ISSN 1880-8468
Technical Report ofInternational Development Engineering
国際開発工学報告

TRIDE-2016-03

May 9, 2016

Abstracts of Master Theses
Presented in February 2016

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Preface
Master theses of Department of International Development Engineering, Tokyo Institute ofTechnology were presented successfully on August 4, 2015 and February 12, 2016,
respectively. This technical report consists of the abstracts of those theses.

TECHNICAL REPORT OF INTERNATIONAL DEVELOPMENT ENGINEERING
TRIDE-2016-03

TABLE OF CONTENTS(COMPLETING IN SEPTEMBER 2015)
A NEW COMPOSTING OPERATION REDUCING ODOR EMISSION AND
ELIMINATING PATHOGEN............................................................................... CHOI WONKYUNG 1
TREATMENT OF ACID MINE DRAINAGE USING INDONESIAN NATURAL ZEOLITES................................................................................................. FERNANDO 5
UNDERSTANDING FACTORS AFFECTING PRIMARY SCHOOL TEACHERS’ USE OFICT FOR STUDENT-CENTEREDEDUCATION IN MONGOLIA................................................................................................. LI SHENGRU 9
WAREHOUSE LOCATION DETERMINATION FOR HUMANITARIAN RELIEF
DISTRIBUTION IN NEPAL................................................................................................. MAHARJAN RAJALI 13
DEVELOPMENT OF CHANNEL SOUNDER PROTOTYPE FOR OUTDOOR
DISTRIBUTED WIRELESS NETWORK................................................................................... SRISOOKSAI TOSSAPORN 17
11 GHZ URBAN MICROCELL RADIO CHANNEL ESTIMATION USING SAGEALGORITHM................................................................................................. XU CHAO 21(COMPLETING IN MARCH 2016)
FACTORS INFLUENCING TIME SERIES ELECTRICITY CONSUMPTION TREND AT
TOKYOTECH CAMPUS................................................................................................. YUJI HIRAI 25
EFFECTS OF SELF-DISCHARGE ON CHARGE-DISCHARGE EFFICIENCY OF
ELECTRIC DOUBLE-LAYER CAPACITOR....................................................................................................... KENTA KOH 29
EXPERIMENTAL STUDY ON APPLICATION OF ANODE ELECTRODESIN ELECTRO-CHEMICAL REPAIR WITH WATER SUPPLY CURING METHOD....................................................................................................... SHU YAMAMOTO 33
COMPARISON OF TRANSPORTATION EQUIPMENT COMPANIES IN TERMS OFENVIRONMENTAL AND MANAGEMENT EFFICIENCY....................................................................................................... MASATAKA KASHIWA 37
CORRIDOR CHOICE OF TRANSIT CARGO TRANSORT IN LANDLOCKED
COUNTRIES OF EAST AFRICA....................................................................................................... MASASHI SOTA 41
STUDY ON THE SUSTAINABLE MANAGEMENT AND USE OF PARTICIPATORYEDUCATION DATABASE: CASE OF NATIONAL EDUCATION SYSTEMS AND
POLICIES IN ASIA-PACIFIC (NESPAP) OF UNESCO....................................................................................................... TAKAERO INOUE 45
ANALYZING THE IDEAL PROPERTIES OF DATA VISUALIZATION IN THE WORLDDEVELOPMENT REPORT............................................................................................... MA. SAYORI D. KARAKAMA 49
COMPETITION BETWEEN LAND CORRIDOR AND MARITIME TRANSPORT
IN CROSS-BORDER FREIGHT TRANSPORT....................................................................................................... WATARU SAITO 53
CAMERA CALIBRATION USING GLOBAL PROJECTION TRANSFORMATION....................................................................................................... YANG CHEN 57
ENERGY TRANSFER IN ADHESIVE CONTACT BETWEEN PDMS PLATE AND GLASSLENS......................................................................................... GUANDA FU 61
PERFORMANCE COMPARISON OF SPACE-TIME CODING ON POLARIZED AND
NON-POLARIZED LINE-OF-SIGHT MULTIPLE INPUT MULTIPLE OUTPUT (MIMO)PROPAGATION CHANNELS...................................................................................................... DJIBY MAREMA DIALLO 65
GEOMETRY BASED DYNAMIC CLUSTERING OF THE DOUBLE-DIRECTIONALWIDEBAND MULTIPATHS ESTIMATED FROM AN INDOOR MEASUREMENT AT 11
GHZ..................................................................................................... PANAWIT HANPINITSAK 69
INTERANNUAL VARIATION OF SEA-BREEZE IN KANTO-PLAIN IN SUMMER...................................................................................................... YUTA NEJIME 73
SATELLITE-DERIVED URBAN AERODYNAMIC PARAMETERS FOR MESOSCALE
MODELING IN MEGACITIES.............................................................................................. NISRINA SETYO DARMANTO 77
PROCESS TO RECOVER UNREACTED ALCOHOL IN BIODIESEL FUEL PRODUCTIONFROM INEDIBLE OILS....................................................................................................... ZHENQIANG SHI 81

ROLE OF PARATRANSIT MODES ON ACCESSIBILITY IN KHULNA, BANGLADESH..................................................................................................... DHRUBO ALAM 85
APPLICATION OF WIRELESS SENSOR NETWORK FOR CROWD DYNAMICSMONITORING....................................................................................................... STANISLAV LYKOV 89
EFFECT OF SUB-CRITICAL WATER TREATMENT OF RAW MATERIAL ON THE
COMPOSTING....................................................................................................... TAKAHIRO HINOMIZU 93
FIELD SURVEY AND NUMERICAL SIMULATION OF INUNDATION IN THE MEKONG
DELTA’S LARGEST CITY....................................................................................................... CHIAKI TSURUDOME 97
TRANSIT CHOICE OF ASIAN HUB AIRPORTS IN INTERCONTINENTAL FLIGHTS....................................................................................................... SHUYANG KANG 101
LIFETIME PREDICTION METHOD USING ACCELERATION TEST FOR PAINTCOATED STEEL AND MORTAR-COVERED STEEL IN MARINE ENVIRONMENT....................................................................................................... KYOHEI SHIMURA 105
STUDY ON SUSTAINABLE MANAGEMENT OF PONDS AND WETLANDS IN WORLDHERITAGE USING GIS: CASE OF LUANG PRABANG, LAO PEOPLE’S DEMOCRATIC
REPUBLIC....................................................................................................... NORIFUMI IRIE 109
EVALUATION OF THE EFFECTIVENESS OF THE PROPOSED MITIGATIONSTRUCTURES AFTER THE 2011 GREAT EAST JAPAN EARTHQUAKE....................................................................................................... KOKI MATSUMOTO 113
DECOMPOSITION OF NITROUS OXIDE OVER CU MODIFIED INDONESIANNATURAL ZEOLITE.................................................................................................... RYUSUKE KATAGIRI 117
WATER VAPOR RESISTANCE OF FE-MN/(TI,ZR)2O4 FOR NO REDUC....................................................................................................... TOMOAKI MITSUI 121
MIXED ANTI-WASHOUT CONCRETE IN COLD ENVIRONMENT....................................................................................................... YUMA YOSHIDA 125
DESIGN AND CONTROL OF SWITCHING REGULATOR FOR ENERGY HARVESTINGCONSIDERING OUTPUT CHARACTERISTIC OF GENERATOR....................................................................................................... DAISUKE IWASAKI 129
DEVELOPMENT OF DIRECTIONAL CHANNEL SOUNDER USING USRP AND GNU
RADIO....................................................................................................... TIANYANG MIN 133

LIFETIME PREDICTION OF REINFORCED SEAWATER CONCRETE MIXED WITHFLY ASH WHICH WERE PRODUCED IN JAPAN OR PHILIPPINES AGAINSTCHLORIDE ATTACK....................................................................................................... CHENG YI 137
EFFECT OF TANGENTIAL FORCE ON ADHESION BETWEEN AN ELASTIC BEAM
AND A RIGID BODY....................................................................................................... SHIN SHIMAHARA 141
DEVELOPMENT OF SIMULTANEOUS MEASUREMENT SYSTEM OF MIMOCHANNEL RESPONSE AND BODY MOTION FOR STUDY OF DYNAMIC WBAN
CHANNEL....................................................................................................... GAOCHAO CHEN 145

A NEW COMPOSTING OPERATION REDUCING ODOR EMISSIONAND ELIMINATING PATHOGEN
Student Number: 13M51520 Name: Wonkyung CHOI Supervisor: Kiyohiko NAKASAKI࢔ࣥࣔࢽ࢔࢞ࢫⓎ⏕ࡢᢚไ࡜⑓ཎ⳦ẅ⳦ࡢࡓࡵࡢ᪂つ࡞ࢥ࣏ࣥࢫࢺ໬᧯స
ᓲ※ᬒ
In this study, a new composting method which reduces odorous ammonia gas emission and removes
pathogen from the composting process is suggested. 40 °C was the optimum composting temperature with thelowest ammonia gas emission, and it was found that high heat treatment at 80 °C in the early stage of composting
could eliminate pathogenic bacteria. Also, good quality of the final compost product was achieved. Thus,
preliminary heat treatment followed by composting at 40 °C was proposed as the innovative composting operation
reducing ammonia gas emission and removing pathogen.

(Alles-G) were mixed in a ratio of 10:9:1 on dry
weight basis, followed by mixing with sterilized waterto set the initial moisture content of 50 % for thecompost raw material. The initial pH was set at 7.5 byadding slaked lime.
Schematic diagram of the composting system isshown in Fig. 1. The raw composting mixture of 15 g
was placed into each mini-reactor in the incubator.
The air flow was adjusted to the rate of 5.5 mL/min
with flow meter, and was flown into NaOH flaks totrap external carbon dioxide gas, and then into
distilled water in the flasks to be moisturized. The air
was then moved to the mini-reactors in the incubators,
and then to the Tedlar bags. The ammonia gas andcarbon dioxide gas emitted from composting werecollected in the Tedlar bags for daily measurement of
the concentrations and calculation of the cumulativeemission for each gas.

1㻌 Introduction
Due to its eco-compatibility and relatively easy
operational procedures [1], composting is asustainable way in which large amounts of organic
waste can be converted into useful products [2]. It isan environmental friendly method for recyclingorganic wastes such as food wastes, animal manure, orsewage sludge as the substrates to produce compost
products which may further be used as organic
fertilizers. Compost made from animal waste andother organic refuse can serve as a valuable nutrient
resource to the agricultural fields, and decrease
environmental load if they are treated properly [3].
Although composting has many benefits, there
are some significant problems in composting process.
A major problem in composting is the odor caused by
the release of ammonia [4] which may also cause
atmospheric pollution [5]. Conversion of nitrogenfrom protein of composting material is converted into
microbial cell mass or the ammonia gas. Furthermore,
it is possible that pathogenic bacteria are notcompletely destroyed and survived in the compost [3]in that they were not exposed to high temperature ofheat. In this study, it was aimed to suggest a newcomposting operation reducing odorous ammonia gas
and eliminating pathogen.

2 Material & Methods
Fig. 1 Schematic diagram of the composting system.

2. 1. Composting with temperature control for
reduction of ammonia gas emission
Rabbit food, sawdust, and seeding material

Three different composting Run A, Run B, andRun C, of which temperature were at 30, 40 and 60 °C

1

were conducted. Low composting temperatures weremore focused as low ammonia gas emission was
expected due to large microbial cell mass in compost.
All of the composting processes were run for 10 days
with constant temperature. The samples of day 0, 1, 2,
3, 5, 7, and 10 were collected to monitor the moisture
content and pH. The total cell density of bacteria fromthe final compost products of Run A (30 °C), Run B(40 °C), and Run C (60 °C) was compared byreal-time PCR.

days, the pH of composting mixture was increased to
7.5 with addition of slaked lime. At this point, Alles-G
was also added to the composting mixture as theseeding material.
Carbon dioxide and ammonia gas concentration
was measured daily for further analysis. For this run,compost samples on day 0, 1, 2, 3, 4, 5, 7, 9, and 12
were collected to measure the moisture content, pH,
and cell density of mesophilic bacteria, thermophilic
bacteria and E. coli. In addition, germination index
was conducted to test the phytotoxicity of the final
compost product.

2. 2. Effect of heat pre-treatment in composting
operation for elimination of pathogen
Rabbit food, sawdust, and seeding material(Alles-G) in a ratio of 10:9:1 on dry weight basis wasused in this chapter. The moisture content was
adjusted to 50 %. In addition, as a model ofpathogenic bacteria, inoculum of Escherichia coli wasprepared by culturing E. coli K12 on Luria Bertani(LB) agar plate at 37 °C for 24 hours followed bytransferring the colonies into LB liquid medium in aflask which is shaken in 120 rpm for the next 24 hours.
The prepared inoculum of E. coli was supplied to theraw composting mixture at the start of composting.
Two types of composting experiments, Run D
and Run E were performed. Run D was conducted at
40 °C for 10 days, and Run E was carried out withheat pre-treatment at 80 °C for 2 days prior to 10-day
composting at 40 °C. Carbon dioxide and ammonia
gas concentration was measured daily and cumulativeemission of those was calculated. Also, composting
samples of day 0, 1, 2, 3, 5, 7, and 10 were collectedin Run D, and those on day 0, 1, 2, 3, 4, 5, 7, 9, and 12
were collected in Run E for analysis of cell density ofmesophilic bacteria, thermophilic bacteria, and E. coli.
The experimental apparatus of these experiments was
the same with those described above.

3 Results & Discussion
3. 1. Composting with temperature control for
reduction of ammonia gas emission

2. 3. Composting operation with pH control andinoculation of seeding material after heat pretreatment
The integration of 2-day preliminary heat
treatment at 80 °C and 10-day composting at 40 °C
was applied in Run F. Run F was conducted with thecontrol and the inoculation of the seeding material
after the heat pre-treatment to accelerate organic
matter degradation.
In Run F, raw composting mixture of rabbit food,sawdust, and seeding material (Alles-G) in a ratio of
10:9:1 on dry weight basis was used. E. coli was
inoculated for as well. After heat pre-treatment for 2

Fig. 2 The courses of temperature, pH, evolution rate ofCO2, cumulative emission of NH3, and log. relative celldensity of total bacteria for Run A, Run B, and Run C.

2

Cumulative emission of ammonia gas of Run E
was the lowest among Run A – E. That of Run D washigher than that of Run B (40 °C), but it was still
lower than that of Run A (30 °C). Lastly, E. coli wasstill observed until the last day of compost product in
Run D while it was totally eliminated in Run E.
Therefore, Run E showed high efficiency in removalof pathogenic bacteria and reducing ammonia gas inspite of low cumulative emission of carbon dioxide.

The courses of temperature, pH, evolution rate ofCO2 (carbon dioxide), cumulative emission of NH3(ammonia), and log. relative cell density of total
bacteria for Run A, Run B, and Run C are shown in
Fig. 2. The moisture content of all runs weremaintained within the optimum range of 40 – 60 %.
The pH of all runs increased to approximate value of
8.5 on the last day of composting. Carbon dioxide
evolution rate peaked on day 2 for all runs as there
was vigorous organic matter degradation by microbialactivity in the early stage of composting. The
cumulative emission of ammonia gas was the highest
for Run C, of which composting temperature was
60 °C. Unexpectedly, the lowest emission of ammonia
was observed in Run B which was conducted at 40 °C.
Although it is recently generalized that moreammonia gas is emitted at higher composting
temperature, ammonia gas was emitted even less at
Run B (40 °C) than at Run A (30 °C) in this research.
In fact, the relative cell density of total bacteria incompost products of Run B determined by real-time
PCR was the highest among three runs. It isconsidered that specific bacteria of which optimum
temperature are 40 °C propagated. More bacteria
existing in compost products of Run B was consideredassimilate more ammonia, resulting in the least
cumulative ammonia emission.

3. 2. Effect of heat pre-treatment in composting
operation for elimination of pathogen
In Fig. 3, the courses of temperature, pH,evolution rate of CO2, cumulative emission of NH3,and log. cell density of E. coli for Run D and Run E
are demonstrated. The moisture content of both runs
was retained within 40 – 60 %. The pH decreased onlyin the early stage of composting and increased alongwith the composting.
Carbon dioxide evolution rate peaked on day 2 of
Run D, which is similar to CO2 evolution in Run A –C. Organic matter was decomposed during this earlystage of composting. On the other hand, carbon
dioxide barely increased in Run E as the
microorganisms in the composting mixture were
inactive due to high heat process. Thus, distinct
microbial activity was not observed until the latter
phase of Run E as carbon dioxide evolution rate
peaked on day 9 of Run E. This indicates that organic
matter degradation was delayed in Run E.

Fig. 3 The courses of temperature, pH, evolution rate ofCO2, cumulative emission of NH3, and log. cell densityof E.coli for Run D and Run E.

3. 3. Composting operation with pH control andinoculation of seeding material after heat pretreatment

3

Fig. 4 presents the courses of temperature, pH,evolution rate of CO2, cumulative emission of NH3,and log. cell density of E.coli for Run F. Optimum
range of moisture content of 40 – 60 % was attained.
The pH decreased during heat pre-treatment, but was
successfully adjusted to 7.5 at the beginning of 40 °C
composting.

emission of CO2 between Run E and Run F.
Cumulative emission of NH3 was still very low. Lastly,
no appearance of E. coli was observed since heatpre-treatment. The germination index of the final
compost product of Run F was 86.4 %, indicating thatheat pre-treatment at 80 Ȕ followed by composting at
40 Ȕ results in good compost quality as well.

4 Conclusions
Among composting temperatures of 30, 40, and
60 °C, 40 °C is the optimum composting temperaturefor suppression of odor emission as NH3 was reduced
the most by conversion of nitrogen into microbial cellmass. Since complete elimination of E. coli was
impracticable in composting at 40 °C, the heatpre-treatment step was added. In fact, the heat process
at 80 °C was effective in eliminating E.coli K12,
which was selected as the representative bacteria forpathogen. Also, germination index of the compost
product was over 50 % and good compost quality wasconfirmed in that it can be used in the farmland.
In conclusion, heat pre-treatment at 80 Ȕ
followed by composting at 40 Ȕ is suggested as theinnovative composting operation for reducing odor
emission and eliminating pathogen.

References[1] Sullivan, D.M., Bary, A.I., Thomas, D.R., Fransen,
S.C., Cogger, C.G., 2002. Food waste compost effects
on fertilizer nitrogen efficiency, available nitrogen,and tall fescue yield. Soil Sci. Soc. Am., J. 66,
154-161.
[2] Raut, M.P., Prince William, S.P.M., Bhatttacharyya,
J.K., Chakrabarti, T., Devotta, S., 2008. Microbial
dynamics and enzyme activites during rapid
composting of municipal solid waste – a compost
maturity analysis perspective. Bioresour. Technol. 99,
6512-1118.
[3] Gong, C.M., 2007. Microbial safety control ofcompost material with cow dung by heat treatment. J.
Environ. Sci. (China) 19, 1014-1019[4] Hong, J.H., Park, K.J., 2005. Compost biofiltrationof ammonia gas from bin composting. Bioresour.
Technol. 96, 741-745.
[5] Pagans, E., Barrena, R., Font, X., Sanchez, A.,
2006. Ammonia emissions from the composting ofdifferent organic wastes. Dependency on processtemperature. Chemosphere 62, 1534-1542.

Fig. 4 The courses of temperature, pH, evolution rate ofCO2, cumulative emission of NH3, and log. cell densityof E.coli for Run F.

There was a very small peak of CO2 evolution
rate on day 1, but it was almost 0 after completion ofheat process. After inoculation of heat treated seeding
material (Alles-G), the carbon dioxide emission
increased for Run F to 0.01 mol/day. The peak
appeared earlier than Run E, revealing that the lagtime of organic matter degradation was shortened.
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 !"                          #      $            $ %                           &$      '          ( %     
$             '                     %         )  * +       *', ', ' (  #  -         %               + '     %          &                  $   .                 $               /                 %         0    /123' (   /114'      /115                %     '6 '  #  '        %                                     %     %                                          $     %    %  ' &    '                                 $          &                    &           -/,CD3 /C' 6,CD" /C' ,CD" /C  .,CD4 /C   )#  + 6  '(                  &            %             $    %             211< 21=> 3 0 #      #       #      3117 /D1     &
 &     %        
6+=0,                 &        
           %         ,#
,78   8  89:   ' )  * %      '         ;     %   2"1 <21=>                                                8                %      %                    $       

5  +     $      '     '        -             %   &      /,CD-      &       
&     -/,CD3 /C'6,CD" /C'     ,CD" /C .,CD4 /C-          
:    
/1<21=>
J      ' 1K3L       ' 1K# =3L
1M/4<21=/       ' 1/M" ;   /,CD                    E %   ,# 8     %  ' K# L2<21=3(           
E  8     K# L
1M2<21=3
6     K L/D1 K7L
311

       %     $        #      #        3117  /D1   &     %        6+=0,                       %    !
      /                 
 %         6+=0,   
               '                 %                    $             %     , # 
, 7         %      $    /1<21=> 3  
  $          /D1       #         311 7 &       %        
6+=0,                     %    %            ,H      606           %    
$    '       ?:           %                %          1I/ /D1 E ' #      %            ;      %           
'             %     ' -' 6' E' 7'              %        #       ,  #D             '                  $           &                       
               $       -3G,78 
89:'      6/G   ./G     
89: '   %                 &     '
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8Understanding factors affecting primary school teachers’ use of ICT for student-centered educationin Mongolia
Student Number: 13M51550 Name: Shengru LI
Supervisors: Shinobu YAMAGUCHI and Jun-ichi TAKADAIn recent years, Mongolian education policies focus on student-centered education (SCE) and the use of
information and communication technology (ICT) in teaching and learning. This study aims to understand the factors
affecting teachers’ perceptions on use of ICT for SCE as there lacks its study in Mongolian context. Both quantitativeand qualitative analysis were conducted. The study found: 1) teachers’ professional competency and perception of
benefits on use of ICT are significant factors affecting the use of ICT tool for SCE; 2) teacher cooperation is affecting
their perceptions on use of digital contents for SCE; and 3) endogenous teacher level factors such as teacher’s job
satisfaction and self-confidence are affecting their perception on the use of ICT for SCE.
Practicing educational change can arise on different
levels, for instance, teachers, school administrators, anddistrict administrators. Educational change on teacher levelis discussed here because teachers are core stakeholders inpracticing educational change since they are the closest to
instruction and learning. The major aspect of practicing
new programs and policies in a classroom is not
unidimensional, but instead, it contains at least threefollowing dimensions: 1) the possible use of new
instructional materials, curriculum materials or
technologies; 2) the possible use of new pedagogies(teaching strategies and activities); and 3) the possible
alteration of beliefs (pedagogical assumptions and theories
underlying particular new policies or programs) (Fullan,
2007, p.30). Among these three dimensions, the third
dimension for teacher’s educational belief is considered
fundamental to realize sustainable educational change.
There are multiple studies both in developed anddeveloping countries illustrating the dimensionality ofeducational change. They also examined the importanceof the dimension concerning teachers’ educational belief.
For example, Bussis et al. showed a case where teachers
provide substantial materials to the classrooms with thefaith that they will promote certain learning priorities [3].
In this study, teachers developed new instructional
materials based on the belief that new materials would
promote learning outcomes. For another example, Johns etal. examined three dimensions of educational change
among secondary school teachers in the context of physicaleducation in Hong Kong [4]. His study showed that thesituational and personal-social factors prevented untested
ideas to be transformed into firm beliefs and values. Thesestudies indicate the importance of teachers’ educational
belief in introducing new programs and policies into
schools.

1 Introduction
The Mongolian education system experienced
decentralization since early 1990s. It resulted in increased
roles and responsibilities of local governments and schools.
During such transitional period, Mongolian primaryeducation faced decreases in the school enrollment rate andschool completion rate [1]. Given this situation, Mongolian
government implemented educational reforms with newpolicies to assist the changes in education sector.
Specifically, the Ministry of Education, Culture andScience (MECS) issued “New Education Standard” in2003 to promote the concept of student-centered educationin school curriculum. In 2015, “Core Curriculum” wasintroduced for the teachers to apply student-centerededucation.
Further, “Master Plan to Develop Education ofMongolia in 2006 - 2015” formulated policies toimplement ICT in education. This Master Plan emphasized
the utilization of ICT for teacher training programs topromote ICT integration into classroom teaching. With thispolicy guidance, there is a growing interest among teachers
to integrate ICT into teaching.
However, there are limited number of studies
conducted in Mongolia to understand primary school
teachers’ perception on use of ICT for student-centerededucation. In particular, it is important to discuss what is
influencing teachers to motivate and experiment effective
use of ICT for their teaching and other educational
activities with quantitative and qualitative approaches.

2 Literature Review
2.1 Educational change
Educational change has been discussed widely insupporting educational reforms, educational technology
innovations and any other changes associated withtransformation in educational policies. Fullan has
established the comprehensive theory for school
administrators, teachers and other stakeholders tounderstand and practice educational change successfully[2].

2.2 Factors affecting teacher’s educational useof ICT
There exist multiple studies focusing on factors affecting
ICT implementation in school in general, with theassumption that the use of ICT in education will lead tochange in learning arrangement and pedagogies. However,the factors influencing ICT use for student-centered9education are not specifically addressed [5][6]. Drentestablished a framework for identifying and categorizing
factors affecting innovative use of ICT from teacher’s
perspective [7].
Under Drent’s framework, factors can be categorized
into exogenous and endogenous, school and teacher levelfactors. Exogenous conditions refer to the nonmanipulative conditions while endogenous conditions
involve conditions that can be changed. Specifically, four
categories of factors are endogenous teacher level,
exogenous teacher level, endogenous school level andexogenous school level. For example, endogenous teacher
level factors are teachers’ professional competency,
perception on digital teaching materials and job
satisfaction. For exogenous teacher level, age and gender
are included. As for endogenous school level, ICT
infrastructure and support from school management are theexamples. At last, exogenous school level has school
location and type of school as example factors.

during 2012 - 2013 [8]. There are three justifications to use
this baseline survey data: 1) the data cover four provinces
and the capital city representing local characteristics of five
regions in Mongolia with 838 respondents; 2) the datacontain multiple aspects of information with regards toteachers’ use of ICT in education; and 3) the data werecollected in 2102 - 2013, reflecting current perception ofprimary school teachers in Mongolia.

3.3 Data analysis procedure
Based on research objective, dependent and independent
variables were identified from baseline survey instrument.
Two dependent and seven categories of independent
variables were determined. Table 1 shows the details of
dependent variables. Seven potential categories ofaffecting factors were selected based on literature review
of general factors affecting teachers’ use of ICT. 36 survey
items were fitted into the potential seven categories basedon the understanding of each category. The sevencategories of independent variables are: 1) teacher’squality; 2) technical support; 3) access to ICT resources; 4)time; 5) teacher’s educational beliefs; 6) location anddemographic factors; and 7) school management andleadership.

2.3 Importance of endogenous level factors in
Mongolian context
The survey was conducted in 2014 to find out the categoryof factors that teachers believe is affecting on their use of
ICT for SCE. The questionnaire was developed containing
47 potential factors based on literature review and related
documents of education in Mongolia. Four demographic
questions were added. The survey utilizes 5-point Likert
scale to enquire to what extent teachers agree that eachfactor affect their perception on the use of ICT.
Respondents of the survey (N=153) were located in two
provinces and the capital city in Mongolia. The collectedsurvey data were analyzed and interpreted together withinterviews and observations.
The analysis shows that endogenous teacher levelfactors are affecting teachers’ perception on use of ICT for
SCE. Such examples include teacher’s professional
competency, basic ICT skills, and perception on
effectiveness in use of digital materials.

Table 1: List of dependent variables
Dependent Variable
Perception on use of
ICT tool for SCE
Perception on use ofdigital contents for
SCE

Measurement Item from Baseline
SurveyI think the use of ICT tool makes a
progress towards student-centerededucation
I think the use digital contents makesa progress towards student-centerededucation

Note. Measurement is recoded in 4-point Likert scale (1=completely
disagree to 4=completely agree)
Source: developed by author based on baseline survey, 2015

After dependent and independent variables were identified,
the following steps were proceeded. Four steps wereinvolved in data analysis procedure. First, the selected
items for dependent and independent variables went
through descriptive analysis to detect any irregular missing
patterns and outliers. Second, after data cleaning, theoriginal 5-point Likert scale questions were recoded into 4point Likert scale. The “I don’t know” option was recoded
into system missing according to current literatures anddiscussions with baseline survey designer [9] [10]. As forthe location categorical variable, it was recoded into twodummy variables using dummy coding. Third, after thedata treatment, exploratory factor analysis was conducted
on items identified under seven potential categories offactors. Specifically, principal component analysis with
Varimax rotation method was utilized to detect highly
correlated items and the result justified the categorizationof factors into latent variables. Fourth, linear multiple
regression analysis was utilized to analyze the relationships
between potential affecting factor with teachers’

3 Methodology
3.1 Research objective
The study aims to understand factors that affect teachers’
perception on use of ICT for SCE in Mongolia context.
Based on the survey result and the research question, thefollowing hypothesis was formulated.
Hypothesis: Endogenous teacher level factors are affecting
teachers’ perception on use of ICT tools and digitalcontents for SCE in primary schools in Mongolia.

3.2 Data source
The study utilized the data from the survey conducted in
JICA funded project “Quality improvement of primaryeducation teachers through development of training
materials using ICT.” This survey was to review school andteacher condition on the use of ICT in primary education10

perception on use of ICT tools and digital contents towards
SCE.

benefits on use of ICT by 1 scale score, their perceptions
on use of ICT tool for SCE increases by 0.281 scale score.
This model explains 27% of the variance in perception on
use of ICT tool for SCE by two factors, namely,
professional competency and benefits on use of ICT.

4 Data analysis
4.1 Demographics
Most of the teacher respondents to the baseline survey werefemale (95.6%). The respondents’ age were distributed infour ranges, with more than a third aged between 31 to 40
years old (39.5%). Teaching years were also distributed infour ranges, with the biggest portion of respondents having
1 to 10 years of experience (36.9%). Finally, it can be seenthat the respondents belong to schools in Ulaanbaatar(41.1%), province center (31.7%), and village center(27.2%), relatively equally.

PTSCE = 0.976 + 0.394(PC) + 0.281(BICT)(1)

Equation (2) below shows that three factors, namely,
professional competency, teacher cooperation and benefits
on use of ICT have statistical significance as independent
variables to explain teachers’ perception on use of digitalcontents for SCE. The model explains that if teachers
perceive higher professional competencies of themselvesby 1 scale score, their perception on use of digital contents
for SCE will change positively by 0.421 scale score.
Furthermore, if teachers perceive higher benefits on use of
ICT by 1 scale score, their perception on use of digitalcontents for SCE will increase by 0.234 scale score. In
addition, if teachers perceive better teacher cooperation by
1 scale score, their perception on use of digital contents
will increase by 0.147 scale score. This model explains that
29% of the variance in perception on use of digital contents
for SCE by three factors, namely, professional competency,
teacher cooperation and benefits on use of ICT.

4.2 Exploratory factor analysis
The exploratory factors analysis was conducted on 30
potential items towards teachers’ use of ICT for SCE.
There were 6 items exempted from the factor analysis for
two reasons either due to high missing rate or due todemographical and categorical nature. The item measuring
technical support from school was excluded from the factor
and regression analysis due to high missing rate (66%).
Demographical and categorical items were carried directly
into multiple regression analysis, namely, computer
ownership, age, gender, years of service and location ofschool (recoded into two dummy variables).
Through principal component analysis and Varimax
rotation, items with component loadings greater than 0.6
were considered significant. As a result, 23 items out of 30
potential items were categorized into seven latent variables.
The naming for these latent variables were decided upon
the consultation with educational experts, namely, 1)
professional competency (PC), 2) Supportive school
environment for ICT integration (SE), 3) quality of school
computers (QSC), 4) teacher cooperation (TC), 5) benefits
on use of ICT (BICT), 6) available time for use of ICT in
education (T), and 7) resistance to change (RC). Eachlatent variable was constructed by averaging its
constructing items.

PDCSCE = 0.766 + 0.421(PC) + 0.234(BICT) + 0.147(TC)(2)

5 Discussion
This section summarizes the major findings based on bothquantitative and qualitative analysis.
First, professional competency of teachers was found to
be a factor influencing both teachers’ perception on use of
ICT tool for SCE and teachers’ perception on use of digitalcontents for SCE. This finding agrees with current
literatures on teacher’s use of ICT in education. Existingstudies and literatures emphasize the importance ofteacher’s professional competency in successfully utilizing
ICT in educational contexts [11]. In Mongolian context,
under the government policy support, multiple teacher
training programs are implemented at central and local
levels, reaching out teachers with hand-on experiences ofusing ICT into their classroom teaching. This finding canbe interpreted that such efforts are influencing teachers tobelieve the importance of professional competency.
Second, the study found that teachers’ perception of
benefits on use of ICT also affects both teachers’
perception on use of ICT tool for SCE and teachers’
perception on use of digital contents for SCE. The finding
supports multiple literatures indicating that teachers are
more likely to make use of ICT if they are aware of theadvantages of using technology in their teaching [12].
During fieldwork, specific benefits of integrating ICT into
teaching were observed in both rural and urban schools.
For example, teachers in Ulaanbaatar utilized animations
to explain solar system in the galaxy, illustrating theexistence of Earth relative to the other planets in the space.

4.3 Multiple regression analysis
Multiple regression analysis was applied to find out what
are the factors affecting teachers’ belief on use of ICT for
SCE. Twelve independent variables which include sevenlatent variables and six demographic variables were
included in the analysis. Two regression models were
established using two dependent variables. The results ofregression models are summarized below.
Equation (1) below shows that two factors, namely,
professional competency (PC) and benefits on use of ICT(BICT) have statistical significance as independent
variables to explain teachers’ perception on use of ICT toolfor SCE. The model explains that teachers perceive higher
professional competencies of themselves by 1 scale score,
their perceptions on use of ICT tool for SCE increases by
0.394 scale score. Furthermore, if teachers perceive higher

11

provinces and Ulaanbaatar. The study found thatprofessional competency and benefit on use of ICT arefactors affecting on both teachers’ perception on use of ICT
tools and digital contents promoting student-centerededucation. Teacher cooperation was found to be the factor
affecting teachers’ perception on use of digital contents forstudent-centered education.
Three factors identified to affect teachers’ perception
on use of ICT for student-centered education are all
endogenous teacher level factors. This confirms the
hypothesis of this study. This implied while investing
resources in infrastructure development, policy makers
could also consider designing training programs to
improve teacher’s professional competency, by conveying
the benefits of using ICT tools and digital contents in
education. It is also very important to create learning
environment for teachers to promote their cooperation
through collaborative tasks on use of ICT.

Teachers in Bayankhongor province found videos
containing recordings of local instrument useful forstudents’ understanding of music theory. These qualitative
data collected in schools indicate that teachers recognized
the usefulness and benefit of ICT as a part of their lessons.
This implies that future teacher training programs need toemphasize clear objectives of use of ICT in school settings.
Third, the study finding supports that teacher
cooperation affects teachers’ perceptions on use of digitalcontents for SCE. Existing literatures report that teachers
view technology as a constant area of change where
teachers have shared need to learn it for creating newclassroom teaching contents [13]. During the local teacher
training session in rural Mongolia, it was observed thatteachers from different schools cooperated when theypresented, discussed and shared opinions to improve theirdigital teacher training materials. This finding may suggest
school administrators that building an environment forteachers to create, share and exchange ideas for digitalteaching materials could potentially promote teachers’
perception on use of ICT for SCE.
Fourth, in this study, school location did not influenceteachers’ perception on use of ICT tools and digitalcontents for SCE. A study in South Africa found that theteachers from rural and urban areas differ in the perception
on adopting ICT in education due to the difference inteachers’ exposure to opportunities [14]. Further, aprevious study in Mongolia discovered the different
conditions in rural and urban areas could affect teachers’
frequency in use of ICT for communications [15]. With
Mongolian government policy to introduce ICT in schools,
infrastructure and human resources have been developed
rapidly, especially in rural areas. While the different levelof development in schools of different locations may affectthe utilization of ICT, the finding of this study explains theimportance of policies and its implementation regardless of
the location.
Fifth, in this study, ICT infrastructure was not found to
be a factor affecting teachers’ perception on use of ICT for
SCE. Current literatures support that basic ICT
infrastructure is needed for teachers to utilize ICT for
administrative tasks, but teachers’ use of ICT for
educational purposes may also depend on teachers’
pedagogy and educational philosophy [16]. While
computer infrastructure is important for teachers to use ICTin education, this finding contributes to the literaturearguing that whether teachers can effectively integrate ICT
into classroom teaching and other educational activities
requires more than infrastructure itself [16].

References[1] J. Engel, A. Prizzon, and G. Amgaabazar, “From decline to recovery:
post-primary education in Mongolia,” 2014.
[2] M. Fullan, The New Meaning of Educational Change. New York:
Teacher College Press, 2007.
[3] A. M. Bussis, E. A. Chittenden, and M. Amarel, Beyond surface
curriculum: an interview study of teachers’ understandings. 1976.
[4] D. P. Johns, A. S. C. Ha, and D. J. Macfarlane, “Raising Activity
Levels: A Multidimensional Analysis of Curriculum Change,” Sport.
Educ. Soc., Aug. 2010.
[5] H. J. Becker, “Findings from the teaching, learning and computing
survey: is Larry Cuban correct?” Irvine, US, 2000.
[6] W. J. Pelgrum, “Obstacles to the integration of ICT in HGXFDWLRQௗ
results from a worldwide educational assessment,” Comput. Educ., vol.
37, no. 2, pp. 163–178, 2001.
[7] M. Drent and M. Meelissen, “Which factors obstruct or stimulateteacher educators to use ICT innovatively?” Comput. Educ., vol. 51, no.
1, pp. 187–199, Aug. 2008.
[8] Mon Educ LLC., “Report of the baseline survey of ‘Quality
improvement of primary education teachers through development oftraining materials using ICT’ project funded by JICA, Japan,”
Ulaanbaatar, 2013.
[9] K. K. Tsang, “The use of midpoint on Likert scale: the implications
for educational research,” Hong Kong Teach. Cent. J., vol. 11, pp. 121–
130, 2012.
[10] J. Sukhbaatar, “Personal communications on understanding
measurement instrument in baseline survey,” 2015.
[11] K. A. Bingimlas, “Barriers to the Successful Integration of ICT in
Teaching and Learning Environments: A Review of the Literature.”
EURASIA J. Math. Sci. Technol. Educ., vol. 5, no. 3, pp. 235–245, Nov.
2008.
[12] BECTA, “A review of the research literature on barriers to the uptakeof ICT by teachers.” 2004.
[13] S. Dexter, K. R. Seashore, and R. E. Anderson, “Contributions ofprofessional community to exemplary use of ICT,” J. Comput. Assist.
Learn., vol. 18, no. 4, pp. 489–497, Dec. 2002.
[14] K. Cassim and S. Obono, “On the factors affecting the adoption of
ICT for the teaching of word problems,” Proceedings of World Congress
on Engineering and Computer Science, 2011.
[15] O. Tsogtsaikhan, “Factors Affecting Information Communication
Technology (ICT) Integration of Primary School Teachers in Mongolia,”
Tokyo Institute of Technology, 2014.
[16] D. Watson, “Pedagogy before technology: Re-thinking therelationship between ICT and teaching,” Educ. Inf. Technol., 2001.

6 Conclusion
During the recent educational reforms in Mongolia, use of
ICT in primary education has been identified as means toraise quality of education. The study aimed to answer thequestion on “what are the factors affecting teachers’
perception on use of ICT for student-centered education?”
Both quantitative and qualitative analysis were utilized,
covering 838 responses of primary school teachers of four

12

WAREHOUSE LOCATION DETERMINATION FOR HUMANITARIAN RELIEF
DISTRIBUTION IN NEPAL
Student Number: 13M51566

Name: Rajali MAHARJAN

Supervisor: Shinya HANAOKA

Warehouse for storing emergency relief items has been proven to improve overall efficiency and effectivenessof disaster response while also decreasing the cost incurred in process. Apart from that, warehouses also reduce
response/delivery time, problems arising in field due to unsolicited goods while overcoming failures due touncertainty in transport modes. The ultimate goal is to build resilience and achieve better outreach of relief aid.
Due to high vulnerability to disasters in Nepal, there is an alarming need for improving national resilience.
Thus in an effort to suggest improvement in preparedness, this study determined appropriate number andlocation for placing warehouses. The results can be implemented in real life with some modifications to adjust
resource availability and organizational capacity.

1

Despite the importance of preparedness less has been
done in Nepal in terms of inventory prepositioning.
Located in the central of the Himalaya range, Nepal isprone to various types of natural disasters due to its fragile
geophysical structure, very high peaks, complex geology,
variable climatic condition, active tectonic processes,
unplanned settlement, and weak economic and politicalcondition.
Based upon an assumption that predetermination of
optimal locations for warehousing by the concerned
authorities will encourage inventory preposition, this study
aims to determine the location for constructing warehouses
for storage of humanitarian relief. This study has following
specific objectives:
1. Determine the optimal number of warehouses fordesired coverage.
2. Determine the optimal location of warehouses.
3. Perform sensitivity analysis using number of
warehouses and constraints.

Introduction

Disasters destroy the very infrastructure of the country,
affecting the social, financial, economic and physicalstructure of the society and can be triggered by natural,political and economic events (Whybark 2007), also
evident from Nepal Earthquake 2015. The least developed
countries are the ones most vulnerable to disasters (Kovacs
and Spens 2007). The overarching aim of the humanitarian
sector is to save lives, meet the basic needs of people andalleviate suffering (Van Wassenhove 2006), which ispartly contingent on the speed of humanitarian response(Campbell et al 2008).
Over 90 percent of international relief aid is still
dedicated to disaster response meaning much attention andinvestment has been made for immediate response withless investment for preparedness in advance. However the
success and failure of disaster response activities highly
depends upon the level of preparedness beforehand. Hence,focus on preparedness as emergency planning,
constructionofemergency
operationcenters,
prepositioning of emergency supplies can translate tobetter outreach of aid and a better level of service tobeneficiaries.
Among several activities which can be done to improve
preparedness, this study focuses on warehousing for
inventory prepositioning. In addition to merits mentioned
above, warehousing also encourages cooperation andcollaboration between large numbers of governmental,
non-governmental,
nationalandinternationalorganizations working in the field of disaster managementin Nepal while making them wary of important factors inadvance. The figure below shows details of disaster
operation and role of warehouses.

2. Methodology
The problem of determining optimal location and optimal
number of warehouses for disaster relief distribution
during immediate aftermath of disaster can be modeled as
Maximum covering location problem (MCLP) withadditional constraints. The model includes candidate
points which can serve as a potential location for placing
warehouse and the demand point which needs to becovered. In addition to coverage distance, the selection of
warehouse location is also influenced by transportation
accessibility, level of development and disaster safety of
the respective candidate points. In facility location
covering models, a source of demand is defined covered
if it is located within a specified response distance or timefrom a facility. User accessibility in terms of response
distance or time are two criterion that determines theselection of warehouse location. This study uses thenotion of response distance.
Demand point selection is based on disaster mapping
which is essentially the districts highly vulnerable toearthquake, flood, and landslide. Disaster mapping has
been done considering return periods of 100, 50 and 25
years for earthquake, greater than and equal to ten

Figure 1-1: Disaster operation and role of warehouse
prepositioning
13

disaster safety index of candidate points should be greaterthan or equal to the average minimum value to be selected
as warehouse location. The mathematical formulation of
MCLP is shown as:

kilometers of land area expected to be inundated for flood,and rainfall induced and earthquake induced landslide
scenarios. Union of the districts susceptible to all threedisaster scenarios has been considered as demand points
which counts for 55 out of 75 total districts of Nepal.
Whereas all the 75 districts are eligible for candidate
points, however due to lack of data only 71 districts has
been considered as candidate points and 54 as demandnodes.
The study uses development index to interpret level ofdevelopment, transportation accessibility index for easeof access, and disaster safety index for safety of candidate
points. Human development index has been used as aproxy to represent level of development, which isessentially a measure of life expectancy, education, andper capita income indicators. Transportation accessibility
index has been derived from road density data which
represents abundancy of road network in each district.
Three disaster scenarios; Earthquake, Landslide, andFlood has been used to come up with disaster safety index.
Higher values in case of development index andtransportation index means higher level of development
and better ease of access and higher value for disastersafety index reflect safer locations. The selection of higher
values of development index is preferred in the candidate
points assuming that higher values ensures better
availability of labor force and technological support
which is one of the key factors for warehousing
requirement. Distances between candidate points anddemand nodes has been calculated using shortest pathmethod made available by Department of Roads, Nepal.
Figure 1-1, below shows the framework of methodology
used in this study.

Where,
‫ ܫ‬ൌ ݀݁݊‫ݏ݁݀݋݊݀݊ܽ݉݁݀݂݋ݐ݁ݏ݄݁ݐݏ݁ݐ݋‬‫ ܬ‬ൌ ݀݁݊‫ݏ݊݋݅ݐܽܿ݋݈݁ݏݑ݋݄݁ݎܽݓ݂݋ݐ݁ݏ݄݁ݐݏ݁ݐ݋‬ܽ௜  ൌ ݀݁݉ܽ݊݀ܽ‫ܫ א ݅݁݀݋݊ܽݐ‬ܲ ൌ ݊‫݁ݐܽܿ݋݈݋ݐݏ݁ݏݑ݋݄݁ݎܽݓ݂݋ݎܾ݁݉ݑ‬ͳ݂݅ܽ‫ܬ א ݆݁ݐ݅ݏ݁ݐܽ݀݅݀݊ܽܿݐܽ݀݁ݐܽܿ݋݈ݏ݅݁ݏݑ݋݄݁ݎܽݓ‬‫ݔ‬௝ = ቊ‫݁ݏ݅ݓݎ݄݁ݐ݋݋‬‫ݕ‬௜ = ቊͳ݂݅݀݁݉ܽ݊݀݊‫݀݁ݎ݁ݒ݋ܿݏ݅ܫ א ݅݁݀݋‬‫݁ݏ݅ݓݎ݄݁ݐ݋݋‬்ܰ ൌ ‫݊݋݅ݐܽݐݎ݋݌ݏ݊ܽݎݐݎ݋݂݁ݑ݈ܽݒ݉ݑ݉݅݊݅݉݄݁ݐ‬ܽܿܿ݁‫ݔ݁݀݊݅ݕݐ݈ܾ݅݅݅ݏݏ‬ܰ௏ ൌ ‫ݔ݁݀݊݅ݐ݊݁݉݌݋݈݁ݒ݁݀ݎ݋݂݁ݑ݈ܽݒ݉ݑ݉݅݊݅݉݄݁ݐ‬ܰோ ൌ ‫ݔ݁݀݊݅ ݕݐ݂݁ܽݏݎ݁ݐݏܽݏ݅݀ݎ݋݂݁ݑ݈ܽݒ݉ݑ݉݅݊݅݉݄݁ݐ‬ܶ௝ ൌ ‫݊݋݅ݐܽݐݎ݋݌ݏ݊ܽݎݐݎ݋݂݁ݑ݈ܽݒ݈ܽݑ݀݅ݒ݅݀݊݅݄݁ݐ‬ܽܿܿ݁‫݆݁ݐ݅ݏݎ݋݂ݕݐ݈ܾ݅݅݅ݏݏ‬ܸ௝ ൌ ‫݆݁ݐ݅ݏݎ݋݂ݐ݊݁݉݌݋݈݁ݒ݁݀ݎ݋݂݁ݑ݈ܽݒ݈ܽݑ݀݅ݒ݅݀݊݅݄݁ݐ‬ܴ௝ ൌ ‫݆݁ݐ݅ݏݎ݋݂ݕݐ݂݁ܽݏݎ݁ݐݏܽݏ݅݀ݎ݋݂݁ݑ݈ܽݒ݉ݑ݉݅݊݅݉݄݁ݐ‬The following figure shows the solution approach used for
calculation:

Figure 1-2: Solution approach

3. Calculation Result and Analysis
Calculations were done for three different scenarioswith different coverage distances of 100 Km, 200 Km and
300 Km. Each scenario determines the maximum coverageobtained with minimum number of warehouses. In each of
the scenarios, the demand points are treated as binary
variables. Binary solution seeks to cover each of the

Figure 1-1: Methodology framework

Traditional MCLP formulation has been adapted to reflect
the situation of Nepal by adding additional constraints (4),(5), and (6). These constraints stipulate that the individual
values of transportation accessibility, development and

14and map shows the spatial distribution of warehouse
locations.

demand point at least once, multiple coverage does not
improve the quality of the solution.

3.1 100 Km Scenario
The objective here is to find minimum number of
warehouses for maximum coverage for 100 Km coverage
distance. From the graph below we can see the coverage
rates provided by varying number of warehouses. A totalof 26 warehouses will be required to cover 100 percent
demand. Redundant coverage will be obtained by further
increasing the number of warehouses above 26. However
our objective does not seek for redundant coverage. Figure
3-1, below shows the coverage rates for all three scenarios.
Figure 3-3: Warehouse locations for 200 Km coverage distance

Figure 3-1: Combined coverage for 100 Km, 200 Km & 300 Km
scenario

Figure 3-4: Warehouse locations for 300 Km coverage distance

3.4 Demand distribution
This section shows the details of demand allocation ofdifferent districts to warehouses for 300 Km scenario. As
the warehouse are considered incapacitated, demand
distribution is non-uniform, hence some warehouses are
subjected to large number of demand points whereas others
to very few. The figures below shows the details of demandnodes allocated to each warehouse.
Figure 3-2: Warehouse locations for 100 Km coverage distance

3.2 200 Km Scenario
Upon increasing the coverage distance, the minimum
number of warehouses required to cover all of the demand
points decreases while also decreasing the service level. A
minimum of 12 warehouse will be required to obtain 100
percent demand coverage when the coverage distance is
200 Km. Redundant coverage is not considered in this
scenario too. Figure 3-3, shows the location of warehouses.

3.3 300 Km Scenario

Figure 3-5: Demand node allocation for 300 Km scenario

When the coverage distance was further increased to 300
Km, only seven warehouses were sufficient to cover 54
demand nodes. The graph below shows the coverage rates

15

3.6 Discussion
The importance of warehousing in Nepal can be
explained in relation to recent Earthquake in April 2015.
15 out of 75 districts suffered huge damages due to thisdisaster and to my surprise these districts were alreadyknown to be susceptible to disasters from the studies
conducted earlier. 14 of these districts are among the 54
demand nodes which have been considered for this study.
Warehouses were established in the form of Mobile
Storage Units in various locations to provide relief items
to victims. However less had been done to prepare for
immediate response causing delay in response which could
have been avoided while increasing its efficiency andeffectiveness with earlier warehousing efforts.
Nepal is also home to hundreds of NGOs and INGOs
working in the field of disaster management. These
organizations have their own warehousing and stock
prepositioning strategies. Nevertheless, lack of
coordination and cooperation between government and aidorganization has been identified as one of the majorbottleneck. Government of Nepal can make use of theirabundant presence and willingness to help by determining
appropriate locations for placing warehouses andencouraging consolidation of their efforts. This study
provides several alternative options to choose from forselecting appropriate location. Thus we believe
implementation of this kind of study with somemodifications to meet and reflect the needs on ground willdefinitely improve national resilience against futuredisasters.

Figure 3-6: Spatial diagram for demand allocation to warehouses for
300 Km scenario

3.5 Sensitivity Analysis
Sensitivity analysis has been done to understand how thechange in number of warehouses and values of constraints
will affect overall coverage provided by combination offacilities. Figure 3-7, below shows the impact of changing
number of warehouses to four and to one. Demand point
coverage decreases drastically for 200 Km and 100 Km
scenario when the warehouse numbers were decreased,
apparently due to smaller coverage distance. Warehouse
location in Bhaktapur was found to be important when thenumber of warehouse was limited to one in all three
scenarios, as it was common to all three solutions.
Sensitivity analysis was also done to see the impact ofeach values, however there was no significant decrease incoverage until these values were raised as high as 60
percent. Thus only the output of three constraint valuechange has been included which is shown in figure 3-8.
Coverage rates seemed to decrease with increase inminimum required values of constraints.

4.

Conclusion

This study determined various combination of warehousenumbers coverage provided by each and their respective
location taking into consideration geographical features,level of development and disaster safety which is very
unique to Nepal. Three different scenarios were considered
to take into account appropriate response distance and also
to weigh the pros and cons of having larger number of
warehouses versus larger coverage distance. The
overarching aim is to reduce overall response time andassociated uncertainty arising due to disaster therefore
alleviating impacts of disasters to affected people.

Figure 3-7: Impact of changing no. of warehouses for 300 Km

References
Balcik, B. and Beamon, B.M. (2008) “Facility location in humanitarian relief”,
International Journal of Logistics: Research and Application, Vol. 126, no. 2, pp.
101-121.
Basdemir, M.M. (2004) “Locating search and rescue stations in the Aegean andwestern Mediterranean regions of Turkey”, Journal of Aeronautics and SpaceTechnologies, vol. 1 no. 3 (63-76)
Church, R. and Revelle, C. (1974) “The maximal covering location problem”,
Papers of the Regional Science Association, Vol.32, pp 101-118.
Rawls, C.G. and Turnquist, M.A. (2010) “Prepositioning emergency supplies fordisaster response”, Transportation Research Part B: Methodological, vol. 44, no. 4,
pp. 521-534.
Klose, A. and Drexl, A. (2005) “Facility location models for distribution systemdesign”, European Journal of Operational Research, vol. 162, no. 1, pp. 4-29.

Figure 3-8: Change in coverage due to change in constraints for 300 Km

16

DEVELOPMENT OF CHANNEL SOUNDER PROTOTYPE FOR OUTDOOR
DISTRIBUTED WIRELESS NETWORKS
Student Number: 13M51572 Name: Tossaporn Srisooksai Supervisor: Jun-ichi Takada

Abstract
Although, the traditional channel sounders were successfully utilized by existing works to measure anddevelop the correlation model for indoor distributed channels in distributed wireless networks (DWNs), it
is difficult to implement such a system in large scale of outdoor distributed measurement which requires thenumerous transmitter and receiver nodes. Therefore, the well-suited channel sounder prototype for the outdoor
DWNs is developed by adopting the standalone system of the open source USRP hardware. The time-gridacquisition of received signal is implemented in order to overcome the drawback of the limited data rate within
USRP in this work. By this solution, the data rate can be successfully reduced to 1/8 without sacrificing
the accuracy of the channel characteristics. To extend the developed system for distributed measurement, theframework of the synchronization and the protocol of communication among nodes for obtaining the correlationin the distributed channels in large scale of outdoor distributed measurement is pointed out and proposed.

1

Introduction

the channel sounder could be utilized to measure anddevelop the correlation model for indoor distributed
channels. However, to measure the wireless channels
in large scale of outdoor DWNs, it requires the numerous transmitter (Tx) and receiver (Rx) nodes. In
such condition, it is difficult to implement the traditional channel sounder because it lacks of portability
and also requires big amount of cost.
Therefore, the objective of this research is to develop
the channel sounder prototype which is able to capture the correlation behavior of the distributed wireless channels and to be customized flexibly for different
kinds of the outdoor DWN measurements which commonly require the small and portable channel sounders
and the scalable deployment in large scale area. Although, the wireless sensor devices were developed
for the outdoor distributed measurement in [1], suchdevices are able to measure only the received signal
strength (RSS) which cannot be used to determine
the small scale fading or multipath fading of the distributed channels. The authors in [4] pointed out thatknowing the exact model of small scale fading is essential when considering the partner selection algorithm ofcooperative wireless sensor networks since each small
scale fading model has the different effect to the evaluation of the error probability over the wireless channel.
This error probability significant impacts to the efficiency of the partner assignment algorithms. Therefore, our proposed channel sounder which is able tomeasure the small scale fading, can be implemented toeliminate such a limitation.

Recently, distributed wireless networks (DWNs) areconsidered as an alternative for the next generation
wireless networks. While each communication in thecentralized wireless networks, e.g. mobile network, iscontrolled by the base station, the devices are able tocommunicate each other independently in DWNs. This
improves the robustness of the network, since each device has more than a communication link. Especially,in an emerging wireless application such as disaster
network, it strongly requires the robustness operation
during earthquake, tsunami or fire.
To maximize the robustness of such DWNs, the cooperative distributed algorithms are essential in whichknowing the correlation model of the distributed channels is required. Without such information, the algorithms can only be investigated using the model-based
mathematical assumptions which can not be guaranteed in some real scenarios. The example of such a
issue is pointed out in [1], in which the probabilityof path failure in multi-hop wireless network, which
is one of DWN scenarios, is considered. Although,in such case, independent and identically distributed(i.i.d.) log-normal shadowing is typically assumed asa simple and mathematically tractable model for evaluation, they found that it is not the suitable model
for such scenario when comparing with the correlated
shadowing model extracted from the real measurement.
This raises the importance of the correlation channel
model and the measurement approach in DWNs. However, the existing testbeds for DWNs, e.g. in [2], aim
to measure the network performance, e.g. bits/second,rather than the wireless channel parameters which arenecessary in developing the accurate complex correlation model. In addition, the existing testbeds dependon the commercial devices and drivers of IEEE802.11(WLAN). This causes the difficulty in customizing
such testbeds for measuring the correlation behavior
of DWNs. In contrast to those testbeds, the channelsounder is more flexible. The study in [3] showed that

1.1

Design consideration

1.1.1

Channel estimation by channel sounderTo estimate the channel parameters, the transmitter(Tx) part of channel sounder sends the signal throughthe environment which consists of several scatterers to
the receiver (Rx) part. Then, the channel characteristics can be estimated by performing the signal processing on the received and transmitted signal. The trans17
1

!

!

Figure 1: Channel parameters for the example of two
path channel
mitted signal in this work is the unmodulated complex
Newman phase multitone (NPM) signal same as [5].
This NPM signal is periodic for every tp which is referred as a symbol duration.
Fig. 1 illustrates two channel characteristics which
are channel transfer function (TF) and channel impulse
response (CIR) corresponding to the simple environment which consists only a scatterer and two paths ofsignal. The path gain fluctuation due to multipath interference which is usually called small scale fading canbe observed in this figure. Please note that CIR canbe obtained by taking inverse fast fourier transform(IFFT) on TF. In real environments, the multipath is
not the only source of fading. If there are any objects
such as buildings or trees along the path of the signal,
some part of the transmitted signal is lost through absorption, reflection, scattering, and diffraction. This effect is called shadowing fading. These channel transfer
function and impulse response can be analyzed further
by varying the distance between Tx and Rx. It is simply called distance-path loss relation. Furthermore, in
distributed channels, each of these major effects may
not be independent. The measurement results in [1]reveal that there exists the correlation between shadowing fading of the various communication links over
distributed space. The correlation among these major
effects are also possible, e.g. the correlation between
path loss and small scale fading observed in [3].
1.1.2 Proposed channel sounder and design issue

Figure 2: Outdoor distributed measurement in large
scale
+*'()
!
(

* '()

,
-

$%&'()"#!

Figure 3: Block diagram of each node of proposed channel sounderverter (DUC) unit by a factor of four to the digital toanalog converter (DAC) rate of 128MS/s. Please notethat the Interp unit is realized by FPGA implementation, however the DUC unit is realized by AD9862
mixed-signal front-end processor. Then, the signal istransmitted over wireless channel by radio frequency(RF) daughter board. In Rx flow, the digital down
converter (DDC) and decimation unit (Decim) are responsible for decimating the signal obtained from analog to digital converter (ADC) from the rate of 64 MS/s
to the rate which is lower than or equal 4 MS/s in order
to be able to sent it to the host.
Regarding the proposed architecture, it is obviousthat the measurement bandwidth is limited by the datatransfer rate from the host to FPGA (4 MS/s) andvise versa. According to the sampling theorem, thischannel sounder would have only 2 MHz bandwidth
which is small comparing to 22 MHz bandwidth ofIEEE802.11a/b/g. The solutions for this hardware limitation and its validation result are presented in Sect. 2.
While the hardware issue is considered for individual
channel sounder, the synchronization and protocol ofcommunication among nodes for obtaining the possible
correlation in the distributed channels are consideredand proposed for extending this proposed system forthe distributed measurement in Sect. 3.

In this study, the large scale of outdoor distributed
measurement as shown in Fig. 2 is considered. A channel sounder is represented as a single node which consists a Tx and a Rx. Each node is be able to communicate with other nodes located in its coverage area. The
proposed channel sounder is designed based on the software defined radio (SDR) platform. The open source
SDR called universal software radio peripheral (USRP)
is selected as the main tool because of the flexibility incustomization.
With the characteristic of DWNs, each sounder ornode has to be a portable and compact one. The E1x0 2Hardware implementation
model which is the embedded series of USRP produced
and validationby Ettus Research Inc is suitable. The block diagramof each node based on such a device is shown in Fig. 3.
In Tx flow, the transmitted signals is generated in em- 2.1 Hardware implementation
bedded computer board or host and is sent from host to
field-programmable gate array (FPGA) though 4 MS/s
By considering the specification of Tx and Rx in thisdata bus. The interpolation unit (Interp) is responsi- system at center frequency (fc ) 2.45 GHz, it is able toble for interpolating the signal to the sample rate of 32 capture CIR’s maximum signal delay duration (τmax )MS/s which is further interpolated by digital up con- at 5.8 µs. Thus, in this work, tp of the NPM signal is182Channel impulse response
−60
−65

−65

−75
−80

−80

−85
0

2

2.2

4

6

8
−7

x 10

−85

designed at 8 µs which is greater than τmax in order tocapture all the delay paths. The simple way to solve
the limitation of data rate in Tx flow is to store one
period with length of tp of NPM signal into FPGA andtransmit it out repeatedly. In such case, based on the
fixed interpolation rate which is four of the AD9862
chip, the maximum sampling rate of transmitted signal is 32 MS/s or maximum 16 MHz bandwidth when
considering the sampling theorem. In Rx flow, since
the sampling rate of transmitted signal is 32 MS/s, thesampling of received signal is also 32 MS/s, consequentially. Sending such a sample rate to the host through4 MS/s data bus, time-grid of received signal acquisition concept is applied as depicted in Fig. 4. Thisacquisition concept is introduced for solving the storage issue of received signal in developing the MIMO
channel sounder for 11 GHz [5], while in this work,
this scheme is modified for solving the data rate limitation. As shown in the figure, only one tp periodin every 8 × tp of the full received signal at 32 MHz
sampling rate is transfered to host. This does not sacrifice the accuracy of the channel characteristics, since
8 × tp = 64 µs is lower than 2f1d = 5 ms, where fd
is maximum doppler frequency and the doppler shift
when scattering objects are in motion is considered asstatic in such a period. Then, the solutions for bothtransmitter and receiver paths are realized by modifying the FPGA of E1x0 device.

Path Gain (dB)Path Gain (dB)

−62

−70

−75

−63
−64
−65

−90

−66

−95

−67

−100
0

Measured by E1x0
Measured by VNA

−61

−60

−70

Figure 4: Time-grid of received signal acquisition

Channel transfer function−60

Measured by E1x0
Measured by VNA

2
4
6
Delay time (second)

8

−68

2.445

−6

x 10

2.45
2.455
9
Frequency (Hz)x 10

Figure 5: Indoor measurement result
Table 1: Comparison of the reference clocks
Reference Clock

OCXO

Rubidium Cesium

GPSDO

Short-term
stability (Avg.
time = 1 s.)
Accuracy
Size (cm3 )Weight (kg.)
Cost (JPY)

1×10−112×10−11

1×10−113

5×10−85×10−11
1.5×2.8×1 9×21×33
0.09
4
lowest330k

5×10−12

5×10−13 1×10−12
13×42×52 2×4×1
30
0.1
highest96k

Extending the System for
distributed measurement

In this section, the framework of extending the proposed channel sounder for DWNs are constructed.

3.1

Distributed synchronization

All the system clocks in the proposed channel sounderare generated from 10 MHz signal of the reference
clock. To obtain the accurate estimation of the absolute signal delay and the channel transfer function,
it requires the precise time and frequency synchronization. This can be simply realized by using a highly stable atomic oscillator as reference clock and sharing it
by wired connection. Since, this system is designed forlarge scale measurement, it is obvious that the wirelesssharing is essential. Considering the existing referencesin Table 1, the easiest way to synchronize the start time(t0 ) to obtain the accurate absolute signal delay without wired connection is to utilize the global positioning system disciplined oscillator (GPSDO) which canprovide the global time and pulse-per-second signal toeach channel sounder. Although, the main drawbackof GPSDO is that the GPS signal is easily shadowed by
the obstacles, other advantages of GPSDO which are
the low cost device, low weight and portable size makeit is feasible to implement in large scale (Table 1).
Another important point, frequency accuracy of eachreference type is related to the frequency offset between
Tx and Rx which causes the long-term phase drift, thus
limiting the estimation accuracy of absolute signal delay. In case of GPSDO, the frequency accuracy fa is inorder of 10−12 . If the carrier frequency fc of proposed
channel sounder is 2.45 GHz, the maximum phase drift
in a second is 2πfc fa = 0.015 radian which can be negligible as analyzed in [6].

System validation

To verify the system validation after implementing theconcept presented in Sect. 2.1 and after applying several calibrations, the measurement results of the proposed system in real wireless channel of indoor environment are compared with the results of commercial
vector network analyzer (VNA). The measurement isconducted in the experiment room of Takada laboratory in Tokyo Institution of Technology, where several
tables, laboratory equipments, computers and metallic
shelves exist. The distance between Tx and Rx is 3
m. Radio wave absorber is placed between Tx and Rx
to avoid clear line of sight (LOS) path. The antennas
used in this measurement are omni-directional verticalantennas at 3dBi Gain. The height of both Tx and Rx
antennas is 0.34 m. The center frequency and bandwidth are set at 2.45 GHz and 16 MHz, respectively.
In the case of channel sounder measurement, the firstE1x0 is set as Tx and the second one is set as Rx. The
result is shown in Fig. 5. There is the good agreement
between VNA and the proposed channel sounder.
19
3

identifying the difference mechanisms between the interference and correlation impact.

4

This research proposes the channel sounder system
which is able to solve the limitations of the existing
distributed measurement systems for large scale of outdoor environment. The validation in laboratory environment in term of hardware implementation is already evaluated. However, it still needs more validation in terms of wireless synchronization and measurement protocol in outdoor environments. Once all theprocesses are completed, this proposed system will be
one of the facilitators in identifying and modeling thecorrelation mechanisms of outdoor distributed channels in future work in order to enhance the robustnessof the distributed wireless application.

(a) Mode 1(b) Mode 2

Figure 6: Distributed measurement protocol

3.2

Conclusion

Distributed correlation
measurement protocol

References
The main objective of the proposed channel sounder is
to capture the correlation behavior of the distributed
wireless channels. Therefore, it needs the protocol ofcommunication among nodes for obtaining the possible
correlation in the distributed channels. There are two
importance insights which should be taken into accountwhen considering such a protocol. The existing worksin [1, 3] reveal the first insight that any links between
the nodes which have either a common Tx or common
Rx or both shows the high value of correlation coefficients. The second insight, as pointed out in [7], isthat the multi-link propagation issues in some scenarios arise because they suffer from both distributed correlation propagation channels and interference among
various links.
Therefore, the measurement protocol in this work
consists of two modes. It is able to measure all common Tx/Rx link pairs regarding the first insight andaims to identify the impact of interference to the correlation level in the propagation channels regarding thesecond insight. The first mode can be realized by utilizing the time-division multiplexing (TDM) scheme asshown in Fig. 6(a) which is the example of conducting
the distributed measurement by using three channelsounders, each has a Tx and a Rx. The link pairs
which have a common Tx in this mode can be measured simultaneously. For instance, the Tx1-Rx2 link
and Tx1-Rx3 link are measured at the time slot t1 . In
contrast, the link pairs which have a common Rx cannot be measured simultaneously, e.g. Tx2-Rx1 link at
t2 and Tx3-Rx1 at t3 . However, the good point of thismode is that the interference impact can be eliminated.
The second mode shows as Fig. 6(b) is done by implementing the the multi-tone frequency-division multiplexing (MTFDM) [8] in TDM manner. In this mode,
the link pairs which have a common Rx can be now
measured simultaneously, e.g. Tx2-Rx1 link and Tx3Rx1 at t1 . With relying on FDM, the interference may
impact the propagation channel in the second mode.
However, by comparing the correlation result between
the first and second modes may give some insights for[1] P. Agrawal and N. Patwari, “Correlated Link
Shadow Fading in Multihop Wireless Networks,”
IEEE Transactions on Wireless Communications,vol. 8, no. 8, pp. 4024–4036, August 2009.
[2] J. Lee, S.-J. Lee, W. Kim, D. Jo, T. Kwon, and
Y. Choi, “Understanding Interference and Carrier
Sensing in Wireless Mesh Networks,” IEEE Communications Magazine, vol. 47, no. 7, pp. 102–109,
July 2009.
[3] C. Oestges, N. Czink, B. Bandemer, P. Castiglione,
F. Kaltenberger, and A. Paulraj, “Experimental Characterization and Modeling of Outdoorto-Indoor and Indoor-to-Indoor Distributed Channels,” IEEE Transactions onVehicular Technology,
vol. 59, no. 5, pp. 2253–2265, Jun 2010.
[4] P. Castiglione, S. Savazzi, M. Nicoli, and T. Zemen, “Impact of Fading Statistics on Partner Selection in Indoor-to-Outdoor Cooperative Networks,”
in IEEE ICC proceeding, May 2010.
[5] Y. Konishi, Y. Chang, M. Kim, and J. Takada,“Versatile Radio Channel Sounder for Double Directional and Multi-link MIMO Channel Measurements at 11 GHz,” IEICE TRANSACTIONS on
Electronics, vol. E97-C, no. 10, pp. 994–1004, 2014.
[6] M. Lin and I. Wassel, “Impact of Channel SounderFrequency Offset on the Estimation of Channel
Parameters,” in Proc. IEEE 64th Vehicular Tech.
Conf. (VTC-2006 Fall), September 2006.
[7] C. Oestges, “Multi-Link Propagation Modelingfor Beyond Next Generation Wireless,” in Loughborough Antennas and Propagation Conference(LAPC), November 2011.
[8] K. Sakaguchi, J. Takada, and K. Araki, “A Novel
Architecture for MIMO Spatio-Temporal Channel
Sounder,” IEICE TRANSACTIONS on Electronics, vol. E85-C, no. 3, pp. 435–441, 2002.

20
4

11 GHz Urban Microcell Radio Channel Estimation
Using SAGE Algorithm
Student ID: 13M51589

Name: Xu Chao

Supervisor: Jun-ichi TAKADAWith the growing demand for higher data rate of radio channel, higher frequency spectrum and MIMO
system are preferred to be utilized since they can improve the capacity and data rate of system. However, theperformance of MIMO system depends on radio channel conditions. Therefore, considering about systemdesign and antenna selection, we must characterize radio channel to get the best performance of MIMO system. Here we estimate channel behavior of 11 GHz urban microcell radio channel by parametric modeling ofwide-band double-directional measurement data which was measured by building an 11 GHz 24x24 MIMO
channel sounder with 400 MHz, and conducting massive outdoor measurement campaigns.
pectation maximization (SAGE) has been applied
to the process of these measurement data. And aftergetting the results parameters from SAGE, utilize
some factors and estimation method to processthese results in order to characterize wide-band
double-directional channels. these parameters canbe utilized to reconstruct transfer function (TF),
and the method of antenna selection based of TF
has been introduced in [2]. And from the characteristics of delay, direction, power, etc., some behavior
in outdoor scenario at higher frequency can bestudied by comparing channel conditions at highfrequency and channel conditions at lower frequency studied in [3][4]. Further more, these results cangive some advices for system design.

1.Introduction
In 21st century, wireless communication has
attracted more and more attention because it plays
an important role in providing services for bothsocial and personal requirement. In next decade, It
is a trend that the number of users connected into
networks is increasing, and the demand for highdata rate for cellular networks is growing. Since
the frequencies in the lower microwave range (below 5GHz) has been used for current mobile communication while higher frequencies remain unused. Therefore, the utilization of high frequency
spectrum in microcell has a large potential in increasing data rate since wider bandwidth can be
guaranteed. In addition, a method called multipleinput and multiple-output (MIMO) for multiplying
the capacity of a radio link using multiple transmit
and receive antennas to exploit multi-path propagation has become an essential element of wirelesscommunication standards including IEEE 802.11n(Wi-Fi), IEEE 802.11ac (Wi-Fi), HSPA+ (3G),
WiMAX (4G), and Long Term Evolution (4G).
However, these spectral efﬁciency gain often
requires an accurate knowledge of the channel at
the receiver or at the transmitter, sometimes at bothside. Our challenge is that the channel properties at
low frequency has been studied, but for high frequency (especially in the outdoor environment),
they are not clear in the current studies. Therefore,
it is necessary to characterize radio channel athigher frequency spectrum.
To characterize radio channel, 11 GHz 24x24
MIMO channel sounder with 400 MHz bandwidth
has been built, and massive indoor and outdoor
measurement campaigns are conducted[1]. And akind of high resolution parameter estimation technique known as space alternating generalized ex-

2.Extraction of Propagation
Channel Parameters
Measurement data is utilized to analyze radio
channel conditions. And channel model is based on
the measurement data. Before discussing themethod of processing measurement data, ﬁrstly,channel model should be introduced.
Double directional channel model can be de scribed by:L

H MIMO ( f ) = ∑ a R,l (ϕ R,l ,θ R,l , f )⋅ Γ l ⋅ a T,l (ϕ T,l ,θ T,l , f )e− j 2 π f τ l
l=1

(1)

where L is the number of paths, ! f is frequency,! a T and ! a R is antenna array of transmitter and receiver respectively, ! ϕ T ,θ T ,ϕ R ,θ R denote the azimuth and co-elevation at transmitter side called
Direction of Departure (DoD) and receiver side
called Direction of Arrival (DoA), ! τ is time delay,
and ! Γ includes four kinds of polarization complex
path gain. A propagation channel can be character-21

ized by nine parameters, i.e. DoD, DoA, time delay,
and polarization complex path gain.
SAGE[5] is a kind of channel parameter estimation algorithm. It is based on Maximum Likelihood Estimation (MLE). The basic idea of MLE is
to ﬁnd parameters that maximum the likelihood
function. Basing on MLE, SAGE is divided into
two steps to achieve lower complexity: Expectation
step (E-step) which is to decompose data into different signal components and Maximization step
(M-step) which is to search for parameters.

measurement data with initial parameters into
SAGE, and get parameters for every path.

3.2 Path loss characteristics
comparison
Here we choose path loss characteristic to test
the results of SAGE, because it is a fundamental
channel characteristic which explains how the average power level changes when the distance between transmitter and receiver is increased. It is of
most importance in any wireless communication
systems as it determines the coverage of the radio
signal in the given environment with assigned
transmitted power and signal-to-noise ratio (SNR)
requirement at the receiver.
To make comparison, we must calculate pathloss from measurement and SAGE results respectively. And the following ﬁgures are the results.

3.Veriﬁcation of SAGE with
Indoor Measurement data
Our main task is to analyze microcell radio
channel measurement data. Firstly, to verify SAGE
algorithm, we would like to process measurement
data measured in indoor scenario where all of thesnapshots are Line of Sight (LoS) and the moving
directions of Mobile Station are not changeable.

VV

HV

100

100
Free Space
SAGE detected
Measurement

90

90

85

85

80
75
70

80
75
70

65

65

60

60

55
50

Free Space
SAGE detected
Measurement

95

Path loss [dB]Path loss [db]

95

55
1

10

Distance between BS and MS [m]

50

1

10

Distance between BS and MS [m]Figure 2 Path loss comparison

We choose VV-polarization as the example forco-polarization case and HV-polarization as theexample for cross-polarization case. Form the results, we can see that co-polarization contributes
most of the path weight of one snapshot, and thepath loss calculated from measurement data directly and that from SAGE results of both cases match
well, especially for co-polarization case. Therefore, SAGE algorithm is believed to be applicable.

Figure1. The concept of SAGE veriﬁcation

3.1 Measurement campaign
The measurement was conducted in collaboration room of West-9 Hall of Tokyo Tech. Tx which
can also be seen as MS (Mobile Station) is moving
while Rx which can also be seen as BS (Base Station) is ﬁxed. It is 24×24 MIMO system, and bothtransmitter and receiver are Uniform Circular Array
with height of 170cm. Some system parameters canbe seen as the following table.
Center frequency

11 GHzSignal bandwidth

400 MHzTx power per antenna

10 mW

No. of multitones

2048

Tone separation

195 kHz

4.Analysis of Urban Radio
Channel Measurement
After verifying SAGE, we can analyze propagation channel conditions based on measurement data
conducted by channel sounder in outdoor scenario.

4.1 Measurement campaign
The measurement was conducted along thestreets of Ishigaki city. Figure 3 shows the map of
measurement. Tx which can also be seen as MS(Mobile Station) is moving along the Tx route with
red color, while Rx which can also be seen as BS

Table 1. System parameters

After getting the transfer function of each individual frequency by measurement, we can input the22

(Base Station) is ﬁxed on the Rx position with blue
color.

4.2 Data Analysis
By utilizing SAGE, we can get direction, delay,
and path weight parameters of each path. To makethese parameters meaningful for channel estimation, our next step is to analyze them by some factors.

oRx
Tx route

50
100
150
200
250
300
350
400
450
500
550
100

200

300

400

500

600

700

Figure 3. Transmitter route of measurement

Tx is located on a moving car. and the maximum speed is 10km/h. The height of Tx is 3m andthe deﬁnition of 0° at transmitter side is the moving
direction, while the height of Rx is 2.5m and thedeﬁnition of 0° at receiver side is 75° North by
East. Both array response of transmitter and receiver are UCA. But one of the AD board is broken, so
it is 12V12H×12V10H MIMIO system. Systemparameters are same with table 1, and measurement
parameters are shown as the following table.
Antenna gain

7 dbi

Delay resolution

2.5 nsNo. of Snapshots

800

Figure 5. Data analysis process

Here the process has been divided into two parts
as shown in ﬁgure 5. One is some characteristics
that can be calculated by results directly, the otherone is clusters identiﬁcation that must be analyzed
combining with other proper methods.

4.2.1 Characteristics of Radio Channel
1. Path loss and polarization characteristics:
Path loss has been mentioned above. And thepath loss model we will use in this analysis is distance dependence with linear ﬁtting in dB scale.
And an important parameter that characterizes
the channel polarization is the cross-polarization
power ratio (XPR) which indicates the power ratiosof co-polarization to cross-polarization for verticaland horizontal polarization. Similarly, the co-polarization power ratio (CPR) which indicates the power ratio of the vertical polarization (VV) to the horizontal polarization (HH).
Results: By comparing the results with those inlower frequency, linear ﬁtting path loss results arelager. In case of XPR, it is smaller comparing witha typical factor of 10~30 dB. One of the reasons
may be the some small errors of SAGE, On theother hand, the high frequency spectrum requirelarge polarization diversity of antennas.
2. Delay spread:
Delay spread is one indicator of the multi-pathrichness of the radio channel. It can present the
spread of paths in terms of time domain.
Results: Delay spread is 4~33ns which issmaller comparing with 38/50 ns in case of 2GHz
and 22~88 ns in case of 5.3 GHz. And The largest
delay spread appear around the snapshot which obtains the longest distance from BS.

Table 2. Measurement parameters

By checking residual power combined with average path gain of each snapshots. there are fourparts of snapshots that can be analyzed by applying
their data into SAGE. Based on the transmitterroute and their positions, we can divide all of thesesnapshots into two course which are shown in thefollowing ﬁgures.

Figure 4. Two transmitter courses

The residual power of all snapshots is under
30% and the total number is 111, and all are LoS
snapshots.

23

3. Direction spread:We describe a direction as a unit vector Ω (calculation coordinate) which the initial point at O andthe terminal point is located on a sphere and only
consider azimuth.
Results: The direction at MS is more distributed
than that of BS. The reason is that the azimuth ofdeparture ranges among all directions while theazimuth of arrival is limited by the measurement
courses. In addition, we can also ﬁnd that direction
spread is quite large, so we need further analysis bypower spectrum.
4. Delay power spectrum and azimuth power
spectrumThe delay power spectrum (DPS) and azimuth
power spectrum (APS) can be calculated based on
azimuth-beam forming.
Results: From the results of DPS, We can ﬁndthat SAGE detected most of dominant paths successfully. And by plotting APS, we can ﬁnd the direction of LoS component. For other NLoS components, so we detect them by clustering.

5.Conclusion
Finally, the comparison results can be seen as
the following table.
Low Frequency(2 GHz)[3]Low
Frequency(5.3 GHz)[4]High Frequency(11 GHz)

Comparison(for higher
frequency)

Microcellradius

150 m

200 m

150 m

×

Path loss

a=2.2 b=34.0※Based onmodel in [6]a=2.2 b=42.5※Based onmodel in [6]a=2.42b=48.9/53.2

LargerDelay
spread38/50ns

22-88 ns

4-33 ns

Smaller

Direction
spreadAngular ranges
within 40°

Angular ranges
within 40°

Angular ranges
within 60°-70°

largerTable 3. Comparison Results

The most important contributions of this thesis
can be summarized below:• At high frequency spectrum, radio channel
parameters can be extracted by SAGE algorithm
from data measured by channel sounder, and we
can estimate these parameters by comparing
characteristics of path loss, delay spread and direction spread with those of lower frequency
spectrum.
• For path loss, it is larger than the path loss atlower frequency.
• For delay spread, it is smaller than the delay
spread at lower frequency. This results can beused for designing the guard intervals of OFDM
based system.
• For direction spread, it is quite larger than the
direction spread at lower frequency. This results
can be used for designing the antenna pattern inthe system.
• The main scatters are building walls in this
scenario, and for all snapshots, there are 11~17
main clusters can be detected by KPM clustering
algorithm.

4.2.1 Identiﬁcation of clusters
Visual clustering has been used for a long time,
but visual identiﬁcation of clusters contains a certain level of uncertainly and interpretation, also
very difﬁcult to deal with huge amount of data. So
the clustering algorithm will be used here is KPower Means (KPM) clustering.
The basic concept of K-Means clustering is“Clusters are chosen such that they minimize thetotal distance from their centroids”. Different from
K-Means algorithm, KPM also includes the inﬂuence of power to make clustering result more reliable.
We also we analyzed the delay spread and direction spread intra clusters.
Results: From the clustering results, here wecan choose some of the main clusters of one snapshots to draw a rough sketch of clusters combining
with map.
Intra-cluster delay spread is from 0 to 18 ns andmore that 90% are under 4 ns which means that thedelay has been well detected by clustering. Intracluster direction spreads of AoA and AoD are almost same. It reduces a lot compared with the direction spread among all the individual paths but
still a little bit large. The reason may be that The
effects of the scattering of rays from the rough surface of building walls and small objects are more
dominant in the higher frequency.

References[1] M. Kim, J. Takada, and Y. Konishi, “Novel Scalable MIMO Channel Sounding Technique and Measurement Accuracy Evaluation withTransceiver Impairments,” IEEE Trans. Instrum. Meas., vol. 61, no.
12, Dec. 2012.
[2] Molisch, Andreas E. "MIMO systems with antenna selection-an
overview." Radio and Wireless Conference, 2003. RAWCON'03. Proceedings. IEEE, 2003.
[3] Saito, Kazuyuki, Tetsuro Imai, and Yukihiko Okumura. "2 GHz
Band MIMO Channel Properties in Urban Small Cell Scenario in
Crowded Area." Vehicular Technology Conference (VTC Fall), 2014
IEEE 80th. IEEE, 2014.
[4] Zhao, Xiongwen, et al. "Propagation characteristics for wideband
outdoor mobile communications at 5.3 GHz." Selected Areas in Communications, IEEE Journal on 20.3 (2002): 507-514.
[5] Pham Van Hue, Radio Channel Parameters Estimation Using
SAGE Algorithm, Master thesis, International Development Engineering, Tokyo Institute of Technology, Mar, 2014[6] Series, M. "Guidelines for evaluation of radio interface technologies for IMT-Advanced." Report ITU (2009): 2135-1.

24Factors influencing time series electricity consumption trend at TokyoTech campus
Student Number: 13M51514   Name: Yuji HIRAI   Supervisor: Naoya ABEpe¦NÃO¸½cR±\sBÁ@ µ} peÃO¸½cR!»TÈt¾¦N,:)!McR
cRe ¨©w!vÃO¸½cRj=hyz|!¯§ÃO¸½cRKX?e½ÃO!¸ xaÄS 9 UÄj= cR±\cRÅQ[lL{k time, it is important to clarify how electricity consumption
has change over time and what factors could influence thetime series electricity consumption trend at university
settings.

1. Introduction
1.1 Electricity consumption and universities
Realizing higher level of energy saving is one of the mostimportant challenges for achieving sustainable society. In
Japan energy consumption in commercial sector isincreasing markedly and account for 32.5%.
Approximately 70% of energy consumed in commercial
sector is caused by electricity consumption. Therefore
electricity saving in commercial sector is one of urgent
issues.
Tokyo Tech, which electricity consumption isregarded as a part of commercial sector consumption, isa research university that consumes the electricity as muchas 17,000 households do every year, meaning that its
consumption patterns and quantity could draw more
public attentions. Indeed both central government andTokyo metropolitan government enacted various laws toregulate companies, offices and organizations to reduce
electricity consumption for the sake of environmental
conservation. In this regard, Tokyo Tech is obligated toreduce 1% of electricity consumption every year inaccordance of the laws, the Energy Conservation Act by
the central government and the Tokyo Metropolitan
Environmental Security Ordinance. According to theregulations, Tokyo Tech was required to propose its
electricity saving policy. In the policy, Tokyo Techcommitted to conduct effective electricity saving practices
as long as Tokyo Tech can keep its quality of education
and research activities. Under this policy, Tokyo Techfocuses on peak-cut and base-cut by monitoring andcontrolling electricity consumption and real-timeelectricity consumption visualization system or alarming
system. For example they shift a part of machine operation
schedule to midnight (peak-cut) and take a second look at
machine operation to improve electricity use (base-cut).
As a result, by the end of 2014, Tokyo Tech successfully
reduced 2.2% in average in the last 8 years by promoting
those electricity saving actions and installing highelectricity efficient equipment [1].
However detailed electricity consumption
trend over time at university setting have not been
investigated well while cross-sectional consumption
pattern are well reported in each university’senvironmental reports or alike. In order to come up withsustainable and effective actions for electricity saving over

1.2 Current understandings of university
electricity consumption
Electricity consumption in university is divers because of
their different functions, majors, staffs, and students. In
this context, each university in Japan reports theircontribution to electricity saving. For example, Universityof Tokyo and Osaka University insists their roles as
research universities to lead sustainable society throughtheir electricity saving actions [2][3]. Kyusyu University
focused on understanding current electricity consumption
situation [4]. The University of Electro-Communications
considers contribution to environment and reports tosociety is responsibility to establish reliance and openness[5]. Tokyo University of Agriculture and Technology
mentions importance to reduce cost for electricity
consumption to maintain quality of its academic activities
toward rising electricity prices [6]. According to thosereports, investigating electricity consumption data ismeaningful for understanding current consumption
structure to specify energy saving policy and evaluate
policies in order to contribute to sustainable society andfulfill their responsibility as research institute.
Oohashi et al.(2013)[7] and So(2010)[8]investigated electricity consumption pattern at universityand distinguished electricity use related to humanactivities and others to specify electricity use to cut.
Yamahane et al.(2012)[9] and W.P.Wong[10] evaluatedelectricity saving actions and estimated future electricity
consumption pattern and by creating the model. The
review of those reports and previous articles, however, do
not provide enough insights about how electricity
consumption pattern change over time and what factors
could influence the trend.

2. Objectives and Contribution
2.1 Objectives
Tokyo Tech also has unique electricity consumption
pattern caused by different electricity consumption
patterns at different size, function and facilities. In this25

5,000,000

The objective of this study is to identify factors
influencing time series electricity consumption trend at
Tokyo Tech campus under following three perspectives:•
To clarify the point when the electricity
consumption pattern changed based on rate ofelectricity consumption trend change.
•
To identify the buildings contribute to the electricity
consumption trend change.
•
To clarify the factors affecting the trend change.

Administrator

Monthly

3,700,000
3,650,000
3,600,000

Seasonal effect modiﬁed electricity consumption

Figure 2: Seasonal effect modified electricity consumption at
Tokyo Tech campus

3.2.2 Contribution to percentage change
To measure how each building’s electricity consumption
trend change contributes to the university-wide overall
electricity consumption trend change, contribution topercentage change is calculated. Equation (2) shows theformula of the calculation. Difference of electricity
consumption in one building from last month is divided byelectricity consumption as whole campus in last month.
The total amount of contribution to percentage change by
each building is equal to the rate of electricity
consumption change at the whole campus’s electricity
consumption.

Table 1: Summary of electricity consumption data collected

51 Buildings

3,750,000

3,550,000

In this research electricity consumption time series datafrom June 2011 to Oct 2015 is investigated, those data aremeasured, collected and maintained by Tokyo Institute ofTechnology Energy Conservation office.

Monthly

3,800,000

2011-Jun
July
Aug
Sep
Oct
Nov
Dec
2012-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2013-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2014-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2015-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct

Electricity consumption (kWh)3,850,000

3.1 Data collection

Whole campus

3,000,000

Electricity consumption original time series

3. Data collection and methodology

Data

3,500,000

Figure 1: Original electricity consumption time series at Tokyo
Tech campus

From the result of this study, the cause of electricity
consumption trend change is clarified and different
electricity consumption patterns in different buildings are
confirmed, therefore This research will contribute topropose effective and sustainable electricity consumption
reduce policy and actions in the future.

Frequency

4,000,000

2,500,000

2.2 Significance of the study

Area

4,500,000

2011-Jun
July
Aug
Sep
Oct
Nov
Dec
2012-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2013-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2014-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2015-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct

Electricity consumption (kWh)research, the pattern was investigated especially focusedon when and how the trend of electricity consumption
pattern changed and which building contributed to thepattern change.

Duration
2011 Jun ~2015 Oct
2011 Jun ~2015 Oct!! ! ! !

!!! ! !!!! !
!!!!!!!! !!"! !!
!!!!

!!!!!!!!!! ! !!"#$!!!!!!!!!!!!!!"#!!"##!! !!"#!!"#$!
!!! ! !!!!! ! !!"#$%&'($&"#!!"!!"#$"%&'("!!!!"#$!
!!!!!! !!! ! !!"#$%&'$'%(!!"#$%&'()"#!!!!!!!!!"#$%#&'!
!!!!!! ! !!"#$%&'$'%(!!"#$%&'()"#!!"!!!!!!"#$%#&'!
!" !"#$ !"#$!
!!!!!!! ! !!"#$%&'$'%'!!"#$%&'()"#!!"!!!!"#!!"#$%&!
!!!!!!!!!!!!!!!!!!"!!"#$!!"#$!!

Tokyo Institute of Technology
Energy Conservation Promotion Office

3.2 Methodology
3.2.1 Seasonal effect modification
As original time series has seasonal effect fluctuation, theoriginal pattern can be understood as containing Trendseries (T(t)), cycle series(C(t)), seasonal series(S(t)), andirregular series (I(t)). (equation (1)) In this study seasonal
effects are eliminated by moving-average method in orderto define trend change. Figure 1 shows the result of
seasonal effect modification for electricity consumption at
Tokyo Tech campus in 5years by using R program.
Figure1showsoriginal
electricity
consumption time series, and Figure 2 shows seasonal
effect modified electricity consumption time series.

4. Electricity consumption at Tokyo Tech
campus
4.1 Change of electricity consumption trend
Figure 3 shows the rate of electricity consumption trendchange. According to the result, electricity consumption
trend can be classified into 3 terms; Term1: from
December 2011 to September 2012, Term2: from August2012 to December 2013, Term3: from January 2014 toApril 2015. In term 1, the electricity consumption trend
increased continuously. In Term 2, the trend fluctuateddrastically. In term 3, the trend kept small change until
Oct 2014, then gradually shifted to balanced situation.

!!!! ! ! !"!!! ! ! !!!! ! ! !!!! !!!!!!"! !!

26

1.5%

3,800,000.00

1.0%

3,750,000.00

0.5%

3,700,000.00

0.0%

3,650,000.00-0.5%

3,600,000.00-1.0%

Term 2

Term 3Dec
2012-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2013-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2014-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2015-Jan
Feb
Mar
Apr

Term 13,550,000.00

computing center accounted for 50% of contribution toelectricity consumption decrease. In Term 3 until
September 2014, the computing center and the mainbuilding accounted for 20% of contribution to electricity
consumption decrease. After October 2014, the computing
center accounted for 29% of contribution to electricity
consumption increase and the main building accounted for
28% of contribution to electricity consumption.
It can be said that indeed the electricity
consumption trend change was common phenomena
among each buildings, but buildings that consume muchelectricity such as the computing center or the mainbuilding mainly contributed to trend change.
Through analysis of contribution to percentagechange by buildings, the buildings that mainly contributeto electricity consumption trend change were clarified,
however each building has its own trend. Therefore inorder to figure out how those trends affected electricity
consumption trend change, each building is divided into 5groups by using cluster analysis based on its electricity
consumption trend. And contribution to percentage change
by each groups were calculated.
Figure 5 shows contribution to percentagechange of each electricity consumption pattern group andTable 2 shows name of buildings that belong to eachgroups. The numbers beside the name indicate the rankingof electricity consumption among buildings.
Result shows that consumption increase in the
Term1 was caused by increase in the Group 3 and theGroup 4, in the Term 2 the Group 2 shifted from decrease
to increase, the Group 3 shifted increase to decrease and
the Group 4 kept increase trend, consumption decrease in
the Term 3 was caused by decrease in the Group 3 and theGroup 4 but Group 3 was gradually shifting to increase
trend.

Rate of change [%]Electricity consumption [kWh]3,850,000.00

Seasonal effect modiﬁed electricity consumption-1.5%

Rate of Electricity consumption change

Figure 3: Rate of electricity consumption trend change

4.2 Contribution of electricity consumption
trend change in each building
Figure 4 shows the number of buildings contributing
electricity consumption increase or decrease in eachmonth. The result shows the number of buildings that
contribute to increase or decrease of electricity
consumption has been changing as rate of electricity
consumption change. It means that electricity
consumption change in each month was common
phenomenon for each building.

electricity

On the other hand, in Tokyo Tech, electricity
was not consumed equally by each building. For example
the computing center accounted for 25%, the mainbuilding accounted for 10% and top 10 buildings
accounted for 60% of all electricity consumption. Gini
index of electricity consumption among buildings werealso calculated, confirming that the electricity
consumption distribution on the campus is indeed quite
uneven the Gini index is about 0.63 consistently in those 5
years. According to the fact, next question is whether
contribution by each building was even or not.
In Term 1 (from December 2011 to September2012) the computing center and the main building
accounted for 28% of contribution to electricity
consumption increase. In Term 2 (from August 2012 to
December 2013) only 3 buildings accounted for 25% ofeach trend change. Especially after May 2013 the mainbuilding and the south 9th building accounted for 20% of
contribution to electricity consumption increase and the

1.0%

0.5%

0.0%

-0.5%

-1.0%

-1.5%

2012-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2013-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2014-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2015-Jan
Feb
Mar
Apr

Figure 4: Number of buildings contributing
consumption increase/decrease in each monthRate of electricity consumption change [%]1.5%Group1
Group1

Group2

Group4
Group5
Group2 Group3
Group4
Group3Rateof electricity
Rate of electricity
consumptionchangeGroup5
consumption change

Figure 5: Contribution to percentage change by each electricity
consumption pattern group
Table 2: Electricity consumption trend group
Group 1

Group 2Ishikawadai Bldg. 5 (12)Venture Business(30)

South Bldg. 5(51)Group 3

Group 4

Group 5South Bldg. 9(3)Computing center(1)

Main Bldg.

East Bldg.1(10)South Bldg. 8(4)West Bldg.8W

South Bldg. 3(11)South Bldg. 7(6)West Bldg.9(7) Van de Graaff Lab. (34)

Ishikawadai Bldg.1st (13)South Bldg. 1(8)West Bldg.8E(14) Midorigaoka Bldg.5 (39)Institute Library(9) Ishikawadai Bldg. 6 (18)West Bldg. 4(15)

East Bldg. 2(16) Ishikawadai Bldg.2 (20)South Bldg. 6(23) Ishikawadai Bldg.3

South Bldgs. 4

Cafeteria(2)Midorigaoka Bldg. 1 (17)(5)

North lab. Bldg.3b (32)Tokyo Tech Front (22)West Bldg.3(28)(25) The Centennal Hall (33)North Bldg.1

(29)

(35)

West Bldg.7(31)North Lab. Bldg. 1 (43) International House (36)Global Bldg.

(38)

Research center for low
temperature Phsics(24)

Ishikawadai Bldg. 4 (27)West Bldg. 1
Environmental safety
management Bldg.

North Bldg. 2

North lab. Bldg.5(44) North lab. Bldg 2b (37) Midorigaoka Bldg. 3 (40)(45)

The 70th Anniversary
Auditorium(41)

The 80th
Anniversary Hall(48)

North lab. Bldg. 4-1 (42)
Health Service Center (46)

North lab. Bldg. 6 (47)

(Number in parenthesis: Electricity consumption ranking)

27

(49)

(19) Radioisotope Lab. (50)(21) Administration Bureau (26)

5. Influencing factors

6.Conclusion

Based on the electricity consumption situation in last 5
years, influencing factors are examined. First, correlation
between electricity consumption trend and temperature
trend are considered. Figure 6 shows electricity
consumption trend change and temperature trend change.
A correlation coefficient of electricity and temperature in
the Term1, the Term 2, and the Term3 is -0.032, -0.151
and 0.985 respectively. From the result, continuous
reduction in the Term 3 could be caused by temperaturechange.
The correlation coefficients in Group 1, Group2, Group 3, Group 4 and Group 5 are -0.265, 0.073, 0.203,
0.640 and 0.765 respectively. Electricity consumption
trend change at Group 4 and Group 5 could be caused bytemperature change.
The introduction of high efficient equipment
could cause electricity consumption trend change. Fivebuildings among ten that introduced high efficientequipment in 2014 belong to Group 4 that caused
reduction trend in the Term 3.
Figure 7 shows Electricity consumption trend
and Electricity charge trend. Rising electricity charge is
big incentive for Tokyo Tech to reduce electricity
consumption. However electricity consumption reduction
was not enough to reduce electricity charge. From January
2013 electricity consumption reduced but payment toelectricity bill increased. To make up the cost caused byrising electricity price from 2011, 9,547,406 kWh must be
cut. That is 21.5% of electricity consumption in 2015.

Regarding current electricity consumption situation in thelast 5 years, electricity consumption trend at Tokyo Techis divided into 3 terms based on the rate of electricity
consumption change. Electricity consumption trendchanges in both Term 1 and Term 2 were relatively large
and short-term change. On the other hand, electricity
consumption trend change in Term 3 was small andlong-term change.
The increase in electricity consumption from
December 2011 to September 2012 was mainly caused byincrease trend at the computing center and the mainbuilding (Term 1). Drastic trend change from August 2012
to December 2013 was mainly caused by the transition
from continuous decreasing trend to increasing trend at thesouth 9th building and by the transition from continuous
increasing to continuous decreasing in the computing
center (Term 2). Continuous decreasing from January
2014 to April 2015 was mainly caused by the decrease
trend at main building and the computing center (Term 3).
Based on current electricity consumption
situation, influencing factors were investigated. Especiallyin Term3, two factors such as temperature trend change
and installation of high efficient equipment are exploredas influencing factors. Those factors could affect theelectricity consumption reduction at the buildings belong
to Group 4 such as the main building that caused
electricity consumption reduction at Tokyo Tech campus.
For the future effective and continuous
electricity consumption reduction, the most important
thing is to maintain drastically lower electricity
consumption at the buildings, which consume muchelectricity such as the computing center, the main building
and the south 9th building. However, considering thefact that those buildings are also quite important on thecampus for research and educational activities, otherapproach and countermeasures for the further reduction ofelectricity consumption needs to be also seriously
considered. For that end, further study is necessary toinvestigate empirically how the people on the Tokyo Tech
campus are actually using the electricity and how thechange of their electricity consumption-related behaviors
could actually contribute to the campus-wide consumption
reduction. It is also important to clarify in-what-formand how the necessary information and incentives could
be provided to the people for the electricity saving
behaviors, as the simple provision of electricity
consumption information would not contribute to theactual reduction of electricity consumption.

Figure 6: Electricity consumption trend and temperature trend
3,850,000

Electricity charge (yen)70,000,000

3,800,000

60,000,000
3,750,000

50,000,000
40,000,000

3,700,000

30,000,000

3,650,000

20,000,000
3,600,000

0

Oct
Nov
Dec
2012-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2013-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2014-Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2015-Jan
Feb
Mar
Apr
May
Jun

10,000,000

Electricity charge trend

Electricity consumption (kWh)80,000,000

Reference[1] ]£efD Cpef (2015) CpefbaY 2015[2] ]£efD Cef (2015)
CefbaY 2015[3] ]£efDeÀef (2015) eÀefbaY 2015[4] ]£efDAoef (2015) AoefbaY 2015[5] ]£efDÃºJef (2015) ÃºJefbaY 2015[6] ]£efD
C¹pef (2015) C¹pefbaY 2015[7] eq9inu9<W@(2013)e²ªVef~³"/5$:¸hy¿ ¡ÃO¶­«h,:)! eÀef"/5$:¸wM
v¥fGb¦µ}Â¤ 78 r¤ 684
U,193-201.
[8] g_`(2010)d¢´'(+2 ^ef#380("/5$:¸hy´ ¡¢´Z9°pfGµ}ÂNo.156.
[9] m¬`9F®HE9P®e·9>I;Æ2012Ç
6-7&1*'4.
8%! >¼e#380("/5$:¸½M
ªVpf
¤ 24 r 47-54.
[10] W.P. Wong R.F. Fellows A.M.M. Liu, (2006),"Use of electrical energy inuniversity buildings: a Hong Kong case study", Facilities, Vol. 24 Iss 1/2 pp. 5 - 17

3,550,000

Electricity consumption trend

Electricity consumption trend
Electricity charge trend

Figure 7: Electricity consumption trend and Electricity charge
trend

28

Effects of self-discharge on charge-discharge efficiency of electric double-layer capacitor
Student number: 14M18052 Name: Kenta KOH Supervisor : Kunio TAKAHASHI㟁Ẽ஧㔜ᒙ࢟ࣕࣃࢩࢱ࡟࠾ࡅࡿ⮬ᕫᨺ㟁ࡀ඘ᨺ㟁ຠ⋡࡟ཬࡰࡍᙳ㡪㧗 ೺ኴᮏ◊✲࡛ࡣ㟁Ẽ஧㔜ᒙ࢟ࣕࣃࢩࢱࡢ඘㟁㐣⛬ࠊ⁀ࡵࡓ㟁Ⲵࢆ౑⏝ࡍࡿࡲ࡛ࡢᚅᶵ᫬㛫୰࡟Ⓨ⏕ࡋ࡚࠸ࡿ࢚ࢿࣝࢠ࣮ࣟ
ࢫࢆ࢖࢜ࣥᣑᩓ࡜ෆ㒊᢬ᢠ࡟ࡼࡿࡶࡢ࡜ศ㢮ࡋࠊゎᯒࡍࡿࡇ࡜࡟ࡼࡾ⮬ᕫᨺ㟁ࡀ඘ᨺ㟁ຠ⋡࡟ཬࡰࡍᙳ㡪ࢆㄪ࡭ࡓࠋࡇ
ࢀ࡟ࡼࡾ㟁Ẽ஧㔜ᒙ࢟ࣕࣃࢩࢱࢆ⵳㟁ࢹࣂ࢖ࢫ࡜ࡋ࡚฼⏝ࡍࡿ㝿࡟ᚅᶵ᫬㛫࡟ᛂࡌ࡚࡝ࡢࡼ࠺࡟඘㟁ࢆ⾜࠼ࡤ඘ᨺ㟁ຠ⋡Ⰻࡃ౑࠼ࡿ࠿ᥦ᱌ࡍࡿࠋ

1. Introduction

2. Energy loss in charge-discharge process

Electric double-layer capacitors (EDLC), forming
double-layer which is several nanometers are currently
discussed as a storage device. They are used cases ofchanging storage device is difficult, maintenance freeand so on.
Because of their small internal resistance and long
cycle life, they have less volumetric and gravimetricenergy density to compared with rechargeable battery.
Storage devices have self-discharge that is thedecrease of its open circuit voltage as shown Fig1. The
self-discharge of EDLC is much higher than that ofrechargeable batteries.

We consider energy loss in charge discharge processclassify into ion diffusion and internal resistance[1]Ion diffusion, accumulation of an excess ionicconcentration occur in charging process.

Fig2 schematic ion distribution model
2.4

When the capacitor is disconnected from the charging
circuit, the charge of double-layer will stay in place but
the excess ionic concentration near the carbon surface
will diffuse to an equilibrium state as shown in Fig2.
So the area increase, the capacitance‫ ܥ‬also increase.
And the open-circuit voltage of the capacitor decrease.
In this paper, to determine ‫ܥ‬, we use output charge ofdischarge capacitor. Precisely, we should use stored
charge in capacitor, but it is enough as a first
approach. So we use ‫ ୲ܥ‬which is the ratio of output
charge and voltage of capacitor as capacitance. The
case ion diffusion is over, ‫ ୲ܥ‬ൌ ‫ ҄ܥ‬is constant. Energy

Voltage[V]

2.3
2.2
2.1
2
1.9

0

10

20

loss by ion diffusion is difficult to make model. So weinvestigate energy loss by ion diffusion withexperiment.
Energy loss by internal resistance, the model asshown in Fig3.

Time [hour]
Fig1 Voltage drop by self-discharge
In research[1-3], the self-discharge consists of a
relatively fast ion diffusion process and a slower
leakage current. They estimate voltage drop of
capacitor under condition that capacitance is constant.
This estimation doesn’t contain effect of charge
current. In addition, capacitance is changed byinternal state of capacitor. So we cannot estimate
energy of capacitor from open circuit voltage. Chargedischarge efficiency is significant parameter forstorage devices to design for energy harvesting and soon.
Therefor purpose of my research is investigating
effects of self-discharge on charge discharge efficiency
and suggest how to charge with high charge discharge
efficiency.

Fig3 model of internal resistance
Leak current is occurred by disappearing a part ofcharges between double-layer and flowing charge on
surface of capacitor. Energy loss occur by parallelinternal resistance ܴଵ in charge process and open
circuit process. ܴଵ is changed by ion diffusion andinternal state of capacitor [4]. However after enough29

time pass, the circuit consist of ideal capacitor andideal resistance because capacitance is constant. In
this state, voltage of capacitor in open circuit process
obtained as‫ݐ‬ܸ୵ ൌ  ‫  כ‬൭െ൱ሺͳሻܴଵ ‫҄ܥ‬3. Experiment and result
3.1 Equipument
We determine each parameter in experiment.
The circuit and equipment are shown in Fig5 andTable1.

So the Energy loss by parallel internal resistance, in
range of capacitance is constant, obtainedܸ୵ ଶ‫୔ܧ‬୪୭ୱୱ ൌ නሺʹሻܴଵEnergy loss by series internal resistance occur whencharging process and discharge process. Constantcurrent charging is desirable charging method
typically, we use constant current charging in thispaper. So we use ‫ܫ‬ୡ as charge current, the energy loss
is obtained‫ܧ‬ୗ୪୭ୱୱ ൌ ܴଶ ‫ܫ‬ୡଶ ሺ͵ሻ

Fig5 charge-discharge circuit

On the other hand, discharge process, the energy loss
is dependent on load. The smaller the load is, thehigher the energy loss. In this paper, the load which isconnected in discharge process is bigger than seriesinternal resistance. So we ignore this energy loss.
Using input energy ‫ܧ‬୧୬ , output energy ‫ܧ‬୭୳୲ , energy
loss by ion diffusion and internal resistance before
enough time passed ‫ܧ‬ୈ୪୭ୱୱ , total energy loss ‫ܧ‬୘୪୭ୱୱ
defined as

0 charge  㻌wait  㻌 discharge  㻌Power source

Matsusada ECD5-5‫ܫ‬ୡ 㸸charge currentܶ୵ 㸸wait timeܳ୧୬㸸input charge㻌ܳ୭୳୲ 㸸output chargeܸ୧୬ 㻌 䠖initial voltageܸ୭୳୲ 㸸output voltage㻌 㻌ொ‫ ୲ܥ‬㸦ൌ ౥౫౪ 㸧:capacitance㻌

135

2

134
133

௏౥౫౪

ܳ୭୳୲ Ȁܸ୧୬ 㻌

1

GRAPHTEC GL900

3.2ResultExperiment is done with ‫ܫ‬ୡ ൌ ͲǤͳሾሿ, ܸ୧୬ ൌ ʹǤͶሾሿ
because we can observe ion
and leak current.
on diffusion
dif
Fig6 shows whole process. We calculated capacitancewith output charge of discharge
harg and output voltage.
The capacitance is constantant from 12h from Fig7.

Voltage[V]Voltage(V)ܸ୭୳୲ 㻌

Voltage measure

We choose EDLC which has capacitance of 100F
(nominal) and voltage logger which has impedance of1Mȳ, that is several ten times bigger than ܴଵ .
In this experiment, the parameter we can determine
are ‫ܫ‬ୡ , ܶ୵ , ܳ୧୬ , ܸ୧୬ . And the parameter we canmeasure are ܳ୭୳୲ ǡ ܸ୭୳୲ . So we can calculate ‫ ୲ܥ‬.

We have to estimation ܴଵ and ܴଶ and ‫ܧ‬୘୪୭ୱୱ to know
effect of ‫ܧ‬ୈ୪୭ୱୱ on charge discharge efficiency byexperiment.

2

Elna DYNACAP DZTable1 equipment‫ܧ‬୧୬ െ ‫ܧ‬୭୳୲ ൌ ‫ܧ‬୘୪୭ୱୱ ൌ ‫ܧ‬ୈ୪୭ୱୱ ൅ ‫୔ܧ‬୪୭ୱୱ ൅ ‫ܧ‬ୗ୪୭ୱୱ ሺͶሻܸ୧୬ 㻌

Capacitor

132

1

131
130

Time

129

0

Fig4 Experiment process and parametersdischarge
㻌
charge wait 㻌 㻌 1280

To estimate ܴଵ enough time passed, we use fitting
method with equation (1) and ‫ ҄ܥ‬. So we do

1

2

0

3

10

20

Time[hour]Time[hour]

Fig6 charge-discharge processs

Fig7 Capacitance change

experiment changingܶ୵, we obtain ‫ ୲ܥ‬and equation ofvoltage drop.
To estimate ܴଶ , we use voltage drop οܸ just after
charging. In charging process, the voltage drop is

So fitting wait time to equation(1)uationfrom 12h to24h, weobtainedοܸ ൌ ܴଶ ‫ܫ‬ୡ ሺͷሻܴଵ ൌ ʹʹͲʹͶሾȳሿܴଵ ‫ ҄ܥ‬ൌ ʹǤͻͷͳ ‫଺Ͳͳ כ‬‫ ҄ܥ‬ൌ ͳ͵ͷǤͶ

In the same way, voltage drop on condition that initial
voltage are 0.6V, 1.5V, 2.4V. Fig8 shows the initial
voltage is higher, effect of ion diffusion is the bigger
and diffusion time is the shorter.

It is unknown that which series internal resistancechange or not by charge current, initial voltage and so
on.

30

2.4

experimentfittingܸ୧୬ ሾሿ
0.2

οܸሾሿ
0.066

0.033

0.6

0.060

0.03

1.6

1.2

0.069

0.0345

1.4

1.8

0.066

0.033

1.2

2.1

0.074

0.037

2.4

0.067

0.0335

2.2

Voltage[V]

2
1.8

1

ܴଶ ሾȳሿ

0.8

Table3 Dependence of ܴଶ on initial voltage

0.6
04. Discussion of charge discharge efficiency

20㻌

10

We divided discussion of charge-discharge efficiency
with wait time because there is large difference ofinternal state of capacitor.
4.1 Case of ࢀ‫ ܟ‬ൌ ૙
If you want to use energy just after charging,‫୔ܧ‬୪୭ୱୱ ൌ Ͳ
From equation(4)‫ܧ‬ୈ୪୭ୱୱ ൌ ‫ܧ‬୘୪୭ୱୱ െ ‫ܧ‬ୗ୪୭ୱୱ
‫ܧ‬୘୪୭ୱୱ is measured value and ‫ܧ‬ୗ୪୭ୱୱ is calculated fromequation(3).

Time[hour]
Fig8 Deference voltage drop each initial voltage
Fig9 shows change of ܴଵ by initial voltage. From thisgraph, ܴଵ is depend on initial voltage. And initial
gr
voltageis small, leak current is also small.
vo

0.3A
1A
2A

0.4

6
60000

0.3
40000
4

‫ܧ‬ୗ୪୭ୱୱ Ȁ‫ܧ‬୧୬㻌

Parallel internal resistance [ȳ]㻌

8
80000

0.2

2
20000

0.5

1

1.5

2

2.5

0.1

Fig9 dependence of internal resistanceon initial voltage
Table2 shows that οܸ and calculate ܴଶ each current
And Table 3 shows that οܸ and calculate ܴଶ each
initial voltage with charge current is 2A. From thisresult, series internal resistance is independent on
charge current and initial voltage. So we use averagevoltage drop and determine

0

0

1

ܸ୧୬ ሾሿ㻌2

Fig10 Effect of ‫ܧ‬ୗ୪୭ୱୱ on charge discharge efficiency
0.16

ܴଶ ൌ ͵͵Ǥͷሾȳሿ

0.3A
1A
2A

‫ܫ‬ୡ ሾሿ
1

οܸሾሿ
0.034

0.034

2

0.063

0.0315‫ܧ‬ୈ୪୭ୱୱ Ȁ‫ܧ‬୧୬ 㻌

0.14

ܴଶ ሾȳሿ

3

0.093

0.031

0.1

0.12

Table2 Dependence of ܴଶ on charge current

0.08
0

1

ܸ୧୬ ሾሿ㻌2

Fig11 Effect of ion diffusion and leak current before
enough time passed on charge discharge efficiency

31

Fig10 shows series internal resistance big effect on
charge discharge efficiency when initial voltage is low.
Fig11 shows ion diffusion and leak current big effect on
charge discharge efficiency when initial voltage ishigh. So there is the highest efficiency point between
0V to 2.5V as Fig12. From this graph, charge current isthe bigger, highest efficiency point is also bigger.

‫ܧ‬ୈ୪୭ୱୱ Ȁ‫ܧ‬୧୬ 㻌

0.35

0.3

0.9

0.8

‫ܧ‬୭୳୲ Ȁ‫ܧ‬୧୬ 㻌

0.25

0.7

0.5

1.5
2
2.5
ܸ୧୬ ሾሿ㻌Fig14 Effect of initial voltage on ‫ܧ‬ୈ୪୭ୱୱ (‫ܫ‬ୡ ൌ ͲǤͳሾሿ)

0.6

0.5

0.4

5. Conclusion

0.3A
1A
2A
0

1

ܸ୧୬ ሾሿ㻌We cleared Effect of ion diffusion and internal
resistance depend on initial voltage, wait time andcharge current.
Ion diffusion depend on initial voltage and charge
current. Series internal resistance do not depend on
initial voltage or charge current. Parallel internal
resistance depend on initial voltage.
So charge-discharge efficiency depend on initial
voltage and charge current. We suggest that how tocharge high charge-discharge efficiency each wait time.

2

Fig12 Charge discharge efficiency
4.2 Case of ࢀ‫ ܟ‬is enough long time
Case of storage energy long time, effect of ‫୔ܧ‬୪୭ୱୱ is big
when wait time is long. ‫ܧ‬ୈ୪୭ୱୱ can be represented‫ܧ‬ୈ୪୭ୱୱ ൌ ‫ܧ‬୘୪୭ୱୱ െ ‫ܧ‬ୗ୪୭ୱୱ െ ‫୔ܧ‬୪୭ୱୱ

Reference[1] B.W. Ricketts, C. Ton-That, 2000,” Self-discharge ofcarbon-based
supercapacitorswithorganic
electrolytes”,Journal of Power Sources 89. 64–69[2] Hengzhao Yang, Ying Zhang, 2011, ”Self-discharge
analysis and characterization of supercapacitors forenvironmentally powered wireless sensor network
applications”, Journal of Power Sources, v 196, n 20, p
8866-8873[3] Li Zhongxue, Wu Fu, 2011, ”Diagnostic Identificationof Self-Discharge Mechanisms for Carbon-Based
Supercapacitors with High Energy Density͇,Power andEnergy Engineering Conference (APPEEC), 2011 AsiaPacific, 25-28[4] Julia Kowal, Esin Avaroglu, 2011, ”Detailed analysisof the self-discharge of supercapacitors”, Journal ofPower Sources v196, Issue 1, 1 p573–579‫ܧ‬୘୪୭ୱୱ is measured value and ‫ܧ‬ୈ୪୭ୱୱ is calculated fromequation(3) and ܴଶ ., equation(2) and ܴଵ .
From Fig13 and Fig14, capacitor can be used highcharge discharge efficiency with small charge current
and small initial voltage

00.42‫ܧ‬ୈ୪୭ୱୱ Ȁ‫ܧ‬୧୬ 㻌

0.4

0
0.38

0
0.36

0
0.34

0
0.32

0.2

1

0.4

0.6
0.8
1
‫ܫ‬ୡ ሾሿ㻌Fig13 Effect of charge current on ‫ܧ‬ୈ୪୭ୱୱ (ܸ୧୬ ൌ ʹǤͶሾሿ)32

EXPERIMENTAL STUDY ON APPLICATION OF ANODE ELECTRODESIN ELECTRO-CHEMICAL REPAIR WITH WATER SUPPLY CURING METHOD
Student ID: 14M18187

Name: Shu YAMAMOTO

Supervisor: Nobuaki OTSUKI⤥Ỉ㣴⏕᪉ἲࢆ⏝࠸ࡓ㟁Ẽ໬Ꮫⓗ⿵ಟᕤἲ࡟࠾ࡅࡿྛ✀㝧ᴟᮦࡢ㐺⏝࡟㛵ࡍࡿᐇ㦂ⓗ᳨ウᒣᮏ ࿘ᮏ◊✲࡛ࡣ㸪⤥Ỉ㣴⏕᪉ἲࢆ⏝࠸ࡓ㟁Ẽ໬Ꮫⓗ⿵ಟᕤἲࡢ㝧ᴟᮦ࡜ࡋ࡚㸪ᚑ᮶ࡼࡾࡶ⤒῭ⓗ࡞ྛ✀㝧ᴟᮦࡢ㐺⏝ྍ⬟ᛶࢆ᳨ウࡋࡓ㸬ࡑࡢ⤖ᯝ㸪ࡑࡢ⿵ಟຠᯝ࡜౯᱁࠿ࡽ㸪ྛᕤἲ࡟᭱㐺࡞㝧ᴟᮦ࡜ࡋ࡚㸪㟁╔ᕤἲ࡟ࡣ࢔࣑ࣝ㸪⬺ሷᕤἲ࡟ࡣᑟ㟁ᛶࢩ࣮ࢺ㸪෌࢔ࣝ࢝ࣜ໬ᕤἲ࡟ࡣࢫࢳ࣮࣓ࣝࢵࢩࣗ㸪㟁╔ᕤἲᚋ࡟๤ⴠ㜵Ṇᕤࢆ⾜࠺ሙྜ࡟ࡣⅣ⣲⧄⥔ࢩ࣮ࢺࢆᥦ᱌ࡍࡿࡇ࡜ࡀ࡛ࡁࡓ㸬electrode of electro-chemical repair, the combination
repair is efficient and waste material can be reduced.
(2) Specimens
In this study, two types of specimens and one existing
structure are used.
One specimen is a prism mortar specimen. The size
is 40*40*160mm and rebar is embedded in the center. It
is used for investigation in which many cases are needed.
Specimens having several conditions are shown in Table
2. Repairing system is shown in Fig 1.

1. Introduction
Nowadays the deterioration of reinforced concrete
structures is widely reported all over the world. It isnecessary to maintain these structures to use for longtime. Electro-chemical repair [1] is one of repair methods
to RC deterioration related to steel corrosion. In this case,
it is necessary to provide the external solution on
concrete surface in the case of applying electro-chemical
repair to RC. Water Supply Curing Method [2] can beapplied as one of the reliable solution providing methods.
However, problems remain in practice ofelectro-chemical repair. The major problem is highconstruction cost. And titanium mesh which is used as
anode electrode accounts for large part of the cost.
In this study, the application of economical anodeelectrodes to electro-chemical repairs and improvementof the repair effect by replacement of anode electrode isinvestigated. Therefore the objective of this study is tosuggest optimal anode electrode for electrodeposition,
desalination, re-alkalization, and strengthening after
electrodeposition.

Table 2: Parameters of mortar specimens
Chapter

Crack

Initial Cl-

3
4
6

Crack
Non
Non

0kg/m38.0 kg/m3
0kg/m3Covering
depth
10mm
15mm
15mm

2. Overview of anode electrodes and specimens(1) Anode electrodes
In this study, target anode electrodes can be roughly
divided into three kinds except titanium mesh forcomparison. Their resistances and unit prices are shownin Table 1. The unit prices are lower than titanium mesh.
The first anode is metal. Metal is thought to beinappropriate because it is eluted by electrification. And
eluted metal has a possibility to affect the repair effect.
The second one is conductive sheet which consists ofcarbon black and plastic. It is not clear whether the
resistance of this sheet is low enough or not because it is
not so much low as titanium mesh.
The third one is carbon fiber sheet. If this sheet canbe used for strengthening even after usage as anodeThe other specimen is a RC beam deteriorated undermarine environment for 40 years (150*300*1800mm). It
is used for investigation of simulation of field practicewith Water Supply Curing Method. The rebar
arrangements are shown in Fig 2 and repairing with
Water Supply Curing Method is shown in Fig 3.
Φ6

A

D13

Φ6

(mm)

20

300

20

D13

20

20 75

250

A

3@100=300 150

250

Φ6

1800
Φ6

150

20

D13

A

Φ16

250

75

250

75

D13

300

Unit price(yen/m2)
15,000
448
160
2,400
2,000
1,000
6,000

150 3@100=300

20

Table 1: Anode electrodes
Kind of anode
Resistanceelectrodes(Ω࣭cm)
Titanium mesh
4.92E-05
Aluminum
2.65E-06
Steel
1.00E-07
Metal
Copper
1.68E-08
Zinc
6.02E-08
Conductive sheet
9.45
Carbon fiber sheet
1.87E-02

Fig 1 : Repairing system

A 3@200=600

20
150

75

250

6@50=300

150

250

150 6@50=300

1800

Fig 2 : Rebar arrangement(upper : chapter.3,4, lower : chapter.6)

33

䠉

䠇

(2) Visual investigation
In electrodeposition method, electrodeposits cover theconcrete surface. Fig 7 shows RC beam specimen after
repairing (shown in Fig 3) with aluminum or titanium
mesh. From the pictures, electrodeposition method withaluminum has higher surface covering effect than thatwith titanium mesh.

Rebar

Feed pipe䠉
Water supply curing sheet

Bubblewrapping䠇DC power
supplySuction pipe
Suctionpipe

Non-wovenfabricSolution tankAnodeelectrode

Feed Pump

Suction machine

Fig 3 : Repairing system with Water Supply Curing
MethodThe existing structure is an interior wall deteriorated
by carbonation. It is used in chapter.5 to investigate theapplicability of steel mesh to field practice of
re-alkalization method. The repair area is 1.7m*30m.

3. Electrodeposition
Aluminum, steel, copper, and zinc are easily-available
metals. These metals are thought to be eluted withelectrification and affect the property of electrodeposit.
In this chapter, resistivity against Cl- permeation isinvestigated with mortar specimens. And aluminum isapplied as anode electrode to electrodeposition methodwith Water Supply Curing Method.
(1) Resistivity against Cl- penetration
After repairing (shown in Fig 1), specimens had been
immersed 10% NaCl water for 7 days. Then Clpenetration depth is measured in silver nitrate spray test.
And Cl- on rebar is measured in test shown in Fig 4. The
results are shown in Fig 5 and Fig 6. From the results,
electrodeposition method with aluminum and copper
have higher or equivalent repair effect to improve
resistivity against Cl- penetration compared with thatwith titanium mesh.

Fig 7 : Appearance after repair

4. Desalination
In this chapter, desalination effect is investigated withmortar specimens. And effective range of conductive
sheet is investigated from current broadening.
(1) Desalination effectDesalination effect of each anode electrode is evaluatedfrom Cl- content in external solution during repairing(like Fig 1). The results are shown in Fig 8. From theresult, conductive sheet and carbon fiber sheet haveequivalent desalination effect as titanium mesh. And
aluminum and steel have lower desalination effect thantitanium mesh.
Removed Chloride
ion amount(g)0.2

Pinsetter

Samplecontainer

Electricbalance Weighing

Taking out thereinforcing bar

Reweighing after drying for
24 hours in a desiccator
after weighing(measurement of adherent)

Distille
d water

Cl- penetration
depth (mm)

6

4
2
䜰䝹䝭

㕲

Al

Fe

㖡

ள㖄

Cu

Zn

䝏䝍䞁

Ti

䜂䜃䛒䜚

No repairFig 5 : Cl- penetration depth
Cl- on steel bar(µg/cm2)

1

2

3

4

5

6

25

A

20

A

A

A

A

A

15

10

5
0

zero
0
1

Al

7
2

3

Fe

Cu

4

Zn

7

8

(2) Effective range of conductive sheet
In order to evaluate resistance of conductive sheet,current broadening is investigated. Current which flows
in each specimen is measured when repairing system islike Fig 9. And the lowest integrated current density oftitanium mesh case is estimated as minimum standard.
Fig 10 shows the integrated current density. From theresult in case of conductive sheet, the length in whichcurrent can flow appropriately is thought to be thedistance to third specimen, 48cm at maximum.

No crack
Crack

0

0.05

Time (week)Fig 8 : Desalination effect of each anode electrode

Covering depth

8

Ti meshConductive sheet
Aluminum
Steel
Carbon fiber sheet0

Fig 4 : Measuring method of Cl- on rebar
10

0.1

0

Measuring the Clconcentration ion wash
liquorWashing with
distilled water

0.15

5

Ti

6

No repair

6

5

4
3
Anode

2

Fig 9 : Repairing system-

Fig 6 : Cl amount on rebar
34

1

䐥

䐤

䐣

䐢

䐡

䐠

Conductive sheet

In conventional field practice of re-alkalization method,steel mesh can be applied because the period of thismethod is shorter. In this chapter, the applicability ofsteel mesh to field practice of re-alkalization methodwith Water Supply Curing Method is investigated. The
repair area is 1.7m*30m in total.
(1) Current density
Design current density is 1.0 A/m2 and maximum limit ofvoltage is 40V. The appearance of interior wall during
repairing is shown in Fig 14. Fig 15 shows the current
density and voltage. From the result, design current incase of steel mesh can flow in appropriate voltage except
first several days like as titanium mesh case.

Standard

Titanium mesh

䐟

䐥

䐤

䐣

䐢

䐡

䐠

䐟

Fig 10 : Integrated current density

Chloride ion on
steel bar (µg/cm2)

And Fig 11 shows the Cl- on rebar after repairing.
Measuring method is same as in chapter.3. The highest
Cl- on rebar of titanium mesh case is estimated as
maximum standard. From the result in case of conductive
sheet, the length in which desalination effect can beobtained appropriately is thought to be the distance tothird specimen, 48cm at maximum. This length is sameas that in which current can flow appropriately.

Titanium mesh

7
6
5
4
3
2
1
0

Steel meshFig 14 : Appearance during repairing

Titanium䝏䝍䞁meshCurrent density (A/m2)䐟 䐠 䐡 䐢 䐣 䐤 䐥 䐟 䐠 䐡 䐢 䐣 䐤 䐥Conductive᪂䝕䞁䜹 sheet

Fig 11 : Cl- amount on rebar
These results are confirmed in investigation ofsimulation of field practice with Water Supply Curing
Method. Fig 13 shows desalination rate of each point
indicated in Fig 12. Desalination rate is calculated fromchloride content in concrete before and after repairing.
From the result, there is little deference between thedesalination effects of two points. Therefore, it can besaid that conductive sheet can be applied within about
45cm in this experiment.

40

1.0
0.8

Voltage (V)Integrated current
density (A / m2 䞉 day)

5. Re-alkalization

45
40
35
30
25
20
15
10
5
0

0.6
0.4
0.2

Steel mesh

30
20

Steel mesh
10

Titanium mesh

Titanium mesh

0.0

00 2 4 6 8 10 12 14Time (days)

0

2

4

6

8 10 12 14Time (days)

Fig 15 : current density and voltage(2) Re-alkalization effectRe-alkalization effect is evaluated from carbonated depth
before and after repairing. Fig 16 shows the core
discolored by phenolphthalein reaction. From the result,re-alkalization method with steel mesh is completed likeas that with titanium mesh.
Steel mesh

Titanium mesh

Before repairing

Fig 12 : Measuring points
Fig 16 : Corbonated depth

Covering depth : 10mm
Desalination rate
(%)

100

6. Strengthening after electrodeposition
Carbon fiber sheet has been used for strengthening theconcrete. It has high conductivity and it can be applied toanode electrode in electro-chemical repair. It is thoughtthat this sheet can be easily pasted on concrete bypouring low-viscosity epoxy resin into space between
concrete and water supply curing sheet after
electro-chemical repair. In this chapter, the strengthening
effect by carbon fiber sheet is investigated with mortar
specimens and RC beam. And the applicability to prevent
concrete falling is investigated by bond strength test.

ձCenterղFront edge

80
60
40
20

0
0㹼1cm

1㹼2cm

2㹼3cm

3㹼4cm

Depth from concrete surface
35

(2) Bond strength
Bond strength is criterial parameter for prevention
concrete falling. It is evaluated after loading test of RC
beam like Fig 20. Table 4 shows the result. From theresult, although the bond strength cannot be accurately
measured in almost points (ձ), at least measured bondstrength is over 1.5N/mm2 which is standard in
Procedure of Design and Construction of bridge
structures (Shutoko Engineering Company) [3].

(1) Strengthening effect
The maximum loading capacity of mortar specimens
after repairing and pasting carbon fiber sheet is measured
in loading test shown in Fig 17. The size of carbon fiber
sheet is 40mm*90mm and it is pasted on under surface
and side surface. Table 3 shows the condition and result.
From the result, the strengthening effect is obtained.
P
40

40

90

(mm)

100

Fig 20 : Bond strength test

Fig 17 : Loading test of mortar specimens
Table 3: Parameters and loading capacity
Pasting carbon
Loading
Electrodeposition
fiber sheet
Capacity (N)Conducted
Conducted
13,125
Conducted
Non-conducted
7,475
Non-conducted
Non-conducted
7,250

Table 4 : Bond strength
Number of measured points
ձDisbonded between attachment and carbon
ղDisbonded between carbon and concrete
Average bond strength of ձ(N/mm2)
Average bond strength of ղ(N/mm2)

In case of investigation with RC beam, strengthening
effect is evaluated by load-deflection relation. At first,
electrodeposition method is applied to 3 RC beams (likeFig 3). Anode electrodes are titanium mesh, aluminum
foil, and carbon fiber sheet. Then, carbon fiber sheet ispasted by low-viscosity epoxy resin on the carbon fiber
sheet anode case. Finally, loading test is conducted. The
way to set the equipment and load is shown in Fig 18.
Fig 19 shows the result of load-deflection relation ofeach case. From the result, there is little deference among
4 patterns. This result conflicts the result of mortar test.
The reason is thought to cause from deference ofcross-section ratio of (cross-section area of carbon fiber
sheet in tensile side) / (cross-section area of RC beam).
The cross-section ration of mortar specimen is 0.28%
and that of RC beam is 0.06%. The smaller this value is,the smaller strengthening effect is. Therefore, it isthought that strengthening effect cannot be obtained incase of RC beam.

250

7. Conclusions
The following conclusions are derived from theinvestigations of this study:1) Aluminum showed higher repair effect as anodeelectrode than conventional anode electrode(titanium mesh) for electrodeposition method.
2) Conductive sheet can be applied to desalination
method in the appropriate length (in this study:
about 45cm).
3) Steel mesh can be applied to re-alkalization methodwith Water Supply Curing Method and the repair
effect can be obtained.
4) It was suggested that carbon fiber sheet can be used
to prevent concrete falling after electrodeposition. If
cross-section ratio of (cross-section area of carbon
fiber sheet in tensile side) / (cross-section area of
RC beam) is large, carbon fiber sheet can be usedalso for strengthening.
Considering from the results and the price of each anodeelectrode, optimal anode electrode for electrodeposition,
desalination, re-alkalization, and strengthening after
electrodeposition can be suggested as Table 5.
Table 5: Optimal anode electrodes
Electrodeposition
Aluminum
Desalination
Conductive sheet
Re-alkalization
Steel meshStrengthening after
Carbon fiber sheet
electrodeposition

P/2

P/2
700

400

700

450

450

250

Repair or strengthening range
π gauge

Displacement meter

12
11
1
3.05
1.86

Strain gauge

Fig 18 : Equipments and loading and support position
350

Load (kN)300

8. Reference

250[1] Japan Society of Civil Engineers: Recommendation
for Design and Construction of Electrochemical
Corrosion Control Method; 2001.11[2] Hazama Ando Corporation: Water Supply Curing
System of Concrete Structure, “Aqua Curtain”;2013.4[3] Shutoko Engineering Company: Procedure of Design
and Construction of bridge structures; 2008

No repair
Ti meshAl foil
Carbon fiber sheet

200
150

100
50
0

0

10

20

30

40

50

Deflection (mm)Fig 19 : Load-deflection relation of RC beams

36

Comparison of Transportation Equipment Companies in terms of Environmental andManagement efficiency
Student Number: 14M18023  Name: Masataka Kashiwa  Supervisor: Naoya Abe⎔ቃຠ⋡ᛶ࡜⤒Ⴀຠ⋡ᛶ࠿ࡽࡳࡓ㍺㏦⏝ᶵჾ௻ᴗ㛫ࡢẚ㍑
᯽ 㞞㈗ᮏ◊✲࡛ࡣࠊ2014 ᖺᗘ࡟⎔ቃሗ࿌᭩ࢆⓎ⾜ࡋࡓ㍺㏦⏝ᶵჾ௻ᴗ 9 ♫ࢆᑐ㇟࡜ࡋࠊྛ௻ᴗࡀබ⾲ࡋ࡚࠸ࡿᐃ㔞ⓗ⎔ቃࢹ࣮ࢱ࡜㈈ົ㛵㐃ࢹ࣮ࢱࢆ⏝࠸࡚ࠊDEA (Data Envelopment Analysis)࡟ࡼࡾࠊ⎔ቃຠ⋡ᛶ࡜⤒Ⴀຠ⋡ᛶࢆ⟬ฟࡋࡓࠋࡲࡓࡇࢀࡽࡢ⟬ฟࡉࢀࡓຠ⋡ᛶ᝟ሗ࡟ຍ࠼ࠊẸ㛫ㄪᰝᶵ㛵࡟ࡼࡿ⎔ቃ⤒Ⴀᗘㄪᰝ࡟ࡼࡿ⎔ቃ⤒Ⴀ࡟㛵ࡍࡿࢹ࣮ࢱࢆ⏝࠸࡚ࠊ⤒Ⴀάື࡜⎔ቃάືࡢ㛵ಀᛶ࡟ࡘ࠸࡚⪃ᐹࡋࡓࠋ

1 Introduction

approximately 20,000-30,000 points per one and many
affiliates are involved in car manufacturing. The
emission from this industry was assumed as one of themain air and water pollutants sources and thismanufacturing activities have been regulated byvarious environmental law such as Revised Energy
Conservation Act, PRTR raw and End-of-life Vehicle
Recycling Law. Thus it is significant to focus on this
sector in this study.

1.1 Background and literature review
Corporate activities, which are assumed as tomaximize profit, have caused some environmental
issues in Japan from 1890s. Because of enacted
environmental law and regulations on and after late1960’s and general citizen`s request  for  better
livingconditions,
corporations
includingmanufacturing enterprises, had to comply with legal
requirements. These correspondences have changed to
do pollution prevention, competitive strategy andsustainable management [1]. Accomplishing theimprovement of management activity and profitability
and the reduction of environmental burden byincorporating an environmental consideration into
every corporate activity independently is called“Environmental Management”.
There are many arguments about the relationshipbetween Environmental activity and Management
activity. Levy(1995) and Kagata(2006) argued thatenvironmental management don`t always generate
profit for the company and even become additional cost,depending on the situations. [2,3]. Kaneko (2015)
argued that there is no significant correlation between
Environmental
efficiency(Sales/CO2)
andManagement efficiency (ROA)[4].
This research focused on the transportationequipment industry which is assumed as a majorindustry in Japan and is related to several industries
from material procurement to production, sale andmaintenance. The value of shipments of manufactured
goods of this industry occupies 20.1% of whole
manufacturing industry. Many numerical parts

1.2 Objective of the study
This research will do inter-enterprise comparison to
grasp the actual state of environmental management of
the transportation equipment industry. This research
will clarify the relationship between an environmental
activity and a management activity with efficiency thatwas calculated by corporate quantitative data and therelationship between an efficiency and another datasuch as corporate information, Environmental
Management survey ranking.

2 Analytical method & Data
2-1 DEA (Data Envelopment Analysis)
In this research selected Data Envelopment Analysis(DEA) that Charnes, Cooper and Rhodes proposed in
1978. This is a non-parametric method for evaluating
Decision Making Units (DMUs) can be such ascompany and people with the efficiency. CCR and BCC
model are two basic DEA models, where the former isunder constant return to scale technology and the latteris under variable return to scale technology. The
difference of CCR and BCC model in DEA was showed
in Figure 1. DEA can deal with multiple inputs, outputs

37and different unit data for the evaluation. The source of
inefficiency and the reference for DMU can be clarified.

calculates the efficiency under optimal weight. These
are repeated for every DMUs.
2-2 Target companies and Usage data
This research selected target companies which
published environmental report in 2014 and whose
sales are over 1 trillion yen and divided 9 target
companies into 2 groups by accounting method as
Table1. The scope of consolidated accounting included
a subsidiary and an affiliate around the world against
only one`s company of unconsolidated accounting inthis research.

Figure 1: The difference of CCR and BCC model in DEA

Table 1:

Consolidated accounting group

At first, assuming that four DMUs (A, B, C, D) spendsome fortunes and produce some fortunes. The inclineof the straight line to link the origin of DMU B is thebiggest. In other words, the maximum ratio of output /input represents most efficient DMU. All DMUs except
B are wrapped up under this efficient frontier and arerecognized inefficient.

Mitsubishi motors
Kawasaki heavy industry
Yamaha motors
Isuzu motors
Hino motors
Total 4 companies
Total 5 companies
Total 9 companies

Table 2:σ ௨ ௬σೞ ௨ೝ ௬ೝೕσ೘ ௩೔ ௫೔ೕ൑ ͳሺ݆ ൌ ͳǡήήήǡ ݊ሻ‫ݒ‬ଵǡ ‫ݒ‬ଶǡ ‫ݒ ڮ‬௠ ൒ Ͳ‫ݑ‬ଵǡ ‫ݑ‬ଶǡ ‫ݑ ڮ‬௦ ൒ Ͳ൏ ‫ܲܮ‬௢ ൐ ݉ܽ‫ߠݔ‬௢ ൌ σ௦௥ୀଵ ‫ݑ‬௥ ‫ݕ‬௥௢σ௠௜ୀଵ ‫ݒ‬௜ ‫ݔ‬௜௢ ൌ ͳ(1.3)

Efficiency

Input

OutputManagement

Employees
Capital investment
Research anddevelopment expenses
Amount of energy
Amount of raw material
Amount of water
Environmental
preservation cost
Amount of energy
Amount of raw material
Amount of water
Environmental
preservation cost
CO2 emmision
Waste amount
WastewaterSale
Net profit

Environmental

(1.4)
(1.5)

Environmental
Management

(1.6)
(1.7)

‫ݑ‬ଵǡ ‫ݑ‬ଶǡ ‫ݑ ڮ‬௦ ൒ Ͳ(1.9)‫ݒ‬ଵǡ ‫ݒ‬ଶǡ ‫ݒ ڮ‬௠ ൒ Ͳ

Input variables and Output variables(1.2)σ௦ ‫ݑ‬௜ ‫ݕ‬௜௝ ൏ σ௠ ‫ݒ‬௜ ‫ݔ‬௜௝ 

Unconsolidated accounting group

Nissan motors
Denso
Komastu
Kubota൏ ‫ܲܨ‬௢ ൐  ݉ܽ‫ߠݔ‬௢ ൌ σೞ ೝ ೝ೚ (݅ ൌ ͳǡήήήǡ ݉Ǣ ‫ ݎ‬ൌ ͳǡ ‫ ڮ‬ǡ ‫ݏ‬ሻ (1.1)೘ ௩೔ ௫೔೚

Group validation(1.8)

In this approach, n DMUs and m inputs variable, s
outputs variables are assumed and ‫ܷܯܦ‬௢ is defined astarget DMU and ‫ݔ‬ଵ௢ , ‫ݔ‬ଶ௢ ,㺃㺃㺃 ‫ݔ‬௠௢ as input data of‫ܷܯܦ‬௢ , ‫ݕ‬ଵ௢ , ‫ݕ‬ଶ௢ ,㺃㺃㺃, ‫ݕ‬௦௢ as output data of ‫ܷܯܦ‬௢and ‫ݒ‬ଵ  , ‫ݒ‬ଶ  , 㺃 㺃 㺃 , ‫ݒ‬௠ as weights of input, ‫ݑ‬ଵ  ,‫ݑ‬ଶ ,㺃㺃㺃, ‫ݑ‬௦ as weights of output. The efficiency ratio
is represented in the formula (1.1). The formula (1.2)
represents that the efficiency shall not exceed 1.0.
Because it`s difficult to find optimum solution withfractional programming model, this model can beconverted into a Linear programming model in (1.5)
under constraint conditions (1.6). Linear programming
model gives several variables the best weights andCO2 emmision
Waste amount
WastewaterSale
Net profit

Management efficiency was defined as the degree of
how to produce maximum for constant resource input.
Environmental efficiency means the degree of how to
reduce environmental burden maximum for constantenvironmental
resource input. Environmental
management efficiency means the degree of how to
produce maximum and reduce environmental burden
for constant environmental resource input. Because
there are great scale differences of the data which wasused to calculate the management efficiency within
unconsolidated accounts group, BCC model wasapplied only to these against CCR model.

38

3 Results

Kubota was the most efficient company in terms of bothactivities. On the contrary to this, Denso was the mostinefficient company. As for environment activity,
Nissan was proposed the improvement of all input
variables and large improvement of CO2 emissions andwaste water. Denso was proposed large improvementof amounts of energy and water. As for management
activity, the reason why there are differences ofmanagement efficiency between Kubota and Nissan
and Denso is Kubota made a moderate profit by a few
inputs and Nissan made a large profit by a large inputs
and Denso made a moderate profit by a large inputs.

3-1 Inter-enterprise comparison
Using two efficiencies, we conducted Inter-enterprisecomparison and proposed points of improvement and acompany to be referred.

Environmetal Efficiency

1
0.8
0.6
Mitsubishi
Kawasaki
Yamaha
Isuzu
Hino

0.4
0.2

3-2 The relationship between environmental activity
and management activity
This section revealed the relationship between two
efficiencies and corporate informations, theenvironmental management degree.
The company which is high in sales and the numberof employees was lower in environmental efficiency.
Several trends were revealed that management
efficiency improved with a rise in net profit and sales
except Nissan, Denso which theirs were by far highfrom others and the statistical significant difference ofmanagement efficiency among three groups whichwere distributed by net profit. The number of
employees was the factor which decrease both
efficiencies. There were positive correlations between
Environmental efficiency, Management efficiency andEnvironmental management efficiency. These results
were same in unconsolidated and consolidated
accounting group. Contrary to expectation,
environmental efficiency of the company which spend
a large amount of the cost on Environmental
preservation was low as figure4.
Using the environmental management degree which
was evaluated by Nihon Keizai Shimbun, Inc, therelationship between the efficiency and synthesis scoreof five indexes (full marks 500 points) was verified inthis sector. The company which is high in net profit ishigher in the environmental management degree.
About the relationship between the number of
employee and the degree of environmental
management, there were different tendencies between
high and low degree groups. So this research focused
individually.
The difference of the environmental management
degree corresponds with the difference of account

0
0

0.2
0.4
0.6
0.8
Management Efficiency

1

Figure2: The efficiency of unconsolidated accounts group

Environmental Efficiency

1
0.8
0.6
Nissan0.4

Komatsu
Kubota

0.2

Denso
0
0

0.2
0.4
0.6
0.8
Management Efficiency

1

Figure3: The efficiency of consolidated accounts group

About 5 unconsolidated accounting companies, Isuzu
was the most efficient company in terms of bothactivities. On the contrary to this, Kawasaki was themost inefficient company. As for environment activity,
Kawasaki and Mitsubishi were proposed theimprovement of all input variables and Mitsubishi and
Hino were proposed large improvement of waste
amounts and CO2 emissions. As for management
activity, the reason why there are differences ofmanagement efficiency between Mitsubishi and
Kawasaki, Yamaha is there are differences of the profit
for constant resource input.
About 4 unconsolidated accounting companies,

39

output in this research.
About a target company, there are different firm sizes,
so it was difficult to handle as same group. Especially
Nissan, Denso, there are different characteristics fromanother companies. There were few comparable data intarget companies, and a count range was different inconsolidated and unconsolidated accounting group. So
this research had to analyze with very few samples.
This research found that a large cost on Environmental
preservation doesn`t contribute the improvement ofenvironmental efficiency from the relationship between
these two.
There was different tendency within 2 accounting
groups in the relationship between the environmental
management degree and the employee number. It isconceivable that consolidated accounts companies canafford to carry out environment management and alarge extent of environmental consideration have been
evaluated high than unconsolidated accounting
companies from the result that the degree ofenvironmental management of all consolidated
accounts companies was larger than that of all
unconsolidated accounting companies.
Further study
This research should consider how improvement ofefficiency contributes to environmental management
and focus on production volume and should concern
about the selection of company and variable withconsidering the tradeoff of these. This researchevaluated company activity with using the quantitative
data, but comparing the evaluation of this research withactual environment activity is expected in the future.

1
Environmental efficiency

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
500 1,500 2,500 3,500 4,500 5,500 6,500
Environmental preservation cost
Figure4: Environmental efficiency and preservation cost

procedure and the degree of all consolidated accounting
companies was larger than that of all unconsolidated
accounts companies. Among consolidated accounts
group, a company with large number of employees
was higher in environmental management degree. On
the contrary this, among unconsolidated accounting
group, a company with small number of employees washigher in degree.
Although there are different efficiencies of Nissan,
Denso and Komatsu in the consolidated accounts group,
the environmental management degree was similar.
The degree of Kubota is low although environmental
and management efficiency is 1.0.

4 Conclusions
About the efficiency, if management efficiency
increase, sales and net profit increase, but even if
environmental efficiency increase, sales and net profit
doesn`t increase, rather decrease. As an example, theenvironmental efficiency of the company which madea high profit was low in Nissan, Denso.
An inconsistency between efficiency and theenvironmental management degree was appeared a lot.
Especially the efficiency of Nissan and Denso was very
low although their degree was very high. The reason forthis can be considered that there is difference between
what this research wants to clarify with using efficiency
which was defined and the meaning of evaluating withthe environmental management degree. The
environmental efficiency expressed whether thecompany can effectively treat input which affects
environment and can reduce environmental burden as

Reference[1]Richard

Welford(1995)

Environmental

Strategy

andSustainable Development: The Corporate Challenge for the
Twenty-first Century,

Routledge[2] Marc A.Levy (1995) “Is the Environment a National Security
Issue?” International Security Vol.20 pp.35-62[3] Kagata(2006) “An Empirical Study of the Relationshipbetween Environmental Performance and Financial Performance”
Journal of Policy Studies No.21 pp.121-142 (in Japanese)[4] Kaneko, Nishitani, Fujii, Komatsu(2015) “Study on
environmental policies and corporate behavior in the epoch ofproactive corporate environmental management” Environmental
Economics࣭Policy Research Vol.8, No1, 61-73(in Japanese)40

CORRIDOR CHOICE OF TRANSIT CARGO TRANSORTIN LANDLOCKED COUNTRIES OF EAST AFRICA
Student Number: 14M18098

Name: Masashi SOTA

Supervisor: Shinya HANAOKA

ᮾ࢔ࣇࣜ࢝ෆ㝣ᅜࡢࢺࣛࣥࢪࢵࢺ㈌≀㍺㏦࡟࠾ࡅࡿᅇᗯ㑅ᢥ♽⏣ ┿ᚿ ᮏ◊✲࡛ࡣࠊ⤒῭ᅇᗯࡢ஺㏻࢖ࣥࣇࣛᩚഛ࡟㛵୚ࡍࡿࢫࢸ࣮ࢡ࣍ࣝࢲ࣮ࡢᙺ๭࡜άື┠ⓗࢆ≉ᐃࡋࠊ࢚࣮ࢪ࢙ࣥࢺ࣮࣋
ࢫࣔࢹࣝࢆ⏝࠸࡚ࢫࢸ࣮ࢡ࣍ࣝࢲ࣮㛫ࡢ┦஫స⏝ࢆࣔࢹࣝ໬ࡋࡓࠋᮾ࢔ࣇࣜ࢝ࢆᑐ㇟࡜ࡋࠊࢣࢽ࢔࠿ࡽ࢘࢞ࣥࢲ࡟᥋⥆ࡍࡿ໭㒊ᅇᗯ࡟ᶆ‽㌶ࡢ᪂つ㕲㐨ᩜタࢆ᝿ᐃࡍࡿࢩࢼࣜ࢜ࠊᆅᇦඹྠయࡀᮾ࢔ࣇࣜ࢝ᆅᇦෆࡢ࢖ࣥࣇࣛᩚഛࢆពᛮỴᐃࡍࡿࢩࢼࣜ࢜ࠊእⓗኚ໬ࢆ᝿ᐃࡋ࡞࠸࣮࣋ࢫࢩࢼࣜ࢜ࡢẚ㍑࡟ࡼࡾྛᅇᗯࡢ㍺㏦≧ἣࡢኚ໬ࢆ⪃ᐹࡋࡓࠋࡑࡢ⤖ᯝࠊ᪂つ㕲㐨ࡢᩜタ࡟ࡼࡾ௚᪉ࡢᅇᗯࡢᩚഛࡀಁ㐍ࡉࢀࡿࡇ࡜ࠊࡲࡓᆅᇦඹྠయࡢᩚഛィ⏬⟇ᐃ࡟ࡼࡾࢫࢸ࣮ࢡ࣍ࣝࢲ࣮ࡢ⥲཰┈࡜Ⲵ୺ࡢᾘ㈝⪅వ๫ࡢྜィ࡛࠶ࡿ♫఍ⓗ౽┈ࡀ᭱኱࡜࡞ࡿࡇ࡜ࡀᐇドࡉࢀࡓࠋ

1.

2. Methodology
2.1. Shippers’ and forwarders’ preference forroute choice

Introduction

Landlocked Developing countries (LLDCs) heavily
depend on transport infrastructures in Transit Countries (TCs)to access to ports, since LLDCs are entirely enclosed by land.
Transporting cargos to LLDCs requires multiple clearances andtransloading from one mode of freight. These circumstances
force LLDCs to face higher transportation cost and longer
transport time than TCs. As a result, it negatively influences on
LLDC’s economic growth.
LLDCs face various complicated problems, such as poorinterconnections due to inconsistency in technical standards oftransport infrastructure, lack of interoperability caused bycomplicated custom procedures for international transport, andinsufficient transport infrastructure (Arvis et al, 2011).
Nevertheless, there is no fully functional transit regime toimprove the situation LLDCs face, in spite of lots of measures
implemented by various regional authorities. What is worse,
LLDC’s right to infrastructure development in transit countries
are limited, even if LLDC attempts to develop it by themselves(United Nations, 2003). Consequently, LLDCs are placed at acompetitively disadvantage to change the situation they arefacing.
However, there is possibility of taking advantage of being
LLDCs. Being surrounded by several countries, LLDCs havemultiple route choices to the seaport. TCs can expect transit
cargos from LLDCs can bring economic benefits for the market
growth of the relevant industry. Taking this into consideration,
TCs compete with each other for transit cargos. Utilizing thecircumstances enables LLDCs to induce TCs to improve thesituation of LLDCs transporting cargo to the seaport. This
study thus clarifies whether LLDCs can take advantage ofpossessing multiple routes by developing the model to simulate
stakeholders’ interactions corresponding to corridor choice oftransit cargo transport in LLDC under the competitive
circumstances between TCs.
The objectives of this research are the following: (1) To
identify the most influential factors for shippers’ andforwarders’ route choice, (2) To determine main stakeholders
who can affect the performance of a corridor and clarify howeach stakeholder affect it, (3) To develop model: eachstakeholder’s decision-making process and understand howtransport network dynamically changes corresponding tostakeholders’ decisions by Agent-Based Model (ABM). This
research finally focuses on East Africa as a case study.

Literature review enables to identify factors on whichshippers put significance for route choice. Factors influencing
route choice to seaport is split into two parts, ones related toport choice and hinterland transport to ports. The mostinfluential factors relevant to port choice are quality of terminal
operation, adequacy of infrastructure and frequency of shipping
visits. As for the factors related to inland transport, the totallogistic cost, transport time, time reliability and safety are
regarded as important factors in the past studies.
Notwithstanding that a great number of factors related toport choice and inland transport identified in past studies, mostof the factors have connections with transport time andtransport cost. These two attributes are utilized for theestimation of cargo distribution on each corridor.

2.2. Stakeholder’s interest and their relationship
Various kinds of stakeholders get involved in thedevelopment and maintenance of corridors. In case of corridor
management, the willingness of each stakeholder to support theenhancement of corridor performance largely depends on thebenefits they can obtain. In addition, each category of
stakeholders focuses on different factors for the performance ofcorridors. Therefore, it is necessary to figure out eachstakeholder’s objective and roles in order to examine the transit
Table 1: Main stakeholders’ primary interests
Stakeholders
Shipper

Forwarders

Port Authority
Road
Authority
Railway
Authority

41

Main interests- Consignment moved from origin to destinationin the shortest possible time and at minimum
cost- Safe transportation and handling- Reduction in operating cost
- Increase volume of cargo handled- Fast clearance process, Harmonization of
documents- Improve cargo throughput- Increase port utilization- Enhancing port competitiveness- Asset preservation through axle load control.
- Infrastructure cost recovery.
- Recover the initial cost and ensure maintenancecost sustainably.
- Enhance competitiveness to transport morecargo.

The components of that defines travel disutility include port
charge (PCx), cost for hinterland transportation (Cip), cost forcrossing border (BCx), Road Charge in RCs (RCx) andmonetary units of trip duration from ports to destination (Dip).
Let βm and lj denote fare per kilometer per TEU and the
distance of link j, respectably. Cost for hinterland transportation(Cip) can be expressed in Equation (2). Travel duration for apath p (Dip) is defined as the dwell time of port x (tx) and thesum of travel time for all links (Equation (3)). The dwell timeof port (tx) is represented by ‘free flow travel time’ (tff,x) andport throughput (Qx) / the capacity (Kpx) ratio. ‘Free flow travel
time’ expresses ideal service time without port congestion. In
second term of right hand side of Equation (3), the transport
duration of link (tj) is also similarly represented with ‘free flow
travel time’ of links (tj), cargo flow on each link (qj) andcapacity of each link (Khj)
The hinterland transportation paths are aggregated into asingle link to avoid biased results because of different numberof hinterland transportation paths per port. A formulation of thegeneralized cost for port n is shown in Equation (4). In thisequation, θ represents the spreading parameter. Cargo
distribution from TCs and LCs are represented in Equation (5).
Let Qn be trade volume of country n. From Equation (5), (6),cargo flow on each link (qj) as well as volume of cargo (Qx)each port handle can be obtained.

corridor development. Shippers, forwarders, port authorities,
road authorities and railway authorities are selected as the mainstakeholders for the reason that these stakeholders have strong
influence on the important factors for shippers’ and forwarders’route choice. The primary objectives and expectations of eachcategory of stakeholders are summarized in the Table 1.

2.3. Agent-Based Model
This study adopts ABM on purpose of simulating
stakeholders’ interactions. ABM simulates actions andinteractions among autonomous intelligent agents that can beseen as a system of capable of interacting independently within
the environment. Utilizing ABM to this study enables to predict
the stakeholders’ reactions against other stakeholders’ actionand estimate shipper’s route choice decided based on changed
corridor’s performance, which represent competitive situation
between multiple corridors connecting LLDCs to ports in TCs.

2.3.1. Flow chart of the model
An overview of the model components andinterconnections are shown in Figure 1, following recent
studies. Trip distribution can be calculated with the stochastic
model utilizing the concept of generalized cost. Generalized
cost is composed of monetary and non-monetary unit, which istransport time in case of this study. On the basis of the trip
distribution, traffic flow on each link can be calculated toestimate each transport authority’s revenue and cost. Based on
the revenues, each stakeholder make decisions on investment
for capacity expansion and pricing to maximize their profit,
providing an update network. These decisions are done once ina year. The modeling process iterates annually for 20 years.
Economic growth is taken as exogenous variables because
economic growth cannot be completely explained only by the
corridor development. The transition of corridor performance isobserved in terms of shippers’ surplus, revenue of each agent,
generalized cost and capacity of hinterland network and port
facility.

‫ܥܩ‬௜௣ ൌ σ௫ሺܲ‫ܥ‬௫ ൅ ‫ܥܤ‬௫ ൅ ܶ‫ܥ‬௫ ሻߜ௫௣ ൅ ‫ܥ‬௜௣ ൅ ߙ௩௢௧ ή ‫ܦ‬௜௣‫ܥ‬௜௣ ൌ σ௝ σ௠ ߚ௠ ݈௝ ߜ௠௝ ߜ௝௣‫ܦ‬௜௣ ൌ ‫ݐ‬௫ ൅ σ௝௝ ‫ݐ‬௝ ߜ௝௣
ொ

௄௉ೣ

௽

‫ܥܩ‬௜ ൌ σ௣ ೔ǡ೛‫ݍ‬௜௣ ൌ ܳ௡ ή௄௛ೕீ஼೔೛ ή௘௫௣൫ିఏήீ஼೔೛ ൯
೵೔ǡ೛

σ೛

௘௫௣൫ିఏήீ஼೔೛ ൯

೵೔ǡ೛

σ೛

(2)

ఙమ

ߜ௝௣ ൰ ൰ (3)(4)௘௫௣൫ିఏήீ஼೔೛ ൯(5)௘௫௣ሺିఏήீ஼೔ ሻ(6)௽ܳ௫ ൌ σ௝ σ௣ ೛Ǥ೔ ‫ݍ‬௜௣ ߜ௫௣

Economic growth(7)

2.3.3. Stakeholder’s objective function

Year tYear t+1

Trip Distribution

Trip Distribution

A)

Port Authority’s profit
The profits generated by Port Authority is calculated
based on total revenue from port charge (PCxy) minus operating
cost and capacity expansion cost of port. The total cost for theoperation and maintenance cost is represented with port
capacity (Kpxy) and marginal capacity cost (Mport) representing
marginal production cost to increase one unit of capacity.

OD cargo volume

Stakeholder make decision about capacity expansion
and charges௤ೕ‫ݍ‬௝ ൌ σ௜ σ௣ ‫ݍ‬௜௣ ߜ௝௣ ߜ௣௜

A multinomial logit model is used to estimate route
choice of shippers. Travel disutility of OD i and path p isrepresented as Generalized travel cost (GCip) (Equation (1)).

OD cost ,timeఙమൌ ‫ݐ‬௙௙ǡ௫ ቀͳ ൅ ߪଵ ቀ ೣ ቁ ቁ ൅ σ௝ ‫ݐ‬௙௙ǡ௝ ൬ͳ ൅ ߪଵ ൬

2.3.2. Trip distribution

Year t-1

(1)Updatedcapacity,
charges

Port authority
Set port capacity and Port charges to maximize theirprofitRoad authority
Set road capacity and Road charges to maximize theirprofit௬

Railway Authority
Set Railway capacity and freight fare to maximize theirprofitMeasure of effectiveness: Customer Surplus, Revenue, Generalized cost, Capacity ofhinterland network and port facility

௬

௬

௬

‫ߨܺܣܯ‬௫ ሺܲ‫ܥ‬௫ ǡ ‫݌ܭ‬௫ ሻ ൌ ܲ‫ܥ‬௫ ή ܳ௫ െ ‫ܯ‬௣௢௥௧ ή ‫݌ܭ‬௫

Subject to:B)௬Ͳ ൑ ܲ‫ܥ‬௫ ൑ ʹܲ‫ܥ‬௫଴
௬ିଵ

‫݌ܭ‬௫

௬

൑ ‫݌ܭ‬௫

(8)
(9)
(10)

Road Authority's profit
The profits of Road Authority is calculated based on
total revenue from collecting road charge (TCny) minus
operating cost and capacity expansion cost of road. Let Mroad
and Khj y denote marginal capacity cost and capacity of link j,

1

Figure 1: Flow chart of the model

42

respectively.

Initialization Step

ஈ
௬
௬
௬
௬
‫߶ܺܣܯ‬௡ ൫ܴ‫ܥ‬௡ ǡ ‫݄ܭ‬௝ ൯ ൌ ܴ‫ܥ‬௫ ή ‫ݍ‬௝ െ σ௝ ೕǡ೙ ‫ܯ‬௥௢௔ௗ ή ‫݄ܭ‬௝ ߜ௠௝

Subject to:

௬ିଵ

‫݄ܭ‬௝

C)

(11)
Calculate cargo flow on each link௬Ͳ ൑ ܴ‫ܥ‬௡ ൑ ʹܴ‫ܥ‬௡଴

(12)

௬

൑ ‫݄ܭ‬௝

Solve unconstrained maximization
problem for transport authority(13)

Railway Authority’s profit
The profits of railway are calculated based on therevenue composed of freight fare (βmy) minus operating costand capacity expansion cost of railway. The first term of righthand side denotes revenue from freight fare. Let Mroad and Khjy
denote the marginal capacity cost of railway and capacity oflink j, respectively.

Update capacity and charges

Constrained
satisfied?
Obtain optimal capacity and charges

14)

Subject to:

Figure 2: Solution Algorithm for each transport
authority

(15)

௬ିଵ
௬
‫݄ܭ‬௝ ൑ ‫݄ܭ‬௝

(16)– Jeeves pattern search, one of the pattern search methods, isutilized into this study (Hooke et al, 1961). The algorithm foreach transport authority’s profit can be shown in Figure 2. As
initial step, select initial charges/freight fares and capacity.
Initial value for penalty parameter is set. As iterative step, theHooke-Jeeves pattern search technique is used to find theequilibrium solutions for each transport authority’s profit. For
every movement of variables, cargo flows and profits oftransport authority are calculated to get closed to the optimalvalue. If the constraints are not satisfied, increase the value ofpenalty parameter by γ (γ = 5 in this research) and back to theiterative step.

2.3.4. Solution algorithm
This study develops the heuristic approach for eachtransport authority to identify the optimal pricing and capacity.
It is assumed that the capacity and charges of other transport
authorities will remain unchanged at the point optimal pricing
and capacity are calculated for each authority. Constrained
maximization problem in Equations (8)-(10), (11)-(13) and(14)-(16) are transformed into an unconstrained maximization
with penalty functions as follows:
௬

௬

௬

௬

௬

ȫ௫ ሺܲ‫ܥ‬௫ ǡ ‫݌ܭ‬௫ ሻ ൌ Ɏ௫ ሺܲ‫ܥ‬௫ ǡ ‫݌ܭ‬௫ ሻ െ ܿ௫ ൫݉ܽ‫ ݔ‬ଶ ሼͲǡ ܲ‫ܥ‬௫ െ ʹܲ‫ܥ‬௫଴ ሽ ൅௬ିଵെ ‫݌ܭ‬௫ ൟ൯௬ିଵെ ‫݄ܭ‬௝ ൟ൯݉ܽ‫ ݔ‬ଶ ൛Ͳǡ ‫݌ܭ‬௫௬

3. Case Study
3.1. Inputs for the simulation

(17)

௬
௬
௬
௬
௬
Ȱ௡ ൫ܴ‫ܥ‬௡ ǡ ‫݄ܭ‬௝ ൯ ൌ Ԅ௫ ൫ܴ‫ܥ‬௡ ǡ ‫݄ܭ‬௝ ൯ െ ܿ௡ ൫݉ܽ‫ ݔ‬ଶ ሼͲǡ ܴ‫ܥ‬௡ െ ʹܴ‫ܥ‬௡଴ ሽ ൅௬݉ܽ‫ ݔ‬ଶ ൛Ͳǡ ‫݄ܭ‬௝

௬

௬

௬

௬

௬ିଵ

݉ܽ‫ ݔ‬ଶ ൛Ͳǡ ‫݄ܭ‬௝௬This study applies the developed model to East Africa ascase study. In order to make the network reflects the current
situation simply without compromising, the network in Figure
3 is assumed in this study. Blue and Red Solid line represent
the road network of the Northern Corridor and Central Corridor,
respectively. Blue dot line shows railway network. Blue dots
represents national border and red dots are capital city ofcountries. In case of DRC and Tanzania, Goma and Muzani arerecognized as representative city.
This simulation sets assumptions as follows;- Representative city of each country except borders in Figure
3 are origin of export and destination of import.
- Import cargos of each country come from one of the ports
and export cargos of each country go to one of the ports.
- Cargo volume of each country will increase as shown in
Table 2.

(18)

௬

଴ሽ
൅
Ȳ൫ߚ௠ ǡ ‫݄ܭ‬௝ ൯ ൌ ɗ௡ ൫ߚ௠ ǡ ‫݄ܭ‬௝ ൯ െ ܿ൫݉ܽ‫ ݔ‬ଶ ሼͲǡ ߚ௠ െ ʹߚ௠െ ‫݄ܭ‬௝ ൟ൯(19)cx, cn and c are a penalty parameter. Variables that are notsatisfied with constraints influence variables through a penalty
that equals to the square of the violation. If the value of penalty
parameter is appropriately large, the augmented objectivefunction avoids a penalty and produces optimal value following
constraints.
In order to solve this augmented objective function, Hooke

Northern Corridor

KENYA

MombasaPort

UGANDADRC
KatunaMalaba(border)

KampalaGoma

NairobiTable 2: Average annual trade growth 2015-2030(border)

Kigali

Import (%)
Kenya
Tanzania
UgandaRwandaBurundiEastern DRC

RWANDA
Rusumo(Border)

BURUNDI

Bujumbura

No

Yes

ஈ
ஈ
௬
௬
௬
௬
‫߰ܺܣܯ‬௡ ൫ߚ௠ ǡ ‫݄ܭ‬௝ ൯ ൌ σ௝ ೕǡ೙ ߚ௠ ή ݈௝ ‫ݍ‬௝ ߜ௠௝ െ σ௝ ೕǡ೙ ‫ܯ‬௥௔௜௟ ή ‫݄ܭ‬௝ ߜ௠௝ (௬Ͳ ൑ ߚ௡ ൑ ʹߚ௡଴

Increase Penalty
parameter

MuzaniKabanga

TANZANIA(border)

Central Corridor

Dar es Salaam
Port

Export (%)
7
7
4
6
3
7

5
7
6
12
9
13

3.2. Scenario buildingA)
Figure 3: Simplified network in East Africa

43

Base case scenario
There is no exogenous change other than trade growth.

Table 3: Capacity change on each scenario at 2035

B)

Railway expansion scenario in Northern Corridor
This scenario assumes project scheduled to be
implemented. This project is planning to construct a standard
gauge railway network connecting their countries from the Portof Mombasa. Corresponding to actual plan, this study expects
the capacity of the line between Mombasa and Nairobi will be
12.95 million ton per year at 2017, and the capacity between
Nairobi and Kampala will increase to 1.66 million ton per year
at 2019. In addition, this scenario assumes free flow transport
speed of each railway link will be half when this project isfinished.
C)
Organization restructuring
This scenario assumes that there is one regionalorganization authorized to operate the complete system of ports
and hinterland networks, controlling capacity expansions andpricing of all transport modes. Pricing and investments decision
for ports and hinterland networks are decided based on theobjective function of the regional organization, Equation (20).
The left hand side of the Equation is the social benefit as thesum of total profits from ports, roads and railway, and shippers’surplus. The first three terms of the right hand side are theprofits from ports, roads and railway, respectably. The fourthterm of the Equation is consumers’ surplus between year 1 andyear t, which is approximated by the rule of half.

(mil.TEU/year)
Port
Mombasa
Dar es Salaam1
Road
Mombasa-Nairobi
Nairobi-Malaba
DES-Muzani
Muzani-Kabanga
Muzani-Rusumo
Railway
Mombasa-Nairobi
Nairobi–Kampala1൅ σ௜ σ௣

ଶ

B

20.03
10.14

35.21
36.57

38.43
40.01

111.3656.38

128.07
63.81

22.05
7.89
6.64
0.31
2.21

34.96
7.89
13.50
0.32
3.67

23.98
8.64
24.83
8.64
12.64

72.78
21.16
29.17
0.38
4.82

44.74
16.31
28.61
0.44
4.99

1.00
0.13

1.34
0.27

44.13
4.04

6.12
0.70

25.92
3.24

Scenario
C-1

C-2

DES = Dar es Salaam(mil. $/year)
A
Authority’s Benefit
Mombasa Port
1,317
DSM Port
798
Kenya Road
1,131
Tanzania Road
562
Railway
343
Shippers’ Surplus
TCs-13,594
LLDCs-6,027
Social benefit-15,468

B

Scenario
C-1

C-2

1,379
726
393
500
4,865

855
432
-188
-60
232

824
439
-91
-28
1,256

-69
-2,904
4,892

3,667
9,835
14,744

6,843
11,331
20,575scenarios of “Organization restructuring”, shippers’ surplus isremarkably improved. Especially, the circumstances forshippers in LLDCs become better off. As a result, social benefit
is higher than other two scenarios, despite its low revenue.

௡

బ
೟
బ
೟
൫ொ೔೛
ାொ೔೛
൯ሺீ஼೔೛ିீ஼೔೛ሻA

Table 4: Authority’s benefit and shippers’ surplus at 2035

ܹܵ ൌ ෍ ߨ௫ ൅ ෍ ߶ ൅ ߮௫Initialinputs(20)

In case of organization restructuring, two scenarios are
assumed depending on maximum annual capacity growth.
C-1) Regular investment scenario
Maximum annual capacity growth is less than 10%C-2) Progressive investment scenario
Maximum annual capacity growth is less than 20%.

4. Conclusion
The transition of corridor performance is observed fromthe results of the simulation. Firstly, in “Base case scenario”,
the corridor performance 20 years later gets worse than current
situation. Management structure where each transport authority
only cares about their benefits isn’t efficient management
structure in this case. The results of Railway expansion
scenario show that the development of one corridor can induce
the other corridor’s development. Even though the expansion ofrailway on the Northern Corridor does not directly influence
the Central Corridor, transport authorities that belong to theCentral Corridor take actions to expand capacity more thanBase Case Scenario. The results of “Organization restructuring”
indicate this management structure can be more effective thanrailway expansion in terms of social benefit. The removal ofinconsistency in each stakeholder’s actions contributes to theefficient development of the corridors. Given the situation
where stakeholder’s motivation are interacted each other,
organization restructuring itself is unrealistic. However, theresults show that organization restructuring can lead to better
circumstances than inconsistent investment done by eachstakeholder.

3.3. Results
The results of scenario are compared herein. Table 3
shows the capacity change on each scenario In case of“Railway expansion scenario”, more capacity expansion occurs
in both of the ports and roads except roads connecting cities in
TCs and ports. In addition, transport facilities in Central
Corridor are improved. Considering this phenomena, railway
expansion has an influence on the development of Central
Corridor. Stakeholders relevant to Central corridors takeactions to enlarge the capacity, in order to prevent cargo in
LLDCs from choosing Northern Corridors. In both scenarios of“Organization restructuring”, all transport infrastructure’s
capacities will be expanded than “Base case”. However,investment allocation is different in scenarios of “Organization
restructuring”. While investment is focused on road expansion
in “Regular investment scenario”, railway capacity expansion
is done in “Progressive investment case.
Table 4 summarizes authority’s benefit and shippers’surplus at the end of simulation. Comparing “Base casescenario” with “Railway expansion scenario”, railway
authority’s benefit dramatically increases owing to increased
volume of cargo. The situation for shippers is also improved asshown in the decrease of shippers’ surplus. Regarding both

References[1] Arvis, J., Carruthers, R., Smith, G., and Willoughby, C., 2011.
Connecting Landlocked Developing Countries to Markets, The
World Bank, Washington, D.C.
[2] United Nations, (2003). Transit transport issues in landlocked andtransit developing countries, Economic and Social commission for
Asia and the Pacific Landlocked developing counter series, No.1
[3] Hooke, R., Jeeves, T. A., 1961. Direct search solution of numericaland statistical problems, Journal of the ACM, 8, 212–229

44

STUDY ON THE SUSTAINABLE MANAGEMENT AND USE OF PARTICIPATORYEDUCATION DATABASE: CASE OF NATIONAL EDUCATION SYSTEMS AND POLICIES IN
ASIA-PACIFIC (NESPAP) OF UNESCO
Student Number: 13M51508

Name: Takero INOUE

Supervisor: Shinobu YAMAGUCHI and Jun-ichi TAKADAཧຍᆺᩍ⫱ࢹ࣮ࢱ࣮࣋ࢫࡢᣢ⥆ⓗ㐠⏝ཬࡧ฼⏝࡟㛵ࡍࡿ◊✲: UNESCO ࡢ
National Education Systems and Policies in Asia-Pacific ࡢ஦౛ࢆ⏝࠸࡚஭ୖẎ㑻 ᝟ሗ㏻ಙᢏ⾡ࡢά⏝࡟ࡼࡿ᝟ሗඹ᭷ࡢ୍⎔࡜ࡋ࡚ࠊࣘࢿࢫࢥࡣཧຍᆺᩍ⫱ᨻ⟇ࢹ࣮ࢱ࣮࣋ࢫ NESPAP ࢆ㛤Ⓨࡋࡓࠋᮏ◊✲ࡣ NESPAP ࡢᣢ⥆ⓗ࡞㐠⏝࠾ࡼࡧ࣮ࣘࢨ࣮ࡢࡼࡾ✚ᴟⓗ࡞ࢥࣥࢸࣥࢶࡢᥦ౪ࢆ┠ⓗ࡜ࡋࠊ㐠⏝యไࡢタィ࠾ࡼࡧ࣮ࣘࢨ࣮ࡢ฼⏝ືᶵࡢㄪᰝࢆ⾜ࡗࡓࠋศᯒࡢ⤖ᯝࠊ㐠⏝ࡣᙺ๭ẖ࡟ಶูࡢࢫࢱࢵࣇࡀᢸᙜࡍࡿ
ࡇ࡜ࡀຠᯝⓗ࡛࠶ࡾࠊ࣮ࣘࢨ࣮ࡣ NESPAP ࡬ࡢࢥࣥࢸࣥࢶᥦ౪ࢆ㔜せ࠿ࡘ▱㆑ྥୖ࡟ᙺ❧ࡘ࡜⪃࠼࡚࠸ࡿࡇ࡜
ࡀ᫂ࡽ࠿࡟࡞ࡗࡓࠋ

1 Introduction

2.1 Web content management

UNESCO has a policy to promote universal access to
information [1]. Following this policy, UNESCO
Regional Bureau for Education in Asia and the Pacific(UNESCO Bangkok) has developed a participatory,
National Education Systems and Policies in Asia-Pacific(NESPAP), to promote discussion on education
development in UNESCO’s networks in the Asia-Pacificregion and beyond.
Tokyo Institute of Technology (Tokyo Tech) has beencollaborating with UNESCO Bangkok in the development
and management of NESPAP since 2013. The joint team
identified three problems: 1) the originally developed
platform was not suitable for the sustainable management
and maintenance; 2) the usability of the website was low;and 3) the software architecture was too complex.
Further, there are two challenges in establishing asustainable mechanism to manage and maintain NESPAP.
First, limited resources were available. Second, there is aneed for defining workflows for staff members of
UNESCO Bangkok can constantly engage in managing
and maintaining the database.
It is also necessary to update the content of NESPAP
reflecting users’ motivations. It is because users’
motivation should be a key driver to make them contribute
their knowledge and skills for no monetary rewards.
However, little is known about why education experts
contribute their expertise to the content of participatory
databases.
Given this background, the research objectives are tofocus on two aspects related to NESPAP. First, this study
identifies factors of sustainable management andmaintenance of NESPAP. Second, this study investigatesusers’ motivations to participate in the education database.

With the Web changing into large and rapidly changing
sites, there is a growing need to understand content
management of a website. McKeever (2003) provided aframework of the web content management lifecycle [2].
The framework defines two iterative stages of web content
lifecycle, namely, the collection of content and thedelivery or publishing of that content. NESPAP is acombination of different functions, and users cancontribute different types of content. In order to develop
workflows that are effective in the management of suchcomplex content, the analysis of NESPAP needs toconsider frameworks for web content management.
McKeever’s web content management lifecycle is suitablein the research context because the framework considers
simultaneous processes of multiple content collection anddelivery.

2.2 Evaluation of websites
Nakagawa et al. (2001) proposed the Web Usability Scale(WUS) to obtain quantitative information of users’
perceptions on the usability a website [3]. The evaluation
is developed based on multiple existing survey methods.
WUS covers seven factors of the usability: familiarity,
usefulness, reliability, operability, configuration,
readability and responsiveness. WUS is suitable for
evaluating NESPAP because it covers a broad range of theusability of a website, and thus, can be used to perform acomprehensive analysis of the website of the database.

2.3 Volunteer Functions Inventory
There is a growing attention on why people participate inuser-generated content (UGC) outlets such as YouTube
and Wikipedia since UGC is created by users’ voluntary
contribution [4][5]. Clary et al. (1998) created Volunteer
Functions Inventory (VFI) to understand volunteers’
motivation [6]. The VFI defines six motivations to

2 Literature review

45development of manuals; and 3) training of staff membersof UNESCO Bangkok.

volunteer, namely Values, Understanding, Social, Career,Protective and Enhancement. The VFI has been widelyadopted to study people’s voluntary behaviors, including
participation in UGC outlets [7][8].
Houle et al. (2005) applied this framework to examine
the relationship between people’s volunteer motivations
and specific volunteer tasks [9]. Two major findings areemphasized: 1) different tasks can be characterized bydifferent motivations; and 2) people choose tasks thatserve their specific motivations. The framework is a goodreference to explore the relationship between the use of
NESPAP functions and users motivations.

3.3.2. Survey based on Web Usability Scale
Two questionnaires are conducted to analyze users’
perception toward NESPAP, covering seven factors
defined in WUS: familiarity, usefulness, reliability,
operability, configuration, readability and responsiveness.
The questionnaires employ a 5-point symmetric Likert
scale, ranging from “Strongly agree” to “Strongly
disagree”. Respondents also indicate other web services
that they use, and provide comments for NESPAP
improvement. Mean value and frequency are calculated in
the analysis.

3 Methodology

3.3.3. Survey based on Volunteer Functions Inventory
A questionnaire is conducted to analyze users’ motivation
to participate in the database. The questionnaire employs
the six motivations of VFI: Values, Understanding, Social,
Career, Protective and Enhancement (Table 1).
The questionnaire is composed of three parts. First,
respondents indicate how likely they plan to use sevenfunctions of NESPAP. Second, participants indicate howmuch each motivation drives their use of each function. A
5-point Likert scale is used, ranging from “Very much” to“Not at all”. Third, they provide demographic information.
For analyzing data, demographics are examined byfrequency. Mean value analysis is conducted forlikelihood to use specific functions of NESPAP, andmotivations in the use of functions.

3.1 Research objectives and questions
This study focuses on two aspects related to NESPAP.
First, this study identifies factors of sustainable
management and maintenance of NESPAP. Second, it
investigates users’ motivations to participate in the
education database. To achieve these objectives, thisresearch aims to answer specific research questions asfollows:
1) What are the factors that affect the sustainable
management of online education research database?
2) What motivations drive people to participate in online
education research databases?

3.2 Research framework
Regarding the management and maintenance of NESPAP,
this study adopts McKeever (2003)’s web content
management lifecycle to develop workflows to befollowed by UNESCO staff members. In addition, thisstudy conducts a questionnaire survey, based on Web
Usability Scale, to capture users’ perception toward the
database. Regarding research objective two, for users’participation, this study incorporates Clary’s Volunteer
Functions Inventory (VFI) to explore the relationshipbetween users’ specific motivations and functions of
NESPAP.

Table 1 Six motivations of VFI (Source: Clary et al., 1998, pp. 1517-1518 [6])Motivation
Description
ValuesExpressing or acting on important values

Understanding
SocialLearning more about the world and/or exercise skills
that are often unused
Strengthening one’s social relationship

CareerGaining career-related experience

Protective

Reducing negative feelings, such as guilt, or for
addressing personal problems
Growing and developing psychologicallyEnhancement

3.3 Research process

4 Analysis and interpretation

3.3.1. General framework
The research process is composed of three phases. Phase I
focuses on data collection. This phase includes four steps:
1) discussion on roles for the management andmaintenance; 2) development of the software system of
NESPAP; 3) survey on the usability of the database, based
on WUS; and 4) survey based on people’s participation
motivations, based on VFI.
Phase II focus on the analysis of data. This phaseincludes three steps: 1) analysis on the management andmaintenance of NESPAP; 2) analysis on people’s
motivation to participate in NESPAP; and 3) interpretationof the results.
Phase III related to the application to UNESCO
activities consists of three steps: 1) discussion on the
promotion of NESPAP at UNESCO Bangkok; 2)

4.1 Analysis and interpretation onsustainable software system
4.1.1. The roles for the management and maintenance
Multiple discussions identified four roles and their
specific tasks in managing and maintaining NESPAP, IT
administrators, content moderators, users and visitors. IT
administrators are responsible for maintaining thesoftware system, and content moderators are in charge of
maintaining the quality of the contents and activities in the
education database. The users are expected to contributeto the contents in addition to participating in site activities,
and the visitors do not have site account and thus can only
view the site contents.
Specific tasks for each roles are as follows. IT
administrators update software (the system core, plug-ins,

46

were distributed, and 50 were returned, yielding a
response rate of 64.1%.
Summarizing demographics, most respondents
identified themselves as researcher, followed by professor.
Gender, age, years of experience in the education field
were diverse. Fifty-eight percent of them had experienceof exchanging and sharing knowledge online. Seventynine percent of the participants spend five or more hours
on computer and other devices per day.

and theme), make back-ups, and restore the system.
Content moderators validate user-contributed contents andupdate site contents. Some content moderators are also incharge of assigning and designing other content
moderators. Users view and add contents, and participate
in other site activities such as discussion with other users.
Finally, visitors view contents.
4.1.2. Development of the software system of NESPAPIn developing and improving NESPAP, two basic
principles were agreed: 1) to explore system platforms
which require a minimum amount of maintenance effort;
and 2) to consider two approaches, namely WordPress(CMS) and mashup (web application hybrid) as potential
solutions.
These two solutions were compared from theperspective of regular maintenance, bug fixing, quality ofservice, simplicity of software and compliance withorganization policy. WordPress was selected as thesolution because bug fixing was easy and software wassimple. It also allows storing the site data at UNESCO,
which complied with the organization’s policy about thelocation of data storage.
The implementation took two steps for developing thedatabase. First, eMap, eForum and eResources were
implemented. Second, ESP and two new components,
namely eJob (job board) and eConf (event management
system) were implemented.

4.2.2. Likelihood to use seven functions of NESPAP
There were four major findings from the mean value
analysis. First, participants gave relatively higher ratings
to four functions, namely sharing documents, creating
online group, sharing news and discussing specific topics.
Second, a function for sharing documents showed thehighest score, indicating that users are most interested inutilizing NESPAP to share their documents andinformation. Third, respondents are interested (mean scoreover four) in three functions for creating user groups,
sharing news and discussing specific topics. This can beinterpreted that users are interested in activities about
specific events, projects and topics. Fourth, a function forrecruitment showed the lowest score, implying thatparticipants were less interested in using NESPAP for
recruitment purposes.
4.2.3. Motivations in the use of functions
There were three findings from the result (Table 2). First,
the motivation related to “Understanding” and “Values”
had highest mean values, 3.90 and 3.84, respectively. Thisimplies that these are the most important motivations of
the respondents. Second, scores related to “Social” and“Career” motivations were relatively high, 3.49 and 3.44,
respectively. Third, scores related to “Protective” and“Enhancement” were relatively low, 2.42 and 2.65,
respectively.

4.1.3. Survey based on Web Usability Scale (WUS)
The first survey was conducted with members of a
UNESCO’s regional education network in November
2014. Respondents included education experts from 14
countries and regions in the Asia-Pacific. A total of 43questionnaires were distributed, and 26 were returned,
yielding a response rate of 60.5%. The second survey was
conducted between October and November 2015. Emailwith a link to the survey was sent to those who haveexperience of using NESPAP. Questionnaires were
distributed to 65 education experts, including 25 staff
members of UNESCO Offices. A total of 24 were returned,
yielding a response rate of 37.5%.
Analyzing two questionnaires, there are five major
findings. First, the participants had a good impression on
the website, especially its design and contents. Second, thewebsite’s configuration (site structure) and responsivenesswere the points of improvement. Third, among six
components, ESP, eForum and eResources were identified
popular components. Fourth, the participants found most
NESPAP functions useful. Fifth, functions for recruitment(eJob) were found less attractive among users.

Table 2 Mean scores on motivations in the use of functions
Function\
ValUnderSoCaMotivationues
standing
cial
reer

4.2 Analysis and interpretation on users’participation motivations

Discussion(general)Discussion(specific)
Online group

3.91

4.10

3.47

3.52

Protective
2.26

Enhancement
2.63

3.73

4.02

3.51

3.53

2.47

2.70

3.89

4.02

3.60

3.44

2.36

2.73

Sharing
documents
Self-archiving
documents
Sharing news

4.09

4.02

3.38

3.33

2.36

2.73

3.59

3.80

3.25

3.18

2.45

2.51

4.07

3.95

3.66

3.41

2.50

2.66

Recruitment

3.59

3.39

3.57

3.64

2.52

2.57

Mean

3.84

3.90

3.49

3.44

2.42

2.65

4.2.4. Relationship between the likelihood to use
functions and motivations in the use of functions
Correlations were computed between the two sets ofitems: mean scores on participants’ likelihood to use
functions, and average mean scores for each motivation
regarding the use of functions. The result showed that

4.2.1. General outline
The VFI survey was conducted with 36 education experts
in November 2014 and 42 staff members of UNESCO
Bangkok in February 2015. A total of 78 questionnaires

47was found to be a valuable reference when exploring the
motivation of a group of users of educational database.
Future studies is suggested to increase the sample size
including different group of respondents such as school
teachers and principals to achieve a more comprehensive
coverage.
The result is expected to contribute to the literature on
utilizing UGC in international development as well as tothe practitioner involved in participatory education
database.

“Values”, “Understanding” and “Social” weresignificantly correlated with all functions at the 0.01 level(Table 3). The motivation of “Career” were significantly
correlated with all functions except “discussion (specific)”(.390). “Protective” was significantly correlated with“discussion (general)” (.409), “discussion (specific)”(.464) and “self-archiving documents” (.420).
“Enhancement” was significantly correlated with“discussion (general)” (.478), “discussion (specific)”(.425), “self-archiving documents” (.564), “sharing news”(.522) and “recruitment” (.415)
There were two findings. First, the motivations of“Values”, “Understanding”, “Social” and “Career” weresignificant in relation with people’s likelihood to use
functions of NESPAP. Second, the motivations of“Protective” and “Enhancement” were only partiallysignificant in the use of functions.

References[1] UNESCO. (2005). UNESCO World Report: Towards Knowledge
Societies. Retrieved fromhttp://unesdoc.unesco.org/images/0014/001418/141843e.pdf[2] McKeever, S. (2003). Understanding web content management
systems: evolution, lifecycle and market. Industrial Management &Data Systems, 103(9), 686-692.
[3] Nakagawa, K., Suda, T., Zempo, H. & Matsumoto, K. (2001). The
Development of Questionnaire for Evaluating Web Usability. Human
Interface Society, 421, 424.
[4] Jenkins, H. (2009). What Happened Before YouTube. Retrieved
from
http://web.mit.edu/uricchio/Public/television/jenkins%20before%20you
tube.pdf[5] Ransbotham, S., & Kane, G. C. (2011). Membership turnover andcollaboration success in online communities: Explaining rises and falls
from grace in Wikipedia. MIS Quarterly-Management Information
Systems, 35(3), 613.
[6] Clary, E. G., Snyder, M., Ridge, R. D., Copeland, J., Stukas, A. A.,
Haugen, J., & Miene, P. (1998). Understanding and assessing the
motivations of volunteers: a functional approach. Journal of personality
and social psychology, 74(6), 1516.
[7] Nov, B. O. (2007). What Motivates Wikipedians? Communicationof the ACM, 50(11), 60–64.
[8] Fugelstad, P., Dwyer, P., Filson Moses, J., Kim, J., Mannino, C. A.,
Terveen, L., & Snyder, M. (2012, February). What makes users rate
(share, tag, edit...)?: predicting patterns of participation in online
communities. In Proceedings of the ACM 2012 conference on
Computer Supported Cooperative Work (pp. 969-978). ACM.
[9] Houle, B. J., Sagarin, B. J., & Kaplan, M. F. (2005). A functional
approach to volunteerism: Do volunteer motives predict task
preference?. Basic and applied social psychology, 27(4), 337-344.

Table 3 Relationship between likelihood to use functions and motivations in theuse of functions
ValuesUnderSoCaProEnhance
standing
cial
reer
tective-mentDiscussion.700**.657**
.517*
.434*
.409*
.478**(general)***Discussion.648**.603**
.516*
.390*
.464*
.425**(specific)**
Online.608**.685**
.627*
.595*
.383*
.384*
group
*
*
Sharing.628**.689**
.632*
.547*
0.187
0.289
documents**
Self.549**.582**
.653*
.764*
.420*
.564**
archiving***
documents
Sharingnews.659**.679**

Recruitment.526**.541**

.743*
*
.696*
*

.733*
*
.668*
*

.380*

.522**.350*.415****. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

4.2.5. Interpretation
Analysis on the likelihood of using different NESPAP
functions suggests that functions oriented for specifictopics, events and projects are more likely to be used.
Analysis on participants’ motivations in the use offunctions indicated that people participate in NESPAP
because it is valuable and it is knowledgeable.
Relationship between likelihood to use functions andmotivations in the use of functions indicates that people
with the “Values”, “Understanding”, “Social” and “Career”
motivations are more likely to use NESPAP.

5 Discussion and conclusion
This study focused on two aspects of a participatory
education database, namely, sustainable management andmaintenance, and users’ motivation to participate in online
education database.
Study on the sustainable management and maintenanceof NESPAP identified that McKeever’s web content
management lifecycle as a useful reference whendeveloping the workflows. This study also demonstrated
that WUS can be applied to evaluate a website which has
been just launched. The study utilized Clary’s VFI toanalyze influence of users’ motivation to participate. VFI

48

ANALYZING THE IDEAL PROPERTIES OF DATA VISUALIZATION IN THE WORLDDEVELOPMENT REPORT
Student Number: 13M51543

Name: Ma. Sayori D. KARAKAMA

Supervisor: Prof. Naoya ABE

Data visualizations (datavis) are used to transform raw data into something readable and understandable. Existing
literatures show that evaluating datavis does not guarantee that readers are able to correctly comprehend its contents. This research aims to identify the ideal properties of datavis in the World Development Report (WDR) andrecommend evaluation criteria for future authors, readers, and researchers. The Analysis of WDR figures, compilation of relevant literature findings, and trend analysis were performed to identify three properties. Those are color,size, and data sources, which are considered as some of the general ideal properties of datavis, color usage being
the most important to portray the message of datavis to the readers. This understanding may contribute to the further enhancements of the World Development Report and other development related materials.

12Introduction

Literature Review

1.1 Research overview

2.1 History of Data Visualization

The World Development Report is an annual publication and considered as one of the major analytical publication of the World Bank where it focuses on a particularand important aspects of international development over
the years (The World Bank, 2015). Other than WDR,
there are more organizations who publish developmental
reports such as the Human Development Report and Inclusive Wealth Report. The increase of developmental
reports portrays that the world is facing more and moreissues. The role of such reports is to provide updates or amessage about what is happening to our world whether
there is an improvement or none. There had been various
means to address the message not only to the experts, but
to the general audience as well. Other than reading thecontext of the reports, another way of delivering suchmessages is by the use of data visualization. Data visualization (datavis) can be briefly defined as the “visual representation of data or information using graphical techniques” aimed to communicate its message in any fieldand support people to amplifying cognition through visualmeans. Studies show that using visuals provide more insights and impact to the readers (Cukier, 2010).
The WDR contains information about statistical data
represented by either tables or visualized data throughfigures or graphs. This study aims to identify the trends ofdata visualization and its ideal properties in the WDRs by
analyzing the figures present in the report. By tracing thedevelopment of data visualization in the report, on how
the report represents its data, and how it changed over theyears, future reports might be able to follow or refer to
how WDR used its datavis to deliver a message to its
readers. Although, it can be argued that the visualized
data in the report may have flaws such as the figures
might be difficult to understand especially for the generalaudience. Earlier releases of WDR did show such problems in their use of data visualization. Later releases, on
the other hand, showed improvements especially with theuse of colors. Some existing development-related reports
might be facing the same problems as WDR used to have.
Therefore, it is important to learn the development of
datavis in the WDR so that current or future reports may
refer to it and have insights on how to improve their useof data visualization. This research aims to contribute to
the authors or organizations of developmental reports andto the researchers of data visualization itself.

The birth of data visualization can be traced back before
the 16th century where there was a trend for geometric
diagrams and maps to aid in navigation and exploration(Friendly, 2007). Data in this context refers to facts, numbers, or records. Over the years, visualization had evolved
to a more complex yet useful form supporting demographic statistics, decision-making, and communication.

2.2 Data Visualization Properties and Evaluation
Criteria
Data visualization studies show that there is scare information on how to evaluate data visualization (Plaisant,2004). Kelleher and Wagener (2011), however, provided
ten proposed guidelines for effective data visualization to
reduce common pitfalls in the pursuit to effective convey
information in the context of scientific publications andpresentations:
1. Create the simplest graph that conveys the information you want to convey.
2. Consider the type of encoding object and attributeused to create a plot.
3. Focus on visualizing patterns or on visualizing details, depending on the purpose of the plot.
4. Select meaningful axis ranges.
5. Data transformations and carefully chosen graph aspect ratios can be used to emphasize rates of change
for time-series data.
6. Plot overlapping points in a way that density differences become apparent in scatter plots.
7. Use lines when connecting sequential data in timeseries plots.
8. Aggregate larger datasets in meaningful ways.
9. Keep axis ranges as similar as possible to compare
variables.
10. Select an appropriate color scheme based on the typeof data.
Arslan and Toy (2014) specified colors, fonts, formats,layouts and icons play an important role in visualization
and analyzed the visual problems on infographics where
the common challenges are layout mistakes, irrelevant
colors, wrong choice of formats, overuse of pictographic
icons, and off topic visual elements. Steele and Illinsky(2010) say, “color is one of the more abused and neglected tools in data visualization” implying that every used49

color in any graphic should serve a purpose of clarifying
the meaning of the content. Sperandei (2014) and Finan(2015) stressed that graphic presentations are powerful
instruments for the communication of research results andare also prone to misunderstanding and manipulation.
Every aspects of graphic design such as scales, color, andshapes can influence how results are interpreted(Sperandei, 2014).
This study applied the proposed ten guidelines as reference and modified it accordingly to match the datavis
characteristics of the WDR. Thus, leading to the formulation of data visualization evaluation criteria that can beapplied in the WDR and other development related materials.

3

properties of datavis as one of the objectives of this research. WDR contains several numerical data represented
by either tables or visualized data through figures orgraphs. Since the focus of this research is about data visualization, by definition is the “visual or graphical representation of data or information” from authors of several
academic literatures, figures were selected as the datavis
data. Data regarding the figure and table usage frequency
were also collected for analysis to learn more about theusage of datavis in the WDR. A total of 1,370 figureslisted on the table of contents of WDRs were collected.
Table 1 shows a summary of what kind of data were extracted from the figures. Performing a thorough analysisof the all those figures required a huge amount of time. To
overcome that ordeal, specific topics were selected basedon the extracted frequency of keywords from the contentsof the reports programmed in Ruby.

Research Methodology

3.1 Overview
The WDR annual publications from 1978 to 2015 were
purposively selected as the main source of data as it features a long history of international development reports
catering several examples of data visualization (datavis)
showing how it evolved through time. There are a total of
37 WDRs. Report from 1999 was not included as somereleases included report from two years (1998-1999,
1999-2000, and 2000-2001) and was therefore represented by 1998, 2000, and 2001 respectively.

3.2 Keyword selection process
Keyword selection was a necessary part of this research toidentify important topics in terms of international development to trace the trends on how data visualization
evolved in the WDR and how were its properties different
or similar over time. A keyword extraction tool was programmed to filter and extract the keywords from the text
version of the reports. The program was iterated until the
top results return distinguishable words, nouns, verbs, or
adjectives. Results by the program were not fully accurate
and would therefore be considered as an estimate. The
estimated frequency of each unique keyword per report
was then computed together with its accumulated occurrence in all the reports and was compared. The focus on
women, health, and poor were the results of computing
the frequency or occurrence of those terms in each report(see Figure 1) and comparing it to the general occurrenceof those keywords in all the reports (see Figure 2).
It can be assumed that most keywords in the report
can be regarded as important, but there are some which
are exceptionally more significant than others. In the caseof this research, it was measured by how frequently it was
mentioned in the reports. For example, in the term “women”, it appeared the most in the year 2012 where the topic
was about “Gender Equality and Development”, which
was an obvious result since that was the focus of the report. However, it does not necessarily signify that the
topic “women” is highly regarded in the WDR from 1978
to 2015. But in the case of the “women” keyword, it appeared on both Figure 2 and Figure 3, so we can conclude
that the term was indeed highly regarded in the WDR ingeneral. Another example was the keyword “climate”.
The term “climate” appeared the most in 2010 as the topic
is about Climate Change. That was understandable. However, computing for its overall occurrence in the reports, it
did not appear that much in the other WDRs. Although it
was ranked 3rd in the keyword occurrence per report, it
was barely mentioned in other reports. Whereas in comparison to the keyword “health”, it appeared the most in
year 1993, but also had frequent mentions in other years
which means that it is one of the important topics in the
WDR. Comparing the similar keywords on both tables,
topics related to “women”, “health”, “jobs”, “economic”,
and “poor” appeared on both. To further narrow it down
further, the remaining 5 keywords were correlated to the
MDGs to select the final 3 keywords to be used in thisTable 1: Summary of dataset in WDR figures
Observations 1,370 figures
WDR 1978 – 2015
SourceDatasetFigure Color data
͌ Number of colors used per report͌ Mean and Mode of color usage
͌ Maximum and Minimum number ofcolors͌ Meaning of each color per report
Mean
7

Mode
8

Min
2

Max
11

Med
9

Figure Size data (in pixels)* zoom size was set to 75% relative toletter size (215.9 mm × 279.4 mm)
paper*
͌Average figure height and width perreport͌Minimum and Maximum height andwidth per report
For height (in pixels):
Mean Mode Min
410
N/A
279

Max
603

Med
324

For width (in pixels):
Mean Mode Min
366
333
192

Max
508

Med
374

Other data
͌Data source inclusion in figures status(boolean)(Source: by the author)
The data collection involved collecting that figure andtext data from the WDRs to analyze and identify thetrends of data visualization and to determine the ideal50

study. MDGs indeed came to life in year 2000, but it covers several topics in development through its 8 goals.
Topics regarding “women” were related to MDG goal 3:
promote gender equality and empower women, and goal 5:
improve maternal health. Topic about “health” was related to MDG 4, 5, 6. For keyword “jobs”, it was not implicitly shown in the MDGs and was therefore screened out.
The same case with the keyword “economic”. Lastly,
keyword “poor” was related to the MDG goal 1: eradicate
extreme poverty and hunger. Therefore, “women”,“health”, and “poor” were selected as the final keywords
to be used in this research to analyze the trend of datavis
in those specific topics.

4

Results and Discussion

4.1 Trends of data visualization in the WDR
There were three parts in the trend analysis in this research. Table 2 shows a summary of the different type ofanalyses performed in this research and the respective
data used for each analysis.
Table 2: Data visualization analysis data usage
ANALYSISDATA USED

General Datavis
Analysis· Figure countFigure Properties Analysis· Color frequency

Specific Topic
Analysis· Keyword frequency· Table count· Figure size (height and width)· Color, Size, Data source(Source: by the author)
In the general analysis, data in the WDR was represented with either tables or data visualization throughfigures that was why a comparison was made between thetwo (see Figure 3). Although WDR is a well-known report, it was possible that it was not making use of datavisualization. By learning this trend, it can be known if
the use of data visualization is a necessity or not for developmental reports, at least for the WDR.
Figure 1: Results of estimated keyword occurrence perreport (Top 10 results)

Figure 3: Comparison of figures and tables in the WDR(Source: by the author)
In case the result showed that the use of tables weremore prominent even in the recent years, it might say thatthe use of tables might be more appropriate in such reports rather than data visualization through figures. But
since it showed that the use of figures did increase andovertook the use of tables, it means that the World Bank
sees that it was necessary or appropriate to use data visualization in the WDR, that tables alone are not enough toexpress the message of the data. Tables were used as frequently as figures in the earlier years. Changes started tooccur from mid-1990s where data visualizations became
more frequently used over the tables. The increase of datavisualization usage can indicate that the topics in theWorld Development Report in the later years started torequire more attention from its readers wherein tables
alone were not sufficient to deliver the message.

Figure 2: Results of accumulated keyword occurrence
from all reports (top 20 results)(Source: by the author)

3.2 Datavis Evaluation Criteria Formulation
Analyzed datavis trends in the World Development Report revealed its ideal properties and were used to create
evaluation criteria applicable for the report. The 10 proposed guidelines for effective data visualization by Kelleher and Wagener (2011) from the literature review werealso used as a reference to develop some evaluation criteria for the data visualization in the World Development
report. Some of the proposed guidelines from Kelleher
and Wagener (2011) were not applicable in the World
Development Report and were therefore modified in accordance to its datavis properties.
51

the figures of the reports by analyzing topics about women, health, and poverty. Answering the first objective of
this study and identifying the properties of data visualization in the WDR lead to the development of datavis evaluation criteria (see Table 2) adequate for WDR.
Table 2: Proposed evaluation criteria based on the research results and findings

The second part of the analysis was the actual observation of all figures per report. Data visualization properties were observed as to how it changed over the years. At
first, all known datavis properties based from related literatures were taken into consideration such as dimension,color, size, fonts, graph scale, data source, and data labels.
By observing the figures from each year, the most distinguishable changes in the datavis properties can be observed in the figure color usage, size, and data source
availability. Trends can be observed from those datavis
properties as to how the WDR changed over the years interms of data visualization.

(Source: by the author)

5

Conclusions and Recommendations

Tables were commonly used in the earlier WDR to represent data and changed from mid-1990s where figures
were more frequently used over tables which may imply
that the World Bank sees that it was necessary or appropriate to use data visualization in the WDR where tables
alone were not enough to express the message of the data.
Analyzing the trends of datavis in terms of specific keywords such as “women”, “health”, and “poor” revealed
some specific findings about the how datavis properties
such as color, size, and data source availability were different or similar over the years. The mentioned datavis
properties were some of which that showed the most patterns in its evolution in the WDRs. Based on the findingsof the trend analysis, datavis properties, and guidelines
proposed by Kelleher and Wagener (2011), “Readability”,“Meaningfulness”, and “Reliability” were concluded as
datavis evaluation criteria adequate in the WDR.
This research can be further improved by testing therecommended evaluation criteria results by conducting aproper survey, advisable for its continuous development
and analyzing the trends for other significant keywords.
This research may be used as a reference or model in alarger scale such as comparing different developmental
reports from other organizations that could expose moreinteresting findings regarding data visualization in developmental reports that may result to the progress of a more
standardized data visualization evaluation criteria andmethod applicable to most reports not limited to developmental ones.

Figure 4: Sample compiled figures from year 1990 – Poverty (top) and 2001 - Attacking Poverty (bottom)(Figures compiled by the author)
The third part of the trend analysis is to analyze thetrends of data visualization regarding some important
specific topics. Figure 4 shows an example of how thedata visualization for the topic “poor” changed in year
1990 (Poverty) and year 2001 (Attacking Poverty). Both
topics of those years were focused on poverty. It was evident that there was an improvement in the color usage.
According to Steele and Iliinsky (2010), every used color
in a graphic should serve a purpose of clarifying themeaning of the content. In the case of the datavis for
keyword poor, the earlier year used around 8 different
colors, but its usage was not clear as to what kind of message it wants to deliver to the readers. On the other hand,
year 2001 used fewer colors that made the figures easierto read. However, the message of the colors was rather
vague as well. There were some similarities observed on
both sets. Although the uses of colors were not clearly
defined, some certain colors were used to represent countries experiencing poverty that can be found consistent inall figures. In terms of figure size, both years used relatively large figures that occupies around half of a page inmax that may imply that the larger the size, the more emphasis the authors wanted to make.
Overall observation of figures per WDR revealed datavisualization properties applicable to the report. Three (3)datavis properties were deemed necessary to develop recommendable data visualization evaluation criteria for
WDR. Those were color usage, size, and data sources.
The color property showed the most pattern of usage inReferences[1] Friendly, M. (2007). A brief history of data visualization.
Handbook of Computational Statistics: Data Visualization, Vol.
3, 1-34. Heidelberg: Springer-Verlag.
[2] Kelleher, C. & Wagener, T. (2011). Ten guidelines for effective data visualization in scientific publications. Environmental
Modelling & Software. pp.1-6[3] The World Bank, (2015). World Development Report. Retrieved fromhttps://openknowledge.worldbank.org/handle/10986/2124[4] Steele, J., & Iliinsky, N. (2010). Beautiful visualization:
looking at data through the eyes of experts. Sebastopol, CA:
O'Reilly.
52

COMPETITION BETWEEN LAND CORRIDOR AND MARITIME TRANSPORT
IN CROSS-BORDER FREIGHT TRANSPORT
Student Number: 14M18069

Name: Wataru Saito

Supervisor: Shinya Hanaokaࢡࣟࢫ࣮࣎ࢲ࣮㍺㏦࡟࠾ࡅࡿᅇᗯ࡜ᾏ㐠ࡢ➇த㰻⸨ ர ࢡࣟࢫ࣮࣎ࢲ࣮㍺㏦࡛ࡣప㈝⏝ࡢᾏ㐠㍺㏦ࡀ⏝࠸ࡽࢀࡿࡇ࡜ࡀከ࠸ࠋࡋ࠿ࡋࠊ㏆ᖺࠊ㝣㊰㍺㏦࡛࠶ࡿᅇᗯࡀᩚഛࡉࢀ฼⏝ࡉࢀ࡚࠸ࡿࠋ
ᮏ◊✲ࡣࠊୡ⏺ྛᅜ㛫ࡢࢡࣟࢫ࣮࣎ࢲ࣮㍺㏦ࢆᑐ㇟࡟ࠊᅇᗯ฼⏝ศᢸ⋡ࢆ⿕ㄝ᫂ኚᩘ࡜ࡋ࡚ࣟࢪࢫࢸ࢕ࢵࢡᅇᖐศᯒࢆ⾜࠸ࠊᅇᗯࢆ฼⏝ࡍࡿせᅉࢆ᫂ࡽ࠿࡟ࡍࡿࠋࡲࡓࠊṧᕪศᯒ࡟ࡼࡾᆅᇦಶูࡢせᅉࡶ᫂ࡽ࠿࡟ࡍࡿࠋศᯒࡢ⤖ᯝࠊෆ㝣ᅜࢲ࣑࣮ࠊ㍺ฟᅜࡢရ┠౯್ࠊ2ᅜ㛫㊥㞳ࠊ㞄ᅜࢲ࣑࣮ࠊ㝣㊰࢖ࣥࣇࣛࣞ࣋ࣝࠊ࢝ࣥࢺ࣮ࣜࣜࢫࢡࠊ ‴࢔ࢡࢭࢫࠊᾏ㐠㍺㏦㢖ᗘࡀ᭷ព࡞ኚᩘ࡛࠶ࡾࠊ୰࡛ࡶ㝣㊰࢖ࣥࣇࣛࣞ࣋ࣝᨵၿࡀᅇᗯࡢศᢸ⋡ࢆ኱ࡁࡃୖ᪼ࡉࡏࡿ஦ࢆ♧ࡋࡓࠋࡲࡓࠊᮾ࢔ࢪ࢔ࡢᾏ㐠㍺㏦㢖ᗘࡢከࡉࠊᅜࡢ୺せ⏘ᴗࡀ㈠᫆ရ┠࡟୚࠼ࡿᙳ㡪ࠊᆅᙧࠊࢸࣟ࡞࡝ࡢ࢝ࣥࢺ࣮ࣜࣜࢫࢡࡀࠊᆅᇦᅛ᭷ࡢせᅉ࡛࠶ࡿࡇ࡜ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ

nominal value are more than 1%.

1. Introduction
A cross-border land corridor is the bundle ofinfrastructure such as highways, railways and borderfacility that links 2 or more urban areas in different
countries. To facilitate the trade by cross-border land
corridor, many projects have been carried out byinternational donor and various countries in theworld. ”New Silk Road” concept connecting the EU andChina proposed by the Chinese government in 2015 isgood example of cross-border land corridor project.
Land corridor and maritime transport compete eachother because maritime transport costs less than land
corridor while maritime transport is much slower.
Cross-border transport is relatively longer distance andmore complex due to border procedure than domestictransport, so that maritime transport can easily besuperior to land corridor. Additionally, in developing
countries, since the value of commodities is not as highas in developed countries, forwarders tend to choose
the maritime transport over land corridor. Therefore, incompetition with maritime transport, how much land
corridor will be used for cross-border freight transport
has not been revealed yet even though there are manycross-border land corridor projects in the world.
The main objectives of this study is to identify thesignificant factors to select cross-border land corridor
by inductive approach and this can be broken down into
2 sub objectives as follows.
1) To develop the model that estimates the land ratio bylogistic
regression
analysisusing
country
Origin-Destination (OD) pairs.
2) To investigate the specific OD pairs situation by
residual analysis.

2.2 Logistic regression analysis
In this study, to identify the significant factors todetermine the land ratio of selected OD pairs, logistic
regression analysis is applied. Logistic regression aims
to find the best fitting model to describe therelationship between explained variable and a set of
explanatory variable with generating the coefficientand its significant levels of formula. Logistic regression
analysis is modeled as follows.
‫ݕ‬ൌ݁ ఈାఉభ ௫భାఉమ ௫మାǡǡǡାఉ೛ ௫೛ͳ ൅ ݁ ఈାఉభ ௫భାఉమ௫మାǡǡǡାఉ೛ ௫೛‫ ׷ ݕ‬‫ݔ‬௜ ǣ ߙ ‫ ׷‬ߚ௜ ǣ     ሺͳሻ2.3 Explained variable
To focus a competition between land corridor andmaritime transport, airborne trade nominal value issubtracted
from
total
trade
nominalvalue($Thousands) to calculate the land ratio as follows.
y : Land ratioൌ࣭
ሺʹሻ െ 

2.4 Explanatory variables
In this study, 11 explanatory variables classified into
4 groups are prepared as follows. The average value of
the OD are taken for A1, D9, D10 and D11 while theaverage value of the OD and passing countries aretaken for C7 and C8. The overall information ofvariables is shown in Table 1.
A1: GDP/capita and A2: Landlocked country dummy
are set up to represent the characteristics of country.
GDP/capita is used to represent the economic power ofshippers or forwarders to take charge of higher landtransport cost than maritime. Additionally, landlocked
countries are in specific situation since there is no port
in their territory. Land corridor should be superior tomaritime when either OD is landlocked country.
Dummy variable equals to 1 if either OD is landlocked

2. Methodology
2.1 OD pair selection
To examine the competition between land corridor
and maritime transport, data of the OD pairs bothmodes have share in 2014 are drawn by the database [1].
280 samples are selected by the following 2 principal
criteria.
1) Origin and destination locate in same or near region(following Global Insight classification).
2) Both overland nominal value/total trade nominal
value and seaborne trade nominal value/total trade53

Table 1: Overall information of the variables
Group

A:Characteristic of Country
B:Characteristic of Freight

C:Characteristic of Mode
[Land]

D:Characteristic of Mode
[Maritime]

V ariables

Expected

deviation

sign

Min

Land ratio

98.96%

1.07%

42.80%

37.39%

A1:GDP/capita

73,669.5

819.7

16024.7

15,487.4

A2:Landlocked country dummy

AverageStandard

Max

1

0

0.1

0.32

B3:Export (manufacturing)

94.0

0.1

44.8

30.9

C4:Distance

7,728

81

2496

1,651

C5:Neighboring dummy

1

0

0.33

0.47

C6:Number of border

12

1

2.98

2.25

C7:Infrastructure level2.0333-1.3722-0.1895

0.8909

C8:Country risk

84.64

29.80

53.23

11.18

D9:Port access

16.0

2.0

5.4

2.6

D10:Port infrastructure level

6.7

2.7

4.3

0.7

D11:Frequency of maritime

139.1

4.7

40.7

23.2

+
+
+
䠉
+
䠉
+
+
+
䠉
䠉

Unit

Source%Global InsightdollarWorld Bank

-

-

%

World Bank

km

-

-

-

-

-

-

World Bank

index

Euromoney

days

World Bank

index

World Bank

index

World Bank

connected to global shipping networks based on fivecomponents of the maritime transport sector: numberof ships, their container-carrying capacity, maximum
vessel size, number of services, and number ofcompanies that deploy container ships in a country's
ports. The index generates a value of 100 for thecountry with the highest average index in 2004.


country, 0 otherwise.
A value of freight itself also affects the mode choice.
The commodity with higher value should betransported by land corridor. In this study, the ratio of
manufactures exports in total merchandise exports(B3) is used to represent the average value ofcommodity exported by the origin. Data is derived fromWorld Development Indicator [2].
C4-C8 are the variables to express a situation of each
OD pairs’ land corridor. C4: Distance measured inkilometers between the main city in origin anddestination
connecting
thepassing
countries
represents transport time by land corridor. In land
corridor, border crossing is one of the bottlenecks since
freight has to follow the procedures such as customs,
immigration and quarantine. Dummy variable equals
to 1 if OD share land border, 0 otherwise (C5). This
study also includes the number of border crossing (C6)when freight is transported from origin to destination
by land corridor. Furthermore, land infrastructure level(C7) are measured by composite variables constructedfrom three variables (total road length/land area size,
paved road length/total road length, total railway
length/ land area size) by principle component analysis.
Finally, the values of country risk (C8) is derived from
Euromoney[3] evaluated by consensus survey of theexpert opinion. The scores express social network of
economic, political country risk. Country risk affects
land transport, especially in border area since many
ethnical conflicts and corruption such as a bribe often
happen. The score ranges from 0 to 100 and higherscore means lower risk in this database.
The situation of maritime transport is expressed by
D9-D11. D9: Port access is used to represent theaccessibility of the port from main city. The summationof lead time of export in the origin and lead time ofimport in the destination is inputted. In addition, “Portinfrastructure level index” is a proxy used to represent
how much port infrastructure of the origin anddestination are developed (D10). This index ranges
from 1 to 7 and measures business executives'
perception of their country's port facilities. To represent
a frequency of maritime transport between the origin
and destination, “Linear shipping connectivity index”
derived from World Bank is used (D11). Liner Shipping
Connectivity Index captures how well countries are

3. Results and discussion
3.1 All sample case
All correlation of coefficient between explanatory
variables is less than 0.7, therefore firstly analysis by
all samples with all variables is conducted and result isshown in Table 2 as case (a). By omitting 3 variables
with unexpected sign (A1: GDP/capita, C6: Number ofborder and D10: Port infrastructure level) and 3
samples with more than 3 or less than -3 of absolutevalue of standardized residual error, the model used forresidual analysis in the next section is obtained as case(b). Case (b) has more than 60% of adjusted R squareand 8 factors gets significance. Furthermore, byomitting C8: Country risk, A1: GDP per capita getssignificance since their correlation coefficient isrelatively high (0.66). Unfortunately, the significance of
C6: Number of border and D10: Port infrastructurelevel cannot be found in anyway by this model.

3.2 Excluding 2 inter regional case
2 European related inter regional case (European
Union&Western Asia, Other European country&
Western Asia) has high land ratio even though they
have many border crossings. By excluding thesesamples, the sample size turns out to be 207 and thesignificance of C6: Number of border is confirmed asshown in case (c). Therefore, it can be said that in theregions except the European countries, number ofborder is also significant factor to facilitatecross-border land corridor.

3.3 Stratification analysis
Since border crossing is obviously significant factor
as mentioned above, stratification analysis using same
variables is conducted by separating the samples into 2
groups: the OD locating neighbor and OD locating nonneighbor. In neighboring case, the OD pairs that either
origin or destination is landlocked country are excluded54

Table 2: Estimation results (*: 10%, **: 5%, ***: 1% significance)
(a)
(b)
(c)
(d)
(e)
0.580
0.619
0.520
0.561
0.645
0.563
0.608
0.506
0.537
0.631
Coeffici tsignifi Coeffici tsignifi Coeffici tsignifi Coeffici tsignifi Coeffici tsignifi
entvalue cance ent
value cance ent
value cance ent
value cance ent
value canceIntercept-4.6910 -3.67 -2.5139 -3.56 0.6244 1.27 0.6741 1.15 -1.6915 -1.50 A1
0.0000 -1.52
0.0000 3.07 (***)
0.0000 2.71 (***)
A2
1.5170 4.76 (***)
1.3593
4.55 (***)
0.9396 2.04 (**)
1.54824.29 (***)
B3
0.0189 3.77 (***)
0.01904.07 (***)
0.0122 2.66 (***)
0.0153 2.33 (***)
0.02104.01 (***)
C4-0.0009 -11.05 (***) -0.0008 -13.06 (***) -0.0007 -8.31 (***)-0.0010 -8.23 (***)-0.0007 -10.46 (***)
C5
1.6433 6.13 (***)
1.5005
6.49 (***)
C6
0.2220 2.70 (***)-0.5063 -5.56 (***)
C7
0.3059 1.55
0.69884.49 (***)
0.78324.03 (***)
C8
0.0685 5.18 (***)
0.0513
5.17 (***)
0.05684.51 (***)
D9
0.1093 2.19 (**)
0.09592.16 (**)
0.0694 1.44
0.1213 2.00 (**)
D10
0.1756 0.75
-0.2080 -0.94
D11-0.0127 -1.91 (*)-0.0135 -2.53 (**)
-0.0166 -2.55 (**)
Num of samples
280
277
207
79
187

R squareAdjusted R square

4.2 Sensitivity analysis

from a set of the samples since these OD pairs havehigh land ratio without discussion. The result ofneighboring case is (d) with 79 samples while
non-neighboring case is (e) with 187 samples.
Comparing both groups, non-neighboring case hashigher adjusted R square and more variables withsignificant. D9: Port access is significant only inneighboring case. Since maritime transport time isrelatively shorter, higher accounting ratio of time on
port access makes D9: Port access crucial inneighboring case. D10: Port infrastructure has
expected sign only in non-neighboring case since higher
port infrastructure level is required for longer maritime
transport time.

Sensitivity analysis is a technique that examines theimpact of input change on output. To comprehend thepotential each variable has to change the land ratio,
B3: Export (manufacturing), C7: Infrastructure level,
and C8: Country risk, D9: Port access and D11:
Frequency of maritime are changed ranging frommaximum to minimum value of a set of samples shownin Table 1. The residual error initially each sample
has is assumed to be fixed even when each input
variable is changed.
The result of Brazil (export) - Chile (import) whose
actual land ratio is 10.3% is shown in Figure 2. It canbe said that infrastructure level has the biggest
potential to increase the land ratio by almost 40% inthis case.

4. Residual analysis
4.1 Low and high residual error
By logistic regression analysis, residual error can becalculated by subtraction of the predicted value from
actual value. It can be assumed that the samples withlow residual error is expressed well by the set of
explanatory variables and the samples with highresidual error is not, therefore other factors to explainhigh residual error should be investigated. The
following figure shows histogram of the standardized
residual error by case (b) in Table 2. In this study, thesamples with standardized residual error ranges from-0.5θ to 0.5θ is considered to have low residual error
and sensitivity analysis is conducted. Furthermore, thesamples with more than 2θ or less than -2θ ofstandardized residual error is considered to have highresidual error, and detailed analysis is carried out.

Figure 2: Tornado chart (Export: Brazil, Import: Chile)

4.3 Detailed analysis
The samples with high residual error including
omitted 3 samples are shown in Table 3. The reason
why these samples have high residual error can beclassified into 5 issues as follows.
[1] Land infrastructure level
In EU&Western Asia and North America, the actual
value is higher than predicted value. As shown in Table
4, the average of infrastructure level in EU&Western
Asia is much higher than all samples and this makes
the land ratio possible to be high. North America has
lower infrastructure level contrary to the expectation
since one of the indexes used to composite variable istotal road length per land area and countries in North
America have large land area so that the value getslower than the real situation. Both cases contain long
distance, so it can be implied that distance is not a

Figure 1: Histogram of standardized residual error

55

Table3: Samples with high residual error
Issue

RegionExportImportSpain
TurkeyFinland
EU&Western Asia[1]Land infrastructure
Turkeylevel
Portugal
TurkeyMexicoNorth America
Canada[2]Frequency of maritime
East Asia
ThailandWestern Asia
United Arab Emirates
El Salvador, Honduras, and NicaraguaCentral America
Costa Rica and Panama[3]Detailed commodity&North America
MexicoSouth America
BrazilAlgeria
Burkina Faso, Mali, and Niger
AfricaLibya[4]Geographical features
Burkina Faso, Mali, and Niger
BoliviaSouth America
Argentina
Chile
Egypt, Arab Rep.
IsraelAfrica&Western Asia
Egypt, Arab Rep.
Israel[5]Specific country risk
Central America &Costa Rica and Panama
North America
Indian Subcontinent
Pakistan&Western Asia

Colombia

7.9%

68.7%

-60.8%

Other Western Asia (Iran,Iraq)

3.6%

68.0%

-64.3%[5] Specific country risk
Even though indicator of country risk is included in
the model, it is hardly to be said the score representscountry risk of land corridor itself perfectly for somecountries. For example, the score of Israel is 66.8 andbetter than the average but, in Israel there is area
called “Gaza” specifically dangerous and this area
locates close to the freight route between Africa andWestern Asia. In addition, in Panama, Darien Gap
locates where rebel army have activity and this area isa missing link of Pan-American corridor. Furthermore,in Pakistan, Iran and Iraq, there is much terrorism andIslamic extremist such as Taliban base around here.

critical issue if land infrastructure is developed enough.
Table 4: Average of infrastructure level
C7: Infrastructure level

TurkeySpain
TurkeyPortugal
TurkeyFinland
CanadaMexicoSingapore
Bahrain
MexicoMexicoCosta Rica and Panama
Costa Rica and Panama
Burkina Faso, Mali, and Niger
Algeria
Burkina Faso, Mali, and Niger
Libya
BrazilChile
Argentina
Saudi Arabia
Libya
IsraelEgypt, Arab Rep.

Actual Predicted Residual
value
value
error
81.2%
34.5%
46.7%
80.8%
36.2%
44.6%
80.6%
27.5%
53.1%
79.9%
30.0%
50.0%
79.8%
30.6%
49.3%
79.8%
28.2%
51.6%
87.6%
30.7%
57.0%
85.9%
17.9%
68.0%
26.7%
74.1% -47.4%
2.5%
46.9% -44.4%
52.7%
89.3%
36.6%
70.3%
83.5%
13.2%
78.9%
26.5%
52.4%
82.3%
lower
+
4.8%
57.7% -52.9%
4.1%
60.8% -56.7%
4.1%
59.3% -55.2%
3.8%
63.2% -59.4%
98.8%
54.1%
44.7%
21.7%
70.0% -48.2%
18.2%
64.6% -46.4%
6.0%
55.2% -49.2%
5.1%
65.8% -60.7%
7.1% higher䠉
5.5% higher
䠉EU&Western Asia North America All samples
1.0502-0.2085-0.1895[2] Frequency of maritime
East Asia has extremely high frequency of maritime
transport (the average of East Asia: 90.5, the average of
all samples: 40.7) since Malacca strait is a choke pointof world supply chain. In fact, 9 out of top 10 worldcontainer port locate in East Asia such as Shanghai,
China and Singapore in 2014.
[3] Detailed commodity
Most of Western Asia country except Turkey export
mineral fuels more than 50% of total trade and this canbe the reason of causing high residual error sincemineral fuel is a mode captive commodity of maritime
transport. As for Mexico-Central America and Brazil –Costa Rica and Panama case, according toUNcomtrade[4], Mexico mainly exports manufacturing
products to Central America and Brazil also exports
manufacturing products to Costa Rica andPanama ,even though B3: Export (manufacturing) of
Brazil is not as much high (34.8%). This is thephenomena caused by a big economical gap between
those countries. Additionally, it can be considered thatCentral America exports the lower value commodity to
Mexico by truck to reduce the unloaded return truck.
[4] Geographical features
Burkina Faso, Mali and Niger are landlocked country
but the land ratio with North Africa is not high sinceAtlas Mountain and Sahara desert exists along theroute. In contrast, route between Bolivia and Brazil isrelatively flat so actual value is higher than predictedvalue. Andes Mountain existing between Chile andBrazil makes the land ratio gets lower as well.

5. Conclusion
In this study, to generalize the important factors to
select cross-border land corridor in competition withmaritime transport, logistic regression analysis was
conducted and 8 variables were identified as thesignificant factor. Some variables were found to besignificant in the specific cases. Sensitivity analysis
showed a big potential of land infrastructure. By
detailed analysis, importance of land infrastructure,high frequency of maritime transport in East Asia,
detailed commodity, geographical features and specificcountry risk were found as region-specific issue.
References[1] Database
Online available: https://www.ihs.com/index.html[2] World Bank Development Indicator
Online available: http://wdi.worldbank.org/table[3] Euromoney Country Risk
Online available:
http://www.euromoneycountryrisk.com/Home/Return/Countries[4] UNcomtrade
Online available: http://comtrade.un.org/

56

CAMERA CALIBRATION USING GLOBAL PROJECTION
TRANSFORMATION
Student Number:14M18247 Name:Yang Chen

Supervisor:Yukihiko YAMASHITA

大域的射影変換を用いたカメラキャリブレーション
陳 陽
本研究では, Zhang が提案した平面オブジェクトのカメラキャリブレーション法に基づき, 実世界の座標と画像座
標の間のホモグラフィの推定に, 大域的射影変換 (GPT) 相関法を適用することを提案する。GPT 相関法は画像自体のマッチングによりホモグラフィを求めるため，特徴点が求まりにくい画像でもホモグラフィを求めることができる。そのため，キャリブレーションオブジェクトの選択がより柔軟になる。また，画像全体でマッチングを行うため，高精度化が期待できる。さらに本研究では，キャリブレーション実験により，提案手法の有効性を確かめた。

1

2 Camera Calibration

Introduction

2.1 Camera Model

Computer vision is a technology which makes the machine to have an ability of recognizing the world fromimages. This technology can be used to recognition,
tracking, and measuring. With the development of computer vision, it has been applied for medical science,robotics, character recognition, military reconnaissance,
scene measurement, and so on.
Camera calibration is a necessary step in 3D computer
vision. It is a process to estimate the internal and external parameters of a camera. The accuracy of camera calibration effects the correctness of the following steps incomputer vision directly. It has been studied extensively
in photogrammetry community and computer vision.
Tsai proposed a camera calibration method by observing a 3D object. The calibration object consists of twoplanes orthogonal to each other. This method is very efficient. But because we need to know the geometry of theobject in 3D space with a very high precision, it requires
expensive calibration equipments and delicate setup.
For this issue, Zhang did abundant study and proposeda new flexible method [3] for camera calibration in 1999.
Zhang’s method requires the camera observing a planar pattern under different orientations. Then estimating the homography between each image and the world
by matching several feature points, and the internal andexternal parameters of a camera can be estimated form
those homography. Zhang’s method is quite flexible andeasy to setting up, so that it has been implemented in
OpenCV. OpenCV is an open source library aiming at
real-time computer vision, which was developed by Intel, and later supported by Willow Garage. The libraryis written in C and C++ and runs under various OSs.
It has a high capability for calculating images and matrixes. However, in Zhang’s method, since the first stepis to check out several feature points for matching, thecalibration result correctness is usually effected by theaccuracy of the coordinates of the extracted points.
In this research, I propose using the global projection
transformation (GPT) correlation method to estimate thehomography between an image and the world coordinates. Since GPT correlation method is a region matching method, images without feature points also can beused to estimate the homography, so that it will makethe choice of calibration objects more flexible. And theimprovement of accuracy also could be expected.

Figure 1: Camera model

Figure 1 shows the relationship among the coordinates of a camera model. We assume that a 3D point
P in the world coordinate system is represented by
M=[X, Y, Z]T . Its projected point in the image coordinate system is represented by m = [u, v]T . The relationship between M=[X, Y, Z]T and m = [u, v]T is given byXYs= A[R t]  Z  ,1"#α γ u0with A = 0 β v0 ,
0 0 1
"

u
v
1

#



(1)

where s is an arbitrary scale factor, R and t is the rotation matrix and the translation vector which relate theworld coordinate system to the camera coordinate system, and A is the internal parameter matrix of a camera,
with (u0 , v0 ) the coordinates of the origin of an image,α and β the scale factors, and γ the skewness of the two
image axes.
Camera calibration is to estimate the parameters in A,
R, and t.
57

2.2

Zhang’s Method

estimated, all internal parameters of the camera can becalculated as follows:

Zhang’s method is based on eq.(1). It just uses several (at2least 2) photos taken by observing a planar pattern under)v0 = (B12 B13 − B11 B23 )/(B11 B22 − B12
different orientations. Either the camera or the planar2
λ = B33 − [B13 + v0 (B12 B13 − B11 B23 )]/B11
pattern can be moved freely. The motion does not needp
to be known.
α =
λ/B11
First, we can assume the template plane satisfies Z =p2
β =λB11 /(B11 B22 − B12)
0. Then we can rewrite eq.(1) as follow:2
γ = −B12 α β/λ" #Xuu0 = γv0 /α − B13 α2 /λ
(11)
Y
s v
= A[r1 r2 r3 t]  0 
1After A is estimated, the external parameters for each1"#
image could be calculated. From eq.(3), we haveX
= A[r1 r2 t] Y .
(2)
r1 = λA−1 h1
1
r2 = λA−1 h2
r3 = r 1 × r 2
From eq.(2), we can get a relationship between a 3D
point M and its projection point m by a homography H,
t = λA−1 h3(12)with H = A[r1 r2 t]. The homography can be estimated by an image of a template.
with λ = 1/||A−1 h1 || = 1/||A−1 h2 ||.
We denote H by its column vectors as H =[h1 h2 h3 ]. Then we have[h1 h2 h3 ] = λA[r1 r2 t],

3 Global Projection Transformation
Correlation Method(3)

where λ is an arbitrary scalar. Because r1 and r2 areorthonormal, we have
GPT correlation method is an extension of GAT (Global
Affine Transformation) correlation method.
GPT correlation method uses the normalized crosshT1 A−T A−1 h2 = 0,(4)
correlation as a matching measure. By applying GPT to
T
−T
−1
T
−T
−1
h1 A A h1 = h2 A A h2 .
(5) the input image, a high correlation can be obtained withthe target image. The projection transformation parameFrom eqs.(4) and (5), we can solve the internal param- ters between the input image and the target image can beeters by a closed-form solution. Let
obtained when the correlation value is the maximum.
#"
B11 B12 B13
B = A−T A−1 ≡ B12 B22 B23(6)3.1 2D Projection Transformation
B13 B23 B33
 2D projection transformation is given byv0 γ−u0 β
γ
1

= 

α2
γ
α2 β
v0 γ−u0 β
α2 β
−

− 2
α β
γ2
+ 12
2
2
α β
β
γ(v0 γ−u0 β)v−− 0α2 β 2β2

α2 βγ(v0 γ−u0 β)
v
− 0
2
α β2
β2
2
v0
(v0 γ−u0 β)2+ 2 +1α2 β 2
β
−

.

x′ =

Ax + b
.
1 + hc, xi(13)Because B is symmetric, it can be defined by a 6 dimen- Here, hc, xi is the inner product between c and x. Thisfunction can be divided into the affine transformation
sional vector(AT) part and the partial projection transformation (PPT)b = [B11 , B12 , B22 , B13 , B23 , B33 ]T .
(7) part as follows:x′ = Ax + b
x(≡ P (x))
x′ =1 + hc, xi

By using hi = [hi1 , hi2 , hi3 ]T , we haveT
hTi Bhj = vij
b

(8)

(15)

The AT parameters A, b, and the PPT parameter c canbe calculated respectively.

with
vij = [hi1 hj1 , hi1 hj2 + hi2 hj1 , hi2 hj2 , hi3 hj1 +hi1 hj3 , hi3 hj2 + hi2 hj3 , hi3 hj3 ]T .
From eqs.(4) and (5), it can be obtained that


T
v12
b=0
(9)
(v11 − v22 )T

3.2 Calculating Affine Transformation Parameters
Let’s denote an input gray-scale image as f (x), a template image as g(x), and the transformed input image asf ′ (x). From eq.(14), the correlation value between the
input image and the transformed image is given byas

Vb = 0.

(14)

(10)

For n images of a template, there will be 2n × 6 equations. Since V is known, b can be solved. After b isC(f ′ , g) =
58
2

Z

1
f (A−1 (x − b))g(x)dx.
|A|
K

(16)

Because A and b are directly in the variable of function f , it is difficult to determine A and b. Therefore,
I change the function to eq.(17) by using the Gaussian
window function.
ZZ′
C(f , g) =G(Ax1 +b−x2 )f (x1 )g(x2 )dx1 dx2 .

4 Experiments Results

The necessary condition of maximization of
CGAT (f, g) yields that both derivatives of CGAT (f, g)
with A and b equal to zero. Then A and b can becalculated.

4. Match each input image with the template image to
get the transform matrix.

In experiments, I test many kinds of templates, such as
chessboards, circles, and squares. The templates are
printed on a A4 paper and attached to a board. The input
images are taken by iPhone6 Plus with an original sizeK
as 2448×2448 and they are transformed into 200×200(17) gray scale images.
Here, A and b appear only in the Gaussian window funcThe process of the experiment are as follows:tion.
In order to achieve a higher process speed, I also intro1. Make a template image and print it out.
duce the edge direction function. So the final correlation
function is given by
2. Take a few (at least 2) pictures of the template under
ZZ
different orientations.
CGAT (f, g) =G(Ax1 + b − x2 )δ(∇f (x1 ),K
3. Make the photos to be the same size with the template image as the input images.
∇g(x2 ))f (x1 )g(x2 )dx1 dx2 . (18)

3.3

5. Estimate the homography between the image coordinate and the world coordinate of the print.

Calculating Partial Projection Transformation Parameters

6. Calculate the length between 2 points on the print
from the input image, and compare it with the actual length to evaluate the accuracy of the proposed
method.

The function fp (x) which is transformed by the partial
projection transformation is given by
Figures 2 – 5 show some transformed images by GPTx−1
fp (x) = P (x) f,(19) correlation method. Tables 1 and 2 show the results of
1 − hc, xi
the original correlation value, the GPT correlation value,
the estimated length, and the actual length. Table 3−1
where |P (x)| is the Jacobian of P −1 (x).
shows the comparison of the length calculated by GPTFirst, I substitute fp (x) into eq.(16). Next, I also in- correlation method and Zhang’s method in OpenCV.
troduce the Gaussian window function and the edge diFrom the results, we can see that the template images
rection function to get an objective function. Finally, thewitchtransformed by GPT correlation method become
objective function is given byvery similar to the input images. The correlation values
ZZ
are all improved to 0.9 by GPT correlation method. I alsox1
− x2 )δ(∇f (x1 ),CPPT =
G(
calculated the length of 2 points on the input images by
1 + hc, x1 i
Kthe estimated homography, and compared the length with∇g(x2 ))f (x1 )g(x2 )dx1 dx2 . (20) the true length. There is about 10% error. The lengthshows the availability of GPT correlation method to esThe factor c can be calculated when the derivative of timate homography between the images and the world
CPPT (f, g) by c equals to zero.
coordinats.
To determine the optimal GPT factors, I use the sucHowever, the calculated length still has some errors.
cessive iteration method. As for the whole algorithm,
Thecalculating time was also increased a lot along with
I calculate the affine transformation part and the partial
thesizeof an image.
projection transformation part alternatively until the correlation becomes maximum. Then, output the maximal
correlation value and the transformation matrix.

3.4

Estimation of Homography

The transformation matrix represents the relationship between the input image and the template image. And thehomography between the camera and the world coordinates can be represented as follow:
"
" #
"
# 1
#
0 u0
u
a11 a12 b1
Xdx
1v
a21 a22 b2  0 dy v0  Y=
11
c1 c 2 1
0 0 1
"
#
X
= H Y .
1(a)(b)
Therefore, n kinds of Hs can be estimated by n im(c)ages of a template. Then substituting them into eq.(4)and eq.(5), the parameters of a camera can be calculated.
59
3

Figure 2: Template of chessboard pattern

Table 1: Results of chessboard pattern
Original Cor.

GPT Cor.

0.347
0.323
0.277

0.937
0.953
0.940

Calculated
Length
19.10
21.63
19.80

True
Length22
22
22

Table 2: Results of circles

(a)
(b)
(c)

(a)

Original Cor.

GPT Cor.

0.566
0.611
0.520

0.943
0.922
0.944

Calculated
Length127.44116.98100.97

True
Length112
112
112

Table 3: Calculated lengths by GPT and Zhang’s methods
(b)
(a)
(b)
(c)

Lengthby GPT
19.10
21.63
19.80

Lengthby Zhang’s method
21.51
23.32
21.45

True length
22
22
22

(c)
Figure 3: Left:input images Right:transformed images by
GPT

5 Conclusions
In this paper, I proposed an application of GPT correlation method to camera calibration. We can easily estimate the homography between the image and the world
coordinates. The experimental results show the effectiveness of GPT correlation method for image matching.
From the calculated length of 2 points of the input image,
we can prove the availability of homography estimatedby GPT correlation method. We can use a template without feature points such as circles for camera calibration.
It makes the choice of calibration object more flexible.
For the feature works, we need to improve the algorithm of GPT correlation method to reduce the error. And
accelerating the calculating speed is also an importanttask.

Figure 4: Template of circles

References[1] R.Y. Tsai, “A versatile camera calibration techniquefor high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” IEEE J.
Robotics and Automation, vol. 3, no. 4, pp. 323-344,
Aug. 1987.
(a)[2] Wakahara, T.; Yamashita, Y., “Acceleration of GATcorrelation for distortion-tolerant image matching,”
21st International Conference on Pattern Recognition (ICPR 2012), pp.746-749, 11-15 Nov. 2012.
[3] Zhengyou Zhang, “A flexible new technique forcamera calibration,” IEEE Transactions on PatternAnalysis and Machine Intelligence, vol.22, no.11,
pp.1330-1334, Nov. 2000.

(b)[4] Wakahara, T.; Kimura, Y.; Tomono, A., ”Affineinvariant recognition of gray-scale characters using global affine transformation correlation,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol.23, no.4, pp.384-395, Apr. 2001.

(c)
Figure 5: Left:input images Right: transformed template
images60
4Energy Transfer in Adhesive Contact between PDMS Plate and Glass Lens
Student Number: 14M18253 ɹ Name: Guanda FU ɹ Supervisor: Kunio TAKAHASHI
γϦίϯΰϜͱΨϥεϨϯζͷ‫ڽ‬ண઀৮ʹ͓͚ΔΤωϧΪʔतडၕɹ‫ף‬ୡ
γϦίϯΰϜͱΨϥεϨϯζͷ‫ڽ‬ண઀৮ʹ͓͚ΔΤωϧΪʔतडʹ͍ͭͯ‫͞ڀݚ‬ΕͨɻมҐΛ੍‫͕ͳ͠ޚ‬Βྗͱ઀৮൒‫͕ܘ‬ଌఆ͞Εͨɻ࣮‫݁ݧ‬Ռ͔ΒγϦίϯΰϜͷϠϯά཰ɺΨϥεϨϯζͷ‫཰ۂ‬൒‫ܘ‬ɺ‫ܭ‬ଌ‫ܥ‬ͷ߶ੑ‫ٴ‬ͼ‫ڽ‬ண࢓ࣄΛਪఆ͠ɺ઀৮աఔʹ͓͍ͯͷ֤ΤωϧΪʔΛ‫ͨ͠ࢉܭ‬ɻLoading աఔʹ͓͍ͯɺ઀৮໘Ͱ஝͑ͨք
໘ΤωϧΪʔ͕γϦίϯΰϜͷ஄ੑΤωϧΪʔͱࢄҳΤωϧΪʔʹ౉͠ɺҰํɺunloading աఔʹ͓͍ͯɺ஝͑ͨ
஄ੑΤωϧΪʔ͕ք໘ΤωϧΪʔͱࢄҳΤωϧΪʔʹ౉͢͜ͱ͕෼͔ͬͨɻ

1

Introduction

determined. Then elastic energy, interface energy, stiffness
energy and energy dissipation are calculated and the energy
Understanding of adhesion phenomena of polymers is sig- transfer is discussed.
nificant in engineering filed such as designing grip-releasedevices e.g. adhesive tape[1] and wall climbing robots[2].
Furthermore, the adhesion phenomena is also important in 2 Theoretical consideration
dealing with micro-scale objects[3]. Hertz[4] first introAn schematic illustration of the contact system is shown in
duced the contact theory of two elastic spheres (or one
Fig. 2.1. It is assumed that a axisymmetric parabolic bodysphere and one flat surface) pressed against each other. Basecontacts on a semi-infinite elastic body and their is no fricon the Hertz theory, Johnson, Kendall and Roberts[5] anation between two bodies.
lyzed the adhesion behavior between a soft elastic sphere toZta flat rigid body or, conversely ,a hard rigid sphere to a softkelastic flat surface. Furthermore, Takahashi, Mizuno andkOnzawa[6] added the consideration of the stiffness of the
Rigid
Rigid
measurement system to the JKR contact model[5]. Thesestudies were based on the stable equilibrium condition ofF
R1
the total energy of the contact system. However, during realcontact experiments, the difference of force curve (i.e. theδ
relationship of force and displacement) in the loading andaR2
Elastic
Elastic
unloading (or approaching and detaching) process has been
observed. The contact process is irreversible due to the energy dissipation which is involved as adhesion hysteresis. Fig. 1: Schematic illustration of the TMO contact model. Zt is the gross
The study of adhesion hysteresis is important for clarifying displacement that applied by experimental apparatus, a spring with stiffness k is represented of the stiffness of the measurement system, F is thethe adhesion phenomena and designing applications.
force between rigid body and elastic body, a is the radius of contact area,
Adhesion hysteresis is observed and discussed in the ex- R1 is the radius of curvature of the rigid body, R2 is the radius of curvature
periment of elastic contacts by many authors[7-11], how- of the elastic surface, δ is the penetration depth of the rigid body into theever, the mechanism of adhesion hysteresis is not clear. An elastic body.
impressive study was introduced by Baek et al.,[12]. TheyIn Fig. 1, the force F between rigid body and semi-infinite
attempted to clarify the mechanism of adhesion hystereelastic body is balanced as a relation of F = PH − PB , where
sis under the energy unstable equilibrium condition, which
PH is force of Hertz’s distribution and PB is the force ofmeans the energy dissipation is separated from the energy
Boussinesq’s distribution. F can be expressed asof contact system. And a quasi-static (having a duration be!
tween each step of applying a displacement) was conducted
a2
2Ea
δ−
,
(1)
F=
between a PDMS and a silica glass lens. The results showed
3R
1 − ν2that the energy dissipation during unloading process is proportional to the radius of contact area. The energy dissipa- where E and ν are Young’s modulus and Poisson’s ratio oftion is a result of energy transfer (elastic energy, interface the semi-infinite elastic body. R is an effective radius ofenergy, etc.) during the contact process. However, how en- curvature that can be expressed byergy transfered have not been investigated. In the present
1
1
1
=
+ ,
(2)
study, the way of transfer during the contact process beR R1 R2
tween each type of energy is clarified.
As an approach to the object of this study, TMO point con- R1 and R2 are the radius of curvature of the rigid body andtact model[6] is used to discuss the energy transfer during semi-infinite elastic body. In the perfect condition, which
the contact process. Force and contact radius are measured means that the elastic surface is flat, R2 is infinite, so that R
under the control of displacement by the experimental appa- = R1 . δ is the displacement of the tip of rigid body, and aratus during the contact process (i.e., duration time in fixed is the radius of contact area. The displacement δ is not thedisplacement). Other parameters such as Young’s modu- gross displacement that we can control under the consideralus, stiffness etc. that needed to calculate each energy are tion of stiffness of the measurement system. Thus, a spring61
1

with k is considered as the stiffness of the measurement sys- force and contact radius were measured by electronic baltem, and the gross displacement Zt is expressed asance(c) and microscope(d) 4 times in one second. The measurement error of electronic balance and microscope areF−5Zt = −δ − ,(3) ±0.01g and ±1 × 10 m. A polydimethylsiloxane(PDMS)kplate was fabricated as the semi-infinite elastic body by
together with Eq.(2), we can get the new relationship that heated the mixture of two kinds of liquid (base polymer andcross-linking, Sylgard 184 Silicone Elastomer Kit). The
can be expressed as
contact experiment began at the loading process i.e. glass
!
a2
2kEa
lens and PDMS plate were approaching each other. The
−Zt −
.
(4)
F=
3R
gross displacement was set to 0µm when the glass lens and(1 − ν2 )k + 2Ea
PDMS plate contacted firstly. There were 20steps during
The force F, contact radius a and gross displacement Zt are the loading process. In the unloading process, the motorizedmeasured simultaneously by experimental apparatus. The stage applied displacement towards detachment of the glass
Young’s modulus E, radius of curvature R and stiffness k lens and PDMS plate. During the unloading process, thecan be determined using Eq.(5).
motorized stage conducted until the glass lens and PDMS
The elastic energy stored in the elastic body due to the plate fully detached from each other.
elastic deformation is expressed as
Uelastic =4Ea5
1 − ν2 2F .
+4Ea45(1 − ν2 )R2

(b)

(5)



(a)

The interface energy stored in the contact area is expressedas
Uinterface = −πa2 ∆γ,(6)∆γ is the work of adhesion, means the work needed to separate unit area of the interface. The stiffness energy stored
in the measurement system is expressed as
Ustiffness =

F2
.
2k

(d)
(e)
(c)

(7)

g

The energy of the contact system is expressed as∆Uexternal = ∆Uelastic + ∆Uinterface+ ∆Ustiffness + ∆Udissipation .

Fig. 2: Schematic illustration of the experimental system. (a) Motorizedstage: motorized vertical movement with resolution of 1µm; (b) Microscope: resolution of 1600×1200 pixel; (c) Electronic balance: strain gauge
type with resolution of 0.01g; (d) Lens: silica glass lens with radius of curvature R1 = 0.2076m and diameter of 30mm; (e) PDMS plate: with thedimension of 60 × 60 × 15mm.

(8)

The strain energy release rate GZt (i.e. the energy variation
per contact area) during the fixed gross displacement Zt , isdefined and expressed as∆Uelastic + ∆Ustiffness ∆Udissipation
=
+ ∆γ.
GZt =
∆(πa2 )−∆(πa2 )4 Result and discussion(9)

4.1 Experimental result

GZt can be calculated by
G Zt =(PH − F)2,6πRPH

The force F between PDMS and glass lens and radius ofcontact area a are shown in Figure 3. The force that measured by the electric balance is shown in Figure 3(a). The
force when first jumped to contact was negative, meansthat the adhesive force mainly acted because the force fromBoussinesq’s contact was greater that the force from Hertz’s
contact. During the waiting time, the force decreased. After the waiting time of 60 seconds, 1µm of displacement
was loaded by the motorized stage, so that the force from
Hertz’s contact became larger instantaneously and then decreased again in the waiting time. In the unloading process,the motorized stage conducted the opposite direction to theloading process. When the motorized stage unloaded 1µm,
the applied force became smaller instantaneously. During
the waiting time, the force kept almost the same value at thefirst several steps. After that, the force during the waitingtime increased which is the opposite to the loading process.
As the unloading process progressed, the applied force became the smallest which is referred as the largest adhesive
force before the detachment.

(10)

where PH is force of the Hertzian contact that is expressed
as 4Ea3 /3(1 − ν2 )R. The energy dissipation ∆Udissipation canbe calculated using Equation (2.11), since the work of adhesion ∆γ has been determined. The determination of ∆γ
will be described in section 4.

3

Experiment

The schematic illustration of experimental apparatus isshown in Fig.2. The experimental apparatus was set in aclean bench on a vibration-isolated table. Contact experiment was conducted in the environment of 20◦ C. The grossdisplacement Zt was applied by the motorized stage(a) by
1µm per step. There were 60 seconds of duration of waitingtime between each step(fixed gross displacement Zt ). The
62
2

0.15

0.15

(a) Loading
60s

60s

60s
60s

0.1

Loading60s
60s

Force F (N)Force F (N)F

0.1□: Measured force F• : Calculated force Fcal =Unloading

0.05
Zt

Zt

0

Unloading

-0.05

(

2kEa
a2
−Zt −
2 Ea + (1 −ν 2 )k3R

E = 2.01M Pa
R = 0.2076m
k = 6 ×106 N/m
ν = 0.5

0.05(

Loading

0

-0.05

(b)
0.002500.001

0.00150.002

0.00250.003

Unloading

0.002

Fig. 4: Fitting result of measured force F and calculated force Fcal plottedwith radius of contact area a. The values of E, R, k, ν were applied to theequation in this figure. Fcal is well fitted to F.

Loading

0.0015
Loading 60s

Unloading

0.001

60s

GZt at the boundary between advancing and receding ofcontact radius. That means the value of GZt |amax is the valueof ∆γ. The strain energy release rate is shown in Fig.5 as afunction of contact radius. The region of ∆a ≥ 0 and ∆a ≤ 0
is not clear in the circle part of Fig.5 due to the measurement
error of the contact radius. Thus, that region is the boundary of increasing and decreasing of contact radius. Therefore, ∆γ can be estimated as 0.037 ≤ ∆γ ≤ 0.047. We
assumed the intermediate point where ∆γ = 0.042(J/m2 ) inthis study.

60s
60s

0.0005

60s

60s
Zt

0
-15

0.0005

Radius of contact area a(m)aRadius of contact area a (m)Unloading
-0.1

-10

-5

0

5

Zt

10

15

20

Displacement Zt (μm)
Fig. 3: (a) Measurement force F between PDMS and glass lens at eachdisplacement. (b) Radius of contact area a at each displacement.

Strain energy release rate Gzt (J/m2)

4.2 Determination of R, E, k
About the effective curvature R, we can not ensure that the
PDMS surface is perfectly flat which means R2 has infinite
value, so that the R need to be evaluated. The effective radius of curvature R can be determined using Equation (5)when applied force F = 0, therefore,
a2
R=−.
3Zt F=0(11)

The result is R = 0.2076m, it is the same value with the radius curvature of the glass lens. So it can be assumed from
Equation (3) that the PDMS plate has a nearly flat surface.
According to the assumption in this study, the measured
force F, radius of contact area a and gross displacement
must satisfy the relationship of Equation (5). The value ofradius of contact area a, effective radius curvature R andPoisson’s ratio ν were assigned to Equation (5). The parameters needed to be evaluated (i.e. E, k,) were also assigned to Equation (5) using experienced value. Thus a calculated force Fcal can be obtained. E, k were adjusted tomake calculated value Fcal close to the measured value F
using least squares method. The parameters is determined
as E = 2.01(MPa), k = 6.00 × 106 (N/m). The comparison
with measured force F and calculated force Fcal is shown in
Fig.4. It is suppose that Fcal is well fitted with F.

0.16

amax

Δa ≤ 0

0.05

0.14

0.045

（0.037 ≤ Δγ ≤ 0.047）

0.04

0.12

0.035

0.1

Δa ≥ 00.00266

0.00268

0.00270

0.08

Unloading
0.06
0.04
0.02

Loading

000.00050.001

0.00150.002

0.00250.003

Radius of contact area a (m)Fig. 5: Strain energy release rate plotted as a function of contact radius.
The work of adhesion ∆γ is determined at the boundary of increasing anddecreasing of contact area.

4.4 Results of energy calculation

Since all the parameters were measured and determined in
Section 4.2 and 4.3, each type of energy in contact system can be calculated. The calculation results plotted withcontact radius is shown in Fig.6. It is observed from Fig.6
that the external work done by the experimental apparatus
is received as the elastic energy by PDMS plate and as thestiffness energy by the measurement system. The stiffness
energy negligible since the order of magnitude of stiffness
energy is 3 times less than other types of energy. Therefore,
4.3 Determination of ∆γ
the external energy is almost transfered as elastic energy of
The work of adhesion ∆γ is determined using strain energy the PDMS plate at the moment of applying a displacement.
release rate GZt in Equation (10). It is assumed that the energy dissipation ∆Udissipation ≥ 0 during both loading and4.5 Energy transfer in duration of fixed Zt
unloading process. The change of contact area ∆(πa2 ) is
positive as the contact radius a advanced, and it became Energy transfer is occurred in the duration between eachnegative as the contact radius a receded. Therefore, from displacement. However, in a critical area in unloading proEquation (9), the value of ∆γ is determined to the value of cess at the displacement of 19µm to 17µm, the energy can
63
3not be judged as increasing or decreasing because the variation of elastic energy and contact radius (interface energy)
are within the margin of error. So that the energy transfer isnegligible at the beginning of unloading process. From thedisplacement of 16µm, the contact radius and elastic energy
have a tendency of decreasing, meas the energy transfer began.

Uelastic (J)Uexternal (J)(×10-7)8(a)Energy gradient is calculated at loading and unloading
processes for the discussion of energy transfer. Fig.7 shows
the relation between gradient of each type energy and gradient of total energy Utotal . The total energy is the energy
stored in the contact system which is defined as∆Utotal = ∆Uelastic + ∆Uinterface + ∆Ustiffness .

We can get ∆Utotal ≤ 0 in the duration time of fixed Zt during both loading and unloading process due to the assumption of ∆Udissipation ≥ 0. Because ∆a ≥ 0 during loading
process, the negative value of ∂Utotal /∂a correspond to theloading process. Similarly, the positive value of ∂Utotal /∂acorrespond the unloading process.
In the region of loading process (∂Utotal /∂a ≤ 0), thegradient of elastic energy and energy dissipation have positive values(∂Uelastic /∂a > 0, ∂Udissipation /∂a > 0), meansthat they received energy. Whereas the gradient of interface
energy is negative(∂Uinterface /∂a < 0), means that interface
energy transferred to elastic energy and energy dissipationin the duration time of fixed gross displacement Zt . Thechange of stiffness energy can be negligible due to the smallorder of the energy gradient. On the contrary, the elastic
energy stored in the PDMS plate transfered to the interface
energy and energy dissipation in the duration time of fixedgross displacement Zt .

Loading

Unloading40-4(×10-7)12(b)

Loading
Unloading

8
4

Uinterface (J)0(×10-7)-4(c)Loading

Unloading-8

Ustiffness (J)-12(×10-10)12 (d)

Loading

Unloading

8
4

Udissipation (J)0(×10-7)8(e)Unloading

40

Loading
00.00050.001

0.00150.002

0.00250.003

Radius of contact area a (m)0.0012∂U elastic / ∂a∂U interface / ∂a∂U stiffness / ∂a0.0008 ∂U dissipation / ∂aelastic

5 Conclusion
All kinds of energy were calculated by measuring theforce F, contact radius a, displacement Zt and determining Young’s modulus E, radius of curvature R, stiffness k
and work of adhesion ∆γ. In loading process, the interface
energy stored at the contact area transfered to the elastic energy of PDMS plate and energy dissipation. A stable equilibrium state exists at the beginning of unloading process
which means the energy transfer does not occur. Then, in
the rest of unloading process, the elastic energy stored in
the PDMS lens transfered to the interface energy and energy dissipation.

Fig. 6: Each type of calculated energy with the relation of contact radius
during whole contact area. (a) External energy that done by experimental apparatus. (b) Elastic energy stored in the PDMS plate. (c) Interface
energy stored at the interface between PDMS and glass lens. (d) Stiffness
energy stored in the experimental apparatus. (e) The amount of dissipatedenergy.

∂U / ∂a (J/m)(12)

elastic

0.0004

References
diss

ipat
i

0

on
diss

-0.0004-0.0008[1] W. Hawkes et al.,Trans. on Mechs., 2013 Vol.18, No.2.
[2] M. P. Murphy et al., small 2009, 5, No. 2, 170.
[3] Z. Wei et al., Chin. Phys. Soc., 2004 Vol.13, No.8.
[4] H. Hertz, Macmillan London 1896, p.156.
[5] K. L. Johnson, et al., Proc. R. Soc. Lon., Ser. A 1971 324, 301.
[6] K. Takahashi, et al.,J.Adhesion Sci.Tech. 1995, Vol.9, No.11, 1541.
[7] P. Silberzan,et al.,Langmuir 1994, 10, 2466.
[8] G. Y. Choi, et al.,Langmuir 1997, 13, 6333.
[9] A. N. Gent, Langmuir 1996, 12, 4492.
[10] E.Charrault, et al., Langmuir 2009, 29(10), 5847.
[11] D. Maugis,et al.,J. Phys. D: Appl. Phys., 1978 Vol. 11, No. 14[12] D. Baek et al.,, ”Mechanism of adhesion hysteresis in the elastic

stiffness

stiffness
ipat
i

on

interface

Loading-0.0002

Unloading
00.0002interface
0.00040.0006∂U total / ∂a (J/m)
Fig. 7: Gradient of each type of energy plotted with gradient of total energy. The region of ∂Utotal /∂a ≥ 0 represents loading process, region of∂Utotal /∂a ≤ 0 represents unloading process. Furthermore, the positive
value of energy gradient in loading process means giving energy; the negative value of energy gradient means receiving energy, this is contrary to
the unloading region.

between a silicone rubber and a silica glass lens” a master thesis ofTokyo Institute of Technology(2013).

64
4

Performance Comparison Of Space-time Coding On Polarized and Non-PolarizedLine-Of-Sight Multiple Input Multiple Output (MIMO) Propagation Channels
Student Number: 14M18307 Name: Djiby Marema Diallo Supervisor: Jun-ichi Takada

Abstract
Performance evaluation of an open loop 22 MIMO (Multiple Input Multiple Output) system under Rician
fading conditions is studied in this paper. First, through spatial multiplexing technique and spatial SpaceTime Trellis Code (STTC) technique, and the use of a BPSK (Binary Phase Shift Keying) modulation, theBER (Bit Error Rate) is computing in each case to evaluate the channel performance. Second, by using
co-located and dual-polarized antennas, we also evaluate the channel performance by considering the dualpolarized multiplexing technique and the dual-polarized diversity technique. In order to characterize these two
schemes discussion on the SNR per bit is considering. We Compare of performance of 22 MIMO spatial, anddual polarized STTC and multiplexing schemes based on how the performance of these systems are affectedby the Rician K factor.

1

2Introduction

Channel Modeling

Our goal in this work is to evaluate the performanceof MIMO transmission system using different antennaconfiguration, under Rician fading environment.
Therefore we will provide the MIMO channel representation in the case where antennas are co-locatedand the case where they are spatially separated. The
representation of the channel matrix for a MIMO
system with NT transmit antennas and NR receive
antennas is given by the NT ×NR matrix:


h1,1
h1,2
· · · h1,MT h2,1h2,2. . . h2,MT 


H= .

.
..
..
..

 ..
.
.

Recently, due to the high demand of mobile communication system in increasing data rates, reliability andefficient usage of the spectrum, the use of multiple antennas at both side transmitter and receiver appears asa breakthrough to meet these challenges. Even though
the Multiple-Input Multiple-Output has demonstrated
its importance to meet these challenges faced by thewireless communication designers through the multiplexing and the diversity technique, its capacity may be
reduced when the transmitter does not have any knowledge about the channel propagation, when the line-ofsight (LOS) component is dominant. Even though improvement of the MIMO system can be achieve underthese limits using some techniques such as beamforming, multiplexing technique in close loop system withthe presence of the LOS component, correlated channel, the performance degradation cannot be avoided
in open-loop MIMO system with the presence of LOS.
The LOS component is known to reduce the system
performance due to the high correlation of the individual channel it causes. Since orthogonal polarizations ideally may offer a complete separation between
the individual channel of the propagation environment,
therefore the use of co-located orthogonally-polarized
antennas appears as a space and cost cost effective alternative. In this thesis, the performance evaluation ofa spatially separated co-polarized and co-located dual
polarized in the presence of LOS component is proposed. Specifically the performance of an open-loop
MIMO system will be analyzed based on the bit-errorrate (BER). While modeling a MIMO transmission system, the Rician factor represents the ratio of the powerin the LOS component and the power in the non-lineof sight component. Therefore, in order to analyze thedegradation performance due to the presence of the
LOS component, we will conduct simulation against
the Rician K factor. Diversity technique The validation of this extended framework will be evaluated in
the future work.

hMR ,1hMR ,2···hMR ,MT

Each individual elements of the matrix hj,i represent
the channel gain from the transmit antenna of index ito receive antenna of index j . Thus the mathematicalrepresentation of the MIMO system is given by thefollowing equation:y = Hx + n(1.1)

Where y,x,n are the received signal vector, the transmitted signal vector, and the noise vector respectively.
In this work, the different elements of the channel matrix are supposed to be independent and identically distributed (i.i.d.) zero-mean, unit variance, complex circularly Gaussian random variables. The MIMO channel propagation is composed of the Line-of-sight (LOS)
component and the Non-line-of-sight (NLOS) component. At the mobile receiver, when a signal is affected by several scattering objects, random attenuations, and delays due to the radio propagation environment, then the signal is called a NLOS component.
And when the signal travels from the transmitter tothe receiver without any obstructing object, it is calleda LOS component. When only NLOS are present in
the MIMO channel propagation, therefore the matrix
coefficients can be modeled as complex Gaussian variables with zero mean. The individual elements follows
65
1

receiver, the signal which is the superposition of all
multipath components is degraded due the fading. Increasing the power transmission, or the antenna size,or antennas height are possible solution but not pragmatic. Since the signal is degraded at the received, if
several copies of the same signal, therefore the probability that at least one copy is not corrupted is increased. This scheme of transmission and reception iscalled diversity and is one of the most important technique in MIMO transmission system to mitigate theeffects of fading in wireless communications.

the Rayleigh distribution and the channel is called aRayleigh MIMO channel. Rayleigh MIMO channel matrix has proven to be a very instrumental tool for thecomputation of the average capacity of NLOS channel like in macrocells of mobile communication systems[2],[3]. Although useful, this approach is not suitable to describe communication environment with LOS
components. The Rice fading model is more appropriate as it include the LOS component.
Since we are evaluating the performance of a channel ina LOS environment therefore the Rician fading channel
modeling will be adopted.

3.2
2.1

Spatially
Co-Polarized
Channel ModelMIMO

The design criteria for Space-Time Trellis codes(STTC) was derived in [8] by Tarokh, Seshadri andCalderbank over slow frequency non selective fading channels under the assumption of a large signalto-noise-ratio (SNR). This concept became extremely
popular due its effectiveness of combating fading effects. The STTC can provide maximum diversity andcoding gain with spectral efficiency over fading channels by using simple decoding techniques. The coding
offered by the STTC itself is different of that provided
by the achieved by the block or the convolution codes.
A typical STTC designed for a wireless communication
system has an encoder, pulse shaper, modulator andmultiple antennas at the transmitter, and the receiver
has one or more receive antennas.

A Rician fading environment is a propagation environment having both direct signal components and scattered signal component. The channel matrix of the
Rician fading environment is given by:

H=

r

K
HLOS +
K +1

r

1
HNLOS
K +1

(1)

Where K is the Rice factor and is the power ratio in
the LOS component of the channel to the power in thefading component.

2.2

Space-Time Trellis Coding

Co-located Dual Polarized MIMO
3.3
Channel Model

Encoder structure for STTCThe Rician channel modeling in dual polarized MIMO The encoder for a STTC is composed of one input blockof n bits and v memory blocks of n bits. At each timesystem is given by te following equation:t, the encoder maps the information bit to modulation
symbols, where the mapping function is described by arrtrellis diagram. All the bits of a block are replaced by
K
1
t-j+1
,i=1,...n ofH=
Xc HLOS +Xc HNLOS (2) the n of the previous block. The ith bit bi
K +1
K +1
th
the j block, j =1,... v +1, is associate to nT multiplierk
Xc represents the antenna depolarization matrix and coefficients gi,j ∈ Z2n , k = 1,...nT . Where nT is thenumber of antennas. A space-time trellis encoder isis given by:
Xc represents the antenna depolarization matrix and defined by its generator matrix G.
 1
is given by:
g1,1 . . . g1n,1 . . . g11,v+1 . . . g1n,v+1
 ..
.. 
..
..
 .

√ 
. 
.
...
.
...
...

 k
1
χc
k
k
k

G = g1,1 . . . gn,1 . . . g1,v+1 . . . gn,v+1 
Xc = √

χc
1
 .
..
..
.. 
 ..
...
.
...
.
...
. 
nT
nT
nT
nT
Where χc is the antenna cross-polar coupling and is
g1,1 . . . gn,1 . . . g1,v+1 . . . gn,v+1
assumed to be as equal to 0.

3
3.1

4 Performance Criteria
Space-Time coding for MIMO
We consider a mobile communication system with nT
Systemtransmit antennas and mT receive antennas We assume
a the STTC codeword:

Diversity Gainc = c11 c21 c31 ....cn1 c12 c22 ...cn2 ...c1L c2L ...cnL
In wireless communication,the information is carriedby the electromagnetic waves. Electromagnetic waves Where L is the frame length. The erroneous codeword
are attenuated due to some mechanism that are re- is given by:flection, scattering induced by the propagation envie = (e11 e21 e31 ....en1 e12 e22 ...en2 ...e1L e2L ...enL ).
ronment: this phenomena is called fading. At the66
2In order o obtain a maximum diversity order m.n, the(n×L) difference matrix B(c,e)

en1 T − c11

4.1

...

B(c,e) =e12 − c12e12 − c12e12 − c12
...
..
.
...
e12 − c12

...
...
...
...
..
. ...
...

10-1


e1L − c1L
e1L − c1L 
e1L − c1L 

... 


..

.
1
1
eL − c L

10-2
BER

e11 − c11
 e21 − c11 3 e1 − c11

 ...


..

.


BER Estimation for STTC scheme 2x2 Co-Polarized MIMO System

100

10-3

rank criterion

The rank criterion will optimize the spatial diversity
gain r.m achieved by a STTC over all possible codeword pairs c and c by maximizing the rank of matrix
A(c,e). The minimum rank of matrix A taken over allcodeword pairs is also called the the rank of the code.

4.2

-10

4.3.1

Spatially Separated Co-Polarized System

5

10

15

20

25

30

Rayleigh fading channel propagation, the performanceof the Co-located dual-polarized MIMO approaches theperformance of a single-input single-output propagation channel where the bit-error rate is deterministic
in any case. When increasing K between 0 and 7
which correspond to the Rician fading channel where
both LOS and NLOS components are present, theCo-located dual-polarized 2×2 MIMO system outperforms. Finally when K equals 10dB which corresponds
to the pure LOS environment, the performance of the
Co-located dual-polarized MIMO is almost the same as
AWGN channel.
BER0 Estimation for STTC scheme Dual-Polarized 2x2 MIMO System
10

10-1
10-2
BER

Performance Analysis

0

Figure 1: Co-Polarized STTC scheme

Determinant criterion

4.3

-5

K(dB, Rician Factor)

The determinant criterion optimizes the coding gain.
The coding gain corresponds to the minimum rth rootof the sum of the determinants of all r×r co-factorsof A(c, e) = B(c, e)B ∗ (c, e) taken over all pairs of
distinct codewords c and e [10]. The coding corre1
sponds is therefore specified by (λ1 λ2 λ3 ....λr ) r whereλ1 λ2 λ3 ....λr is the absolute value of the sum of determinants of all the principal (r × r) co-factors of A. To
maximize the determinant of A(c,e) taken over all possible codeword pairs c and e is equivalent to achieve
full rank r=n.

The system performance evaluation is done based on
the Rician K factor. It is observed that when the Rician
K factor is varying between -10 and 0 which correspondto the pure Rayleigh fading channel propagation, theperformance of the spatially- separated Co-polarized
MIMO approaches the performance of a single-input
single-output propagation channel where the bit-error
rate is deterministic in any case. When increasing K
between 0 and 7 which correspond to the Rician fading channel where both LOS and NLOS components
are present, the spatially-separated Co-polarized 2×2
MIMO system outperforms. Finally when K equals
10dB which corresponds to the pure LOS environment, the performance of the spatially- separated Copolarized MIMO is almost the same as AWGN channel.

4.3.2

SNR=0dB
SNR=7dB
SNR=10dB
SNR=15dB

10-4

10-3

SNR=0dB
SNR=7dB
SNR=10dB
SNR=15dB

10-4

-10

-5

0

5

10

15

20

25

30

K(dB, Rician Factor)

Figure 2: Dual-Polarized STTC scheme

5

Summary

The performance evaluation of a STTC spatiallyseparated co-polarized and co-located dual-polarized
MIMO system is performed in this chapter. This performance evaluation is based on the Rician K factor
which determines the importance of the LOS component in the channel propagation. While spatially separated co-polarized should be preferred for rich scattering MIMO system, the co-located dual-polarized
MIMO is recommended for the LOS environment.

Co-located Dual-Polarized System

Similarly to the case of spatially-separated Copolarized MIMO system, the performance evaluation
is done based on the Rician K factor. Although
the bit-error-rate (BER) is in different in the case ofdual-polarized MIMO, the trend is the same.In pure67
3

References[14] M.Kang and M. Alouini Performance Analysis of
MIMO MRC System over Rician Fading Chan[1] C.Oestges, Propagation Channel, Capacity andnels, IEEE VTC2002 fall, pp. 869873, 2002. 1, 7
Error Probability with Dual-Polarized Wireless
R.Calderbank,
A.Constantinides,
Transmissions, Indian Journal of Radio and Space [15] E.Biglieri,
A.Goldsmith,
A.Paulraj,
andH. Poor, MIMO
Physics, vol. 36, pp. 411417, 2007
Wireless Communications. Cambridge University[2] Emre Telatar, ”Capacity of Multi-antenna GausPress, 2007. 1
sian Channels” , European Transactions on
Telecommunications, Vol. 10, No 6, pp. 585-595, [16] S.Zhou and G.B.Giannakis, Optimal Transmitter Eigen-Beamforming and Space Time Block
Nov/Dec 1999
Coding Based on Channel Mean Feedback, IE[3] K. Yu, M.Bengtsson, B. Ottersten and M. Beach,
ICE TRANS.COMMUN., vol. E95-B, no. 11, pp.
Narrowband MIMO Channel Modeling for LoS
34983508, 2012.
Indoor Scenarios,Proceedings XXV II th Triennial
General Assembly of the International Union of [17] K.Kobayashi, Ohtsuki.T, and Kaneko.T, Precoding for MIMO Sytems in Line-Of-Sight EnvironRadio Science, (URSI) Maastricht, the Netherment, IEEE GLOBECOM 2007, 2007
lands, Aug. 2002.
[4] Andrea Goldsmith, Wireless Communications, [18] A. Paulraj and T. Kailath, Increasing capacity
Cambridge University Press, 2005.
in wireless broadcast systems using distributed
transmission/directional reception (dtdr), 1994.
[5] F.Bohagen, P.Orten, and G.E.Oien,On Spherical
US Patent 5,345,599
vs. Plane Wave Modeling of Line of Sight MIMO
Channels, IEEE TRANSACTION ON COMMU- [19] F.Bohagen, P.Orten, and G.E.Oien, Construction
NICATIONS, Vol.57, No. 3,pp. 841-849, 2006.
and Capacity Analysis of High Rank Line of Sight
MIMO channel, IEEE COMMUNICATIONS SO[6] C.Oestges and B. Clerckx, MIMO Wireless ComCIETY/WCNC, vol. 1, pp. 432437, 2005.
munications, From Real Word Propagation toSpace Time code Design. Elsevier Ltd., 2007[20] M.Taguchi, K.Murayama, T.Shitomi, S.Asakura,
and K.Shibuya, Field Experiments on Dual[7] W.L.Stutzman, Polarization in Electromagnetic
polarized MIMO Transmission with UltraSystems. Arttech House,1993
multilevel OFDM Signals Toward Digital
Terrestrial Broadcasting for the Next generation,[8] W.L.Stutzman, Polarization in Electromagnetic
BMSB, IEEE international symposium, pp. 15,
Systems. Arttech House,1993
2011.
[9] V.Tarokh,
N
Seshadri,andA.R.
Calderbank,”Space-Time Trellis Codes for [21] Z. Chen ,J. Yuan and B. Vucetic An improved
Space-time Trellis Coded Modulation Scheme
Wireless Communications: Code Construction”,
on Slow Rayleigh Fading Channel, 0-7803-7097IEEE 47t h Vehicular Technology Conference,
1/01/10.00 copyright 2001 IEEE
vol.2, pp.637-641, Phoenix, Arizona, 4-7 May
1997.

[10] V.Tarokh,
N
Seshadri,andA.R.
Calderbank,”Space-Time Trellis Codes forHigh Data Rate Wireless Communications:
Performance Criterion and Code Construction”
IEEE Transactions on Information Theory, vol.44,
no.2,pp. 744-765, March 1998[11] Z.Chen, J. Yuang, and B. Vucetic, ”An improved
Space-Time Trellis Coded Modulation Scheme on
Slow Rayleigh Fading Chanels”, IEEE International Conference on VCommunications, 2001.
ICC 2001. Vol.4, pp. 1110-1116, Helsinki, Finland,
11-14 June 2001.
[12] N. Yuen, ”Performance Analysis of Space-timeTrellis codes,” Master of Engineering report, University of British Columbia, April 2000.
[13] G.J. Foschini and M.J. Gans,”On limits of wireless communications in a fading environment whenusing multiple antennas,” Wireless Personal Communications, vol. 6, pp. 311-335,1998.
68
4

GEOMETRY BASED DYNAMIC CLUSTERING OF THE DOUBLE-DIRECTIONAL WIDEBAND
MULTIPATHS ESTIMATED FROM AN INDOOR MEASUREMENT AT 11 GHZ
Student Number: 14M18313 Name: Panawit Hanpinitsak Supervisor: Jun-ichi Takada

Abstract
This work proposes clustering approach utilizing scattering points (SPs) obtained from the measurement-based ray tracer(MBRT). After that, multipath components (MPCs) were classified into clusters according to those SPs. The cluster centroids
were tracked along the mobile station (MS) route exploiting 2 cluster tables. The cluster characteristics were extracted from11 GHz channel sounding data in an indoor environment. The clustering accuracy of this algorithm was assessed against theconventional clustering method. The simulation results showed that MPCs were classified into the clusters and the centroids
were tracked accurately. Moreover, the cluster assessment results implied that the proposed method achieved more accuracy.

1 Introduction

and AoA with angular sweep of ±3 degree. Find all possible SPs from all combinations of AoD and AoA sweep.

Recently, the data traffic in the mobile communication has
2. For each SP, calculate two values: 1. the distance beincreased rapidly due to the increase of various services. To
increase the network capacity, the next generation mobile
tween SP and the nearest IO and 2. the delay error which
communication system such as the 5th generation(5G) system
is absolute difference between SAGE estimated delay
and theoretical delay calculated from the location of SPs.
has been proposed. In 5G system, the higher frequency band
above 10 GHz is expected to be utilized for the mobile com3. If the minimum distance between the walls and SPs ismunications. Then the higher order multiple-input, multiplesignificantly small, the SPs that minimize this distance
output (MIMO) transmission technologies which utilize theare selected. Otherwise, SP that minimize the delay error
several antennas will be realized in the those bands.
are selected.
Since the MIMO performance strongly depends on the
propagation environment, an accurate channel model is neces4. In case the launched rays from both Tx and Rx do not insary for system design. Recent channel models also assumes
tersect, or the intersection point is more than 1 m outsidethat the multipath components (MPCs) arrives in clusters withthe room, these MPCs are regarded as the double bounce
similar characteristics. Thus, in order to create an accurate
clusters.
cluster based channel models, it is important that clusters
should be properly defined. Conventionally, KPowerMeans(KPM) [1] algorithm has been widely used. However, the For DB MBRT, the algorithm is as follows
drawback is that the physical locations of interacting objects
1. Launch ray from Tx according to the Anngle-of(IO) in the environment was not considered which decreases
Departure (AoD), find the intersection point between thisclustering accuracy. Thus, this research aims to focus on clusray and all possible objects blocking its paths. This intertering in indoor environment based on scattering points (SPs).
section point is called the SP seen from Tx side (SPTX).
2. The same can be done from the Rx side. The intersection
point in this case is called SP seen from Rx side (SPRX).

2 Proposed Clustering Method
2.1 Scattering Points Estimation

3. After obtaining SPTXs and SPRXs found in the first step,
find all possible MPC travelling distances from all possible combinations of SPTXs and SPRXsFirstly, the measurement was conducted by using 11 GHz
channel sounder [2] which output the result as superposition of paths. Then, space alternating generalized expectation maximizations (SAGE) [3] algorithm was applied to geteach path parameters from measured data. The parametersof each paths consist of propagation delay, angle-of-departure(AoD) from transmitter (Tx) and angle-of-arrival (AoA) fromreceiver (Rx). The SPs of MPCs were estimated by measurement based ray tracer (MBRT) [4]. It inputs the path parameters obtained from the parameter estimation along with themap of the environment into the ray tracing algorithm so that
the SPs and corresponding IO locations can be estimated. The
algorithm was simplified to consider in the azimuth plane.
Moreover, only single bounce (SB) and double bounce (DB)MPCs were considered. SB MBRT algorithm is as follows

4. Calculate the delays from those distances obtained instep 3. by dividing them with the speed of light.
5. Select only one pair of all SPTXs and SPRXs where thedelay is closest to the measured delay which was obtained by SAGE algorithm.
6. If the error between the SAGE delay and calculated delayis than some threshold (in this research, 10 ns was used),adjust the SPTX and SPRX. If the calculated delay ismore than SAGE delay, iteratively move the SPTX andSPRX one step closer to the Tx and Rx until the error isless than 10 ns, otherwise iteratively move them further
away from Tx and Rx. The step value used here is 0.2 m

1. Launch rays from both Tx and Rx according to its AoD
69

1

2.2 Clustering and Tracking Method

a. Find the minimum SPD between the MPC in the current snapshot s and centroid obtained in previous snapshot s − 1
if Choice = 0 then(s−1)
d ← min{SPD(cn, X(s) )}
else
(s)
(s−1)
dT x ← min{SPD(cn,T x , XT x )}After the MBRT calculated the SPs of SB and DB MPCs,
these SPs were used for clustering. The concept of this clustering method is based on KPM [1]. In KPM algorithm, theclusters are identified by their centroid positions. After that, ititeratively optimizes the position of centroid in order to minimize the multiplication between multipath component distance (MCD) of centroid and MPC and path power. In conventional method [5] , MCD is defined as the Euclidean distance between 2 parameter vector which is expressed asxl,c = [τl , cos φlAoD , sin φlAoD , cos φlAoA , sin φAoA
].
l

(s−1)

(1)

where τl , φAoD
and φAoA
denote delay time, AoD and AoAllrespectively. Each dimension in xl,c is normalized to havezero mean and unit variance. In case of proposed method, thisdistance metric or scattering point distance (SPD) is definedas the distance between SPs. Thus, parameter vector for SB
and DB MPCs are expressed as
xl,SB = [sx,l , sy,l ].
xl,DB,T x =xl,DB,Rx =xl,DB ==(s)dRx ← min{SPD(cn,Rx , XRx )}end ifb. Initialized this centroid if this distance is less thansome threshold
if (d < ǫ2 and Choice = 0) or (dT x < ǫ2 and dRx < ǫ2and Choice = 1) then
(s)
(s−1)
m ← m + 1, cm ← cn
end ifend for
3. Assign minimum number of cluster and initialize thecentroids(s) (s)(s)Kmin ← m, C(s) ← [c1 , c2 , ..., cm ]Twhere ǫ2 is the threshold. It is set to 1.5 (SB) and 1 (DB).

(2)

2.2.2 Main Clustering Algorithm[sTx,lx , sTy,lx ]Rx[sRxx,l , sy,l ][xl,DB,T x , xl,DB,Rx ]Rx[sTx,lx , sTy,lx , sRxx,l , sy,l ].

As in conventional KPM, the minimum (Kmin ) and max(3) imum (Kmax ) number of clusters need to be specified.
Since Kmin is already determined, only Kmax needs tobe determined. Kmax was set to 10 for SB and 15
where sx,l , sy,l , represent SB SP in x- and y-coordinates. for DB. The proposed algorithm for snapshot s is as folSimilarly, in case of double bounce, sTx,lx , sTy,lx denotes the lowsRxx- and y-coordinates of SPTX and sRx
x,l , sy,l denotes the
1. Set the parameter Choice according to which types ofx- and y-coordinates of SPRX. For each snapshot s, l-th
MPC is being determined. Set to 0 for SB and 1 for DB.
(s)
MPC is denoted by xl and the n-th SP based cluster
for K = Kmin to Kmax do(s)centroid (SBCC) is denoted by cn . All the MPCs and2. Initialize additional SBCC if K is more than Kmin
(s)
(s)
(s)
if K > Kmin then
SBCCs are stored in matrix X(s) = [x1 , x2 , ..., xL ]T(s) (s)(s) T
Initialize the additional K − Kmin SBCC using deter(s)and C = [c1 , c2 , ..., cN ] respectively where L is theministic
initialization [6](s)number of MPC and N = C
is the number of cluster.
end
if
(s)
Xn denotes the subset of X(s) which belongs to cluster
3. Cluster using KPM algorithm
(s)
(s)
(s)
(s)
cn . Please note that xl is either xl,SB or xl,DB depending
4. Check whether the resulted SBCC is compact or not,on which type of clusters are being considered. The same
break the iteration if it is already compacted(s)applies for other variables. In case of DB clusters, xl,T x andfor n = 1 to C(s) do
(s)
(s)
(s)
if Choice = 0 thenxl,Rx are the simplified version of xl,DB,T x and xl,DB,Rx(s) (s)l(n) ← max{SPD(Xn , cn )}
(s)
(s)
th
and they are stored in matrix XT x and XRx . The n SBCC
else
(s)
(s)
(s)
(s)
(s)
in DB clusters are expressed as cn = [cn,T x , cn,Rx ]lT x (n) ← max{SPD(Xn,T x , cn,T x )}
(s)

(s)

lRx (n) ← max{SPD(Xn,Rx , cn,Rx )}end ifend forif (max{l} < ǫ3 and Choice = 0) or (max{lT x } < ǫ3and max{lRx } < ǫ3 and Choice = 1) thenbreak
end ifend for
where ǫ3 is the threshold. It is set to 0.5 (SB) and 3 (DB).

2.2.1 Cluster Centroid Initialization

This step was done to guess initial centroid location. Deterministic initialization [6] was used in the first snapshot. The
strongest path is initialized to be the first centroid. Then, foreach MPC, calculate the minimum distance to the centroids
available. Finally, the MPC having the maximum distance ischosen to be the next centroid. For the subsequent snapshotss, the SBCCs were initialized using the previous snapshot s −1 SBCCs as shown in the following
2.2.3 Optimal Cluster Number Selection
1. Set the parameter Choice according to which type of
MPC is being determined. Set to 0 for SB and 1 for DB.
Optimal cluster number was selected by calculating and2. Set m ← 0
combining several cluster validation indices (CVIs) using avfor n = 1 to C(s−1) doerage rank aggregation [5]. In this method, the indices of all
70

2

Table 1: An Example of an Instantaneous Cluster Table

possible cluster numbers are calculated. After that, the rank
number is attach for each type of index. If the CVI is optimized based on maximizing the index, the highest rank number is given to the maximum value, the second highest number is then given to the second maximum value and so on.
If the CVI is optimized based on minimizing the index, therank number assignment is reversed. Finally, these rank numbers are summed up for each cluster number. The optimalcluster number is then selected based on maximum numberof that summation. Seven validation indices which are XieBeni, PBM, Calinski-Harabasz, Davies-Bouldin, Silhouette,
Kim-Parks, and Dynamic indices were used.

Cluster index

Position

Counter1[x1 , y1 ]04

[x4 , y4 ]15[x5 , y5 ]3

2.2.4 Shape Pruning
Shape pruning [1] was done to remove outlier MPCs in DB
clusters. Firstly, weak singleton clusters were removed until
the power is less than 95 % of original power of all MPCs.
After that, outlier MPCs for other clusters were removed byremoving the MPC that has maximum distance from the centroid until the total MPC power is less than 90 % of original
power of all MPCs.

Figure 1: Channel sounder antenna array.
Table 2: C HANNEL S OUNDER PARAMETERS
Parameter

Value

Frequency, Bandwidth

11 GHz, 400 MHz

Tx/Rx antenna arrays

12 elements

2.2.5 Cluster Tracking Methoddual-polarized UCA

After all clusters in all measurement snapshots were calculated, all the cluster centroids were used for cluster tracking
which is based on 2 types of tables: 1. Cluster database table(CDT) and 2. Instantaneous cluster table (ICT).
CDT keeps the databases of cluster centroid’s location of
every snapshots. After each snapshot, the resultant cluster
centroids were input into this table. The row and column ofthis table are cluster indices and snapshot numbers respectively. If a cluster does not exist in some snapshot, the table isfilled with an empty vector.
ICT provides the information of the clusters instantaneously at each snapshot. An example of this table is shownin Table 1. There are three columns in this table:
1. Cluster index: This column provides the information
which cluster is active at that particular snapshot.
2. Position: This column gives the geometrical position ofcluster centroids.
3. Counter: This number shows how many snapshots hasgone without this cluster appears. This number is reset to zero
if the cluster is found. If Counter exceeds some predefinedvalue, this cluster is discarded from this table.
The criteria to track the cluster centroids is the Euclidean
distance between the current and previous cluster centroids.
If this distance is significantly small, the clusters are regarded
to be the same. This threshold was set to 1 m for SB and 3 m
for DB. The disappearance and reappearance of clusters arecontrolled by Counter. This value is incremented by one after
each snapshot if a cluster disappears. If a cluster disappears
for several snapshots, this cluster is discarded from the ICT.

Antenna gain, Tones number-20

6 dBi, 2048
CircularcounterSpiral stairs

-15

x
y

Wooden shelf 2-10 Glass wall 1-5

Rx's baseband
Woodenportrait0

Rx

Glass wall 2

Woodenshelf 1

Tx direction
Thin fabric wall
Pillars

Rest area with chairs and tables
Glass panel
Concrete wall

[m]

30

20

10

0

Figure 2: The considered indoor environment.

Figure 3: Panorama photo of measurement environment.
moved along the course at 0.25 m/s. Before running the algorithm, the Line-of-Sight (LOS) path and MPC where the delay
is around the LOS delay were removed as there is no IO associated with them. Fig. 2 and 3 shows the environment floor
plan and panorama photo seen from Tx at the first snapshot
and Table 2 shows the specification of channel sounder used.
Fig. 4 and 5 shows locus plot of SB and DB clusters respectively in the environment. Each tracked cluster is denotedby different combination of marker and color. The clusteringresults shows that most of the tracked clusters are associatedwith one IO or few IOs which means that the proposed method
can estimate cluster locations very accurately. However, someclusters are still sparse in DB results. This is because thethreshold to track the cluster centroid for DB is more than SB.

3 Results and Discussion
The proposed method was validated with channel sounding
data in an indoor hall environment in 11 GHz band [2]. The
configuration of the sounder is shown in Fig. 1. The channel was measured continuously while the transmitter (Tx) was
71

3

-50
Proposed
Conventional
SAGE

-60

-15
-10

Eigenvaluemedian[dB]y-coordinate [m]

-20

Tx

-5
Rx

0
5
30

20

10

-70
-80
-90
-100
-110
-120

0

0

x-coordinate [m]

10

15

20

25

Eigenvaluesnumber

Figure 6: Median of Eigen-values comparison between proposed and conventional method.

Figure 4: SPs locus of single bounce clusters.

4 Conclusion-20

y-coordinate [m]

5

-15
-10

This work presented a novel clustering method exploiting
MBRT. Utilizing the SPs in the clustering algorithm, an accurate clustering and tracking could be achieved. All the tracked
behaviour of clusters could be accurately justified from thelocation of Tx, Rx and IOs associated with them. The eigen
value comparison between proposed and conventional clustering method also implied that the proposed method achieved
higher accuracy by around 4 dB. These information would bevital for the analysis of the channel characteristics and modeling at 11 GHz in the future.

Tx

-5
Rx

0
5
30

20

10

0

x-coordinate [m]Figure 5: SPs locus of double bounce clusters.
Table 3: Cluster distribution for channel simulation
Parameters

Distribution

AoA, AoD, EoA, EoD, delay

Laplace

Power, polarization ratio

NormalPhase (φ)

Uniform

References[1] N. Czink, P. Cera, J. Salo, E. Bonek, J. P. Nuutinen,
and J. Ylitalo, “A framework for automatic clustering ofparametric mimo channel data including path powers,” inIEEE 64th Vehicular Technology Conference (VTC 2006
Fall), Sept 2006, pp. 1–5.

Moreover, since DB clusters have to undergo double bounce, [2] Y. Konishi, Y. Chang, M. Kim, and J. Takada, “Versatile radio channel sounder for double directional andthe probability that non-specular scattering occurs is higher
multi-link mimo channel measurements at 11 ghz,” IEwhich makes the cluster characteristics changes rapidly.
ICE Transactions on Electronics, vol. E97-C, no. 10, pp.
The proposed clustering results was validated and com994–1004, Oct 2014.
pared with the conventional method where xl,c from equation
1 was used. The assessment was done by generating 11 MPCs [3] B. Fleury, M. Tschudin, R. Heddergott, D. Dahlhaus,
and K. Ingeman Pedersen, “Channel parameter estimationfor each cluster according to the distribution in table 3. Afin mobile radio environments using the sage algorithm,”ter that, channels were reconstructed for both MPCs obtainedIEEE J. Select. Areas Commun., vol. 17, no. 3, pp. 434–
from proposed and conventional clustering by superposing the450, Mar 1999.
MPCs. Singular value decomposition (SVD) were applied tothese channel matrices and also SAGE reconstructed channel [4] J. Poutanen, K. Haneda, J. Salmi, V. Kolmonen,
matrices to obtain all 24 eigen values of all snapshots. These
A. Richter, P. Almers, and P. Vainikainen, “Developeigen values are the square of channel gain which describe
ment of measurement-based ray tracer for multi-link douhow much gain is achieved if MIMO channel is decomposedble directional propagation parameters,” in 3rd European
into parallel independent channels. Thus, they are the good
Conference on Antennas and Propagation, 2009. EuCAP
indicator for modeling accuracy evaluation. The median of
2009., March 2009, pp. 2622–2626.
these Eigen-values are shown in Fig. 6. In case of first two
eigen values, proposed and conventional method have similar [5] L. Materum, J. Takada, I. Ida, and Y. Oishi, “Mobile station spatio-temporal multipath clustering of an estimatedperformance. However, from the third eigen value onward,
wideband mimo double-directional channel of a small urconventional method overestimates the channel. This is beban 4.5ghz macrocell,” Eurasip J. Wireless Commun. Netcause some clusters from the conventional method might beworking, vol. 2009, no. 1, p. 804021, 2009.
the mixture of strong clusters scattered from different IOs.
This makes that cluster has a very high power, angular and [6] S. Mota, M. Garcia, A. Rocha, and F. Perez-Fontan,
delay spread with centroid location not corresponding to any“Clustering of the multipath radio channel parameters,” inof the IO. Thus, the MPCs generated from this cluster would
Proceedings of the 5th European Conference on Antennas
have higher power than usual which strongly contributes toand Propagation (EUCAP), April 2011, pp. 3232–3236.
the overestimation of eigen values.
72

4Interannual variation of sea-breeze in Kanto-plain in summer
Student Number: 14M18135

Name: Yuta Nejime

Supervisor: Manabu Kanda

ኟᮇᮾி‴ᾏ㢼ࡢ㛗ᮇᖺ௦ኚ໬ᐷ༨ ♸ኴ ༞ㄽ࡟࠾࠸࡚ࠊ㐣ཤࡢ㛗ᮇ㛫ࡢẼ㇟ほ ࢹ࣮ࢱࡢศᯒ࠿ࡽࠊᮾி‴ᾏ㢼ࡢෆ㝣࡬ࡢ౵ධ᫬้ࡀ᭷ព
࡟ኚ໬ࡋ࡚࠸ࡿࡇ࡜ࢆ♧ࡋࠊࡑࢀࡀẼೃኚື࡜㒔ᕷ໬ࡢ୧᪉ࡢᙳ㡪࡟ࡼࡿࡇ࡜ࢆ♧၀ࡋࡓࠋᮏㄽᩥ
ࡣࠊኟᮇࡢᮾி‴ᾏ㢼࡟ࡘ࠸࡚ࠊ1960 ᖺ௦࡜ 2010 ᖺ௦ࡢᅵᆅ฼⏝ࡸẼೃ᮲௳ࢆ෌⌧ࡋࠊ㒔ᕷ໬ࡸẼೃኚື࡟కࡗ࡚ࠊᮾி‴ᾏ㢼ࡸ┦ᶍ‴ᾏ㢼ࡢᣲືࡀ࡝ࡢࡼ࠺࡟ኚືࡋࡓࡢ࠿ࢆࠊWRF㸦Weather Researchand Forecasting Model㸧ࢆ⏝࠸࡚ࠊᩘ್ィ⟬࡟ࡼࡗ࡚ゎᯒࡋࡓࡶࡢ࡛࠶ࡿࠋ

1. Introduction
The impact of urbanization to sea-breeze behavior has

urbanization to the sea breeze behavior in Kanto-

been a topic of interest [1] due to its mitigation effectplain.

on urban heat island. Both simulations [2] and data

2. Methodology

analyses [3] have suggested that urbanization near thecoast delays sea-breeze penetration inland. However,

The effect of 1960s’ and 2010s’ climate, and 1960s’ andanother study, statistical comparison [4] of sea-breeze

2010s’ land morphology to sea breeze was investigatedpenetration time between 1960s and 2010s in Tokyo,

using the weather model, Weather Research andsuggests that,lForecasting Model (WRF).

Sea-breeze penetration inland of Tokyo was

2.1 Reconstruction of 1960s’ land morphology

delayed by ~30 min. due to surface drag fromdense urban areas; andlTo reconstruct 1960s’ land morphology for weather

Sea-breeze in 2010s tends to pass the shoreline

simulation, it was necessary to construct a distribution

earlier than it was in 1960s due to the increasingof the following parameters [5] (additional description

land-sea pressure gradient possibly triggered bycan be found in [6]),global climate change.
The findings were based solely from analyses of pointlLand use categoryobservation data. Simulation is another tool to validatelthe earlier findings in Tokyo. Conversely, no study haslPlain Area Index (ߣ௣ )included an accurate reconstruction of past real landlmorphology and climate, with specific focus onlinterannual variation of sea-breeze.

lRoughness length (‫ݖ‬଴)The objective of this research are as follows,lAnthropogenic Heat Emission (AHE)Ø

To re-create 1960s’ urban and climate condition.

ØTo find the separate effect of climate change andFrontal Area Index (ߣ௙ )Average Building Height (‫ܪ‬௔௩௘ )Displacement Height (݀଴ )The earliest digital map of land use produced by MLIT
(Ministry of Land, Infrastructure, Transport andTourism) was retrieved for the year 1976 with high73

spatial resolution, 100m × 100m, and was assumed toapproach to sea surface temperature as well. For thishave the same land use as 1960s.

purpose, MGDSST (Merged satellite and in situ dataߣ௣ and ‫ܪ‬௔௩௘ were estimated from family-unit density

Global Daily Sea Surface Temperature) was introduced.

in 1970 produced by MIC (Ministry of Internal Affairs

The spatial resolution is 1.0° × 1.0° and its temporal

and Communications). ߣ௙ , ‫ݖ‬଴ and ݀଴ are estimatedresolution is monthly.

by ߣ௣ and ‫ܪ‬௔௩௘ using regression formula [5]. Then,
AHE

was

estimated

[7]

using

Final

2.3 Simulation setting

EnergyConsumption in Japan, 1965. The Fig.1 shows assumed

In this research, the modified [6] WRF-ARW ver.3.3.1‫ܪ‬௔௩௘ in current and 1960s.

was used with 7 days spin-up days, and the simulation
domain and resolution are shown in Fig.3 and Table1,

2.2 Reconstruction of 1960s’ climate condition

respectively. 18 days with clear sea-breeze were
statistically analyzed [4].

In this research, a reverse approach of the Pseudo-

Three cases were simulated in this research (Table.2).

global Warming Method [8] (PGW) was used to re-“Control” case had simulated with 1960 urban

create 1960s’ climate. The climate difference (ex.

morphology

Temperature, horizontal and vertical wind velocity and

and

2010

climate.

Urbanization’s

influence to sea-breeze behavior could be represented

humidity) between 2010s’ and 1960s’ were calculatedby the comparison of “Current” and “Control”. On theby subtracting from 10-yr average of 2010s (2004-

other hand, climate change effect could be represented2013) to that of 1960s (1960-1969). Then, 1960s’ daily

from the comparison of “Past” and “Control” cases.

climate data were re-created by subtracting it from2010s’ daily climate data (Fig.2). For this method,
JRA-55 (Japanese 55 years Reanalysis) was usedbecause it has available records from 1958 with arelatively high spatial resolution is 0.5° × 0.5°.
Sea breeze is driven by pressure gradient generated by
the temperature differences between the land and sea.
Thus, it was necessary to apply the reversed PGW

Fig.3 Simulation domain
Table.1 domain information
Fig.1 Average Building height㸦lefe:1960s’, right:2010s’㸧

Fig.2 Modified PGW method for 1960s’ climate
74

Number of grid

Grid size

Domain 1

62×56×61

30000

Domain 2

171×171×61

6000

Domain 3

201×181×61

1200

3. Result

Table.2 Combination of urban and climate

3.1 Climate differences
Fig.4 shows the temperature and wind difference

Current

Past

Control

Urban

2010

1960

1960

Climate

2010

1960

2010

between 2010s and 1960s on August; and Fig.5 showsthat of sea surface temperature (SST). Left figure
shows the wider domain (domain 1 in Table 1) and rightfigure shows domain 3 which is Kanto region. Positive
value means the temperature and wind were more
intense in 2010s compared to that of 1960s.
The temperature in Japan had increased about 0.3 to
0.7°C (Fig. 4). Especially in North-west side of Kantoarea, the increment of temperature exceeds 1.0°C.

Fig.4 Temperature and wind difference

Southwesterly winds were enhanced above the sea inbetween 2010s and 1960s on Aug

2010s’. This means that easterly winds were stronger inthe 1960s’.
According to Fig.5, SST has increased around Japan,
especially at northern part. SST at Tokyo and Sagamibay also increased about 0.2~0.3°C. The increment of
SST was found to be smaller than that of surface
temperature above Kanto region.
Fig.5 Sea Surface Temperature between

3.2 Statistical result

2010s and 1960s on Aug
Here, effect of urbanization and climate effect are
Table.3 Average time of sea breeze arrival

discussed statistically based on the simulation. Seabreeze arrival at Otemachi from Tokyo bay are 5 cases

Current

Control

Past

and at Kumagaya from Tokyo and Sagami bay are 9

Otemachi

9:06

8:51

8:22

cases. Because Sagami bay sea-breeze are dominant toKumagaya

15:12

14:33

14:27

penetrate to Kumagaya. In Other cases, there arerainfall event or wind from Kashima-bay and so on.

than in “Control” case. It can be deduced from this large

The average time of sea-breeze arrival at Otemachi andtemperature differences that sea breeze tend to

Kumagaya are shown in Table.3. At Otemachi, sea

penetrate in “Current” case. However, sea-breeze

breeze arrival in “Past” case is earlier than others due

penetration above land in “current” case is much slower

to the climate effect. Fig.6 shows that the ensemble

than that in “Control” because of the surface drag.

average of 2-m temperature and wind distribution

Fig.7 shows that same as Fig.6, but in whole Kanto-

around Otemachi (star) at 9. Sea breeze in the “Control”

plain at 15:00 local time. Due to the urbanization, 2-mcase was weaker due to climate change (Fig.4). In terms

height temperature in “Current” is highest andof urbanization (Current v.s Control), temperaturetemperature gradient between land and sea is higher.

difference between land and sea in “current” are higher

However winds were weaker because of surface drag.

75

So, the sea-breeze arrival time at Kumagaya in“Current” case are slower than the others. On the other
hand, sea breeze arrival time in “Past” and “Control”
don’t have so much difference. Inland temperature
gradient in the “Control” was higher than in the “Past”
resulting to a more intensified acceleration of sea
breeze in the “Control” than in the “Past”. This can be
obvious at the later time when sea-breeze front in“Control” has caught up with that of the “Past” case. As
shown in Table 3, sea-breeze arrival time at Kumagaya
shows little difference between “Past” and “Control”.

4. Conclusion
Fig.6 2-m height of temperature (upper) and windlLand modification and climate in the 1960s was

distribution (lower) at Otemachi at 9reconstructed to study separate effects ofurbanization and climate change to sea breeze
behavior;l

It is difficult for sea breeze to penetrate inland in
the present because of the enhanced surface dragdue to increasing building volume and extent in
Kanto region (urbanization);l

Sea-breeze penetration tends to be delayed in thepresent climate at Otemachi when no change inurban areas is assumed; However,lInland temperature is warmer in the present
climate (assuming 1960s urban areas) resulting to

Fig.7 2m height of temperature (upper) and windlarger temperature gradient. Consequently, sea-

distribution (lower) in Kanto at 15

breeze arrival time at Kumagaya in the present

Meteorology, 1989.

climate becomes similar as that of the “past” (in[4]. Nejime, Y. et al.: Journal of Japan Society of Civil

spite of the earlier penetration of sea breeze atEngineers, 2015.

Otematchi).

[5]. Kanda, M. et al.: Boundary-Layer Meteorology,
2013.

5. Reference[6]. Varquez, C, G, A. et al.: Boundary-Layer
Meteorology, 2014[1]. Yamamoto, H. et al.: Journal of Geography, 2011.

[7]. Dong, Y. et al.: (Preparation for Submission).

[2]. Kusaka, H. et al.: Journal of Meteorological[8]. Hara, M. et al.: Journal of Japan Society of Fluid

Society of Japan, 2000.
[3]. Yoshikado,

H.

et

al.:

Mechanics, 2010.

Boundary-Layer

76

SATELLITE-DERIVED URBAN AERODYNAMIC PARAMETERS FORMESOSCALE MODELING IN MEGACITIESStudent Number: 14M18299
Name: Nisrina Setyo DARMANTO
Supervisor: Manabu KANDA, Alvin C. G. VARQUEZ

⾨ᫍ⏬ീࢆ⏝࠸ࡓ㒔ᕷࣃ࣓࣮ࣛࢱࡢ⟬ᐃ࡜࣓࢞ࢩࢸ࢕࣮࡟࠾ࡅࡿ    ࣓ࢯࢫࢣ࣮ࣝ㒔ᕷẼೃࣔࢹࣝ࡬ࡢᛂ⏝ࢲ࣐ࣝࣥࢺ ࢽࢫࣜࢼ ࢭࢸ࢕ࣚ
This research aims to derive 1km high-resolution urban parameters from global satellite images asfoundation on calculating WRF urban aerodynamic parameterization scheme. Empirical equations in deriving
satellite images into urban geometric parameters were developed and resulted in good agreement with real ones.
Simulations on Jakarta and Istanbul megacities were done by utilizing the new satellite-derived urban
parameters to assess their performance in advanced WRF. Simulation results in these two megacities showedthat application of satellite-derived urban parameters improves model results in representing temperature, windspeed, and other urban atmospheric phenomena.
available for all cities, especially those in developing countries.
On the other hand, there is high potential to define twodimensional urban morphology ߣ௣ from recent LANDSAT 8
satellite imagery due to its 30m resolution and more sensitive
sensors in differentiating land use/cover. Furthermore, Digital
Elevation Model (DEM) satellite ASTER launched in 2011 has
tendency to give offset biases on measuring ground elevation
due to forest or buildings canopy [3]. Thus, it is very likely thatbuilding canopy height could be determined by subtracting
these offsets with real ground elevation. GMTED2010 was
proofed in providing real ground elevation correctly [4] andwill be utilized together with ASTER in estimating ‫ܪ‬௔௩௘ .
In this study, we aim to calculate ߣ௣ , ߣ௙ , ‫ܪ‬௔௩௘ , d and‫ݖ‬଴ in megacities. This is a novel method in creating detailed
high resolution 1km urban parameters from satellite images
and prospected to be applied for global extent. Furthermore,high-resolution 1km urban climate simulation using WRF
coupled with modified single-layer UCM with urban
parameterization scheme was done. These simulations wereconducted for Istanbul and Jakarta and is aimed to assess thecapability of these satellite-derive urban parameters in defining
the megacities actual urban climate condition.

1 Introduction
Urbanization, despite offering improved living standards andsignifying rapid economic growth for the country, it also
introduces social, economic, and environmental threats. One of
the major environmental impacts of urbanization ismodification of urban climate due to increase of heat andpollutant emissions, and changes in surface cover. The most
discussed phenomenon in urban climate is urban heat island(UHI), a condition where an urban area is significantly warmer
than its surrounding rural areas due to anthropogenic heat andcomplex urban morphology.
Recently, the said phenomenon is analysed by using stateof-the-art 3D weather and climate model which capable on
medium to fine resolution such as mesoscale model. One of thesophisticated mesoscale model widely used in urban climate
research is Weather Research and Forecast (WRF) model due
to its capability to directly couple with urban canopy models(UCM).
From large-eddy simulation of real buildings in Tokyo, a
new urban aerodynamic feedback parameterization [1] indisplacement height d and aerodynamic roughness length ‫ݖ‬଴parameters which can adequately consider the heterogeneity ofurban geometry within a grid (bulk indices). Calculation ofthese parameters requires the urban morphology represented infive geometric parameters: plane area index ߣ௣ (i.e., the ratioof the plane area occupied by buildings to the total floor area),
the frontal area index ߣ௙ (i.e., the ratio of the frontal area ofbuildings to the total floor area), the average building height‫ܪ‬௔௩௘ , the maximum building height ‫ܪ‬௠௔௫ , and the standard
deviation of building height ߪு . Incorporating the new urban
aerodynamic feedback parameterization in 1km resolution to
WRF mesoscale model coupled with modified single layer
UCM for Tokyo resolved the sea breeze fairly well along withthe surface wind speed reductions at densely built-up areas [2].
Although weather modelling of urban areas improve upon
inclusion of realistic parameters, distributed urban parameterdata for most cities is still scarce. Setting up the urban
parameters often requires realistic building data which is not

2 Urban Parameters Construction
for Megacities
2.1 Determining empirical equation on ࣅ࢖ fromLANDSAT 8

Before deriving satellite images to ߣ௣ and ߣ௙ , land use
classification in defining types of land use/cover (e.g., water,
vegetation, bare soil, and agriculture area) from these images
should be done. To classify the land use from LANDSAT 8
image, supervised classification process was conducted. This
process was done by using Semi-Automatic Classification(SCP) plugin available for QGIS [5]. For this work LANDSAT
8 Band 2–Band 7 were used. This classification concept isusing image corrections and Spectral Angle Mapping (SAM)
77algorithm. The method will maintain the classified land use in
30m LANDSAT 8 resolution.
This classification method applies the concept of machinelearning by selecting Region of Interests (ROIs) as learning
samples. SCP is supported with region growing algorithm
during the ROIs collection which detect similar pixels value onsurrounding areas. In creating an area-specified ROIs, userneeds to give attention during the creation to define highrepresentation of real land use data. This could be done bymatch it with high-resolution satellite image (i.e., Google
satellite image). The collected ROIs for each different land use
classification is then averaged to create spectral signatures.
These spectral signatures will be the based for land use
classification itself by using spectral angle mapping algorithm.
Since this method utilized an area-specific ROIs, the spectralsignatures will be valid in a specific area of study. There are 10classified Region of Interests (ROIs) as the base in making thespectral wavelength; water, 3 vegetation classes (e.g., mixvegetation, grassland, mix-forest), 2 bare soil classes (e.g.,general bare soil and quarry), agriculture, and 3 urban classes(e.g., medium-dense, high-dense, and industrial/commercial)(Figure 1).

taken from MAPCUBE provided by Pasco Geospatial (http://
www.pasco.co.jp/eng/products/3d_city/). ߣ௣ for Istanbul
were taken from Istanbul real building data provided by Turkey
government. ߣ௣ for Jakarta were extracted from map image
provided on Nokia Here map by using simple image processing
to extract building footprint from the map.
Ordinary Least Squares (OLS) method was utilized todetermine coefficient value on each urban ratio as regressor
variables towards λp value from original building data as
dependent variable. In this OLS method, we used seven real λp
database (Table 2.2): Tokyo real λp; Istanbul real λp; Jakarta
real λp; Tokyo and Istanbul combined real λp; Tokyo andJakarta combined real λp; Jakarta and Istanbul combined real
λp; and Tokyo, Jakarta, and Istanbul combined real λp. We used
OLS prediction equation (2) to find three coefficientsߙଵ ǡ ߙଶ ǡ ߙଷ which corresponding directly to ‫ݎ‬ଵ଴ଶ , ‫ݎ‬ଵ଴ଷ , and ‫ݎ‬ଵ଴ସ
respectively. Range of applicable ߙଵ ǡ ߙଶ ǡ ߙଷ to be used inderiving λp value from Landsat 8 images on other cities are:ߙଵ ൌ ͲǤͲͳ̱ͲǤͲ͵, ߙଶ ൌ ͲǤͶ̱ͲǤͷ, and ߙଷ ൌ ͲǤͳ̱ͲǤ͵.
ߣ௣ ௣௥௘ௗ௜௖௧ ൌ ߙଵ ሺ‫ݎ‬ଵ଴ଶ ሻ ൅ ߙଶ ሺ‫ݎ‬ଵ଴ଷ ሻ ൅ ߙଷ ሺ‫ݎ‬ଵ଴ସ ሻ ,(2)ߣ௙ ൌ ͳǤͶʹߣଶ௣ ൅ ͲǤͶߣ௣ ,(3)Equation (2) has been proofed to have >0.86 Pearson R
correlation coefficient of real λp and predicted λp in Tokyo,
Istanbul, and Jakarta.
To create frontal area index ߣ௙ , we directly derived itfollowing ߣ௙ empirical equation correlation with function ofߣ௣ (2.3) (Kanda et. al., 2013). ߣ௙ predict created from ߣ௣
predict has Pearson R value of 0.87 with ߣ௙ real in Tokyo,
which conclude that this method is impressive to represent real
1km ߣ௙ in urban area.

2.2 Determining empirical equation on ࡴࢇ࢜ࢋfrom ASTER GDEM, GMTED2010, andNightlight DMSP
Figure 1: Land use classification (left column) and extracted
urban land use (right column)

To create empirical equation in deriving 1km ‫ܪ‬௔௩௘ , thesatellite images used are: 30m ASTER GDEM, 7.5 arc-second
GMTED2010 – and calibrated nightlight images – 1km
Nightlight Defense Meteorological Satellite Program (DMSP)
satellite image for Tokyo area. ASTER GDEM and
GMTED2010 –interpolated into 30-m resolution- satellite
images (Fig. 2(a) and (b)), both with 1-m vertical resolution
were utilized as primary images in this method. Nightlight datainterpolated into 30-m resolution (Fig. 2(c)) was utilized as
secondary since its high potential in correcting averagebuilding height on high-rise commercial area which is analyzed
lacked from ASTER GDEM and GMTED2010 due toinadequacy on urban morphology detection [6]. For validation,
1-km spatially averaged building height data from MAPCUBE
was utilized. 1km average building height created from
MAPCUBE then will be called ‫ܪ‬௔௩௘ real.
From 1-km gridded spatially average raster data of
ASTER GDEM and GMTED2010, we subtract ASTER value
with GMTED2010 value on the same grid. The result is filteredwith urban area mask (ߣ௣ ). Nightlight data also filtered withurban area mask to be paralleled with ASTER-GMTED data.
OLS of ASTER GDEM, GMTED2010 and Nightlight as

From the created urban extracted area (Figure 1), we
created urban ratio which later used in comparison with ߣ௣real to satellite-derive ߣ௣ predict. Urban ratio is the ratio of
total area occupied with certain urban class to the total area of
all urban and non-urban value in 1km grid. Therefore, there
will be 3 urban ration in area inside approximately 1km
resolution grid and derived as:‫ݎ‬ଵ଴ଶ ൌ‫ݎ‬ଵ଴ଷ ൌ‫ݎ‬ଵ଴ସ ൌ௧௢௧௔௟௔௥௘௔௢௙ଵ଴ଶ௜௡ଵ௞௠௚௥௜ௗ௞௠మ
௧௢௧௔௟௔௥௘௔௢௙ଵ௞௠௚௥௜ௗ௜௡௞௠మ

,௧௢௧௔௟௔௥௘௔௢௙ଵ଴ଷ௜௡ଵ௞௠௚௥௜ௗ௞௠మ

(1b)௧௢௧௔௟௔௥௘௔௢௙ଵ௞௠௚௥௜ௗ௜௡௞௠మ

௧௢௧௔௟௔௥௘௔௢௙ଵ଴ସ௜௡ଵ௞௠௚௥௜ௗ௞௠మ
௧௢௧௔௟௔௥௘௔௢௙ଵ௞௠௚௥௜ௗ௜௡௞௠మ

(1a)

,

(1c)

where ‫ݎ‬ଵ଴ଶ , ‫ݎ‬ଵ଴ଷ , and ‫ݎ‬ଵ଴ସ denote medium density urban
ratio, high density urban ratio, and commercial urban ratio. The
result of each urban ratio in one 1km grid will be compared toߣ௣ real in the same corresponding grid. ߣ௣ for Tokyo were78

regressors variable and ‫ܪ‬௔௩௘ real as dependent variable wasconducted to estimate a formula that define the correlation
among them. The OLS result is shown in (4a), with adjustment
apply for Have predict >19m as stated in (4b). This adjustment
was done since (4a) tends to underestimate high-rise building
value. ‫ܪ‬௔௩௘ predict was calculated as follows:‫ܪ‬௔௩௘ ‫ ݐܿ݅݀݁ݎ݌‬ൌ ͲǤ͵Ͳͷሺ‫ܪ‬஺ௌ்ாோ ሻ ൅ ͲǤͲͳሺܰ‫ܮ‬ሻ െ ͳǤͶʹ ,‫ܪ‬௔௩௘ ‫ ݐܿ݅݀݁ݎ݌‬൐ ͳͻ݉ ൌ ͸Ǥ͵͹ ൈ ሺ‫ܪ‬௔௩௘ ‫ݐܿ݅݀݁ݎ݌‬ሻ଴Ǥହଷ ,

2.3 Urban Feedback Aerodynamic Parameters:ࢊ and ࢠ૙ construction

Two urban feedback aerodynamic parameters of displacement
height d and roughness length ‫ݖ‬଴ were calculated using
method 1 in new aerodynamic parameterization for real urban
surfaces as stated in equation (5) with ܽ଴ ൌ ͳǤʹͻǡ ܾ଴ ൌͲǤ͵͸ǡ ܿ଴ ൌ െͲǤͳ͹and (6) with ܽଵ ൌ ͲǤ͹ͳǡ ܾଵ ൌ ʹͲǤʹͳǡ ܿ଴ ൌെͲǤ͹͹ [1]. Required urban morphology parameters in theseequations were taken from satellite-derive urban morphology
parameters described before.

(4a)

(4b)

where ‫ܪ‬஺ௌ்ாோ denotes the value of ASTER subtracted by
GMTED2010 (in meters), and NL denotes digital number
value of nightlight images.
(a)

ௗ

ு೘ೌೣ

௭బ

(b)

௕

ൌ ܿ଴ ܺ ଶ ൅ ൫ܽ଴ ߣ௣బ െ ܿ଴ ൯ܺǢ ܺ ൌ௭బ ሺ௠௔௖ሻఙಹ ାுೌೡ೐
ு೘ೌೣൌ ܾଵ ܻ ଶ ൅ ܿଵ ܻ ൅ ܽଵ Ǣ ܻ ൌǡ Ͳ ൑ ܺ ൑ ͳǤͲ

ఒ೛ ఙಹ
ுೌೡ೐

ǡ Ͳ ൑ ܻ

(5)
(6)

3 WRF Simulation using Satellitederived Urban Parameters
In this work, simulation on Jakarta megacity was done.
Another one simulation was done for Istanbul [7]. Two nested
domains and one parent domain with 30km, 6km, and 1.2km
resolution for Domain 1, Domain 2, and Domain 3 respectively
with one-way nesting system were set. There are 3 validation
points in this simulation: Kemayoran station (KMY)
106.8133E,6.1833S; Priok station (PRK) 106.8778E,6.111S;
and Cengkareng station (CGK) 106.65E,6.167S.
Two cases of simulation were conducted in this work toanalyze the effect of urban parameterization on Jakarta urban
climate. One case was coupled with satellite-derived urban
parameterization –next will be called as Urban_case-, andanother one was done without urban parameterization –next
will be called as Control_case-. Both cases used updated
MODIS 15’ 20-categories land use on urban area andAnthropogenic Heat Flux (AHF) generated from global AHF
estimation based on population and Nightlight intensity [8].
For initial and lateral meteorological boundary condition on
both cases, National Centers for Environmental Prediction
Final (NCEP-FNL) archive data in 1° and 6-hour resolution
were used. Both used 25-days spin-up time. MODIS 4km
resolution gridded Sea Surface Temperature (SST) data wasused in both cases to improve NCEP-FNL low-resolution SST.
Both cases used same physical settings: short wave radiation
follows Dudhia scheme, long wave radiation follows RRTM
scheme, microphysics follows New Thompson scheme,
cumulus parameterization follows Kain-Fritsch scheme, andplanetary boundary layer follows Mellor-Yamada 2.5 scheme.
The simulated result on Jakarta was compared withobservation data –hourly synoptic report- from 3 stations
mentioned before. Figure 4 shows temperature validationgraphs for Control_case and Urban_case on KMY and CGK
on fine days on August 22-30 2014. Figure 5 shows wind speed
validation graphs on August 22-30 2014.
Figure 4 shows that Control_case and Urban_case has
similar performance in predicting maximum temperature in
Jakarta and both of them are very close to observation value.
Urban_case predicts minimum temperature better thanControl_case as clearly seen on KMY and CGK stations. Table
1 shows simulation and observation averaged Root Mean(c)

Figure 2: ASTER GDEM (a), GMTED2010(b), andNightlight DMSP (c) for Tokyo
Correlation between ‫ܪ‬௔௩௘ real and ‫ܪ‬௔௩௘ predict has
Pearson correlation R value of 0.67 for Tokyo which isconsiderably acceptable for this study purpose. However, for
Istanbul, application of equation (4a 4b) gives low Pearson
correlation value of -0.10. Based on spatial distribution (Figure3) the spatial distribution of real and predicted ‫ܪ‬௔௩௘ has
similar pattern in Tokyo. Nonetheless, Istanbul shows different
pattern of real and predicted ‫ܪ‬௔௩௘ . This problem is likely due
to different timestamp of real ‫ܪ‬௔௩௘ –which was taken on
2005- and predicted ‫ܪ‬௔௩௘ –which was taken on 2011-. The
real ‫ܪ‬௔௩௘ Istanbul in 2005 was underestimate compared with2011 building data, since this city builds significant number ofskyscrapers that was just finished around late 2010 until early
2011. For Jakarta case, since there is no available real buildingheight data such as Tokyo and Istanbul, equation (4a and 4b)
were directly applied.

Figure 3: Spatial distribution of ‫ܪ‬௔௩௘ from MAPCUBE real
3D building data (left) and satellite-derived ‫ܪ‬௔௩௘ (right) for
Tokyo

79

Squared Error (RMSE) from 3 observation stations.
Urban_case simulation decreased error between simulation andobservation with 1.53 RMSE value for Urban_case and 1.77
for Control_case. This shows that adding satellite-derive urban
temperature in Jakarta in modified WRF could improve
simulation performance as its getting closer with point
observation value.

4 Conclusions
Empirical equations in deriving LANDSAT 8 satellite images
into ߣ௣ and ߣ௙ were proposed. Validation with real urban
parameters were conducted and it resulted in good agreement
between the real urban parameters and satellite-derived one.
Validation other than mentioned cities is still in progress since
it is a difficult matter to collect the data. ASTER GDEM,
GMTED2010, and DMSP Nightlight image were combined tocreate empirical equation in deriving urban geometry
parameters of ‫ܪ‬௔௩௘ from satellite images based on Tokyo area.
Validation on ‫ܪ‬௔௩௘ gave a good agreement between real andsatellite-derived urban parameters in Tokyo.
Two simulation cases were conducted in Jakarta to evaluate
satellite-derived urban parameters performance in WRF model
coupled with modified single-layer urban canopy model for
urban aerodynamic parameterization scheme. It appears thatsimulation results using urban parameters have better
agreement on temperature and wind speed with observed datacompared to another case which not utilizing urban parameters.
It concludes that satellite-derived urban parameters havepromising potential for being source dataset in creating urban
parameters, especially for most cities without distributed
building morphological information.

Figure 4: Temperature comparison of Jakarta simulation caseswith observation
Figure 5 shows that simulation result overestimatesobserved data both in KMY and CGK, which is happened due
to WRF sensitivity in analysing near-ground wind speed. As
shown in Figure 5, Urban_case predicted lower near-groundwind speed, thus made it more similar with observed data.
From Table 1, Urban_case has significant lower RMSE value
compared to Control_case.

References[1] Kanda, M., Inagaki, A., Miyamoto, T., Gryschka, M. and
Raasch, S. (2013) ‘A New Aerodynamic Parameterization
for Real Urban Surfaces’, Boundary-Layer Meteorology,148(2), pp. 357–377.
[2] Varquez, A. C. G., Nakayoshi, M. and Kanda, M. (2014)‘The Effects of Highly Detailed Urban Roughness
Parameters on a Sea-Breeze Numerical Simulation’,Boundary-Layer Meteorology, 154(3), pp. 449–469.
[3] Tachikawa, T., Kaku, M. and Iwasaki, A. (2011) ASTER
GDEM Version 2 Japan’s Validation Report.
[4] Carabajal, C. C., Harding, D. J., Boy, J.-P., Danielson, J. J.,
Gesch, D. B. and Suchdeo, V. P. (2011) ‘Evaluation of the
global multi-resolution terrain elevation data 2010
(GMTED2010) using ICESat geodetic control’,International Symposium on Lidar and Radar Mapping
2011: Technologies and Applications.
[5] Congedo Luca, Munafò Michele, Macchi Silvia (2013).
“Investigating the Relationship between Land Cover andVulnerability to Climate Change in Dar es Salaam”.
Working Paper, Rome: Sapienza University.
[6] Makabe, T., Nakayoshi, M., Varquez, A. C. G. and Kanda,
M. (2014) ‘Database of meteorological urban geometricparameters of Japan and extension to global scale’, Japan
Society of Civil Engineers, 70(4), pp. 331–336. (inJapanese)[7] Yucel, M., Varquez, A.C.G., Darmanto, N., Kanda, M.
(2015) ‘Improvements of urban representation in weathermodels using global datasets’, Journal of HydraulicEngineering, JSCE, 72.
[8] Dong, Y., Varquez, A.C.G., Kanda, M. ‘Global high spatial
resolution anthropogenic heat flux database’ (to besubmitted).

Figure 5: Wind speed comparison of Jakarta simulation caseswith observation
Table 1: Average RMSE for both cases on observation data
Jakarta cases
Control_case
Urban_case

Average
Temperature RMSE
1.77
1.53

Average Wind
speed RMSE
3.53
2.80

80

Process to Recover Unreacted Alcohol in Biodiesel Fuel Production from Inedible Oils
Student Number: 14M18282

Name: Zhenqiang SHISupervisor: Ryuichi EGASHIRA

㠀㣗⏝Ἔࢆཎᩱ࡜ࡋࡓࣂ࢖࢜ࢹ࢕࣮ࢮࣝ⇞ᩱ〇㐀࡟࠾ࡅࡿᮍ཯ᛂ࢔ࣝࢥ࣮ࣝᅇ཰ࣉࣟࢭࢫ▼ ᣺ᕪࣔࢹࣝΰྜ≀࠾ࡼࡧࢪࣕࢺࣟࣇ࢓Ἔࢆ࢚ࢫࢸࣝ஺᥮ࡋ࡚ᚓࡽࢀࡓᐇΰྜ≀ࢆ⏝࠸࡚ᾮᾮᖹ⾮ࢆᐇ ࡋࡓࠋࡑࢀࡒࢀࡢ
ᡂศࡢᖹ⾮㛵ಀࢆ᫂ࡽ࠿࡟ࡋ㸪ࡲࡓࣂ࢖࢜ࢹ࢕࣮ࢮࣝ┦࡜๪⏕ᡂ≀࡛࠶ࡿࢢࣜࢭࣜࣥ┦࡟Ꮡᅾࡍࡿ࣓ࢱࣀ࣮ࣝࡢ≀㉁㔞
ࡣྠ⛬ᗘ࡛࠶ࡾ୧┦࠿ࡽ࣓ࢱࣀ࣮ࣝࡢᅇ཰ࡀᚲせ࡛࠶ࡿࡇ࡜ࢆ☜ㄆࡋࡓࠋࡇࢀࡽࡢᐇ㦂⤖ᯝ࡟ᇶ࡙࠸࡚㸪ࣂ࢖࢜ࢹ࢕࣮
ࢮࣝ┦࠿ࡽࡢ࣓ࢱࣀ࣮ࣝᅇ཰࡟⵨␃㸪Ỉ࡟ࡼࡿᢳฟ㸪࠶ࡿ࠸ࡣࢢࣜࢭࣜࣥ࡟ࡼࡿᢳฟࢆ㐺⏝ࡋࡓ3ࡘࡢ␗࡞ࡿࣉࣟࢭࢫ
ࢆྜᡂࡋ㸪ࡇࢀࡽࢆᶍᨃィ⟬࡟ࡼࡾẚ㍑ࡋࡓࠋᡤせ࢚ࢿࣝࢠ࣮࠾ࡼࡧᚲせ࡞᧯సࡢほⅬ࡟࠾࠸࡚ࢢࣜࢭࣜࣥ࡟ࡼࡿᢳฟ
ࢆ⏝࠸ࡓࣉࣟࢭࢫࡀ᭱ࡶඃࢀ࡚࠸ࡓࠋ

1.

oil is shown in Table 2.1 together with those of othervegetable oils. The free fatty acid, water, etc.
contained in the feed jatropha oil used here were
removed before the transesterification 5). Thus
purified jatropha oil and methanol weretransesterified with the catalyst of sodium hydroxide
under 333 K to obtain fatty acid methyl ester, i.e.,
biodiesel, and glycerol5). The glycerol phase waswashed by aqueous solution of H3PO4 to remove salt
(soap).
Measurement of liquid-liquid equilibrium: The
liquid-liquid equilibrium appearing in thedownstream after transesterification were measured.
The conditions of the measurements are summarizedin Tables 2.2 and 2.3. Methyl palmitate (PAME),
methyl stearate (SAME), methyl oleate (OAME), andmethyl linoleate (LAME) were selected as fatty acidmethyl esters (FAME), since the precursors of theseFAMEs were major contents in the typical vegetableoils as given in Table 2.1. Each single FAME ofthem or the mixture of all these FAMEs was used asa model biodiesel phase. The phase immiscible withbiodiesel was glycerol or water to simulate theheterogeneous system just after the transesterification

Introduction

The production of biodiesel fuel has been
intensively studied as an alternative and carbon
neutral diesel fuel made from renewable biologicalsources such as vegetable oils and animal fats.
Nonetheless, biodiesel fuel commercialization has
not sufficiently been promoted due to the limitation
on feed supply and its high cost of production. To
make the variety options of feed oils, the used frying
oil and inedible plant oils have been popular due to
its lower price and environmentally friend resourcefor biodiesel fuel production 1). For the production
process, the transesterification of the feed oil withlower alcohol is reversible reaction and excessive
alcohol is required to attain higher conversion of
triglycerides2). The recovery of the excessive
unreacted alcohol is one of the important challenges
to improve the production efficiency. The distillationis popular method to recover the unreacted alcoholfrom biodiesel and glycerol phases after thetransesterification 3). The solvent extraction has also
been studied as a promising method of alcohol
recovery, in which water or glycerol, a byproduct intransesterification, was used as solvent 4). However
the alcohol recovery process by solvent extraction
has not sufficiently been studied yet.
The objective of this study was to develop theappropriate process for recovery of unreacted alcohol
for its reuse in the biodiesel fuel production. In thefirst, the liquid-liquid equilibrium appearing in thisproduction was studied. Secondly, based on theabove liquid-liquid equilibrium study, possible
alternatives of the alcohol recovery process weresynthesized, simulated, and compared. In thesestudies, jatropha oil, inedible vegetable oil, andmethanol, one of the widely used alcohols, wereconsidered as feed oil and alcohol fortransesterification.
2.

Liquid-liquid equilibrium in Biodiesel Fuel
Production

2.1 Experimental
Transesterification: The composition of jatropha81

or in the extraction recovery of methanol in thedownstream. The methanol was added to thisheterogeneous liquid-liquid system to measure theequilibrium. The equilibrium in the heterogeneous
system obtained after the above transesterification
was measured as well.
2.2 Results and discussion
Figures 2.1 and 2.2 show the phase diagrams in
the cases with single FAME component. The
methanol distributed more in glycerol or aqueous
phase than in FAME phase in all cases. The twophase region in the diagram shrunk, that is, thesolubilities among the components increased, as thenumber of double bond between carbons in amolecular of FAME increased.
These results were correlated using UNIFAC
method. The UNIFAC method is one of thethermodynamic techniques to estimate activity
coefficients in liquid phase with interaction
parameters between functional groups composing
molecular of component and so on. The correlationsin the case of LAME are shown in Figures 2.3 and
2.4 as examples. The UNIFAC method could
favorably estimate the liquid-liquid equilibrium byadjusting the parameters. The parameters aretabulated in Table 2.4.
The distribution coefficient and mass
distribution ratio of methanol, m MeOH and MR MeOH,
were defined as,
mMeOH = x MeOH,Gly ⁄ x MeOH,BDF
MRMeOH = MGly x MeOH,Gly ⁄ (M BDF x MeOH,BDF)
with mass fractions of methanol, x MeOH,Gly, x MeOH,BDF,
and masses of phases, MGly, MBDF . Table 2.5 shows
the results in the case with the mixture of fourFAMEs. Although it was confirmed that the
distribution coefficient of methanol, m MeOH, wasmuch higher than unity also in this system, the massof methanol in two phases were comparable because

mass of biodiesel phase was much larger than that ofglycerol phase in the practical production. The
similar results were obtained in the case of the system
just after the transesterification as presented inTable 2.6. Hence, it was necessary to recoverunreacted methanol from not only glycerol but also
biodiesel phases.
3.

Process to Recover Unreacted Alcohol

3.1 Process outline
The schematic diagrams of three processes,
Processes a, b, and c, are presented in Figures 3.1,
3.2, and 3.3, respectively.
Process a: The unreacted methanol in bothphases are recovered by distillation. Recovered
methanol is reused in transesterification as feed.
After distillation, the biodiesel phase is washed by82

formulated for the process
were solved
simultaneously under the assumptions of ideal stages
for solvent extraction and distillation. The program
written in Visual Basic was used for the calculationof solvent extraction, where the equilibrium wasestimated by UNIFAC method with the parameters
shown in Table 2.4. The distillation was calculated
by a commercial process simulator (PRO/II,Invensys).
The operating conditions and specifications forthe products are shown in Table 3.1. The heats
consumed by heat exchanger and ditillation reboiler
were regarded as the energy requirement for theprocess.
3.3 Results and discussion
Figures 3.4-3.7 show the effects of the numberof stages in the extraction on necessary S ⁄ F ratio andenergy requirement for the process in Processes b andc. The S ⁄ F ratios and energy requirements decreased
with the numbers of stages and were almost constant,where the numbers of stages were more than five.
The numbers of extraction stages in the bothprocesses were, thus, fixed at six, hereafter.
Tables 3.2 shows the product results in three
processes. The yield of methanol in Process a washighest at 0.995. The purity and yield of glycerol inProcess c were the highest at 0.974 and almost unity
with the methanol yield of 0.98. The yield of
biodiesel of three processes were all over 0.99.
The energy requirements for the respective
processes are summarized in Table 3.3. Under thesame conditions and specifications, the energy

water to remove glycerol. Then the water andglycerol are separated by another distillion. Biodiesel
fuel product is obtained after dehydration. Recovery
of unreacted methanol by distillation is the mostnormal way in biodiesel production.
Process b: The unreacted methanol in byproductglycerol phase is recovered by distillation, whilebiodiesel phase is firstly extracted by water toremove the methanol and glycerol. Then biodiesel
fuel product is obtained after dehydration. Methanol
and glycerol are separated from water by distillations.
Thus
recoveredmethanolisreusedintransesterification as feed and water reused in
extraction.
Process c: The unreacted methanol in crude
biodiesel phase is firstly extracted by purified
glycerol, then this glycerol phase is distillated withthe crude glycerol phase to recovery the methanol in.
Recovered methanol is reused as feed intransesterification.
3.2 Calculation
The equations of material balances, enthalpy
balances, equilibrium relationships, and so forth83NomenclatureM= massm= distribution ratioMR= mass ratioS⁄F= flow rate ratio of solvent relative to feedx
= mole fraction in phase<Subscipt>
BDF = Biodiesel FuelD
= Distillation Column
E= Extraction Column
FAME = Fatty Acid Methyl Ester
H= Heat Exchangeri= Component i
LAME = Methyl Linoleate
MeOH = Methanol
OAME = Methyl Oleate
PAME = Methyl PalmitateR= Raffinate
S= Stream
SAME = Methyl Stearate

required for Process a and Process b wereaboutdouble and triple of that for Process c, namely,
Process c could be operated with much lower energy
than Processes a and b.
The unit operations necessary for the respective
processes are presented in Table 3.4. The number ofoperations, especially distillation, in the case ofProcess c was smaller than those of the otherprocesses. Also in this point of view, Process c wassuperior to the other processes.
4.

[g]
[–]
[–]
[–]
[–]

Literature Cited
1) S. Eguchi et al.; J. Cleaner Pro., 101, 245-250, (2015)2) B. Freedman et al.; J. Am. Oil Chem. Soc., 61, (10), 16381643, (1984)
3) A. Anton et al.; Apply Energy, 99, 146-153, (2012)4) J. Ye. et al.; Biosource Technology, 102, 4759-4765, (2011)5) P. Sinthupinyo; Master Thesis, Tokyo Tech (2007)

Conclusions

The liquid-liquid equilibriums in the biodiesel
fuel production were clarified and it was confirmedthat unreacted methanol should be recovered fromnot only glycerol but also biodiesel phases.
Three synthesized processes using extraction
and distillation methods to recover the unreacted
methanol after transesterification were calculated.
Process using by-product glycerol as extraction
solvent requires lower energy and fewer equipments
than others with a high yield and purity of glycerol,
methanol and biodiesel.

84

ROLE OF PARATRANSIT MODES ON ACCESSIBILITY IN KHULNA, BANGLADESH
Student Number: 14M18201

Name: DHRUBO AlamSupervisor: Shinya HANAOKA

Paratransit is an informal transportation mode which includes non-motorized transport; quite popular and widelyused in many developing cities. Khulna, is the 3rd largest city of Bangladesh, with high number of paratransit traffic
among its’ total vehicle fleet. The main objectives of the study are: explaining the traffic situation of Khulna in thecontext of Bangladesh and develop a mode choice model for various modes including the paratransits. Mode choicedepends on travel time for shorter trips. In case of longer trips, fare is also significant. The important socioeconomic
factors are house ownership and house type as proxies for income.
So far, there has been not much published researchon transposition system of Khulna, specially focusing
on paratransit. Common researches on Khulna include
road accident and traffic safety, analyzing efficiency
of certain roads, and modeling Spacio-temporal land
cover growth dynamics. Also, no mode choice modelor utility function for Khulna has been developed.
Mode choice models developed for Dhaka in the
recent past by Hasan [1], Rahman et al. [2] and Enam[3], are important in this context. But, none of themincludes all the paratransit modes (e.g. battery bikes,
rickshaws, autorickshaws etc.) individually; manysocioeconomic factors (e.g. gender, income,household type etc.) or the phenomenon of modechanges during a trip with single trip purpose havebeen analyzed.

1. Introduction
Paratransit is generally, informal, privately owned,
runs without subsidy, either door-to-door or sharedand includes Non-Motorized Transport (NMT). They
are also often unregulated, poorly managed anddesigned (both the service and vehicles). But it
provides accessibility in developing cities, sometimes
even with the presence of a transit mode, usually at a
relatively low cost and investment; but probably at theexpense of travel time and safety.
Many midsized developing cities of the world faceserious issues in providing transport accessibility,
especially to the poor, with their limited budget.
Khulna, Bangladesh has the following special
characteristics of its socioeconomic situation andtransport system: (i) it has high number of paratransit
traffic among its’ total vehicle fleet (ii) large
proportion of low income people among inhabitants,
also presence of high number of students, industrial
workers, refugees and existence of many slums etc.
(iii) prevalence of walking as a mode (iv) no
Compressed Natural Gas (CNG) fueled vehicle, poorbus service with only one route operating in northsouth direction. Also there is no urban rail (v)important effect of gender while choosing transportmodes.
It is the 3rd largest city of Bangladesh and also a
divisional headquarters. It is located in thesouthwestern part of the country. The city is situatedin the northern part of the district, and is mainly an
expansion of trade centers close to the Rupsha and
Bhairab rivers. The Central Business District (CBD) isin the south and the industrial zone is in the north. Oneof the main reasons for having such high number ofparatransit (especially battery bikes) is the proximityof the Mongla sea port, which is about 50 km southfrom the city. Almost all of the battery bikes are from
China and imported via the port, resulting in cheaper
price and high availability.
This study elaborately describe and explain thecontribution, characteristics and transit pattern ofparatransit modes and its network in a midsizeddeveloping city (in terms of population and area),taking Khulna as a case study. The sub objectives ofthe study are as per following: (i) explaining the traffic
situation in the context of Bangladesh (ii) develop themode choice model for various modes (including theparatransits like rickshaws, battery bikes, autorickshaws etc).

2. Methodology
2.1. Survey Design and Data Collection
A questionnaire survey has been conducted for datacollection in Khulna from 6 to 12 December, 2015.
Basic socioeconomic information (e.g. monthly
income, occupation, etc.) along with trip information
was collected. Also, a survey on drivers was
conducted at the same time, to get the situation fromthe supply sides’ perspective and their situation. The
uniqueness of the data was it includes the informationof the number of mode change and the involved
modes in details. In Khulna, significant numbers of
trips with a single trip purpose are made involving
more than one mode and changes.
2.2. Model Development
A discrete choice modeling (Multinomial logit
model) is used to estimate the utility parameters ofdifferent choice. The utility of a choice set i ofindividual n can be expressed as in Equation (1)Uin = βi * Xin + ɛi, " i ϵ Cn
(1)
Where, Xin: Socioeconomic characteristics of theindividualnandattributes
ofdifferentmodes i, βi: Coefficient of Xin, ɛi: Random error term
andCn:
Universal
choicesetorthechoice set determined deterministically for individualn.
For the basic model, two candidate variables wereused, they are: (1) travel time, and (2) fare. In here, thetravel time means in-vehicle travel time. The
definition of the trip is following; a single trip canonly have one trip purpose with a distinct origin and a185

distinct destination. The trip maker can change themode, one or more than one time at different
locations, but if the trip purpose remains same, it isconsidered as a single trip with that Origin-Destination(OD) pair, involving different mode combination.
Walk is considered as a mode only when it is morethan 5 minutes.
The wards (an administrative unit), has been used aszones. The 31 wards are considered as zones and also
for trips from outskirts of the city some neighboringzones were also considered. The distances between thezones are calculated by GIS software between thecentroids of the zones.
In case of non-chosen alternative modes for each
OD pair and each mode, the cost (fare) and travel time
are calculated by multiplying average value of cost(fare) per km and travel time per km with distancerespectively, the distances between the OD pairs areused. The cost (fare), travel time and distance are in
Taka, minute and km respectively. While calculating
average value of cost (fare) per km and travel time per
km of a mode; in case of linked trips with changes,
travel time and cost (fare) of the unlinked part of thetrips are averaged for each different mode between thezones, similarly like the direct trips with OD pair. For
motorcycles, fuel efficiency (km/liter) for Indian cities
and fuel price (96 Taka/liter) of Bangladesh are used.
In case of students, a flat rate of fare of 3 taka per trip
is used. This is because students generally pay around
1,000 Taka per year for bus use (1USD=70 Taka, 1
Taka=1.54 JPY).

share of car is very low (<1%), so it is ignored inmodeling.
Fig 2 shows, in case of modes with at least one
change, the significant combinations are W_BB,
BB_BB and BB_W. In case of trips with changes, it
can be observed that, in most cases, at least one of themodes from trips from the origin or to the destination
is NMTs (e.g. rickshaws, vans, etc.) or walks, which
are mainly used for short distances; they contribute asan access or egress mode. So, only BB_BB isconsidered for the model.

3. Results
3.1. Travel Behavior Survey
The initial sample size was 1,800. It came down to
1,534 after removing the erroneous ones and wrong
entries. Most (74%) of the trips made were direct and
26% of them had at least one change (mostly, with one
change, 22% of all). For trips with mode changes, themain mode or dominating mode is considered as theone which takes longer travel time.

Fig 3 shows, most of trips made were work, school,
shopping, recreational and business trips. Bazaar trips(which means going to the groceries) are mostly
walking, because people do their groceries nearby.
Almost 30% of the trips are made by battery bikes,
irrespective of the trip purpose.

0%9%

R_R, 1%
W_BB_W
, 2%

4%

7%

BB_W,
10%
BB_B, 2%

W_V, 2%

W_R, 3%

B_W,
2%

AR_BB,
1%
AR_AR,
2%
AR_W,
3%

Fig 2: Mode distribution in case of trips with at least
one change (N=408) (both intra and inter zone)

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Van(V)

Walk(W)

7%

AR_R, 2%

W_BB,
19%

Bus(B)

5%

BB_BB,
BB_AR,
11%
2%

W_AR,
5%

Autorickshaw(AR)32%

BB_R, 2%

W_B, 4%

Battery Bike(BB)22%

R_AR, 3%

Others (2Changes),10%

Others (1Change),11%

Rickshaw(R)14%

R_BB, 3%

Bicycle(BC)Rickshaw
Van
Batterybike
Autorickshaw
Bus
Walk
BicycleMotorcycle

Motorcycle(MC)

BB_BB

Car(C)

Fig 1: Mode distribution in case of direct trips(N=1,128) (both intra and inter zone)

Fig 3: Modal share vs. trip purpose (both intra andinter zone)

As seen from Fig 1, in case of direct trips, thehighest number of trips is made by battery bikes(32%), followed by walk, and rickshaws. The modeshares of other modes are less than 10%. The modal

It is observed from Fig 4, that people with lessincome, like 1,000 taka or less, tend to walk more.
Richer people can afford motorcycles, and use themmore often. A lot of the respondents (249) did not286depend on this income. It is quite common for theowner to carry the fuel expenses in case of battery
bikes; so it is then included in the rent. As a result,
50% of the drivers have no fuel cost. In other cases,
the daily fuel cost is less than 200 Taka, which is quite
low. This is also because the battery bikes do not havea high fuel cost like petrol or octane. The daily rent isquite high, in half of the cases; it is about 400-700
Taka.
More than half of the garages (from where thedriver rent their vehicle and keep at night) have 6-20
vehicles per garage. The autorickshaw drivers are
more experienced, licensed and in most cases, driven
something else before. In case of battery bikes, thedrivers are new; less experienced and have no license.
The battery bike driver or owners have too manyorganizations or co-operative societies among
themselves, but none of them has the complete control(e.g. fare determination, routing decisions etc.) overtheir operations. On the other hand, there are two
autorickshaw associations (one in north and the otherin southern part of the city).

answer the income related question, or rather did not
answer honestly.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Rickshaw
Van
Batterybike
Autoricksh
aw
Bus
Walk
BicycleMotorcycle
BB_BB

Fig 4: Modal share vs. monthly household income
level (in Taka) (both intra and inter zone)
It is shown in Fig 5 that, the mode choice changes
significantly when the distance get over 4 km or 6 km.
People use bus and autorickshaw for longer distances.
Also, as expected walking distance is generally lessthan 6 km and use of battery bikes decrease with
distance. So, 4 km and 6 km are used as a thresholdvalue to stratify the inter-zonal samples into twogroups for modeling.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

3.3. Mode Choice Model
Only inter zone trips were used for analysis. Also,
long distance (inter-district) trips are excluded as thestudy focus is on urban mode choice model. The
number of inter and intra zonal trips are 807 and 727
respectively.
People tend not to give income information as it isconsidered a sensitive issue in Bangladesh. Also,
women, children and aged persons are dependent onothers, like their husbands, parents and children
respectively. In these cases, they do not know or do
not have any income. So, house ownership and house
type are used as proxies for income.

Rickshaw
Van
Batterybike
Autorickshaw
Bus
Walk

Table 1: Dummy variable description

BicycleMotorcycle
BB_BB

Fig 5: Modal share vs. trip distances (in km) (onlyinter zone)
Finally, for the multinomial logit model, 9transportation modes are considered, namely: (1) autorickshaw (AR), (2) bus (B), (3) battery bike (BB), (4)bicycle (BC), (5) motorcycle (MC), (6) rickshaw (R),(7) van (non-motorized 3-wheeler) (V), (8) walk (W)and a combination (9) battery bike and battery bike
(BB_BB). In case of the combination, a trip withsingle trip purpose is made, with one change during
the trip.

No

Variable1.

Vehicleownership

2.

Occupation

3.

Gender4.

House
ownership

5.

House Type

Observations
1: if the person owns a
motorcycle or bicycle
0: Those who do not own amotorcycle or bicycle
1: If the person is a student
0: anyone other than student
1: Female
0: Male1: if the person owns a house
0: Those who do not own ahouse
1: if the person live in a built up
house
0: if the person do not live in a
built up house

The following tables (Table 2, 3 and 4) are theresults of basic models and model with dummy
variables with stratified (based on trip distances) interzonal data samples. From Table 2 and 3, it is observed
that, travel time is important for mode choice forshorter trips (less than 6 km) and both travel time andfare is significant in case of comparatively longer trips

3.2. Driver’s Survey
Daily income for the 90% of the driver is less than
1,000 Taka, 70% of them drive battery bikes and 80%of them do not own the vehicle, so they rent from thebusinessmen. Almost 85% of the drivers solely

387dummy variables in the model makes BB_BB
combination significant.

(more than 4 km). Their coefficients are negative forboth cases, which is logical and expected. The model
for less than 6 km trip distances shows better result
than the other model, as it has only one mode (BB_BB
combination) insignificant. So, for the next step, thismodel is used with dummy variables and the results
are shown in Table 4. (*), (**) and (***) denotes 10%,
5% and 1% significance level respectively for all thetables.

Table 4: Utility parameters for the model with dummy
variables (trip distances less than 6 km)
Parameters

Valuet-testAlternative Specific Constants:
autorickshaw
0 bus-2.430-5.420battery bike
2.080
10.810
BB_BB
0.727
1.620
bicycle2.170
4.650
motorcycle
1.580
4.840
rickshaw
1.810
7.300
van
-1.000-2.770
walk
1.420
5.350
Fare
-0.005-0.310Travel Time
-0.120-8.050
Number of observation: 475
Number of estimated parameters: 10
Null log-likelihood: -865.711
Final log-likelihood: -613.969
Adjusted rho-square: 0.279

Significance Level
***
***
***
***
***
***
***
***

Table 3: Utility parameters for basic model (trip
distances more than 4 km)
Parameters

Valuet-testAlternative Specific Constants:
autorickshaw
0 bus-5.650-6.330battery bike
3.490
5.690
BB_BB
2.450
3.230
bicycle4.160
2.090
motorcycle-1.360-1.400rickshaw
5.740
2.520
van
-4.790-0.420
walk
0.350
0.200
Fare
-0.120-1.890Travel Time
-0.330-6.130
Number of observation: 210
Number of estimated parameters: 10
Null log-likelihood: -411.123
Final log-likelihood: -66.679
Adjusted rho-square: 0.813

t-testAlternative Specific Constants:
autorickshaw
0 bus-2.270-4.210battery bike
2.930
9.100
BB_BB
1.550
3.000
bicycle2.850
5.200
motorcycle
0.915
2.050
rickshaw
2.090
5.100
van
-0.809-2.100
walk
2.590
6.220
Travel Time
-0.118-7.790
Dummy and Socioeconomic Variables:
Gender (AR, V)
0.417
1.200
House Ownership
1.230
4.070
(AR, MC, V)
House Type (B,MC)
0.847
3.110
Income (W)-0.000-1.290Occupation (B, AR)
0.511
1.470
Vehicle Ownership(AR, B, W)-0.562-1.710
Number of observation: 475
Number of estimated parameters: 16
Null log-likelihood: -865.711
Final log-likelihood: -594.014
Adjusted rho-square: 0.295

Table 2: Utility parameters for basic model (trip
distances less than 6 km)
Parameters

Value

Signifi
cance
Level
***
***
***
***
**
***
**
***
***

***
***

*

4.

Conclusion
This study describes the key features of Khulna’stransportation, trip behavior and developed a mode
choice model including almost all the modes in
Khulna. Most of the trips made are direct (74%) andless than 6 km in length (72%). The drivers’ profit is
not quite high, but almost all of them (85%) solely
depend on this income. Also, most (62%) of thebusinessmen of the paratransit sector are relatively
small to midsized (operating 11 to 20 vehicles). Apartfrom the trip attributes (travel time and fare), mostimportant socioeconomic aspects for mode choice arehouse ownership and housing type. In case of otherdummy variables, their signs are also logical, eventhough they are not always as significant as these two.
Some socioeconomic factors like age and permanent
residency do not have influence on mode choice.
These results can be used for measuring accessibility
and be a stepping stone for transportation equity
mapping in future.

Significance Level
***
***
***
**
***

*
***

Table 4 shows that, house ownership and housing
type are the most significant socioeconomic factor formode choice. The dummy variables are used with only
the modes stated next to them enclosed by brackets inTable 4. People with higher income, use private mode
motorcycle) and tend to use rickshaws more often
instead of walking. Bus is preferred by students andautorickshaw and van by women. People, who do not
own any vehicle, (generally poorer households), use
bus and van, and tend to walk more. Adding theReferences[1] Hasan, S., 2007, Development of a travel demand model for
Dhaka city, M.Sc. Thesis, Department of Civil Engineering,
Bangladesh University of Engineering & Technology, Dhaka.
[2] Rahman, M.S., Fujiwara, A. and Zhang, J., 2008, Developmentof travel mode choice models for commuting trips in Dhaka city,
Proceedings of the International Conference on Sustainable
Transport for Developing Countries, Dhaka.
[3] Enam, A. and Charishma, F. C., 2011, Methodological issues indeveloping mode choice models for Dhaka city, Transport Research
Record, Journal of the Transport Research Board: 84-92.
488

APPLICATION OF WIRELESS SENSOR NETWORK FOR CROWD DYNAMICSMONITORING
Student Number: 14M51664

Name: Stanislav LYKOVAcademic Advisor: Shinya HANAOKA and Yasuo ASAKURA

This study is dedicated to investigation of possibility of utilization of wireless sensor network as a core component ofcrowd monitoring system aimed to track crowd dynamics. Based on limitations and shortcomings of contemporary crowd
monitoring approaches, as well as the specificity of the problem, modified positioning algorithm for wireless sensor
network, combined with noisy data preprocessing technique is proposed. Furthermore, several physical principles ofinteraction between pedestrians and sensor network were analyzed in details. Moreover, various mathematical models of
crowd dynamics had been analyzed in order to choose the most appropriate one. Additionally, an experiment was conducted
to demonstrate the concept of data collection technique aimed to obtain relevant information regarding crowd movement.
This experiment was coupled with simulation to demonstrate how obtained data could be processed and visualized.

1

Introduction
2.

One of the major trends of 21st century is intensive
urbanization process. This process is leading to increasing
urban population all over the world. As a result cities are
becoming more congested, therefore the probability ofdangerous situations is increasing. Especially, publicvenues such as train stations, stadiums, airports etc. arehighly vulnerable to so-called crowd related disasters, suchas stampede during mass events or emergency situations
during rush hours.
As an example, one can consider a deadly stampede
during the Hajj in Saudi Arabia in 2015, where over 700
people died and nearly 900 were injured [1]. Notable that
this tragedy occurred despite the fact that modern
surveillance systems were being used and a lot of
preparation work had been done in order to provide safety.
Among many other reasons, the key challenge is thecomplexity of the problem. Crowd behavior is difficult topredict, describe and analyze, since people (especiallyduring emergency situations) do not follow strict
movement rules. Therefore, it is clear that more advanced
systems need to be introduced, and safety aspect should beconsidered first with more attention.
Despite the fact that modern surveillance systems arehighly advanced, several major shortcomings are still exist.
Coupled with the complexity of the task, this may lead to
failure of these systems.
In order to overcome some of these limitations this study
propose the concept of the system based on WirelessSensor Network (WSN) as a solution for crowd dynamics
monitoring. Under the term WSN we assume spatiallydistributed autonomous sensors to monitor physical orenvironmental conditions, and to cooperatively pass theirdata through the network.
The ultimate goal of current study is to propose a conceptof a system which could be used in order to track crowd
dynamics and analyze crowd behavior. There are two main
objectives under the scope of this study:
1. To investigate the possibility of utilization of WSN asa core component of crowd monitoring system,
coupled with the analysis of physical principles ofinteraction between WSN and the crowd.
To develop a prototype of data collection system
aimed to gather information regarding crowd
dynamics, and experimentally validate the createdmodel.

2

Methodology

2.1

Proposed Solution

The solution based on wireless sensor network as a corecomponent of crowd monitoring system is proposed. Thissolution is inspired by “Smart Dust” project, introduced by
the researchers from the University of Berkeley startingfrom 1990s. According to the authors [2], this is a "...
system of many tiny microelectromechanical systems(MEMS) such as sensors, robots, or other devices, that can
detect, for example, light, temperature, vibration,
magnetism, or chemicals.".

2.2

Proposed Positioning Algorithm

One of the key challenges in sensor networks is
positioning. Positioning has to be done in order the
network to function as intended. Due to specificity of thesolution, network could be affected with noise, which may
result in decreasing of applicability, therefore noisy datapre-processing has to be considered. Moreover, since“Smart Dust” concept assume a huge number of devices,
we cannot use common approach such as GPS for
positioning, since it would be costly. More precisely, in ourmodel we assume that following entities exist: tiny devices(ref. as MOTEs) equipped with several sensors, and userrelated devices such as smartphones or wearable devices.
This section is dedicated to the description of modified
positioning algorithm, combined with noisy data preprocessing.
1. Phase 0. Assumptions
1.1. Small number of fast moving BEACONs (userrelated devices, equipped with GPS)
1.2. Large number of slowly moving MOTEs,
equipped with several sensors.
1.3. While BEACONs are moving in vicinity of
MOTE, MOTE remains motionless.
1.4. BEACONs periodically broadcast the following
89

2.

3.

4.

5.

2.3

information: ID, current GPS coordinates.
1.5. MOTEs receive this information and use it forthe purpose to determine the position.
Phase 1. Data Smoothing
2.1. Objective: find time slot when BEACONs are
inside MOTE’s sensing radius.
2.2. Problem: due to existence of obstacles received
data from BEACONs could be noisy.
2.3. Solution: using smoothing cubic spline (SCS) tosmooth noisy observations.
Phase 2. MOTEs position estimation
3.1. Objective: estimate MOTEs position using
received information from BEACONs
3.2. Solution: using least-squares circle fit (LSCF)
for positioning.
Phase 3. Cooperative positioning
4.1. Objective: position estimation of MOTEs which
did not receive any signal from BEACONs.
4.2. Solution: geometric location based on timedifference-of-arrival (TDOA).
Phase 4. Dynamic position update
5.1. Objective: update MOTEs positions, since theirlocation could change over time.
5.2. Solution: newly arrived BEACONs broadcast
their position information to MOTEs, so that
adjust their position. Further, if MOTE's position
had been changed, this information alsobroadcasted to nearby MOTEs and so on. This
process repeated continuously.

3.2

Experiment Objectives

The most important objective is to examine proposed datacollection concept and show how to use obtained data forthe purpose of crowd dynamics description. However,examination of practical issues that arose during theexperiment also was subject to investigation.

3.3

Experiment Setup

The targeted region of interest was represented asrectangular area 10m x 5m indoors. Since engineering
aspects were not considered in current research project,instead of using tiny devices which were described above,
Raspberry Pi 2 Model B [3], four items, had been used.
Each of the devices had Planex GW-USNANO2A LAN
adapter, so that all of them were connected. As for clientside, LG Nexus 5, five items, had been used. Via MQTT
communication protocol, which is extremely lightweight
publish/subscribe messaging protocol, clients and MOTEs
communicated.

3.4

Experiment Roadmap

The key idea of the experiment is utilization of Wi-Fi
received signal strength indicator (RSSI) as a metric fordetermining the distance between MOTEs and clients. The
roadmap of the experiment is shown on the figure below:

Positioning Algorithm Simulation Results

Simulation had been done using JAVA programming
language in order to investigate the influence of noisy datapre-processing on positioning accuracy and investigate theperformance of positioning algorithm according to the
MOTEs density. The proposed algorithm had been tested
using 50, 100 and 150 “hypothetical” observations fromBEACONs. Existence of obstacles (leading to noisy
observations) were emulated using non-uniform
distributed observations. As for the results, the proposed
algorithm demonstrated 29.92% (50 observations);
25.62% (100 observations); 24.26% (150 observations)better results than without noisy data pre-processing.

3

Experiment and Simulation

3.1

Proposed Approach

Figure 1. Experiment roadmap.

3.5

Experiment Results

First of all, the dependency of RSSI according to time wasmeasured. The result is shown on figure below.

As a validation part, an experiment was conducted inorder demonstrate the concept of data collection system.
Coupled with simulation, this experiment demonstrates theway how to obtain the data related to pedestrian's
movement and how to use this data in order to describe andvisualize the dynamics of a crowd. In this chapter thedetailed description of experiment, simulation setups, aswell as results is proposed.

Figure 2. Observations of received signal strength indicator according to time.

90This figure is showing the result of observations forpedestrian moving away from the RPI. The dependency of
RSSI according to distance, as intended follows the
exponential attenuation law, which was theoretically
proved.
However, more interesting and practically useful results
are shown on two picture below, where the dependency of
RSSI according to distance is shown. The observations had
been done for five trials. The result is shown below.

Figure 5. Observations of RSSI according to distance for bottleneck scenario.

As clearly see from the figure, there is a region from 4thtill 12th seconds (which corresponds to bottleneck), when
the pedestrian had to wait before enter the bottleneck. This
waiting near the entrance also widens the peaks (marked
red on figure), so that the signal level remains the same
during the waiting time. Of course, more precise detection
and analysis is essential to consider for the purpose of moreaccurate detection, however, the objective of this scenario
was to show the applicability of proposed technique, so
obtaining the precise results was not the main goal.

Figure 3. Observations of RSSI according to distance (average).

According to this figure, the chosen equipment could be
used, for example, up to 1.85 meters (in case of -50 db.
drop) and 3.68 meters (in case of -55 db. drop).
Additionally, simultaneous observations using four RPIs
installed in targeted area had been done. The main
objective of this phase was to demonstrate the chosenequipment and MQTT communication protocol allow to
obtain meaningful results. The result is shown below.

3.6

Model of Crowd Dynamics

Another important point is mathematical representationof crowd dynamics [4]. In other words, this model is a link
between the data collection system and quantitative
analysis. Herein, we assume that targeted area is divided
into the grid:തതതതതȳ ൌ σ௡௠ ‫ܩ‬௡௠ ‫݊׊‬ǡ ݉ ൌ ͳǡܰ(1)Further, for each grid element, every time step and also foreach of the pedestrians we define coordinate and velocity
vectors, as it shown below:்തതതതത‫ݎ‬ప௞ ൌ  ሾ‫ݔ‬௜௞ ǡ ‫ݕ‬௜௞ ሿ் ǡ ‫ݒ‬ሬሬሬሬԦሬሬሬሬሬԦప௞ ൌ  ሾ‫ݑ‬௜௞ ǡ ‫ݓ‬௜௞ ሿ ǡ ‫ ݇׊‬ൌ Ͳǡ ܶ

(2)Using the equations above, we could calculate local
density map:

Figure 4. Observations of RSSI using four RPIs according to time

According to the results, RSSIs from all the RPIs have
similar pattern, therefore the proposed system could be
used in order to obtain RSSIs from all devices
simultaneously, which is an important aspect for proposed
positioning scheme. Moreover, the shape of peaks are
similar (corresponds to the situation when pedestrians arein vicinity of RPIs).
Additionally, under the scope of the experiment, an
artificial bottleneck had been created. In the center oftargeted area a narrow corridor was setup. Five people
were moving simultaneously towards this bottleneck, andthen waited for each other, while one of them were passing
through. The objective of this phase was to observe the
RSSI from all the RPIs in order to detect this bottleneck.
The results of the observations of RSSI are shown below.

(3)
Further, we also can calculate local velocity map:

(4)

Moreover, as one of the most important results is
calculated flow map, which describe the crowd dynamics
in targeted area:

(5)

91

3.7

Simulation Results

The simulation had been done in order to process andvisualize the data. The simulation dealt with data, obtained
from “hypothetical” experiment. Need to mention that thedata used for simulation had the same structure, as dataobtained from the real experiment. More precisely, during
the experiment the RSSI dependency according to distanceis obtained. Further, using calibration results andtriangulation, current position and velocity of pedestrians
are calculated. These results are used as input parameters
for simulation. The simulation had been done for two
setups. First, the crowd was uniformly distributed inside
targeted area. The density map is shown on the figure
below.

Figure 8. Trajectory of pedestrians according to time

4

Conclusion

This study had an attempt to incorporate the idea of"Smart Dust" into the concept of crowd monitoring system.
For this purpose, the solution based on wireless sensor
network, as a core component of crowd monitoring system
was proposed. Relevant challenges regarding thisapplication of wireless sensor network had been
investigated and analyzed. More precisely, modified
positioning algorithm, combined with noisy data preprocessing technique was presented. Additionally,
experiment had been conducted for the purpose to test anddemonstrate the concept of data collection system aimed toobtain the relevant information regarding crowd
movement behavior inside the targeted area. This
experiment was coupled with the simulation in order todemonstrate how the obtained experimental data could be
used. The key novelty of this study is the attempt to merge
several promising technologies, and the attempt to use theirsynergy performance as the approach for crowd
monitoring problem, which require more comprehensive
data gathering techniques, and data processing methods.

Figure 6. Density map in case of uniformly distributed crowd

The second setup describe the situation when crowd is nonuniformly distributed inside the targeted area andconcentrated near the center. In this case, the peak
represents the highest concentrations of pedestrians. The
result is shown on the figure below.

References[1] Holly Y. “717 people dead: what caused the Hajj
stampede?” CNN official report. 2015. [2][2] Warneke B., Last M., Liebowitz B., Pister. K.S.J.,“Smart Dust: communication with a cubic-millimetercomputer”. IEEE Computer, 34:44-51. 2001.
[3] Official Raspberry Pi Page. "Raspberry Pi model B
hardware general specifications". 2015.
[4] Cristiani E. et al. "An overview of the modeling of
crowd dynamics. Multiscale Modeling of Pedestrian
Dynamics". Springer, pp. 73-107. 2014.

Figure 7. Density map in case of non-uniformly distributed crowd

Besides density maps, the trajectory information could
contain an important information and particularly usefulfor the analysis of movements of pedestrians inside area ofinterest. The trajectory information for two pedestrians,
arbitrary moving inside targeted area is shown on the
figure below.

92Effect of sub-critical water treatment of raw material on the composting
Student Number: 14M18141 Name: Takahiro HINOMIZU Supervisor: Kiyohiko NAKASAKIࢥ࣏ࣥࢫࢺཎᩱࢆள⮫⏺Ỉฎ⌮ࡍࡿࡇ࡜࡟ࡼࡿࢥ࣏ࣥࢫࢺ໬࡬ࡢຠᯝᵽỈ Ꮥᘯ
᭷ᶵᛶᗫᲠ≀ࢆࢥ࣏ࣥࢫࢺ໬ࡍࡿ㝿ࠊᗫᲠ≀ཎᩱࢆள⮫⏺Ỉฎ⌮ࡍࡿࡇ࡜ࡢຠᯝ࡟ࡘ࠸᳨࡚ウࢆ࠾ࡇ࡞ࡗࡓࠋ160Υࠊ200Υࠊ240Υࡢள⮫⏺Ỉ࡛๓ฎ⌮ࡋࡓࣔࢹࣝ㣗ရᗫᲠ≀ࡢࢥ࣏ࣥࢫࢺ໬ࢆ࠾ࡇ࡞ࡗࡓ
࡜ࡇࢁࠊ๓ฎ⌮ࡍࡿ࡜ࢥ࣏ࣥࢫࢺ໬ 8 ᪥┠࡛ Germination Index(GI)ࡣ 80%௨ୖ࡟㐩ࡋࠊࢥ࣏ࣥࢫࢺ໬
ࡀ㧗㏿໬ࡉࢀࠊᝏ⮯ࡢཎᅉ࡛࠶ࡿ࢔ࣥࣔࢽ࢔ࡢⓎ⏕ࢆᢚไ࡛ࡁࡿࡇ࡜ࡀ᫂ࡽ࠿࡜࡞ࡗࡓࠋࡲࡓࠊᚑ᮶ࡢ
ࢥ࣏ࣥࢫࢺ໬࡟࠾࠸࡚ࠊࢥ࣏ࣥࢫࢺ໬ࡢ ᗘࢆ㧗ࡵᚤ⏕≀ࡢάᛶࢆ㧗ࡵ࡚ࡶ 10 ᪥㛫࡛ࡣ GI ࡀ 80㸣࡟㐩ࡋ࡞࠸ࡇ࡜࠿ࡽࠊள⮫⏺Ỉฎ⌮࡛ࢥ࣏ࣥࢫࢺ໬ࡀ㧗㏿໬ࡉࢀࡿ࡜⤖ㄽࡋࡓࠋ

1 Introduction
organic waste. Among waste materials, organic

2 Material & Methods
2.1 Sub-critical water pretreatment ofcompost material

fractions such as food waste originating from

The rabbit food and rice were mixed at a ratio of 4:1

households, restaurants, and food processing factories

to prepare the representative model of food waste. 4

can be reclaimed as compost for agricultural use [1].

kinds of organic acids are characteristic of food waste:

Development of an accelerated composting method

acetic acid, propionic acid, butyric acid, and lactic

has been expected to treat a large amount of foodacid [2]. These were all added to the reaction mixture

wastes rapidly. In recent years, food wastes has been

to achieve adjusted concentrations of 2.90, 3.02, 2.43,

treated by sub-critical water based on an assumption

and 12.45g/kg-ds for acetic acid, propionic acid,that the breaking up of the food waste by the

butyric acid, and lactic acid, respectively. The model

pretreatment using sub-critical water would make thefood waste and sawdust were mixed at a ratio of 10:9

food wastes more easy to be degraded by theto prepare the raw mixture and then, the total 20g-ws

microorganisms. In this study, we investigated theof the raw mixture was treated by sub-critical water

effect of the sub-critical water pretreatment of thefor 30 min at 160, 200 and 240ºC.

Composting is one of the methods for treating

model food waste on the performance of thewere carried out in this study. Three of the four runs

2.2 Composting of food wastes
pretreated by sub-critical water withdifferent temperature conditions

were the composting using the raw mixture treated by

The mixtures prepared were mixed with seeding

sub-critical water for 30 min at 160°C (Run A), 200°C

material of compost products at a ratio of 19:1 to(Run B) and 240°C (Run C). Run D was the run using

prepare the compost raw materials. The pH of thethe raw mixture without sub-critical water treatment.

compost raw materials was adjusted to 5.0. A fungus

composting using the pretreated food waste as raw
material. Four composting runs, Run A, B, C and D

having the ability to degrade fran compounds,

Paecilomyces sp FA13 that was isolated in our93laboratory was inoculated to the compost raw material.

Whatman filter paper. Nine ml of the filtrate was5poured into the disposable petri dishes containing 50

CFU/g-ds in the compost raw material. At the start ofkomatsuna seeds (Takii. co. Ltd, Kyoto, Japan)

composting, approximately 10 g of each compost raw

attached on the germination sheet (Fujihira. co. Ltd,

material was put into the mini reactor (Fig. 1). The

Tokyo, Japan). Distilled water was used as control,

temperature was controlled at 30°C until the day 5 andand petri dishes were covered and kept in the dark forthen, raised to 60°C at a constant rate of 2.5 °C h-1, and4 days at 25°C. At the day 4, Germination Index (GI)the 60°C was maintained until the day 10. On the dayof the compost samples were evaluated by counting

5 of composting, the pH was adjusted to 8.0. The

the number of germinated seeds and the length of thecomposting runs were carried out for 10 days. The

root. GI was calculated according to the following

conversion of carbon at a given composting time wasformula[3].

defined as a molar ratio of carbon loss (as CO2) to theGermination index (%)

The concentration of FA13 was adjusted to 10

total carbon present in the rabbit food and rice ofൌ൬compost raw material. The concentrations of organic‫ܮ‬൰ ൈ ൬ ൰ ൈ ͳͲͲ‫ܿܮ‬acids and fran compounds in the compost samples

G: the number of seed germination in extract of

werecompost sample

determined

by

high

performanceliquidGc: the number of seed germination in distilled

matography (HPLC).
chromatographywater
L: the root length in extract of compost sample
Lc: the root length in distilled water

3 Results and discussion
3.1 Composting of representativemodel food waste pretreated bysub-critical water at different heating
condition

Fig. 1 Schematic diagram of the composting system.

2.3 Composting of model food wastewith different temperature.

It was reported that the fran compounds that inhibit

The compost raw material and composting

the microbial activity were generated during thecondition were same as 2.2. The temperature was

sub-critical water pretreatment. In this study, tocontrolled at 40°C until the day 5 and then, raised toovercome the problem of fran compounds generation,

60°C at a constant rate of 2.5 °C h-1, and the 60°C wasa fungus having the ability to degrade fran compounds,

maintained until the day 10 (Run E).

Paecilomyces sp FA13 that was isolated in ourlaboratory was inoculated at the start of composting.

2.4 Seed germination test

Decrease in the concentration of the fran compounds

Seed germination test was conducted to evaluate thewas monitored by HPLC and it was confirmed that thematuration of compost material. Aqueous extracts offran compounds were completely degraded until day 8

compost samples were prepared by shaking 4gof composting. The courses of CO2 evolution ratesubsamples with 40 ml distilled water in a 200 ml

during the composting are shown in Fig. 2.

flask for 1h, 150rpm, using a shaker. Suspensions

In all runs, CO2 evolution rate was almost 0 at day

were centrifuged and then, filtered through No.2

10, suggesting that almost all the decomposing

94

using the raw material without the sub-critical water
pretreatment. The results of the conversion of C

0.01
Run A
Run B
Run C
Run D

0.008

indicated the change of the organic matter to thehardly decomposing material by the sub-critical water

0.006

pretreatment and it was also considered that the
0.004

organic matter containing nitrogen was changed to the
0.002

2

CO evolution rate (mol/d/batch)organic matter was degraded until day 10.

0material that was hardly converted to the ammonia by
0

2

4
6
Time (d)8the microbial decomposition.

10

Fig. 2 The courses of CO2 evolution rate during theSuppression of the ammonia emission can be observed

composting for Run A, B, C and D.

when the ammonia is consumed considerably by the
microorganism as the nitrogen source. However, theresults of the cell density of microorganisms shown in

Fig. 3. The higher temperature sub-critical water

Fig. 5 showed that the highest cell density of

pretreatment resulted in the higher conversion of C on

microorganism was generally attained in Run D

day 0. On the other hand, conversion of C in Run A

denying the above hypothesis.
Cumulative emission of NH (mol/batch)

Conversion of carbon during composting is shown in

was 39.0%, in Run B was 33.9%, in Run C was 39.4%

3

and in Run D was 45.7% on day 10 and Run D

0.0004

showed the highest value of conversion of C among 4
experimental runs and At first, it was expected that the
breaking up of the food waste by the pretreatmentusing sub-critical water would make the food wastes
more easy to be degraded by the microorganisms.

Run A
Run B
Run C
Run D

0.00030.00020.0001

0
0

2

4

6

8

10

Time (d)However, it was suggested that a parts of organic

Fig. 4 The courses of cumulative emission ofmatter was changed into hardly decomposing material

ammonia during the composting for Run A, B, C andfor microorganisms by sub-critical pretreatment.

D.

40

Log. cell densityof fungi (CFU/g-ds)

Run A
Run B
Run D
Run C

50

20
10
0

Run A
Run B
Run C
Run D

8
6
4
2
12

30

Log. cell density of
Mesophilic bacteria(CFU/g-ds)

Conversion of C (%)

60

12
10

0

2

4
6
Time (d)810Log. cell density ofThermophilic bacteria(CFU/g-ds)

Fig. 3 The courses of conversion of C during thecomposting for Run A, B, C and D.
The course of the cumulative emission of ammonia
is shown in Fig. 4. Almost no ammonia emission was

10
8
6
4
2
12

10
8
6
4
2

0

2

4

6

8

10

observed in the composting runs using the raw

Fig. 5 The courses of log cell density of fungi,

mixture treated by the sub-critical water whereas

mesophilic and thermophilic bacteria during thesignificant amount of ammonia was emitted in Run D

composting for Run A, B, C and D.

95

3.2 Germination index

200

Germination Index (%)

The germination index of compost samples duringcomposting are shown in Fig. 6. It was reported that
the GI for mature compost was over 80%. The GI was
0% in Run A, B and C on day 0 though conversion of
C increased after the sub-critical water pretreatment.
The concentrations of fran compounds and organic
acids

increased

after

the

sub-critical

water

150

100

50

0

D-0d
D-1d
D-3d
D-5d
D-6d
D-8d
D-10d
E-0d
E-1d
E-3d
E-5d
E-6d
E-8d
E-10d

pretreatment and it was considered that thosecompounds attributed the low GI value. The
concentrations of fran compounds and organic acids

Fig. 7 The GI of compost products for Run D and E atdecreased with the progress of composting, on day 8

day 0, 1, 3, 5, 6, 8 and 10.

of composting, all those compounds were below thedetectable level and GI of the compost samples

4 Conclusions

attained highest value in Run A, B and C. GI at theIn this study, it was revealed that sub-critical waterend of composting on day 10 was 33.6% in Run A,

pretreatment was effective to suppress the emission of

111.8% in Run B and 81.9% in Run C and they were

ammonia. Furthermore, the compost samples of thehigher than the GI in Run D of 6.6%. These results

raw mixture treated by sub-critical water pretreatment

suggested that compost product of the food waste withat 160, 200 and 240Υshowed high GI value in short

sub-critical

period.

water

pretreatment

becamematurecompost without containing the germination andgrowth inhibitor.

References

The germination index of compost samples during[1] K. Nakasaki, S. Araya, H. Mimoto, Inoculation ofcomposting are shown for Run D and E in Fig. 7. The

Pichia kudriavzevii RB1 degrades the organic acids

temperature was controlled at 30°C and 40Υ until

present in raw compost material and accelerates

the day 5 in Run D and Run E respectively. The GI

composting, Bioresour Technol (2013).

was improved in Run E compare with that in Run D.

[2] C. Sundberg, I. Franke-Whittle, S. Kauppi, D. Yu,

However, The GI at day 10 was 73% in Run E.

M.

H.

Insam,

H.

Jönsson,Characterisation of source-separated household waste

200

Germination Index (%)

Romantschuk,intended for composting. Bioresour Technol, 102,150

2859-2867 (2011).
[3] F. Zucconi, A. Monaco & M. De Bertolodi,

100

Biological evaluation of compost maturity, BioCycle,
22, 27-29 (1981).

50

A-0d
A-1d
A-3d
A-5d
A-6d
A-8d
A-10d
B-0d
B-1d
B-3d
B-5d
B-6d
B-8d
B-10d
C-0d
C-1d
C-3d
C-5d
C-6d
C-8d
C-10d
D-0d
D-1d
D-3d
D-5d
D-6d
D-8d
D-10d

0

Fig. 6 The GI of compost products for Run A, B, C
and D at day 0, 1, 3, 5, 6, 8 and 10.

96

Field Survey and Numerical Simulation of Inundationin the Mekong Delta’s Largest City
Student Number: 14M18112 Name: Chiaki Tsurudome Supervisor: Hiroshi TAKAGI࣓ࢥࣥࢹࣝࢱ㒔ᕷ㒊࡟࠾ࡅࡿỏ℃⌧㇟ࡢㄪᰝ࡜ᩘ್ࢩ࣑࣮ࣗࣞࢩࣙࣥ㭯␃ ༓ᬗ

Abstract࢝ࣥࢺ࣮ᕷࡣ࣓ࢥࣥࢹࣝࢱ࡟࠾ࡅࡿ᭱኱ࡢ㒔ᕷ࡛Ἑཱྀ࠿ࡽ 80km ෆ㝣࡟఩⨨ࡍࡿ. ୖὶ࠿ࡽࡢἙᕝὶ㔞࡜Ἑཱྀ࠿ࡽࡢᾏὒ₻ộ࡟ࡼࡾ, ࢝ࣥࢺ࣮࡛ࡣᗘࠎὥỈࡀⓎ⏕ࡋ, ఫẸࡢ᪥ᖖ⏕ά࡟ᙳ㡪ࢆཬࡰࡋ࡚࠸ࡿ. ࡋ࠿ࡋࠊỏ℃ࡢ࣓࢝ࢽࢬ࣒ࡸ⿕ᐖ≧ἣࡣࡇࢀࡲ࡛༑ศ࡟◊✲ࡉࢀ
࡚࠸࡞࠸. ᮏ◊✲࡛ࡣ, ὥỈࡢᐇែࢆ᫂ࡽ࠿࡟ࡍࡿࡓࡵᕷෆ࡛ఫẸ࢖ࣥࢱࣅ࣮ࣗㄪᰝࢆ⾜ࡗࡓ. ࡲࡓᆅᙧ 㔞ࢆ⾜࠸ヲ⣽࡞ᆅᙧࣔࢹࣝ
ࢆసᡂࡋ, ᕷ⾤ᆅ࡟࠾ࡅࡿỏ℃ࢆゎᯒࡋ, ほ ࡋࡓỈ఩࡜ẚ㍑ࡋࡓ࡜ࡇࢁ㞵Ꮨ࣭஝Ꮨ࡜ࡶ࡟ࡼࡃ୍⮴ࡍࡿࡇ࡜ࡀ♧ࡉࢀࡓ. ᭦࡟, ᾏ㠃ୖ᪼࡜ᆅ┙ỿୗࢆ⪃៖ࡋࡓ」ᩘࡢỈ఩ୖ᪼ࢩࢼࣜ࢜ࢆ᝿ᐃࡋ, ᑗ᮶ỏ℃ᇦࡀ࡝ࡢࡼ࠺࡟ኚ໬ࡍࡿ࠿ࢆ᳨ドࡋࡓ. ࣔࢹࣝෆࡢ㐨㊰⥲ᘏ㛗࡟ᑐࡍࡿỏ℃ࡢ๭ྜࡣ, ⌧ᅾࡣࡲࡔ㝈ࡽࢀࡓ⠊ᅖ࡛࠶ࡿࡀ, ஝Ꮨ࡛ࡣ 40 cm ࡢỈ఩ୖ࡛᪼ 7.1 %, 80 cm ୖ࡛᪼ 40 %, 100 cm ୖ࡛᪼ 90 %, 㞵Ꮨ࡛ࡣ 40 cm ୖ࡛᪼ 81 %, 60 cm ௨ୖࡢỈ఩ୖ࡛᪼ 100 %࡜ᛴ⃭࡟ቑຍࡍࡿࡇ࡜ࡀ♧ࡉࢀࡓ.

1 Introduction
The Mekong Delta widely spreads from the south ofPhnom Penh towards Vietnam, with approximately a
400-km2 basin. About 50% of the national riceproduction is reaped in the Mekong Delta [1], boosting
Vietnam to be ranked at top five in the world withproduction of around 44 million tons as of 2014 [2].
While the delta is rich in agricultural products, the region
is prone to natural disasters. More than 70% of the
population is at risk of water disasters, such as tropicalstorms, floods, and storm surge [3]. Mekong River
Commission [4] estimated that flooding which occurred
during 2000 and 2003 in Vietnam had caused theagricultural losses estimated at 200 to 300 million US$.

80 km

Figure 1.2 Map of Can Tho City
tides dominantly determined water elevation, although
Can Tho is located 80 km inland from the river mouth.
Furthermore, they revealed that the river  discharge
effectively caused tidal damping and lessened the tidal
energy from the river mouth particularly in a rainyseason. Fujihara et al. [7] analysed the water levels from1987 to 2006 observed at the 24 monitoring stations in
the Mekong Delta, showing that the river inflow hardly
affected water-level rise and that the maximum andminimum water elevation is strongly influenced by tidal
regime.
However, the mechanism of flooding in Can Tho has notbeen sufficiently investigated. This study investigates thepresent flood level and frequency in this city through thefield survey and also, examine local people’s awarenessto floods and SLR. Moreover, this research investigates
how the inundation takes place and intrudes into thestreets in the Can Tho downtown area under futureenvironmental scenarios considering SLR and land
subsidence.

Figure 1.1 Urabn floods in Can Tho City
It is necessary to investigate the flood risk and the impactof future environmental changes such as sea level rise
(SLR) and land subsidence in the Mekong Delta, whichplays an important role as a place for livelihood and foodsupply. This study focuses on Can Tho City that suffersfrom seasonal flooding (Figure 1.1). Can Tho is thelargest city in the Mekong Delta located 80 km inlandfrom the river mouth (Figure 1.2) and its population is
1.2 million.
Huong and Pathirana (2013) [5] simulated the influenceof SLR, rainfall and urbanisation in Can Tho City,
showing that flood level will drastically increase under
the significant change in river level. Takagi et al. (2014)[6] carried out the field survey and analysed the tidal
elevation data in Can Tho and showed that the ocean

2 Methods
The present study is consisted of three components: (1)interview with local residents, (2) field survey, and (3)numerical simulation, all aiming at revealing the real
danger of flood in the downtown area of Can Tho city.
97

under the five future water level rise (WLR) scenarios
due to the combination of SLR and land subsidence,
WLR of (1) 20 cm, (2) 40 cm, (3) 60 cm, (4) 80 cm, and(5) 100 cm from the present typical water level in dry
and rainy seasons.

2.1 Interview
The interview was held at An Binh on 11th and 14th
August in 2014, Nin Kieu on 14th August in 2014, Tan
An on 10th March in 2015. The total number of the
respondents was 60. The questionnaire comprised ofmultiple choices was prepared in Vietnamese printed
together with English translations. A part of thequestionnaire is shown in Figure 2.1. The questionnaire
was specifically designed for asking the questions about
frequency and extent of inundations around theirresidents and the respondents’ knowledge, experienceand awareness to flood events.

3 Results
3.1 Interview
The interview reveals that nearly all the respondents
understand the phenomenon of flood, while only lessthan half of the respondents recognise storm surge or
SLR. Seventy five percent of the respondents replied thata maximum of 20-50cm depth water flooded their housesin their life. One of the queries about how they react
when such a flood occurs found that 95% of the
respondents would stay home, whereas nobody answeredthat they would evacuate. Even if water level rises by
50cm in the future, 92% of the respondents would notrelocate their residents into a safer place. Only one
person who lives in the downtown area in Can Tho
answered moving to other place.

2.2 Field Survey
In order to create a precise elevation map for simulation,
a topographical survey was conducted. The depth of themain river and tributaries and the ground elevation in
Can Tho were all measured by the authors themselves.
Actual inundation was visually observed twice a day at ariverbank park in the downtown Can Tho. Inundation
depths were measured by a measuring staff. The groundelevation in the downtown area was examined using alaser distance meter. The ground elevation was measured
at a large number of locations with an interval of 10-15
meters. The depth of the Hau River and its tributaries
was measured using a potable echo-sounder.
Measurement was conducted at three points per
cross-section. The depth used in the numericalsimulation was finally determined by taking an averageof the values.

3.2 Topographical Survey
The inundation depth was measured at the riverside parkin the downtown in the morning on 14th August, 2014
(Figure 3.1). The maximum depth reached around 40cm
from 7:00 A.M. to 8:00 A.M. in local time. In
measurement of ground elevation, the total number of theobservation points was 102.
The heights measured all along the pass were finallyadjusted to minimise errors by the closing the traverse.
The lowest elevation measured in the closed pass was 0.1
m lower than the elevation of the riverside park, where
which was set to be 0 m as a base level. The centre of themain river was 19-meter deep and the bank side of theriver was 3-meter deep, demonstrating that the riverslope is steep.

2.3 Numerical Simulation
Delft-3D was applied to the detailed simulation ofinundation in the downtown area with the bathymetry
and topography in the downtown created from the fieldsurvey data. The topography of those outside of thedowntown area was reproduced from the satellite data of
SRTM 90 m Digital Elecation. Since flooding in Can
Tho is greatly influenced by tides traveling upstream, thetidal variation was inputted on the downstream boundary
in the model. The basic calculation settings are listed inTable 2.1. In order to examine the accuracy of the model,
the authors performed the simulations on 13th August and7th November, 2014 in a rainy season and 9th March,2012 in a dry season.
Table 2.1 Basic calculation settings
Item

Description

Grid size

10 meterTime step

0.01 minute

Boundary conditions(downstream,
upstream)

Tide boundary
Riemann boundary

Roughness(Manning’s n value)

River bottom: 0.02
Land surface: 0.05

Figure 3.1 Measurement of the inundation depth at
the park. Photo taken on 11th August, 2014

3.3 Verification of the model

2.4 Future Scenarios
Flood extent over the downtown Can Tho are predicted98Using topographical data from the field survey and
SRTM 90 m Digital Elevation data, the model fornumerical simulation was created (Figure 3.2). The
bathymetry used in the simulation was modified bylowering the original elevation by 60 cm in order toGiven these results obtained, the authors confirm that themodel would be sufficiently reliable in simulating flood
over the downtown area of Can Tho.

3.4 Future scenarios
If water level will rise up more than 80 cm, theinundation areas would significantly expand from thesituation under 20-cm rise in a future dry season (Figure3.4). The downtown would be totally inundated underWLR 80 cm scenario in a future rainy season (Figure3.5). Figure 3.6 shows how rapidly the percentage of theinundated roads will increase as water level rise in caseof both rainy and dry seasons. Particularly, it changes
dramatically when WLR exceeds 20 cm.

Upstream
Tan An:/5FP

Park

NP

Figure 3.2 The bathymetry model
adjust to the mean water levels at Can Tho. The
simulation was performed for a whole day between
15:00 on 14th and 15:00 on 15th August in 2014. Figure
3.3 shows that the calculated depth at the park coincided
the observed inundation depth. Therefore, it is notnecessary to calibrate the bottom boundary in the model.
In November 2014, the authors found that the inundation
reaches even Tan An where flooding rarely occurs in adry season. The simulation for a typical flood season,covering from 10:00 on 7th to 10:00 8th on November in
2014, shows that most roads in Can Tho were inundated
and Tan An was also inundated by a maximum of 20 cm.
Flood in a typical dry season were also investigated bysimulating for a one-day period from 10:00 on 9th to
10:00 on 10th March in 2012. The result shows that theflooding did not occur in any place of Can Tho.

:/5FP

Figure 3.4 Increase of inundated area comparedwith WLR 20 cm and 80 cm in a future dry season

Simulation
Observation
Figure 3.3 Comparison between the simulated depth
and the observation at the park

Figure 3.5 Colour contour map of water elevation
with WLR 80 cm scenario in a future rainy season99

water level scenarios. The results showed that theinundated area would significantly spread if water level
will rise up to 20 cm or higher, which suggests that
immediate actions are necessary to be undertaken in the
coming years before the situation becomes
unmanageable.

5DLQ\VHDVRQ'U\VHDVRQ
Figure 3.6 Percentage of the inundated roads length tothe total roads length in Can Tho
Newly constructed

4 Discussion

Figure 4.1 The new protection at the park along the
Can Tho River, taken by the author on 29th August in
2015

The interview survey revealed that local inhabitants in
the Mekong Delta accept flooding as a usual event, eventhough their daily life has been obviously annoyed bysuch frequent inundations. They would not evacuate
themselves from their houses even flood levels further
increase in the future.
The simulation suggest that local authorities may
mitigate inundation by raising roads or constructing
dykes if the water level rise is limited (less than 20 cm).
In August in 2015, the local authority has raised thebanks along the Can Tho River by about 70cm (Figure4.1). It seems that any visible inundation did not takeplace because of this reinforcement of the flood
protection when the authors visited the city again on 29 thAugust in 2015, unlike the substantial inundations that
we have observed in August 2014. This observation may
demonstrate the effectiveness of the dyke constructed. If
the rise was remarkable (higher than 20 cm), however,
the inundated area will significantly and rapidly spreadthroughout the downtown area of Can Tho. The current
countermeasures should not be enough to prevent
flooding in the future, which would be exacerbated by
SLR and land subsidence. Once the water from the river
overflows the dykes and pours into the city, the water
will first flood a lower part of the city and cause apersistent inundation which has a great impact on the lifeof local population. Given the unawareness of local
people to potential disasters, it is necessary to take an
immediate action for planning an effective disastermanagement.

References[1] P. T. T. Hanh and M. Furukawa, Impact of sea level
rise on coastal zone of Vietnam, Bull. Fac. Sci.,
University of Ryukyu, No.84, 45-59, 2007
[2] Food and Agriculture Organization (FAO), Statistics
Division,
http://faostat3.fao.org/home/E, accessed on 27th,
December, 2015[3] T. L. Nguyen and D. T. Bui, Flood Monitoring of
Mekong River Delta, Vietnam Using ERAS SAR
Data, 22nd Asian Conference on Remote Sensing, 5-9
November 2001, Singapore, 6pp, 2001
[4] Mekong River Commission (MRC), State of theBasin Report 2010, 232 pp, 2010[5] H.T.L. Huong and A. Pathirana, Urbanization andclimate change impacts on future urban flooding in
Can Tho city, Vietnam, Hydrology and Earth System
Sciences, 17, 379-394, 2013[6] H. Takagi, T.V. Ty, N.D. Tao and M. Esteban, Ocean
tides and the influence of sea-level rise on floods inurban areas of the Mekong Delta, Journal of Flood
Risk Management, Vol 7 Issue 1, 2014[7] Y. Fujihara, K. Hoshikawa, H. Fujii, A. Kotera, T.
Nagano and S. Yokoyama, Analysis and attribution oftrends in water levels in the Vietnamese Mekong
Delta, Hydrological Processes, 33 pp, 2015
(Accepted), doi: 10.1002/hyp.10642

5 Conclusions
The interview revealed that 75 % of the respondents
experienced a maximum of 20-50 cm depth inundation in
their life. 95 % of the respondents stay home even if
flooding occurs. Moreover, even if water level will riseup to 50 cm in the near future, 92 % of them would notrelocate their houses to a safer place. We developed thenumerical model which uses the precise topography dataobtained through our field surveys. The model was
verified with observed water levels in both rainy and dry
seasons and then applied to the projections under future

100

TRANSIT CHOICE OF ASIAN HUB AIRPORTS IN INTERCONTINENTAL FLIGHTS኱㝣㛫ࣇࣛ࢖ࢺ࡟࠾ࡅࡿ࢔ࢪ࢔ࡢࣁࣈ✵Student Number: 14M18276

Name: Shuyang KANG

ࡢࢺࣛࣥࢪࢵࢺ㑅ᢥSupervisor: Shinya HANAOKA

International passengers include those of departure, arrival and transit and only transit passengers among hub
airports have a competition each other. This study focuses on long distance (more than 13,000 km, 13 hours)
continental transit flights between North America and Southeast Asia through Asian hub airports. Firstly, thecurrent transit situation among hub airports is analyzed. Secondly, possible factors affecting transit passengers’
flight choice are selected and its weight are estimated by analytic hierarchy process (AHP). Finally, flight routesof certain ODs are evaluated by TOPSIS among main Asian hub airports.

1

Introduction

International passenger volume based on airport is thesum of passengers who are taking an international flightthat “departure from”, “arrive at” or “transit at” a certain
airport. The international passenger volume of “departurefrom” or “arrive at” a certain hub airport is affected not
only by the flight attributes but also by macro factors suchas the country’s attractiveness, visa policy and economic
development. The international passenger volume of“transit at” a certain hub airport is affected by flightattributes, airlines attributes and transit airport attributes
which shows competition among airlines and hub airports.
This study focuses on the long distance continental
transit flight through hub airports. Long distance here
indicate transit flight which is more than 13,000 km, 13hours. Figure 1-1 shows the routes for transit.
Until now, the longest direct flight is around 14,000 km,
16 hours from/to Sydney and Dallas. Airlines may notserve any more direct flights if the distance and total travel
time are longer than this flight. Such situation happens
mostly in the long distance OD like North America –Southeast Asia. The reason of choosing this OD is to transit
at Asian hub airport.
International transit passenger volume is one of themost visible and important attribute to show thecompetition among hub airports and transit routes.
Although Narita’s international transit passenger volume
is seemed to be decreasing during the past several years,transit competitiveness of routes through Narita and other
Asian hub airports are unclear, which is necessary to beevaluated and compared. Furthermore, from the demandside, the factors affecting transit passengers’ choice
behavior are also uncertain.
To make a brief summary of above research problems,
there are 3 main objectives in this study.

Figure 1-1: Direct and transit flights between
North America and Southeast Asia

2.

Methodology

AHP can help to calculate the weight of eachexplanatory factor even if the factors are supposed to be
insignificant or qualitative. Compared with ANP(Analytic network process), an extension method of AHP,
samples can be collected widely from not only experts but
also transit passengers who experienced transit flightseveral times. Although ANP may explain the relationshipbetween factors more clearly and reasonably, sample may
be unavailable due to the increase of the number ofcomparison sets. Furthermore, co-relationship between
factors is difficult to define.
TOPSIS would be suitable for cases with a large
number of attributes and alternatives, and especiallyhandy if the given data is objective or quantitative. In thisstudy, because evaluation scenarios are divided by several
indicators such as trip purposes and ODs, pairwise
comparison is complicated to structure for evaluation.
Although the number of attributes and alternatives are not
many, large number of scenarios makes the evaluation
complex and detailed. Therefore, TOPSIS is chosen as theflight routes evaluation method in this study.

1) To analyze the current transit situation between North
America and Southeast Asia.
2) To estimate the factors and their weights that affectpassengers’ long distance transit flight choice behavior.
3) To evaluate the transit flight routes through main Asian
hub airports.

2.1 Factors estimation method
Analytic hierarchy process (AHP) is a structured

1101

information, and then normalized it. The second step is tocalculate the weighted normalized decision matrix anddetermine the worst and the best alternatives. The third
step is to calculate the similarity to the worst condition
which is also the final scores result and rank thealternatives based on the scores.

technique for organizing and analyzing complex decisions
based on mathematics and psychology. It has particularapplication in group decision making and is used in a wide
variety fields such as government, business, industry,healthcare, shipbuilding and education. It can be also
commonly used in the normal daily life.
There are mainly 3 steps of AHP method. The first stepis to structure the decision hierarchy by defining thedecision problem and developing the conceptual
framework. Factors which are necessary to estimate need
to be selected and divided into multiple criteria. The
second step is to collect the data from experts or
respondents who have the related experience. Relative
importance of every two factors, which is called pair-wisecomparison are judged by the respondents. The third stepis to estimate the relative weights and mean weights ofmain criteria and sub criteria. Degree of consistency isalso necessary to be checked in this step.
The objective of AHP is to estimate relative weights ofelements. In the next table, group ‫ ܣ‬are the elements tobe compared. Group ܽ are the relative importance ofeach 2 elements. For example, ‫ܣ‬ଵ is ܽଵଶ times
important than ‫ܣ‬ଶ .

3. Data collection
AHP questionnaire was designed based on the criteria in
Figure 3-1. Relative importance was selected to show
respondents’ priority between 2 factors. Respondents
chose which factor is more important and how much therelative importance is to themselves.

Figure 3-1 AHP decision hierarchy

Survey was conducted in Tokyo Narita International
Airport in 2 days from 4th to 5th, January, 2016. Survey
target are divided into 2 groups including the people whohave ever used a long distance transit flight 1st, 2nd or 3rd
and more than 3 times which are belong to the effective
sample group and only more than 3 times which is belongs
to the high reliability group. In this study, samples from
respondents are divided into 6 segments by 2 indicators.
The first indicator is trip purpose which includes business,sightseeing and visiting friends and relatives (VFR). The
second is residence which includes North America and theothers. The sample size is shown in the Table 3-1. Finally,
148 effective samples are analyzed with good balance. 83
effective samples of North America are used as AHP
output and 98 high reliability samples are used as TOPSIS
input.

The following formula (1) shows the way of geometricmean method to calculate the weight. (2) and (3) helps tocheck the consistency of the AHP result.
‫ݓ‬௜ ൌ 

ଵ

భ

೙
൫ς೙
ೕసభ௔೔ೕ ൯భ೙೙σ೙೔సభቀςೕ ௔೔ೕ ൯σ೙          (1)

௔ ௪

(2)㻌

ି௡

(3)㻌

೔ೕ ೕ
ߣ௠௔௫ ൌ σ௡௜ୀଵ ൤ ೕసభ
൨

௡

ఒ

௪ೕ

‫ܫܥ‬ሺ‫ݔ݁݀݊ܫݕܿ݊݁ݐݏ݅ݏ݊݋ܥ‬ሻ ൌ ೘ೌೣ

௡ିଵ

< 0.1

2.2 Flight routes evaluation method (TOPSIS)The Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS) is a multi-criteria decision
analysis method. TOPSIS is based on the concept that thechosen alternative should have the shortest geometricdistance from the positive ideal solution and the longest
geometric distance from the negative ideal solution. It is amethod of compensatory aggregation that compares a setof alternatives by identifying weights for each criterion,
normalizing scores for each criterion and calculating thegeometric distance between each alternative and the ideal
alternative, which is the best score in each criterion. Thereare 3 main steps in TOPSIS. The first step is to create anevaluation matrix which is made up of criteria weight data,score data of transit airports and airlines and flight

Table 3-1 Sample size
Trip Purposes

Business

Sightseeing
Visit Friends orRelatives
(VFR)

2
102

Sample typeCollected
Effective
High reliability࡯ࡵCollected
Effective
High reliability࡯ࡵCollected
Effective
High reliability
࡯ࡵNorth America
27
24
16
2.1%
33
32
21
2.3%
28
27
16
3.3%

Others
25
24
13
4.6%
16
14
11
8.7%
28
27
21
5.9%

4. Results and analysis

4.2 Transit choice factors weighting result
Factors weighting are calculated based on 148
samples. Main criteria weight results of respondents living
in Southeast Asia are also calculated in Figure 5-21. The
result of weight criteria of sightseeing and VFR purpose is
unexpected because the weight of airport attributes takes
relatively large and the weight of flight attributes takes
relatively small. The reason may be because of few sample
size and misunderstanding on the definition of airport
attributes in this situation. Therefore, criteria weight
results of North America respondents are utilized to be the
TOPSIS input to evaluate the transit flight routes.
Figure 4-3 shows the main criteria weighting result ofNorth American segment. From the result, businesstravelers choose airlines as their priority while sightseeing
and VFR passengers consider flight. Detailed reason are
shown in the Figure 4-4, 4-5 and 4-6.

4.1 Current transit situation
Figure 4-1 shows the direct and transit volume change
through 4 main Asian hub airports. They are Narita
Airport in Tokyo (NRT), Incheon Airport in Seoul (ICN),
Hong Kong Airport (HKG) in Hong Kong and Taoyuan
Airport in Taipei (TPE). In this figure, although volume
through NRT was ranked 1st in 2004, it decreased year by
year and dropped to 4th in 2014. On the other hand, ICN
and TPE increased dramatically in this 10 years. Althoughvolume through HKG decreased from 2009 to 2014, it
increased from 2004 to 2014.

VFR - Main

25.4%

42.3%

32.3%

Sightseeing - Main

16.8%

46.2%

37.0%

Business - Main

21.3%

36.4%

42.3%

0%

50%

100%

Airport attributes

Flight attributes

Airline attributes

Figure 4-3 Main criteria weight of North American respondents

Source: NAA
Figure 4-1 Transit volume through main Asian hub airports

VFR - Airport

Figure 4-2 shows the results of transit passenger
volume change based on certain ODs which are the top 19
that have the most transit passenger volume. The volume
through NRT of MNL-LAX, MNL-SFO, BKK-LAX,
BKK-JFK and BKK-JFO decreased which are taken by
ICN, HKG and TPE. MNL is the hub airport in Manila,
Philippines. BKK is the hub airport in Bangkok, Thailand.
LAX is the hub airport in Los Angeles, Western US. SFO
is the hub airport in San Francisco, Western US. JFK is the
hub airport in New York, Eastern US. Because there is an
obvious change of the transit volume through every main
hub airport among BKK-LAX, MNL-JFK and BKK-JFK,
flight routes based on these 3 ODs are chosen to beevaluated by the TOPSIS.

34.1%

Sightseeing - Airport
Business - Airport

17.4% 8.2%

37.8%

25.2%

15.1%

23.8% 7.0% 18.7% 12.7%

29.7%

24.8%

0%
Transit procedure
Transit tour
Shopping & restaurant

8.6% 23.6%

50%
Transit facility
Staff service

13.3%

100%

Figure 4-4 Airport attributes weight of North American respondents

VFR - Flight

27.2%

Sightseeing - Flight
Business - Flight

19.1%

36.2%
26.9%

27.5%

13.0%
16.4%

26.1%

17.8% 8.4%
18.1% 6.7%
18.8% 7.8%

30.2%

100%
50%
Transit time
Departure & arrivel time0%
AirfareTotal travel time
Aircraft typeFigure 4-5 Flight attributes weight of North American respondents

VFR - Airline

44.5%

16.9%

18.9% 10.6% 9.1%

Sightseeing - Airline

36.3%

16.3%

Business - Airline

36.0%

17.5% 16.7% 17.0% 12.8%

0%
Safety level
In-flight service
Frequency

24.8%

11.6% 10.9%

50%
100%
Staff service
Loyalty program

Figure 4-6 Airline attributes weight of North American respondents

Source: NAA
Figure 4-2 Transit passenger volume change through NRT

3103

For airport attributes, transit procedure, facility and staff
service are mainly considered. Business passengers prefera better transit facility condition, sightseeing passengers
prefer a satisfied transit procedure and VFR passengers
consider the shopping and restaurant most. As an optional
choice, transit tour is not considered significantly by all thepassengers. For flight attributes, airfare is significant forsightseeing travelers and total travel time is consideredmostly by business travelers. For airline attributes, safetylevel is concerned mostly by all the passengers. Businesspassengers prefer a better loyalty program. Sightseeing
passengers consider in-flight service more.

Table 4-3 Scores of airlines through Asian hub airports
Airline attributes
NRT
ICN
HKG
Safety level
4.500
4.000
4.750
Staff service
4.637
4.215
4.400
In-flight service
4.322
4.023
4.357
Loyalty program
4.000
4.000
4.000
Frequency
13
932
Source: SKYTRAX, Google flight 1st, April

Table 4-4 Flight routes evaluation results of 18 scenarios

4.3 Flight routes evaluation results
TOPSIS sample data are made up of 3 parts. They arescores of hub airports for transit judged by transit
passengers, flight information on April 1st, 2016 andairline scores from SKYTRAX. Table 4-1 shows theaverage scores of 4 main Asian hub airports. Table 4-2
shows the example of long distance transit flightinformation through 4 main Asian hub airports on April 1st,2016. Table 4-3 shows the airlines score of 5 sub criteria
except frequency that is from the flight information.
Frequency in Table 4-3 is the data of LAX- BKK. Airfare,transit time and total travel time are collected by 2
directions and the mean value of one day flight information
are calculated as input. Departure & arrival time is thenumber of available departure time period. Flight routes
evaluation results are calculated based on 18 scenarios.
These 18 scenarios are divided by 3 trip purposes, 3 ODs
and 2 directions which is showed in the Table 4-4. For eachscenario, flight routes choice through NRT, ICN, HKG and
TPE are evaluated and ranked. The transit routes
evaluation results are compared with current transit
situation. For details, the route evaluation result between
LAX and BKK is same as the current transit situation. On
the other hand, for results between JFK and MNL, JFK and
BKK are different with the current situation especiallytransit routes through ICN and HKG in the Southeast Asia
departure scenarios. Low score may mainly be due to thehigh airfare which has a big variation in different ODs.
NRT
4.364
4.409
3.364
4.318
4.045

ICN
4.235
4.176
3.529
3.765
4.000

HKG
4.250
4.200
3.450
3.600
4.250

Purpose
Route

B

S

V

LAX-BKK
NRT
ICN
HKG
TPE
BKK-LAX
NRT
ICN
HKG
TPE
JFK-MNL
NRT
ICN
HKG
TPE
MNL-JFK
NRT
ICN
HKG
TPE
JFK-BKK
NRT
ICN
HKG
TPE
BKK-JFK
NRT
ICN
HKG
TPE

Rank
4
3
2
1
Rank
3
4
2
1
Rank
3
4
1
2
Rank
1
2
3
4
Rank
2
3
1
4
Rank
2
1
4
3

Rank
4
3
2
1
Rank
3
2
4
1
Rank
3
4
1
2
Rank
2
1
4
3
Rank
2
4
1
3
Rank
3
1
4
2

Rank
4
3
2
1
Rank
2
3
4
1
Rank
3
4
1
2
Rank
2
1
3
4
Rank
1
3
2
4
Rank
2
1
4
3

Transit situation LAXяBKK

Transit situation JFKяMNL

Transit situation JFKяBKK

5. Conclusion
Transit passenger volume through NRT, ICN, HKG and
TPE have had an obvious competition in the past 10 years.
From main criteria, flight attributes are selected as priority
by most transit passengers. From sub criteria, airfare, total
travel time and airline safety are mostly concerned by thetransit passengers. Optimal flight route choice change due
to different route directions, which is affected by the highweighted factors such as airfare and total travel time. Thereis always an optimal transit flight route choice under theconditions of different trip purposes, origins anddestinations.

Table 4-1 Scores of Asian hub airports based on 5 sub criteria
Airports attributes
Transit procedure
Transit facility
Transit tour
Staff service
Shopping & restaurant

TPE
4.250
4.065
4.044
4.000
18

TPE
4.091
3.455
3.091
3.636
3.545

Reference[1] Chang, Y. (2003). Performance evaluation of international
airports in the region of East Asia. Proceedings of theEastern Asia Society for Transportation Studies, Vol.4.
[2] Kopytov, E., Abramov D. (2012). Multiple-criteria analysis
and choice of transportation alternatives in multimodal
freight transport system. Transport and Telecommunication,
13(2), 148-158.

Table 4-2 Transit flight information through Asian hub airports
LAX-BKK
NRT
Airfare ($)
2194.6
Transit time (h)6.2
Total travel time (h)25.1
Departure & arrival time
4
Aircraft type8
Source: Google flight 1st, April

ICN
1295.8
11.3
30.3
3
7

HKG
866.2
13.8
32.9
3
5

TPE
579.4
8.5
27.5
5
6

4
104

LIFETIME PREDICTION METHOD USING ACCELERATION TESTFOR PAINT-COATED STEEL AND MORTAR-COVERED STEEL IN MARINE ENVIRONMENT
Student Number: 14M18081 Name: Kyohei Shimura Supervisor: Nobuaki Otsukiಁ㐍ヨ㦂ࢆ⏝࠸ࡓሬ⿦㗰ᮦ࠾ࡼࡧࣔࣝࢱࣝ⿕そ㗰ᮦࡢᾏὒ⎔ቃ࡟࠾ࡅࡿᑑ࿨᥎ᐃ᪉ἲ
ᚿᮧ ᜤᖹ
ᾏὒ⎔ቃ࡟࠾࠸࡚ࡣሬ⿦㗰ᮦࡸࣔࣝࢱࣝ⿕そ㗰ᮦࡢ⭉㣗ࡀၥ㢟࡜࡞ࡗ࡚࠸ࡿ. ࡑࡇ࡛㐺ษ࡞⿵ಟ㛫㝸ࡢỴᐃࡸࣛ࢖ࣇࢧ࢖ࢡࣝࢥࢫࢺࡢ⟬ฟࡢࡓࡵ࡟ຎ໬㐣⛬ࡸຎ໬㏿ᗘ࡟ࡘ࠸࡚ᢕᥱࡋ,ᑑ࿨ࢆ᥎ᐃࡍࡿ᪉ἲࡀᚲせ࡜ࡉࢀ࡚࠸
ࡿ. ᪤ ࡢ◊✲ 1)࡟ࡼࡗ࡚,ࣇࢱࣝ㓟⣔ሬ⿦㗰ᮦ࡜ࢱ࣮࢚࣏ࣝ࢟ࢩ⣔ሬ⿦㗰ᮦࡢຎ໬㐣⛬࠾ࡼࡧຎ໬ಁ㐍ヨ㦂ࢆ⏝࠸ࡓᑑ࿨᥎ᐃ᪉ἲࡀᥦ᱌ࡉࢀࡓ. ࡋ࠿ࡋ, ሬᩱ✀㢮ࡀ␗࡞ࡿሬ⿦㗰ᮦࡸࣔࣝࢱࣝ⿕そ㗰ᮦ࡟ࡶ㐺⏝࡛ࡁࡿࡢ࠿࡞࡝ࡑ
ࡢỗ⏝ᛶ࡟ࡘ࠸࡚ࡣ᳨ウࡀ୙༑ศ࡛࠶ࡗࡓ.
௨ୖࡢ⫼ᬒࢆ㋃ࡲ࠼, ᮏ◊✲࡛ࡣಁ㐍ヨ㦂ࢆ⏝࠸࡚࣏ࣜ࢘ࣞࢱࣥ⣔ሬ⿦㗰ᮦ࠾ࡼࡧࣔࣝࢱࣝ⿕そ㗰ᮦࡢຎ໬㐣⛬࠾ࡼࡧຎ໬㏿ᗘ࡟ࡘ࠸᳨࡚ウࢆ⾜࠸, ᶍᨃᐇ⎔ቃ୰࡟࠾ࡅࡿຎ໬㏿ᗘ࡜ࡢẚ㍑࠿ࡽಁ㐍ಸ⋡ࢆ⟬ฟࡋ, ᑑ࿨᥎ᐃࢆ⾜ࡗࡓ. ࡑࡢ⤖ᯝ, ձ࣏ࣜ࢘ࣞࢱࣥ⣔ሬ⿦㗰ᮦࡢ₯అᮇ࡟࠾࠸࡚ࡣࣇࢱࣝ㓟⣔ሬ⿦㗰ᮦ࠾ࡼࡧࢱ࣮࢚࣏ࣝ࢟ࢩ⣔ሬ⿦㗰ᮦ࡜ྠᵝࡢಁ㐍ಸ⋡ࡀᚓࡽࢀ, ᑑ࿨᥎ᐃ⤖ᯝࡢጇᙜᛶࡀ☜ㄆࡉࢀࡓ. ࡋ࠿ࡋ, ղ࣏ࣜ࢘ࣞࢱࣥ⣔ሬ⿦㗰ᮦࡢ₯అᮇ௨㝆࡟࠾࠸࡚ࡣࣇࢱࣝ㓟⣔ሬ⿦㗰ᮦ࠾ࡼࡧࢱ࣮࢚࣏ࣝ࢟ࢩ⣔ሬ⿦㗰ᮦ࡜␗࡞ࡿຎ໬㐣⛬ࢆ♧ࡍࡇ࡜ࡀ☜ㄆ
ࡉࢀࡓ. ճࣔࣝࢱࣝ⿕そ㗰ᮦ࡛ࡣ, ಁ㐍ヨ㦂࡛ᑑ࿨᥎ᐃࢆ⾜࠺ࡇ࡜ࡀ࡛ࡁ࡞࠿ࡗࡓࡀ, 㕲➽ࢥࣥࢡ࣮ࣜࢺࡢᑑ࿨᥎
ᐃ᪉ἲ࡜ྠᵝࡢ᪉ἲ࡛ጇᙜ࡞ᑑ࿨᥎ᐃ⤖ᯝࡀᚓࡽࢀࡓ.

1. Introduction
 Steel is easy to corrode in marine environment. To protect
steel from corrosion, paint-coated steel are often used. It is
important to know deterioration process and deterioration
speed and establish lifetime prediction method of these steel
for appropriate repair time and life-cycle cost estimation.
In previous study1), the influence of temperature and solution
on deterioration speed of defective paint-coated steel wasinvestigated, deterioration process and lifetime prediction
method of defective paint-coated steel was proposed.
However, in the study, only 2 types of paint-coated steel(Phthalic type, Tar epoxy type) were used. So its applicablerange was not investigated. In actual environment different
type of paint-coated steel and mortar-covered steel are also
used. Therefore, to make the lifetime prediction method more
usable, investigation on deterioration speed and deterioration
process of other type of paint-coated steel and mortar-covered
steel are also needed.
From the above background, the objectives of this study are
as following:
1. To know deterioration process and deterioration speed ofpolyurethane type paint-coated steel and mortar-covered steel
using acceleration test.
2. To obtain magnification of acceleration comparing thedeterioration speed in acceleration environment and simulated
actual environment.
3. To conduct lifetime prediction and examine the validity ofpredicted lifetime.

(a) Incubation stage(b) Propagation stage(c) Acceleration stage
Fig.1 Deterioration process defective paintcoated steel in submerged zone
defective paint-coated steel and as below.
(a) Deterioration process of defective paint-coated steel
From exposure test in off Suruga Bay and Futtsu, andconsideration of mechanism, deterioration process could be
proposed as shown in Fig.1.

2. Previous Study (Phthalic, Tar Epoxy )Annaka1) proposed the influence of f temperature andsolution on deterioration speed and deterioration process of105

(b) Influence of temperature
Temperature was estimated by activation energy.
Magnification of acceleration was estimated by equation (1).

æ DE æ 1 1 ö ö
V1= expçç a çç - ÷÷ ÷÷
V2
è R è T2 T1 ø ø(1)Where, V: corrosion current (μA/cm2), ΔEa: activation energy(cal/mol), R: gas constant (1.99 kcal/mol/K), T: temperature.
From the experimental result, ΔEa was estimated as
11.0kcal/mol. So, the magnification of acceleration between
20˚C and 50˚C was estimated at 5.8.
(c) Influence of solution
The magnification of acceleration between Sea water and3%Nacl was estimated at 2.3.

Fig.3 Mortar-covered specimen
In this study, polyurethane paint (PU) are used. Different
from Phthalic and Tar epoxy paint, polyurethane paint has
undercoat (50µm) and overcoat thickness was controlled.
Paint thickness is set as 300, 500, 700, 900µm.
3.3 Mortar-covered specimen
In case of mortar-covered specimen, steel plates (thickness:
3.2mm, 10mm×10mm) were used. Soldering was conductedto the backside. Same as actual case, anti-washout mortar isused and casted in artificial sea water (Fig.3). Curing was
conducted for 1day in artificial sea water. Then back and sidesurface was covered with epoxy resin. Mix proportion ofmortar is shown in Table.1. Covering thickness was set as
1cm, 2cm, 3cm.
Table.1 Mix proportion of mortar

3. Experimental procedures
In this study, to investigate deterioration mechanism anddeterioration speed, and specimens are submerged inacceleration environment. And to obtain magnification ofacceleration, specimens are also submerged in simulated
actual environment.
3.1 Environments
Acceleration environment is set as the air saturated 50Υ
3wt% NaCl water (JSCE standard2)). Simulated actual
environment is set as the 20Υ artificial sea water.
According to Chap.2, magnification of acceleration shouldbe 13 .3times.
3.2 Paint-coated steel specimen
In this study, steel plates (thickness: 3.2mm, width: 0.9mm)
divided into rings were provided as shown in Fig.2 (a).
Soldering was conducted to the backside of each steel plate as
shown in Fig.2 (b). Steel plate was embedded in epoxy resin
as shown in Fig.2 (c). By connecting lead wire, the divided
steel plates were considered as one circulated plate. Centercirculated steel was called “R-0”. Other steel plates werecalled “R-1” to “R-5” from inside. The area of each steel plateis as shown in Fig.2 (d). After painting, φ8mm defect wasadded to R-0. Divided specimen is used to measure microcell
and macrocell corrosion current density at defect part.
Not divided steel plate specimen (defective and notdefective) is also used to understand paint coating
deterioration.

(a) Front side
Paint coatedpart(b) Back side(c) After epoxy coating

Defect part

Paint coated part

Unit Weight (kg/m3)
Air W/C
S/C(%) (%)W C S

Admixture
AE
Viscosity Defoaming
SP
Admixture
Agent
Agent

3.0 45.0 1.2 360 800 931

2.50

8.00

3.88

0.12

3.4 Measuring method
In this study, appearance observation was conducted tounderstand deterioration of paint coating or mortar covering.
Polarization resistance was measured by alternating current
impedance method. Microcell current density was calculated
as Stern-Geary constant (In this study, 0.0209V) divided by
Polarization resistance.
Macrocell current was measured by zero resistant ammeter
connecting between each ring.

4. Discussion and experimental results
4.1 Polyurethane paint-coated steel(1) Appearance observation
Fig.4 shows appearance, blisters were oseraved on defective
polyurethane paint-coated steel and their distribution
looked concentrating near the defect. Paint-coating wasdisbonded at large area. Even at not blistered part, paintcoating was disbonded and paint thickness didn’t affectdisbonding width from defect. Spot rusts were observed
under disbonded paint-coating. However, there was no rustunder big blister around defect, so it is considered that the areaaround defect was cathod part.
(2)P aint thickness and duration until blistering
As shown in Fig.5, relation between paint thickness andduration until blistering of polyurethane paint-coated steel was
obtainesd as y = 0.0139x in 50ΥNaCl water and y = 0.1478
in 20Υ Seawater. Then magnification of acceleration was

Ring No R-0 R-1 R-2 R-3 R-4 R-5
Epoxy resin
Lead wire Area(cm2) 2.4 8.6 14.6 20.7 26.7 32.8(d) Cross section and area of each ringFig.2 Paint-coated steel specimen
106

(a) Incubation stage
Fig 4Polyurethane paint-coated steel
㸦50ΥNaCl water, 300µm, 140th day㸧Fig.5 Relation between paint thickness and duration
until blistering(b) Propagation stage(c) Acceleration stage
Fig.7 Deterioration process ofpolyurethane paint coated steel
investigated by using acceleration test.
So lifetime prediction of mortar-covered steel was conductedin the same way as lifetime prediction of reinforced concrete3).
(2) Chloride ion diffusibity in mortar-covering
Chloride ion content of mortar surface, chloride ion diffusion
coeficient and initial chloride ion content in 20Υ seawater are
obtained experimentaly as 10 [kg/m3], 3.3 [cm2/year] and 1.2[kg/m3]. And threshold chloride ion content is 2.1 [kg/m3]4)(3) Oxygen permeation amount and corrosion speed
Oxygen permeation amount of mortar-covered steel (20ΥSeawater, 3cm) was obtained as 1.7E-12 [mol/cm2/s]. Thiscan be converted to corrosion speed as 0.94 [mg/cm2/year].5)(4) Relation between corrrosion amount and adhesion
Adhesion force of electrolytic corroded mortar-covered steel
was measured. As the result, adhesion force decreased by
95% at 10 [mg/cm2] corrosion amount.
(5) Corrosion amont at mortar cracking
As actual structure, mortar-covered steel pipe pile(ȭ100cm,
covering thickness = 10cm) was modeled. Then its corrosion

Fig.6 Corrosion speed of polyurethane paint-coatedsteel at defect part (incubation stage)obtained as 10.6 times.(y: Duration until blistering, x: Paint
thickness)(3) Influence of defect on duration until blistering
In case of Polyurethan paint-coated steel (300µm), blisteroccurred on defective specimen at 4th day and blister didn’t
occur on not defective specimen at 140th day. Then influenceof defect existence was obtained as over 35 times.
(4) Corrosion speed in incubation stage
Fig.4.3 shows microcell and macrocell corrosion speed ofpolyurethane paint-coated steel. As shown in Fig.6,
magnification of acceleration of both of microsell andmacrocell corrosion speed was about 10 times.
(5) Proposal of deterioration process of polyurethane
paint coated steel
Deterioration process of polyurethane paint-coated steel was
proposed in Fig.7 After incubation stage, it shows different
deterioration process from Phthalic and Tar epoxy paint type.

4.2 Mortar-covered steel(1)Appearance observation
In this study, deterioration of mortar-covered steel couldn’t

107

amount at mortar cracking was obtained as 21.2 [mg/cm2] by
Nonlinear finite element analysis.
(6) Deterioration process of mortar-covered steel
Fig.8 shows deterioration process of mortar-covered steel.

4.3 Lifetime prediction of polyurethane paintcoated steel and mortar-covered steel
In case of polyurethane paint-coated steel, lifetime wasdefined as incubation stage. In case of mortar-covered steel
lifetime was defined as incubation + propagation stage.
Lifetime prediction procedure is shown in Table.2. (Phthalic:
Defective, 150µm, Tar Epoxy: Defective, 675µm,
Polyurethane: Not defective, 2500µm, Mortar : Steel pipe
pile[φ100cm, covering thickness = 10cm])

5. Conclusions
1. The magnification of acceleration of polyurethane paintcoated steel in incubation stage was about 10 times. This isalmost same to the magnification of acceleration of Phthalicand Tar epoxy paint-coated steel. And predicted life time ofpolyurethane paint-coated steel was close to actual lifetime.
2. Deterioration process of polyurethane paint-coated steel
after incubation period was different from that of Phthalic and
Tar epoxy paint-coated steel.
3. In this study, deterioration of mortar-covered steel couldn’t
investigated by using acceleration test. And lifetime prediction
method for reinforced concrete was not valid. As future
work, more appropriate lifetime prediction method for mortarcovered steel should be studied.
References
1) T. Annaka, “Research on Corrosion Mechanism and
Influential Factors of Paint Coated Steel with Defect inSubmerged Zone”, 2013
2) JSCE, “Guideline of efficiency evaluation about durability
and load resistance of marine steel structures in marine
environment”
3) H. Ohara, “Lifetime Prediction of Concrete Mixed with
Seawater against Chloride Attack and Carbonation”, 2013
4) JSCE, “Concrete standard specifications (design)”, 20125) S. Maruyama, “Influence of temperature and cement on
steel bar corrosion in concrete induced by carbonation”, 2002
6) Public Works Research Institute, “Corrosion protection
guideline and manual of marine steel structure (Splash zone࣭
Tidal Zone), 1990(a)Incuvation stage(b) Propagation Stage(c)Acceleration stage
Fig.8 Deterioration process of mortar-covered steel

Table.2 Lifetime prediction procedure1)

Phthalic

Lifetime 23.4~58.5
Prediction[day]

ActualLifetime

37 [day]

Accuracy 63.2~158%

1)

Tar Epoxy

2.3~5.7[year]

1~5 [year]

Paint thickness

Polyurethane
0.0139×2500µm = 34.75 [day]DefectOver 35 times

Magnification of
Acceleration

10.6 times

Totalover 12900 [day] = over 35.3
[year]

Mortar-covered steel
Incubation stage

5.3 [year] ͤ1

Propagation
Stage

22.6[year] ͤ2

Total

27.9[ year]

About 40 [year]6)

About 50 [year]6)

88.3 %

55.8 %

ͤ1 Calculated from the results shown in 4.2(2) and Fick’s law
ͤ2 Corrosion amount at cracking 21.2 [mg/cm2] / Corrosion speed 0.94 [mg/cm2/year]

108

STUDY ON SUSTAINABLE MANAGEMENT OF PONDS AND WETLANDS INWORLD HERITAGE USING GIS: CASE OF LUANG PRABANG, LAO PEOPLE’S
DEMOCRATIC REPUBLIC
Student Number: 14M18017 Name: Norifumi IRIE Supervisors: Shinobu YAMAGUCHI and Jun-Ichi TAKADA

GIS ࢆ⏝࠸ࡓୡ⏺㑇⏘ᆅᇦ࡟࠾ࡅࡿ‵ᆅᖏࡢᣢ⥆ྍ⬟࡞⟶⌮࡟㛵ࡍࡿ◊✲: ࣛ࢜ࢫேẸẸ୺ඹ࿴ᅜࣝ࢔ࣥࣉࣛࣂࣥࡢ஦౛ࢆ⏝࠸࡚ධỤ ᠇ྐ
ᮏ◊✲࡛ࡣୡ⏺㑇⏘ࣝ࢔ࣥࣉࣛࣂࣥᆅᇦ࡟࠾ࡅࡿ‵ᆅᖏࡢ஦౛ࢆ⏝࠸ࠊ㏆ᖺࡑࡢ౯್ࡀᅜ㝿ⓗ࡟ぢ┤ࡉࢀࡘࡘ࠶ࡿ‵ᆅᖏࡢಖ඲࡟ࡘ࠸࡚ࠊ
⌧ᆅ㑇⏘ᒁࡢࢽ࣮ࢬࢆඖ࡟ᵝࠎ࡞どⅬ࠿ࡽྲྀࡾᢅ࠺ࠋලయⓗ࡟ࠊᆅ⌮᝟ሗࢩࢫࢸ࣒ࢆ⏝࠸ࡓ‵ᆅᖏኚ໬ࡢどぬ໬ཬࡧศᯒࠊ㉁ⓗ࡞࢔ࣉ࣮ࣟࢳ࡟ࡼࡿ⌧ᆅఫẸࡀᇵࡗ࡚ࡁࡓ▱㆑࡟ࡘ࠸࡚ศᯒࡍࡿࠋࡲࡓ Mixed-methods ࢔ࣉ࣮ࣟࢳࢆᑟධࡋఫẸࡢ‵ᆅᖏಖ඲࡟㛵ࡍࡿព㆑ㄪᰝࢆᐇ᪋ࡋࠊ‵ᆅᖏࡢᣢ⥆ⓗಖ඲࡟ࡘ࠸࡚⪃ᐹࡍࡿࠋ

1. Introduction

temporal changes of the ponds and wetlands from 2001 to2015 using GIS; 2) to identify reasons behind the changeof the ponds and wetlands; and 3) to understand people’s
attitude toward ponds and wetlands preservation.

Luang Prabang, the first World Heritage site in Lao
PDR, is situated in the confluence of the Mekong and
Nhamkhan river. The ponds and wetlands in the town havebeen playing an important role with its various functions
promoting flood prevention, water retention, and healthy
environment with rich biodiversity. Its water network
compliments the unique townscape. 183 ponds andwetlands were registered in the inventory within Naturaland Landscape Zone (ZPP-N) of the safeguard andenhancement plan (PSMV) as part of the Outstanding
Universal Values of World Heritage.
Since the heritage inscription in 1995, the town
experienced the hike of tourist visits and rapid
development. This resulted in increasing pressure on theheritage property including the inventory ponds andwetlands. Department of World Heritage in Luang
Prabang (DPL) has been continuously implementing
preservation programs on ponds and wetlands supported
by international organizations under multiple projects
until 2009. With the reason of recent growing attention to
the ponds and wetlands preservation in the Greater
Mekong Sub-regions where the decrease of wetlands havebeen increasingly reported, the collaboration team
between Tokyo Institute of Technology (Tokyo Tech) andDPL conducted a preliminary survey in 2014. The survey
result identified the needs of updating the available data,visualizing temporal changes of the ponds and wetlands,
and understanding the reasons of the change as well as the
local residents’ situation.
Following such needs, three specific research
objectives were developed as follows: 1) to visualize

2. Literature Review
2.1. Analysis on temporal changes using GIS
With the recognized value of ponds and wetlands
preservation, Geographic Information System (GIS) has
been applied to detect the temporal changes of ponds,
wetlands, and their surrounding area.
There are two common methods on GIS applied tomeasure the temporal changes: 1) inspection of georeferenced multi-temporal satellite images; and 2)
comparison analysis on land cover or use types in thelocation at different time points [1]. There are also uniquemethods available depending on the study context. For
example, the shift of the number of patches of land covertypes was studied over years to see the temporal change of
wetlands in China [2].
2.2. Environmental attitude
Incorporating the environmental attitude into decision
making process will help achieve a long term preservationof nature [3]. Environmental attitude is traditionallycomposed of three factors, namely, cognitive (knowledge),
affective (feeling), and conative (behavioral). Meanwhile,
recently diverse factors were identified. For instance,
demographical characteristics such as age and educationlevel were identified factors [4]. Bonita L. Mc Farlane
insisted that value, the worth of the object, has a certain
degree of linkage to environmental attitude [5]. Another109

study suggested there exist connection between perceived
access to green pathway and environmental attitude [6].
Balram then emphasized on the importance of contextbased studies as the attitude components vary depending
the context [7]. This study follows this argument and, thus,
considers the importance of “context” in understanding
people’s attitude toward the ponds and wetlands
preservation in Luang Prabang.

attitude measurement section includes multiple 5-point
Likert scale items in which the level of agreement ismeasured. Those questionnaire items are reflected fromthe current literatures and the possible factor components
identified in the previous step. The demographic profile
section consists of job occupation, gender, age, educationlevel, and income level. The expected respondents include
10 DPL experts, 10 Urban Development Administrative
Agency members, and 40 pond owners. The
questionnaires are distributed in the drop-off survey style.
Finally, quantitative analysis on survey responses isconducted. Three objectives for the analysis arehighlighted: 1) to identify what ideas people mostly agree
or disagree with; 2) to explore the latent factors of theattitude of people towards ponds and wetlands
preservation; and 3) to comprehend the significance levelof the latent factors.
The mean value of the questionnaire items is analyzed
to find patterns in the items. Factor analysis is conducted
to understand latent attitude factors contextualized in
Luang Prabang. Item-total correlation, Kaiser-MeyerOlkin Measure of Sampling Adequacy (KMO), andBartlett tests are performed for data cleansing. For factor
extraction, EFA adapts Principal Components Analysis(PCA). The reliability of the factors is assured through
Cronbach’s Alpha that demonstrates the level of intercorrelation in each factor.
To interpret the level of significance among theidentified factors, the average mean value of each factor iscompared and analyzed. In addition, Sample T-test and
Demographic analysis of variance (ANOVA) are
employed to identify differences in attitude across various
demographic variables such as age and gender.

3. Methodology
The theoretical framework adapted an instrument
developed by Balram to analyze contextualized
environmental attitude [7]. The study takes the following
five specific steps.
First, the temporal changes in a pilot site are visualized
and analyzed using GIS to provide evident information on
the changes and update data on the ponds and wetlands.
The pilot site for the analysis covers Mano and Phonkham
villages located in ZPP-N with the area of approximately
1 km2. This study analyzed the temporal changes bycomparing various types of data including: 1) green spaceobserved in the satellite images; 2) land cover types; and
3) usage types of the ponds at three different time points(2001, 2008, 2014/2015). A vector map in 2014 is newly
delineated for comparison and updated systematically byinspecting observable changes in the field and referring toavailable spatial data.
Second, interviews are conducted with selectedstakeholders with two purposes: 1) to explore reasons
behind the changing tendency of the ponds and wetlands;
and 2) to identify possible factor components affecting theattitude of people toward ponds and wetlands preservation.
Twelve open-ended questions are developed based on thecommon factors of attitude found in existing studies andsuggestions from DPL and Tokyo Tech. The interview
content is translated from Lao to English and stored as text
data.
Third, a local workshop is organized with the expertsof DPL to share the results. There are two purposes for the
discussion as follows: 1) to confirm the validity of theresults and findings discovered in the previous two steps;
and 2) to reflect opinions and ideas of local experts onto
this study.
As the forth step, the identified possible factor
components are further explored through a questionnaire
survey. The questionnaire has two sections: attitude
measurement and demographic profile section. The

4. Data analysis
First, the green space observed in the satellite image of
2002, 2008 and 2014 were compared on GIS. The
decrease of the green space and wetlands was visuallydetected. Second, the area proportion of land cover types
including building zone and green space was calculated
based on the vector map of 2002, 2008 and 2015 usingarea calculation tool of GIS (Figure 1). There were threefindings: 1) green space had decreased while buildingzone had increased; 2) green space had shrunk more
rapidly between 2008 and 2015 than before; and 3) otherland cover types had rapidly increased between 2008 and
2015.
110

distributed between September and October 2015. A totalof 61 responses were collected. The data cleansing was
performed. As a result, 25 items remained valid for further
analyses. Moreover, EFA using PCA techniques andinterpretation through Varimax identified six factors. 1)
cost of the ponds and wetlands maintenance; 2) pleasantfeeling towards the ponds or wetlands; 3) understanding
about the ponds and wetlands; 4) importance of the ponds
and wetlands preservation; 5) accessibility to the ponds
and wetlands; and 6) usefulness of the ponds. The highvalue of Cronbach’s Alpha in every factor indicated thatthe inter-correlation within the tested factor was reliable.
Through the mean value analysis of the factors, there
were particularly four findings in the context of Luang
Prabang (Table 1). First, the respondents believe ponds
and wetlands are important as importance of the ponds
and wetlands preservation was the factor with the highest
level of agreement (Mean = 1.683). Second, therespondents hold pleasant feeling towards the ponds andwetlands as the factor also marked the high level ofagreement. Third, accessibility is one of the reasons forpreserving ponds as accessibility to the ponds andwetlands showed the high level of agreement. Forth, thecost was highly considered by the respondents as cost of
the ponds or wetlands maintenance showed the high levelof agreement.

100%
80%
60%
40%
20%
0%
2002

2008

2014

Other land cover types
Green space (Forest and wetland)
Building zone

Figure 1: Land cover types' area percentage (2001 – 2014)

Third, the comparison analysis of the usage types ofinventory ponds at three different points (2001, 2008 and2015) identified three key findings: 1) ponds with noactivities had increased from two to 13 between 2001 and2015; 2) one pond with no activities in 2008 wasconverted into land; and 3) once ponds became unused,those ponds tend to be abandoned.
Interviews were conducted between March and May
2015 with 13 interviewees. The interview contents wereanalyzed following Grounded Theory. The reasons of thedecrease of ponds and wetlands were categorized into two
major factors: man-made or natural factors. Man-madefactors include unintentional loss due to new constructions
and intentional reclamation. Natural factors include threecomponents: landslides near ponds; mud accumulation in
the bottom of ponds; and continuous droughts due to thelong dry season. Most of the respondents agreed that thepond quality has become lower compared to the past.
The qualitative analysis identified 21 possible factor
components affecting people’s attitude toward ponds
and wetlands preservation such as usefulness inagricultural activities, knowledge about ponds’ history,
and good atmosphere coming from the beauty andrelaxing effect. These components were classified into
five categories, namely, value, knowledge, feeling, costand others.
The local workshop was held in May 2015. As for thereasons behind the decrease of the ponds and wetlands, it
was emphasized that the man-made and natural factors
have been strongly linked with each other. Further, theworkshop extracted additional possible factor components
such as land price hike and local mythology on ponds.
A questionnaire with 32 items, reflecting theworkshop discussion and existing knowledge on
environmental attitude. The questionnaires were

Factor# of Mean Standard
items Value Deviation
3
1.683 0.740

Importance of the ponds andwetlands preservation
Pleasant feeling towards the 4ponds or wetlands
Accessibility to the ponds and 3
wetlands
Cost of the ponds and wetlands 7
maintenance
Understanding about the ponds 4
and wetlands
Usefulness of the ponds
1

1.787

0.788

1.934

0.809

1.940

0.949

2.254

0.847

2.410

1.006

Table 1: The mean value and standard deviation of factors

ANOVA and T-test found the gap across thegovernmental officials (UDAA and DPL experts) andpond owners in understanding about the ponds’ function
as a water network system and about the policies andregulation of DPL.

111

5. Discussion

and people’ attitude towards ponds and wetlands
preservation. As one of the few studies taken place inWorld Heritage sites in developing countries, the findings
will also help the future studies to explore topics related toponds and wetlands in similar settings.
It is recommended to update information subsequently
as the situation of ponds and wetlands as well as its
stakeholders is constantly changing. Neglecting the data
update will cause the obsolescence of information.
Another recommendation is expansion of the study areafor visualization. Following the same techniques, it ispossible to expand the survey scale.
In the end, it is hoped that this study can contribute to
the sustainable management of the ponds and wetlands inLuang Prabang and possibly other sites in the Greater
Mekong Sub-regions as well as World Heritage sites in theworld facing the similar situation.

As for the visualization of the temporal changes in theponds and wetlands, the study findings supported theobserved trends in ponds and wetlands as well as its
surrounding green space around the Greater Mekong Subregions [8]. It is often the case in the region that buildingzone increases typically meanwhile the land cover types
indicating a part of the nature decrease [1]Using the empirical data, this study reinforced theimportance of human-centered studies. As the heritage
preservation is generally achievable when theparticipation of the stakeholders in both the public andprivate sectors is made, understanding various
stakeholders’ opinions is vital [9]. In fact, this study
empirically found that every resident has their own
perception, experiences and knowledge on the ponds andwetlands, so classifying their opinions could capture avariety of viewpoints inclusively.
Five out of the identified six factors affecting people’s
attitude towards ponds and wetlands preservation
coincided with the factors of environmental attitudecommonly found in the current literatures. An exceptional
factor was cost of the ponds and wetlands maintenance,
which was only found in this study context. It may be
because the ponds and wetlands require industrious
maintenance work while other natural environments suchas forests and agricultural lands do not need the same levelof maintenance work. This unique factor proves theimportance of contextual research as pointed out by
Balram [7].
Further, the findings through the mean value analysisof the factors are expected to be referenced to implement
ponds-related projects in the future. It could be a reference
to design preservation awareness campaign on ponds andwetlands. One of the common factors value was regarded
as less important in this study context. In fact, the ponds
and wetlands had been less utilized as identified throughthe interviews and visualization in GIS compared to 2001.
It may have resulted from the shift of economic activities
as the past studies indicated [1][2].

Acknowledgement
This work was supported by JSPS KAKENHI Grant
Number 25303001.
Reference[1]
W. W. Zhang, L. Yao, H. Li, D. F. Sun, and L. D. Zhou,“Research on land use change in Beijing Hanshiqiao
wetland nature reserve using remote sensing and GIS,”
Procedia Environ. Sci., vol. 10, no. PART A, pp. 583–588,
2011.
[2]
D. Xiao, B. Tian, K. Tian, and Y. Yang, “Landscapepatterns and their changes in Sichuan Ruoergai Wetland
National Nature Reserve,” Acta Ecol. Sin., vol. 30, no. 1,
pp. 27–32, 2010.
[3]
F. G. Kaiser, S. Wolfing Kast, and U. Fuhrer,“Environmental attitude and ecological behaviour,” J.
Environ. Psychol., vol. 19, no. 1, pp. 1–19, 1999.
[4]
P. D. L. V. CHRIS LAKHAN, “Use of Loglinear Models
to Assess Factors Influencing Concern for the Natural
Environment,” Environ. Manage., vol. 30, no. 1, pp. 77–87,
2002.
[5]
P. C. B. Bonita L. Mc Farlane, “Factors Influencing Forest
Values and Attitudes of Two Stakeholder Groups: The
Case of the Foothills Model Forest, Alberta, Canada,”
Society & Natural Resources, vol. 13. pp. 649–661, 2000.
[6]
G. Lindsey, M. Maraj, and S. Kuan, “Access, Equity, andUrban Greenways: An Exploratory Investigation,” Prof.
Geogr., vol. 53, no. 3, pp. 332–346, Aug. 2001.
[7]
S. Balram and S. Dragićević, “Attitudes toward urban green
spaces: integrating questionnaire survey and collaborative
GIS techniques to improve attitude measurements,”
Landsc. Urban Plan., vol. 71, no. 2–4, pp. 147–162, 2005.
[8]
B. Matthew and L. Rebelo, “Wetlands play critical role ineconomic growth of the Mekong,” 2015. [Online].
Available: https://wle.cgiar.org/thrive/2015/02/02/wetlandsplay-critical-role-economic-growth-mekong.
[9]N. F. N. Azhari and E. Mohamed, “Public Perception:
Heritage Building Conservation in Kuala Lumpur,”
Procedia - Soc. Behav. Sci., vol. 50, no. July, pp. 271–279,
2012.

6. Conclusion
This study serves as significant knowledge base for
future decision making on the ponds and wetlands inLuang Prabang. This comprehensive study covered threemajor topics including temporal changes of ponds andwetlands, the identification of the reasons for the changes
112

Evaluation of the effectiveness of the proposed mitigation structures
after the 2011 Great East Japan Earthquake
Student Number: 14M18164

Name: Koki MATSUMOTO

Supervisor: Hiroshi TAKAGIᮾ᪥ᮏ኱㟈⅏ᚋࡢ᪂タῶ⅏ᵓ㐀≀ࡢ᭷ຠᛶࡢ᳨ドᯇᮏ ගᕼ
2011 ᖺ࡟Ⓨ⏕ࡋࡓᮾ᪥ᮏ኱㟈⅏࡟࠾࠸࡚ࡣ㸬ᚑ᮶ࡢ᝿ᐃࢆ኱ࡁࡃ㉸࠼ࡿὠἼࡀく᮶ࡋ㸬ᮾ໭ᆅ᪉ࢆ୰ᚰ࡟⏒኱࡞⿕ᐖࢆࡶࡓࡽࡋࡓ㸬ᮏ◊✲࡛ࡣ㸬ὠἼ࡟ࡼࡗ࡚≉࡟኱ࡁ࡞⿕ᐖࢆཷࡅࡓᐑᇛ┴ࡢ 2 ᆅᇦ㸦▼ᕳᕷ࠾ࡼࡧ
ྡྲྀ࣭ᒾ἟ᕷ㸧ࢆ◊✲ㄪᰝᑐ㇟࡜ࡋ㸬㸱ᗘࡢ⌧ᆅㄪᰝࢆ⾜࠸㸬ㆤᓊࡸ┒ᅵ࡞࡝ῶ⅏ᵓ㐀≀ࡢ᚟ᪧ࣭᚟⯆≧ἣࢆㄪᰝࡋࡓ㸬⿕⅏๓ᚋࡢㆤᓊᙧ≧ࡸᆅ┙㧗➼ࡢࢹ࣮ࢱࢆヲ⣽࡟ 㔞ࡋ㸬ࡑࡢ⤖ᯝࢆ཯ᫎࡋࡓὠἼࡢ㐳ୖ෌⌧ᩘ್ࣔࢹࣝࢆᵓ⠏ࡋࡓ㸬ࡇࡢᩘ್ࣔࢹࣝࢆ฼⏝ࡋ࡚㸬௒ᚋ᝿ᐃࡉࢀࡿὠἼ࡟ᑐࡋ࡚㸬⌧ᅾᘓタࡀ㐍ࡵࡽࢀ࡚࠸ࡿῶ⅏ᵓ㐀≀ࡀ࡝ࢀ࡯࡝ຠᯝⓗ࠿ᩘ್ゎᯒ࡟ᇶ࡙ࡁ᳨ドࡋࡓ㸬

1. Introduction
The 2011 Great East Japan Earthquake that occurredon March 11th 2011 triggered a massive tsunami,
claiming lives of more than 18,000 people and bringing
partial or complete damage to approximately 400,000
building structures. Among several areas that sufferedfrom the wrath of the 2011 event were Ishinomaki and
Natori-Iwanuma, both of which are coastal areas in
Miyagi Prefecture. The event had afflicted 3,000 deathsin Ishinomaki and more than 1,000 in the cities of
Iwanuma and Natori.
According to the tidal records at Ayukawa Station[1],the observed tsunami height was at least 8.6m and theresultant flood extended 73 km2 inward Ishinomaki
City[2]. In the Sendai plains, the tsunami height reachedas high as 8.5m[3] as derived from the water mark at
Sendai Airport, and the inundation expanded to 29 km2
inside Iwanuma City and 27 km2 inside Natori City[2].
This study focuses on investigating the efficiency ofthe proposed mitigating structures based on the 2011
Great East Japan Earthquake in order to reduce thedamage of future tsunami events. The authors expectthat the findings in this study can improve the proposedreconstruction plan and thereby contributing to disasterrisk reduction plan in the future.

Fig 2. Field survey at Ishinomaki

2. Field Survey
Field survey was conducted at three different periods(March 2013, November 2013 and November 2015) inorder to collect input data necessary for the tsunami runup simulation. All three surveys were primarily aimed
to collect ground elevation, water depth and dyke height
information.

Fig 3. Field survey points at Natori and Iwnauma

Fig 1 .Field survey points at Ishinomaki
Fig 4. Field survey at Natori and Iwanuma

113

3. Reconstruction Plan

4. Model Setup

Efforts to reconstruct and establish new mitigating
structures were led by the local government of
Ishinomaki, Natori and Iwanuma. The reconstruction
plan which started in year 2011 and has been on-going
until now was based on the past 1960 Chile tsunami andstorm surge event. A three countermeasuresreconstruction plan in Ishinomaki contains lining up a
7.2m-high dyke along the coastline as part of the firststructure, and accompanied by the second structure thatinvolves the road 1-km away and parallel to thecoastline and raising this road by 2.6-4.5m from theground level. In addition to these, 21 evacuationbuildings and an additional 3 evacuation towers will beestablished as included in the third structure. The spatial
distribution of the three countermeasures is illustrated
in the figure below, along with the details of eachstructure’s status.[3]Delft 3D Flow, a fluid dynamics model, was applied torecreate the lifecycle of the tsunami in 2011 generated
from the deep sea, traversing through the shallow waterand eventually running up towards inland. The present
study employed a 2D horizontal grid equivalent to a
non-linear long wave model commonly used fortsunami run-up simulations.

4.1 Calculation Setting and Calculation Area
Table 1 presents the settings used in the simulation.
The offshore boundary is set aligned between the two
GPS buoys of Central Miyagi and Fukushima. The input
data were created by linearly interpolating the observed
tsunami elevations at two locations. For the purpose ofeffectively running the simulation, the computational
domains were composed of four areas from (a) to (d)with a 540m, 180m, 60m, and 20m grids, respectively.

Table 1. Reconstruction plan in Ishinomaki

Table 3. Calculation Setting and Area

An identical three countermeasures package wasproposed in Natori and Iwanuma. This involves lining
up a 7.2m-high dyke along the coastline as part of thefirst structure. Similarly, the second structure aims at
raising the road 1-km away and parallel to the coastline
by 8m from the ground level. The third structure
incorporates a construction of another road raised 10-m
above ground and called “The Highway of Eastern
Sendai”.[4][5]Table 2. Reconstruction plan in Natori and Iwanuma

4.2 Selecting Manning’s n coefficient during
tsunami run-up over land
In order to incorporate the varying surface roughness
throughout the different stages of flow of the tsunami,
separate values of Manning’s coefficient for the sea andthe land were utilized [6][7]. A uniform Manning’s
coefficient of 0.025 was set to the sea portion; however,different values must be assigned to each land segmentof Ishinomaki and Sendai plains in order to assimilate
the difference of land cover of the studied areas. To doso, several calculations were performed employing
different values of Manning’s coefficient. The simulated
inundation height from different Manning values weregraphed and cross-checked with the observed values in
Figure [8]. The Manning value that generated the bestfit regression line was then selected. It was derived that
the Manning coefficient to be used is 0.16 in

114

high run-up, respectively. Similarly, the proportions forthe second and third group were set to 1/2, 2/3(corresponding to heights of 4.5m and 6m) and 4/5, 5/5(corresponding to 7.2m and 9m) with return periods ofonce in several hundred years and once in a thousand
year, respectively. The same procedure was utilized in
Natori and Iwanuma with a maximum run-up tsunami
height of 5m. Utilizing the identical proportions appliedin Ishinomaki, the corresponding incident waves are
1.25m and 1.67 for 1/4 and 1/3 proportions, 2.5m and3.33m for 1/2 and 4/5 proportions and 4m and 5m forthe 4/5 and 5/5 proportions.

Ishonomaki and 0.08 in Natori and Iwanuma.

Fig 5.Comparing observed and calculated inundation
height data in Ishinomaki

Fig 7.Incident Wave in Ishinomaki

Fig 8. Incident Wave in Natori and Iwanuma
Fig 6. Comparing observed and calculated inundation
height data in Natori and Iwanuma

5.2 Calculation Results
The authors ran the simulation for ten different
locations and selected the results in Mitsumata in
Ishinomaki to show one of the typical trends among all
ten locations Fig10-12 show the consequence of eachtsunami scale for all three terrain conditions at
Mitsumata, a residential area during pre-tsunami andpost-tsunami event of 2011. Fig10 suggests that tsunami
events with occurrence interval of once in a hundredyear will completely disperse the generated waves andhence, will not reach the residential areas. On the other
hand, as depicted on Fig11, a tsunami scale of once
every several hundred years will induce a run-up wavethat may extend towards the residential areas, but thestructures may significantly dissipate the energy
generated by the tsunami. The energy dissipation may
also be attributed to the change in land use after thereconstruction plans. Lastly, tsunamis of scaleanalogous to that of the 2011 Great East Japan
Earthquake may not also inhibit the waves fromstretching onto Mitsumata and the energy dissipation
caused by the structures are likely to be lower than thatof the second case.
Figure 13 illustrates the flood area in Ishinomaki with
1/3 incident wave. It is apparent that the second

5. Evaluation of the effectiveness of the
proposed mitigation structures
In this study, the authors defined three types of tsunami
according to the frequency of occurrence: once in ahundred year, once in several hundred years and once ina thousand year. Moreover, three terrain conditions wereapplied: during pre-tsunami event of 2011, during
present condition, and during post-reconstruction of the
proposed structures. The run-up height values withdifferent scales were carried out to each terrain case andthe spatial distribution of the damage was examined.

5.1 Incident Wave
As mentioned above, the authors opted to divide thesimulations into three categories with varying scales oftsunami run-up which are proportioned according to the
9m-high maximum run-up generated during the 2011
event. To illustrate, in Ishinomaki the run-up values ofthe first category (once in a hundred year occurrence
interval) were calculated by proportioning the 9m
maximum by 1/4 and 1/3, resulting to 2.25m and 3m115

structure can prevent the tsunami run-up.

Fig 13. The flood Area at Ishinomaki in 1/3 incidentwave) (Red: Before 2011 Great East Japan, Blue:
Present condition, Green: After Reconstruction)
Fig 9. Calculation points (Left: Ishinomaki, Right:
Natori and Iwanuma)

6. Conclusion
Since a full protection from another tsunami event witha magnitude similar from that of the previous one in2011 is likely to be unrealistic, the intention of thereconstruction plan is to sufficiently dissipate the future
tsunamis’ run-up to enable the residents to evacuate
before the arrival of tsunami, which is further supportedby the raised road plan (second structure). Based on thesimulation results of this study, the likelihood of future
tsunami event reaching the residential area will be lesswhen the proposed new structures are incorporated.
Evidently, the likelihood will be much lower when theresidents relocate further away from the coastline.
Furthermore, the results of the simulation reveal that theconfiguration of the dyke and the raised road in the nonresidential areas of Ishinomaki, Natori and Iwanuma
may possibly inflict a larger tsunami run-up due to wave
reflection. Accordingly, the authors propose areconfiguration of the position and alignment of theraised road from the dyke aligned along the coastline.

Fig 10. Mistumata (1/4 Incident Wave)

7. References
[1]
[2]
[3]

Fig 11. Mistumata (2/3 Incident Wave)
[4]

[5]

[6]

[7]

Fig 12. Mistumata (2011 Great East Japan Tsunami
Incident Wave)
[8]

116

Japan Meteorological Agency:
http://www.jma.go.jp/jma/press/1106/03b/
The Geospatial Information Authority of Japan:
http://www.gsi.go.jp/chirijoho/chirijoho40022.html▼ᕳᕷ:ᮾ᪥ᮏ኱㟈⅏࠿ࡽࡢ᚟⯆ ࠕ᭱኱ࡢ⿕⅏㒔ᕷ࠿ ࡽ ୡ ⏺ ࡢ ᚟ ⯆ ࣔ ࢹ ࣝ 㒔 ᕷ ࢆ ┠ ᣦ ࡋ ࡚ ࠖ㸪https://www.city.ishinomaki.lg.jp/cont/10181000/8235/99.
hukkoujyoukyou_2.pdf
ᒾ἟ᕷ:ᒾ἟ᕷ㟈⅏᚟⯆ィ⏬࣐ࢫࢱ࣮ࣉࣛࣥ㸪
http://www.city.iwanuma.miyagi.jp/kurasi/matidukuri/docu
ments/master-ver2.pdf
ྡྲྀᕷ:ྡྲྀᕷ㟈⅏᚟⯆ィ⏬㸪
http://www.city.natori.miyagi.jp/shinsai/fukkoukeikaku/no
de_31407/node_33441
Takagi H., Bricker J.. Assessment of The Effectiveness ofGeneral Breakwaters in Reducing Tsunami Inundation in
Ishinomaki, Coastal Engineering Journal, Vol. 56, No. 4,
Page 21, Dec. 2014.
Bricker J., Gibson S., Takagi H., Imamura F.. On the Needfor Larger Manning's Roughness Coefficients in DepthIntegrated Tsunami Inundation Models, Coastal
Engineering Journal, Vol. 57, page 13, Jun. 2015.
ᮾ᪥ᮏ኱㟈⅏ὠἼヲ⣽ᆅᅗ ୖᕳࠖ((2011)ཎཱྀ ᙉ,ᒾᯇ ᬦⴭ)

Decomposition of nitrous oxide over Cu modified
Indonesian natural zeolite
Student number: 14M18030 Name: Ryusuke Katagiri

Supervisor: Hirofumi HINODE࢖ࣥࢻࢿࢩ࢔⏘ኳ↛ࢮ࢜ࣛ࢖ࢺᢸᣢ㖡ゐ፹ࢆ⏝࠸ࡓள㓟໬❅⣲ࡢ┤᥋ศゎ∦᱒ 㝯௓ ᮏ◊✲࡛ࡣࠊᕤᴗ⏕⏘㐣⛬࠿ࡽ᤼Ẽࡉࢀࡓ N2O ࡢศゎࢆ┠ⓗ࡟ࠊ⏘ᆅࡢ␗࡞ࡿ 2 ✀㢮ࡢ࢖ࣥࢻࢿࢩ࢔⏘ኳ↛ࢮ࢜ࣛ࢖ࢺࢆᢸయ࡜ࡋ࡚ࠊྵᾐἲཬࡧ࢖࢜ࣥ஺᥮ἲࢆ⏝࠸࡚ࢮ࢜ࣛ࢖
ࢺᢸᣢ㖡ゐ፹ࢆㄪ〇ࡋࠊN2O ศゎ࡟ᑐࡍࡿゐ፹άᛶࢆホ౯ࡋࡓࠋࡲࡓࠊㄪ〇᮲௳࡛࠶ࡿ㔠ᒓᢸᣢ㔞ࠊ↝ᡂ ᗘ࡟ࡼࡿゐ፹άᛶࡢኚ໬ࢆ᳨ウࡋࡓࠋㄪ〇ࡋࡓゐ፹ࡢ୰࡛ࡣࠊྵᾐἲ࡛
500 Υ࡛↝ᡂࡋࡓ Sukabumi ⏘ኳ↛ࢮ࢜ࣛ࢖ࢺᢸᣢ㖡(5 wt%)ゐ፹ࡀ᭱ࡶ㧗࠸ゐ፹άᛶࢆ♧
ࡋࠊN2O ࡣ 300 Υ࠿ࡽศゎࡋጞࡵࠊ550 Υ࡟࠾࠸࡚᏶඲࡟ศゎࡋࡓࠋ
2 Experimental
2.1 Pretreatment

1 Introduction
Natural zeolite is anticipated to have roles ofpollution control catalyst due to its high surface area
and chemical resistance. However, natural zeolite has
low catalytic activity compared to synthetic zeolite
because it is naturally formed from volcanic ash andcontains impurities, nevertheless its development isdesirable as cheaper pollution control catalyst
compared to synthetic catalyst. In previous studies,
catalytic activity of natural zeolite is improved byvarious modifications.
Nitrous oxide (N2O) is one of the powerfulgreenhouse gases with 310 times effects of the Global
Warming Potential per molecule of CO2. Moreover,N2O is also identified as a contributor to thedestruction of ozone layer in the stratosphere [1]. The
abatement of N2O emissions from industrial plants(e.g. adipic acid production and combustion process)is probably the most feasible by implementing
catalytic processes [2]. However, synthetic zeolite
modified with Rh which has high decomposition
activity is costly [3]. Previous study showed zeolite
supported Cu catalyst was effective on N2O
decomposition [3]. Therefore, in this study, thecatalytic activity of natural zeolite modified by Cu
was investigated.

In this study, Indonesian natural zeolite (NZ) fromdifferent places (Sukabumi and Lampung) was used.
They are designated as NZS and NZL, respectively.
Before the experiment, NZ was crushed, washed anddried at 120Υ with particle size below 150 μm.

2.2 Catalyst preparation
2.2.1 Impregnation method (IM)Cu/NZ (NZ=NZS,NZL) catalysts were prepared byimpregnation method. In the case of preparation of
Cu/NZ,NZ was impregnated in an aqueous solution ofCu(NO3)2×3H2O (99.9%, Wako Co.). Each of thesolutions was stirred at room temperature for 24 hours
followed by 24 hours drying at 80 Υ . Then the
catalysts were calcined at 500Υ for 5 hours under air
flow. In order to reduce pressure drop, the catalysts
were pelletized, crushed and sieved to between 0.71
mm to 1.00 mm pellet size. The following
nomenclature for the catalyst samples are used:Cu(xwt%)/NZ where x means Cu loading levels (e.g.
Cu(1wt%)/NZS means NZS with Cu loading of
1wt%).

2.2.2 Ion-exchange method (IE)
Cu-NZ (NZ=NZS,NZL) catalysts were prepared byion-exchange method. In the case of preparation of
Cu-NZ, NZ was added in an aqueous solution of1117

NZ improved catalytic activity. When Cu loadinglevel was 5wt%, the highest catalytic activity for thedecomposition of N2O was achieved on NZS and NZL.
Decomposition of N2O to N2 over Cu(5wt%)/NZS
catalysts began at 300Υ, and the conversion of N2O
to N2 reached 100% at 550Υ. On the other hand,decomposition of N2O to N2 over Cu(5wt%)/NZL
catalysts began at 300Υ, and the conversion of N2O
to N2 reached 100% at 600Υ. NZS showed better
activity for N2O decomposition than NZL at the same
Cu loading level.

Cu(NO3)2×3H2O (99.9%, Wako Co.). Each of thesolutions was stirred at room temperature for one day
followed by filtering using vacuuming pump anddrying. Then the catalysts were calcined at 500Υ for5 hours under air flow. The catalysts were pelletized,
crushed and sieved to between 0.71 mm to 1.00 mm.
Similar to impregnation method, the following
nomenclature for the catalyst samples are used:Cu(xwt%)-NZ where x means Cu loading levels (e.g.
Cu(1wt%)-NZS means NZS with Cu loading of
1wt%).

2.2 Catalytic activity experiment
The catalytic reaction was carried out in a fixed-bed
flow reactor under atmospheric pressure. The reactant
gas was prepared by mixing N2O and He as a balancegas. The mixed gas of 1000 ppm N2O and He was fed
to the catalyst at a flow rate which corresponded to a
space velocity of 16000 h-1. Gas chromatography(GC323w; GL Science Co., with Porapak N, Porapak
Q and Molecular Sieve 13X columns thermal
conductivity detector) was used to analyze N2O
concentration.
Fig. 1 Catalytic activity of catalysts prepared by

2.3 Catalyst characterization

impregnation method for N2O decomposition

Characterizations of the catalysts were performedby Thermogravimetric Analysis (TG8120), X-ray
Diffraction(MultiFlex),
Nitrogen
Adsorption(Autosorb-1),
Scanning
Electron
Microscope(JSM-6000)-Energy dispersive X-ray analysis(MP-00040EDAP) and inductively coupled plasma
atomic emission spectroscopy (SPS7800(ϩ)).

The BET specific surface areas of NZS and NZL
were 27.9m2/g and 37.7m2/g, respectively. The
projected area of one molecule of CuO on the surfaceof TiO2 was calculated by using the radius of Cu2+ ion
and O2- ion and the bonding distance of Cu-O
molecule (Cu2+˖0.69ύ, O2-˖1.40ύ, Cu-O˖1.95ύ).
The projected area of one molecule of CuO on thesurface of TiO2 was calculated to approximately 7.59
ύ2 by using the geometrical equation in Eq.1.

3. Results and discussion
3.1 Catalytic activity of catalysts preparedby impregnation method for N2O
decomposition(Eq.1)

Figure 1 shows the catalytic activity results of
Cu/NZS and Cu/NZL with different metal loadinglevels for the decomposition of N2O to N2. Horizontal
axis indicates reaction temperature, and vertical axis
indicates N2O conversion. The decomposition of N2O
inside empty tube was also investigated because N2Onaturally decompose at high temperature. Compared
to bare NZL and NZS which only decomposed N2O
12% and 25% respectively at 700Υ, loading of Cu on

When the surface of NZ was assumed to be uniform
and, covered with CuO monolayer, the necessaryamount of CuO molecule to make one full layer on theNZ surface could be calculated. Following theassumption, the optimum Cu loading level of Cu/NZ
was calculated. They were 3.73wt% (NZS) and
4.98wt% (NZL), respectively which closely in2118test showed in Fig.2 indicated that there is no
significant
difference
betweenthecatalyst
performances.

accordance with experimental value.

3.2 Catalytic activity of catalysts preparedby ion-exchange method for N2O
decomposition

3.3 The effect of calcination temperature on
thecatalytic
activityfor
N 2O
decomposition

Figure 2 shows the catalytic activity results of
Cu-NZS and Cu-NZL with different metal loadinglevels for the decomposition of N2O to N2. Eachsample was calcined at 500ć for 5 h.

Figure 3 shows the catalytic activity results of thecalcined (AC) and uncalcined (BC) NZS and NZL for
the decomposition of N2O to N2. Calcination
temperature is 500ć and calcination time is 5 h.

Fig. 2 Catalytic activity of catalysts prepared byion-exchange method for N2O decomposition
Fig. 3 The effect of calcination on catalytic activity for N2O

Decomposition of N2O to N2 over Cu(5wt%)-NZS
catalysts began at 300ć, and the conversion of N2O
to N2 reached 97% at 700ć. Also, decomposition of
N2O to N2 over Cu(5wt%)-NZL catalysts began at
300ć, and the conversion of N2O to N2 reached 85%
at 700ć. Similar to the result of impregnation method,
NZS showed better activity for N2O decomposition
than NZL at the same Cu loading level. Compared toimpregnation method, samples prepared byion-exchange method showed lower activity. Table 1
shows the relationship between the amount of Cu
added and Cu ion exchanged capacity of Cu.

decomposition

It is shown that the conversion temperature of N2O
was shifted to slightly lower temperature if the sample
is not calcined (BC). Decomposition of N2O to N2
over NZS BC began at 400Υ, and the conversion of
N2O to N2 reached 57% at 700 Υ . Also,
decomposition of N2O to N2 over NZL BC began at
400Υ, and the conversion of N2O to N2 reached 37%
at 700Υ. The specific surface area of each sample
was NZL (BC): 49.41 m2/g , NZS (BC): 31.63 m2/g ,NZL (AC): 37.67 m2/g , and NZS (AC): 27.85 m2/g .
This result indicated that there are decreases ofactivity site on the surface because of decreasing of
surface area by sintering. Also, structural destructionof clinoptiolite was confirmed by XRD analysis.

Table.1 The amount and rate of Cu exchanged to each sample

3.4 The effect of calcination temperature on
catalytic
activity
for N2O
thedecomposition
Figure 4 shows the catalytic activity results of NZS
and NZL prepared at different calcination temperature
for the decomposition of N2O to N2. NZS was
calcined at 500Υ-700Υ for 5 h.

From Table.1, in both NZ, there are increases in theamount of Cu exchanged by loading more Cu
precursor. However, the result of the catalyst activity
3
119

(0.72 nm0.44 nm).

Fig. 6 XRD patterns of Cu/NZSIn Figure 6, increased intensity of CuO peak was
observed with increased content of Cu. It indicated Cu
exists as CuO on the surface of NZS. It also
indicates that NZS is not covered with Cu completely
but some of Cu sticks together as can be observed in
the SEM-EDS results.

Fig. 4 The effect of calcination temperature on catalytic
activity for N2O decomposition

It is shown that the conversion of N2O was similar
despite the change in calcination temperature. The
specific surface area of each sample was NZL AC
500Υ: 37.67 m2/g , NZS AC 500Υ: 27.85 m2/g ,NZS AC 600Υ: 20.54 m2/g , NZS AC 700Υ: 19.46
m2/g . They indicate that specific surface area ofcatalyst did not affect the activity significantly forN2O decomposition.

3.5 XRD results

Fig. 7 XRD patterns of Cu-NZS

Figure 5-7 show the results of XRD analysis ofeach catalyst.

In Figure 7, no change in the intensity of CuO
peak was observed with increased content of Cu. It
indicated most of Cu exists as Cu2+ in the structure of
NZS.

4. Conclusions
Cu(5wt%)/NZS exhibited the highest catalytic
activity for N2O decomposition, and N2O wasdecomposed completely at 550ć. In the geometricalcalculation, when NZ surface was covered with CuO
completely, the catalytic activity for thedecomposition of N2O reached the highest value.
Catalysts prepared by impregnation method showed
higher catalytic activity than the catalysts prepared byion-exchange method. NZS with different calcination
temperatures showed similar catalytic activity.

Fig. 5 XRD patterns of NZS and NZL

Figure.5 shows that NZS mainly contains
clinoptilolite and mordenite and NZL contains
clinoptilolite and albite. In the activity test, NZS
catalysts showed higher activity than that of NZL.
This might be attributed to structural differences
between NZS and NZL. Mordenite structure has big
and round pore (0.70 nm 0.65 nm) in which N2O
molecule can easily enter compared to clinoptilolite

References[1] S. Kannan, Appl. Clay Sci. 13 (1998) 347[2] P. Esteves, Y. Wu, C. Dujardin, M.K. Dongare, P.
Granger, Catal. Today 176 (2011)[3] ᒾᮏ ṇ࿴ࠊ ௚ ⦅㞟ࠊ͆⎔ቃゐ፹ࣁࣥࢻࣈ
ࢵࢡ͇ࠊ࢚ࢾ࣭ࢸ࢕࣮࣭࢚ࢫ㸦2001㸧
4
120

Water vapor resistance of Fe-Mn/(Ti,Zr)2O4 for NO reduction
Student Number: 14M18170 Name: Tomoaki Mitsui Supervisor: Hirofumi HinodeFe ࡜ Mn ࢆᢸᣢࡋࡓ(Ti,Zr)2O4 」ྜゐ፹ࡢ NO 㑅ᢥ㑏ඖ࡜⪏Ỉ⵨Ẽᛶ୕஭ ᬛ❶
ᮏ◊✲࡛ࡣࠊ᤼Ẽ࢞ࢫෆ࡟ΰᅾࡍࡿⅣ໬Ỉ⣲ࡢࣉࣟ࣌ࣥࢆ㑏ඖ๣࡜ࡋࡓ NO ࡢ㑅ᢥ㑏ඖ (HC-SCR) ࡟㐺ᛂࡋࡓゐ፹ࡢᐇ⏝໬࡟ྥࡅࠊάᛶ࡜ඹ࡟⪏Ỉ⵨Ẽᛶࡢྥୖࢆ┠ⓗ࡜ࡋࡓࠋྵ౵ἲࠊᶵᲔⓗΰྜἲࠊࡑࡋ࡚ỿ╔ỿẊἲ࡟ࡼࡗ࡚ Fe ࡜ Mn ࢆᢸయࡢ(Ti,Zr)2O4 」ྜゐ፹ୖ࡟ᢸᣢࡋࠊάᛶཬࡧ≉ᛶࡢኚ໬࡟ࡘ࠸᳨࡚ウࡋࡓࠋ
3 ㏻ ࡾ ࡢ ᢸ ᣢ ᪉ ἲ ඲ ࡚ ࡛ ᢸ య ࡢ (Ti,Zr)2O4 ࡼ ࡾ ά ᛶ ࡀ ప ୗ ࡋ ࡚ ࡋ ࡲ ࡗ ࡓ ࡀ ࠊ ỿ ╔ ỿ Ẋ ἲ ࡛ ㄪ 〇 ࡋ ࡓFe-Mn/(Ti,Zr)2O4-DP ゐ፹ࡀ≉࡟㧗࠸⪏Ỉ⵨Ẽᛶࢆ♧ࡋࡓࠋ

catalytic oxidation of NO. Fe has high water vapor
and SOx resistance and Mn has high activity andselectivity in NOx reduction [5]. Thus loading Fe andMn on (Ti,Zr)2O4 might increase catalyst activity andwater vapor resistance.
Therefore, in this study, the catalytic activities of(Ti,Zr)2O4 impregnated, manual mixed anddeposited-precipitated with metal oxides were
investigated. Then, catalytic activity and water vapor
resistance of catalysts were compared to determine thebest method and the optimum ratio of the preparedcatalyst for NOx reduction.

1. Introduction
Nitrogen oxides (NOx) are mainly consists of nitricoxide (NO) and nitrogen dioxide (NO2). NOx causes
atmospheric environmental problems such as
photochemical smog, acid rain, ozone depletion andgreenhouse effects. Furthermore, presence of nitrogenoxides in the atmosphere might cause problems tohuman health such as respiratory system illness. Mostof the NOx are emitted from automobiles (52%) and
fixed sources (35%) using fossil fuels.
Nowadays, three-way catalysts (TWC) are used toreduce NOx from automobiles. TWC can reduce NOx
with high efficiency at specific air-fuel ratio(A/F=14.7). However, the development of lean-burnengines (A/F = 20~60) that have higher fuel economy
and cleaner emissions requires another type of catalyst
for NOx reduction. One common method to reduce
NOx in exhaust gas is selective catalytic reduction ofNO using hydrocarbon as reducing agent (HC-SCR of
NO) [1]. Among several applicable methods, HC-SCR
using hydrocarbon contained in the exhaust is
currently the most studied technology for NOx
reduction.
The main reactions taking place during SCR
process are the following:
2NO+O2 → 2NO2
(1)
3NO+3NO2+C3H6 → 3N2+3CO2+3H2O(2)

2. Experimental
2.1 Materials(Ti,Zr)2O4 catalyst was prepared by co-precipitation
method using Zr(NO3)2࣭2H2O and [(CH3)2CHO]4Ti as
precursors with the same molar ratio of Ti and Zr.
First, Zr(NO3)2 䞉 2H2O was dissolved in deionizedwater with continuous stirring and the pH wascontrolled by addition of HNO3 (pH=0.5-1.0). Then[(CH3)2CHO]4Ti was added to the solution and the pH
was changed by addition of NH3 solution (pH=9-11).
On each steps, the mixed solution was stirred for 30
minutes. The resulting mixture was aged for 8 h,filtered, and dried at 100 Υ overnight and thencalcined at 550Υ in air for 4 h.
Fe-Mn/(Ti,Zr)2O4-IMP catalyst was prepared byimpregnation (IMP) method using Fe(NO3)3࣭9H2O,
Mn(NO3)2 ࣭ 6H2O and (Ti,Zr)2O4 as precursors.
Fe(NO3)3࣭9H2O, Mn(NO3)2࣭6H2O and (Ti,Zr)2O4
were added to deionized water in a beaker with steadystirring at room temperature for 24 h, then dried at100Υ overnight, and calcined at 550Υ in air for 3 h.
Fe2O3+Mn2O3+(Ti,Zr)2O4-MM
catalyst
wasprepared by manual mixing (MM) using Fe2O3, MnO2and (Ti,Zr)2O4. Fe2O3, MnO2 and (Ti,Zr)2O4 weremixed together manually with ethanol, then dried, andPrevious study reported that (Ti,Zr)2O4 composite
used as support material exhibited higher catalytic
activity towards HC-SCR of NO when compared tobare TiO2 [2]. (Ti,Zr)2O4 is also known of its highsurface area and high mechanical strength, and is
widely used in many reactions as a catalyst [3].
Another study demonstrated that Mo+(Ti,Zr)2O4
showed high activity (80% NO removal), however thecatalyst was deactivated by the presence of water
vapor [4]. On the other hand, Fe-Mn/TiO2 shows highactivity and high water vapor resistance in selective
121

calcined at 550Υ in air for 3 h.
Fe-Mn/(Ti,Zr)2O4-DP catalyst was prepared bydeposition precipitation (DP) method using
Fe(NO3)3࣭9H2O, Mn(NO3)2࣭6H2O and (Ti,Zr)2O4 as
precursors. Fe(NO3)3࣭9H2O, Mn(NO3)2࣭6H2O and(Ti,Zr)2O4 were added to deionized water in a beaker
and the pH was changed by addition of ammonium
carbamate (1 mol/L) until pH = 7 with steady stirring
at room temperature for 1 h then dried at 100Υovernight, and calcined at 550Υ in air for 3 h.

than unloaded (Ti,Zr)2O4 catalyst. However, among
IMP catalysts, the higher the loading metal content,
the higher the activity of catalyst. Furthermore,
addition of Fe and Mn have lowered the required
reaction temperature for the reaction to proceed.

2.2 Catalytic Activity Test
The HC-SCR activity of prepared catalysts wasdetermined in a continuous fixed-bed flow reactor.
The reactant gas was composed of 1500 ppm NO,
10% O2, 1500 ppm C3H6 and He as a balance gas.
1.0~1.5 g of catalyst was used with a corresponding
space velocity of 13000 h-1. All prepared catalysts
were pelletized, crushed and sieved to 0.71-1.00 mm.
The temperature was changed stepwise from 150Υ to500Υexcept for (Ti,Zr)2O4.
The NO and NO2 concentrations were analyzed by
NOx analyzer (Shimadzu, NOA-7000) while CO2 wasanalyzed using gas chromatograph with thermal
conductivity detector (GL Science, GC-3200). The
catalysts were characterized by TG-DTA, XRD, N2
adsorption (Autosorb 1MP/TSU, BET analysis) andSEM-EDS.

Fig.2 Catalytic activity of Fe2O3+Mn2O3+(Ti,Zr)2O4
catalyst for the reaction of NO to N2 using C3H6 as a
reductantFig.2 shows the conversion of NO to N2 over
Fe2O3+Mn2O3+(Ti,Zr)2O4-MM catalyst by MM
method. MM catalysts show decreasing activity as theloading metal content increased. MM catalysts havewide temperature zone at which it shows activity.
However, MM method does not lead to activity
improvement, similar to IMP method.

3. Results and Discussion
3.1 Catalytic Activity

Fig.3 Catalytic activity of Fe-Mn/(Ti,Zr)2O4-DP
catalyst for the reaction of NO to N2 using C3H6 asa reductant

Fig.1 Catalytic activity of Fe-Mn/(Ti,Zr)2O4-IMP
catalyst for the reaction of NO to N2 using C3H6 asa reductant

Fig.3 shows the conversion of NO to N2 over
Fe-Mn/(Ti,Zr)2O4 catalyst by DP method. DP method
does not lead to activity improvement, similar to theother 2 methods. Differences with the other 2 methods
are that loading levels do not give significant effect toactivity and that the temperature of activity is 50Υ
lower than the other 2 methods.

Fig.1 shows the conversion of NO to N2 over
Fe-Mn/(Ti,Zr)2O4 catalyst by IMP method with Fe andMn loading levels of 0.075-0.3 and 0.15-0.6 molar
ratios, respectively, compared to (Ti,Zr)2O4 catalyst.
Metal loaded catalyst showed lower catalytic activity

122

3.2 Effect of Water Vapor
 In this section, only the result of catalysts which
are loaded with 0.3 of Fe and 0.6 of Mn for eachloading methods are shown.

Fig.6 Activity comparison of Fe0.3-Mn0.6/(Ti,Zr)2O4-DP
and Fe0.3-Mn0.6/(Ti,Zr)2O4-DP [WV] for the reaction of
NO to N2 using C3H6 as a reductant

Fig.4 Activity comparison of Fe0.3-Mn0.6/(Ti,Zr)2O4-IMP
and Fe0.3-Mn0.6/(Ti,Zr)2O4-IMP [WV] for the reaction of
NO to N2 using C3H6 as a reductant (WV means Water
Vapor was introduced to the system)Fig.6 shows the effects of water vapor to
Fe0.3-Mn0.6/(Ti,Zr)2O4-DP catalyst. Like in IMP and
MM methods, the peak of NO reduction shifts tohighertemperature.
Fe0.3-Mn0.6/(Ti,Zr)2O4-DP
catalyst has the highest water vapor resistance which
is 95.0%.

Fig.4 shows the effects of water vapor on
Fe0.3-Mn0.6/(Ti,Zr)2O4-IMP catalyst. In the system
where water vapor exists, the peak of NO reduction
shifts to higher temperature.
Fe0.3-Mn0.6/(Ti,Zr)2O4-IMP catalyst shows highwater vapor resistance which is 77.6% and has activity
throughout the temperature range of the experimentcompared to (Ti,Zr)2O4 catalyst which shows noactivity between 150~350ΥFe0.3-Mn0.6/(Ti,Zr)2O4-IMP catalyst shows 32% No
to N2 conversion which is the highest activity among 3
loading methods tested with 10% water vapor.

Table.1 Comparison of water vapor resistance
Fe0.075-Mn0.15

Fe0.15-Mn0.3

Fe0.3-Mn0.6

IMP

63.1%

65.5%

77.6%

MM

45.2%

60.9%

50.0%

DP

59.6%

76.8%

95.0%

Table.1 shows the comparison of water vapor
resistances of all catalyst. Here, water vapor resistanceis defined by max NO reduction activity [WV]
divided by max NO reduction activity. For comparison,water vapor resistance of (Ti,Zr)2O4 catalyst is 59.7%.
Table.1 showed that compared to (Ti,Zr)2O4 catalyst
two of the DP catalysts have higher water vapor
resistance while one has similar resistance. DP
catalysts also showed the highest water vapor
resistance. All IMP catalysts have higher resistance,and one of MM catalysts has a higher water vapor
resistance. Therefore, it is concluded that the water
vapor resistance performance of catalysts is as follows,
DP catalysts > IMP catalysts > MM catalysts.

Fig.5 Activity comparison of Fe0.3+Mn0.6+(Ti,Zr)2O4-MM
and Fe0.3+Mn0.6+(Ti,Zr)2O4-MM[WV] for the reaction of
NO to N2 using C3H6 as a reductant

3.3 Catalyst Characterization
TG-DTA, XRD, N2-adsorption method andSEM-EDS are used to characterize each catalyst.
Depending on the loading methods and levels,characteristics varies. In this section, the significant
characteristics of the catalysts are discussed. It isthought that characteristic which affects activity ofNO reduction mostly is shown by SEM-EDS analysis
data.

Fig.5 shows the effects of water vapor to
Fe0.3+Mn0.6+(Ti,Zr)2O4-MM catalyst. Similarly, thepeak of NO reduction shifts to higher temperature andFe0.3+Mn0.6+(Ti,Zr)2O4-MM catalyst shows 50%
water vapor resistance. MM catalysts have wide
temperature zone which shows activity despite thepresence of water vapor.

123

result of Fe ions and Mn ions penetration in pores,
therefore the specific surface area decreased.
Table.2 Comparison of specific surface area
SampleSurface Area
2

(Ti,Zr) O2

240.3 m /g

4

Fe0.075-Mn0.15/(Ti,Zr) O -IMP2

4

Fe0.15-Mn0.3/(Ti,Zr) O -IMP

Fig.7 EDS data of IMP catalysts about Fe and Mn

2

4

Fe0.3-Mn0.6/(Ti,Zr) O -IMP2

4

Fe0.075+Mn0.15+(Ti,Zr) O -MM
2

4

Fe0.15+Mn0.3+(Ti,Zr) O -MM
2

4

Fe0.3+Mn0.6+(Ti,Zr) O -MM
2

4

Fe0.075-Mn0.15/(Ti,Zr) O -DP
2

4

Fe0.15-Mn0.3/(Ti,Zr) O -DP
2

4

Fe0.3-Mn0.6/(Ti,Zr) O -DP
2

4

2

16.5 m /g
2

16.9 m /g
2

90.4 m /g
2210.9 m /g2

169.6 m /g
2

177.4 m /g
2160.0 m /g
2

155.4 m /g2

151.1 m /g

Fig.8 EDS data of MM catalysts about Fe and Mn

4. Conclusions
As Fe and Mn have ability to improve lowtemperature activity, activity became better at low
temperature by adding Fe and Mn. However,maximum activity of Fe-Mn/(Ti,Zr)2O4 catalyst did
not exceed maximum activity of (Ti,Zr)2O4 catalyst
for all loading methods.
Water vapor resistance in NO reduction wasincreased by adding Fe and Mn. The best water vapor
resistanceof
Fe-Mn/(Ti,Zr)2O4
is
95.0%(Fe0.3-Mn0.6/(Ti,Zr)2O4-DP catalyst) compared towater vapor resistance of (Ti,Zr)2O4 (59.7%).
The order of water vapor resistance is as follows, DP
catalysts > IMP catalysts > MM catalysts.
The most important factor influencing the activity ofcatalyst in the presence of water vapor is dispersion
and concentration of loaded metals on support surface.
Specific surface area did not affect the activity
significantly.

Fig.9 EDS data of DP catalysts about Fe and Mn
Fig.7 to Fig.9 show Fe and Mn distribution on
support surface for each of three loading methods.
From Fig.7, Fe and Mn are highly dispersed but
concentrations of Fe and Mn on the surface of the
support are low from IMP catalysts. From Fig.8, Fe
and Mn are concentrated locally for MM catalysts. In
other words, dispersion of Fe and Mn is the poorest in
this method. From Fig.9, Fe and Mn are highly
dispersed and concentrations of Fe and Mn on support
surface are the highest for DP catalysts.
These characteristics affect the activity in thepresence of water vapor, however, it does not affectthe activity when the water vapor is not present.

References[1] R.Burch et al, Appl.Catal.B, 11, (1997) 207[2]M.Haneda et al, Catal. Today, 42, 127-135 (1998)[3]B.M.Reddy et al, Appl. Catal. A, 228 (2002)
269-278[4]Kim Dongil, Master Thesis, Tokyo Institute ofTechnology (2014)[5]Mengying Zhang, et al, Appl. Surface Science 300(2014) 58-65

In general, it is said that the higher the specificsurface area the catalysts have, the higher the catalysts
activity. However, according to the data in Table.2,
specific surface area is not connected to NO reduction
activity in this study.
For example, IMP catalyst that has the highest
activity has low specific surface area. This is because
the pores were filled by Fe oxides and Mn oxides, as a

124

STUDY OF CONSTRUCTION PERFORMANCE AND DURABILITY OF
SEAWATER MIXED ANTI-WASHOUT CONCRETE IN COLD ENVIRONMENT
Student ID: 14M18193

Name: Yuma YOSHIDASupervisor: Nobuaki OTSUKI

ᐮ෭⎔ቃ࡟࠾ࡅࡿᾏỈ⦎ࡾỈ୰୙ศ㞳ᛶࢥࣥࢡ࣮ࣜࢺࡢ
᪋ᕤᛶ⬟࡜⪏ஂᛶࡢ᳨ウྜྷ⏣ ♸㯞 ᮏㄽᩥ࡛ࡣ㸪ᖺ㛫ࡢᖹᆒẼ ࡀịⅬ࡟㏆࠸ᐮ෭⎔ቃ࡟࠾࠸࡚Ỉ୰୙ศ㞳ᛶࢥࣥࢡ࣮ࣜࢺࡢ⦎ΰࡐỈ࡟ᾏỈࢆ⏝࠸ࡓሙྜࡢ㸪᪋ᕤᛶ⬟㸦ᙉᗘ㸪ὶືᛶ㸧࡜ሷᐖ࡟ᑐࡍࡿ⪏ஂᛶࡢᇶ♏᳨ウࢆ⾜ࡗࡓ㸬ᙉᗘ࡟ࡘ࠸࡚ࡣᮦ㱋ึᮇࡢ◳໬ಁ㐍ࡀ࠶ࡾ㸪ᐮ෭⎔ቃ࡟࠾࠸࡚ࡣࡑࡢຠᯝࡀࡼࡾ㢧ⴭ࡟࡞ࡿࡓࡵ㸪ᾏỈࡢ⦎ࡾΰࡐỈ࡬ࡢ฼⏝ࡣ᭷฼࡜࡞ࡗࡓ㸬⪏ஂᛶ࡟ࡘ࠸࡚ࡣỈ୰ᡴタ᫬࡟ከ㔞ࡢሷ໬≀࢖࢜ࣥࡀ㕲➽࡟௜╔ࡍࡿࡇ࡜࠿ࡽ㸪⭉㣗㏿ᗘ࡟ᑐࡍࡿᾏỈ⦎ࡾࡢᙳ㡪ࡀᑠࡉࡃ࡞ࡗࡓ㸬ࡲࡓ㸪ᑑ࿨ࢆ⟬ฟࡋࡓ࡜ࡇࢁ㸪ᐮ෭⎔ቃ࡟࠾ࡅࡿᾏỈ⦎ࡾࡋࡓỈ୰୙ศ㞳ᛶࢥࣥࢡ࣮ࣜࢺࡢ࠿ࡪࡾ 70mm ࡢ RC ࡢሙྜ 32.5 ᖺ࡜࡞ࡗࡓ㸬ࡑࡢࡓࡵ㸪ᮏ◊✲࡛ࡣᐮ෭ᆅ࡟࠾ࡅࡿỈ୰୙ศ㞳ᛶࢥࣥࢡ࣮ࣜࢺ࡬ࡢᾏỈ฼⏝ࡣ༑ศྍ⬟࡛࠶ࡿ࡜⤖ㄽ௜ࡅࡓ㸬

1. Introduction
1.1 Background

2. Experiment outlines
2.1 Compressive strength test

Recent years, a long term plans to exploit the vast natural
gas are scheduled in Arctic region, for example Yamal
peninsula, Russia, and large amount of concrete will be
used. Arctic region has less rainfall and cold temperature,and construction sites exist in backcountry. These
conditions are disadvantageous for water procurement.
Because LNG plant site is at the seaside, seawater is easy
to get. So, if seawater is available to use, construction
cost will be reduced. However, seawater use as mixing
water of concrete is prohibited because of containing
chloride ion which makes reinforcement corrode.
Seawater using affects to some property of concrete.
In term of construction performance, several ions which
are included in seawater accelerate the hardening of
cement. Consistency of seawater mixed concrete tends tobe worse. In term of durability, according to the
Arrhenius theory, temperature affects to speed ofchemical reaction and diffusion, in brief low temperature
makes them slow. It is believed that this theory also canbe applied to the corrosion of reinforcement, so lowtemperature condition contributes to prevention of
corrosion. In past studies, the temperature dependency ofcorrosion is obtained. But, it has been studied only attemperatures above room temperature (20Υ) [1].

The φ50x100mm cylinder mortar specimens are used formeasuring the compressive strength. The mix proportion
have 2 levels of mixing water (freshwater, seawater), 3
levels of cement (OPC, BFC, FA mix), and eachspecimens are cured in 2 temperatures (0Υ, 20Υ).
While the casting, shown as Fig.1, mold is set under
the artificial seawater whose water level is set above the
10cm of top of mold, fall the mortar into mold by spoon,and cast without tamping and vibration. Each specimen
is cured in the artificial seawater until the test age.
Temperature is controlled while the casting and curing.
The tests are done at the time of 3, 5, 7, 14, 28, and 91
day age.

1.2 Objectives

OPC mortar whose mix proportion have 2 levels of
mixing water (freshwater, seawater) and 3 levels of watercontent (350, 365, 380 kg/m3) are used. For control thetemperature, flow test was done in the water. From theresult of preliminary experiment, there was no significant
difference in the in water and in the air.

Fig.1 Casting of anti-washout mortar under water

2.2 Consistency test
In this study, the target is underwater anti-washout
concrete, and following objectives have been set for
discussing the use of seawater as mixing water in cold
environment. In this study, cold environment means 0Υ
which is the average temperature of seawater around the
Yamal peninsula.
1. To clarify the construction performance (in thisstudy, compressive strength and consistency) of
seawater mixed anti-washout concrete in cold
environment.
2. To clarify the durability against chloride attack ofseawater mixed anti-washout concrete in cold
environment.

2.3 Cl- diffusion coefficient calculation
The φ50x100mm cylinder mortar specimens are used for
calculating the chloride ion diffusion coefficient. The
mix proportions have 2 levels of mixing water(freshwater, seawater). Temperature is controlled while
casting-curing and exposure (20Υand 20Υ, 20Υand
0Υ, 0Υand 0Υ). Cement type is only OPC. Specimens125

To evaluate the seawater effect of strength
development promotion in cold environment, strength
prediction curve were made with using the maturity(Eq.1) and Gomperz curve (Eq.2).
(Eq.1)
M = å (T + 10)× Dt(Eq.2)
f c¢ = F exp{- a exp(- b log M )}Note: M :Maturity (Υ࣭day),Dt : Material age (day),
T : Temperature (Υ),
f c¢ : Compressive strength (N/mm2),
F : Ultimate strengtha , b : Constant
From this strength prediction curve, the required days
for remolding (strength for remolding is supposed
14N/mm2) were obtained. Table.1 shows the result ofthem. From the result, seawater use makes required days
3-6 days faster than freshwater use in cold environment.
So, seawater use is advantageous for strength
development in cold environment.

were casted under the artificial seawater same as section
2.1 specimens.
Specimens are coated with epoxy resin except top
surface, exposed to artificial seawater which temperature
is controlled among 3 months.

2.4 Oxygen permeability calculation
corrosion behavior measurement

andThe steel bar mortar specimens are used for oxygen
permeability calculation and corrosion behavior
measurement. φ9x100mm SR235 steel bar is set in thecenter with cover depth is 10mm. To control the cover
depth, sides are covered with epoxy resin. Specimens
were casted as section 2.1 specimens (steel bars wereonce exposed to seawater).
The mix proportion have 2 levels of mixing water(freshwater, seawater), 3 levels of cement (OPC, BFC,
FA mix), and each specimens are cured in 2 temperatures(0Υ, 20Υ). W/C is 50% which is the maximum while
use in marine environment.
Specimens are exposed artificial seawater which
temperature is controlled and measurement by corrosion
monitor will be done continuously among the 5 months.
Until the finish of exposure, self-potential andpolarization resistance was measured in each month.
After the exposure, the polarization curve for oxygen
permeability, Cl- content on steel bar and mortar matrix
are measured.

Table.1 Required day for remold in 0Υ㻯㼑㼙㼑㼚㼠㻌㼀㼥㼜㼑 㻹㼕㼤㼕㼚㼓㻌㼣㼍㼠㼑㼞㻻㻼㻯㻮㻲㻯㻲㻭㻌㻹㼕㼤

3. Performance of anti-washout concrete mixed
with seawater in cold environment
3.1 Compressive strength

㻰㼍㼥

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3.2 Consistency
Fig.3 shows the flow value of each experiment case.
From this result, both of seawater use and cold
temperature contribute to be worse consistency. However,compared with seawater use 0Υ and freshwater 20Υ,the same flow value can be obtained by way of watercontent increase 24kg/m3. In term of consistency,
seawater can be used in cold environment with setting
the design of mix proportion.

Fig.2 shows the strength development and 91days
compressive strength. According to time series of 0Υ
case, strength increases continually. This indicates thatfrost of concrete did not occur.
In early age, cases of seawater mixed show higher
strength than freshwater. However, difference of mixing
water did not appear at the 91 day.
At 91 material day, difference of strength between
20Υ and 0Υ is 2-6N/mm2, the hydration delay of
cement types which is added admixture was large
compared with OPC. However every mix proportions in0Υ have more over 20 N/mm2 strength.

Fig.3 Flow Value

4. Diffusion property of anti-washout concrete
mixed with seawater in cold environment
4.1 Cl- diffusion coefficient
To clarify chloride ion diffusivity, with using specimens
shown in section 2.3. The way to evaluation is conformto Japan Society of Civil Engineers JSCE-G 572-2013.
And the Fick's diffusion equation was used. Diffusion
coefficient was calculated by fitting Cl- distribution data
to the solution of Fick's diffusion equation (Eq.3).

Fig.2 Result of compressive strength test

126

{

(

C (x, t ) = C 0 1ࠉ- erf x 2 Dap × t)}

E a = - A ´ (R log10 e) = -4.58 ´ A(Eq.5)
Note: E a : Activation energy (kcal/mol),A : Slope of the plot,
R : Gas constant (= 1.99103 kcal/K࣭mol),As shown in Fig.6, in this study, logarithm of
permeability and reciprocal of temperature shows thelinear relation and calculated activation energy was about
13-20kcal/mol.

(Eq.3)

Note: C (x, t ) : Cl- content (kg/m3) at x (cm) fromthe surface after t (year) exposure time,
C 0 : Cl- content (kg/m3) at the surface
D ap : Cl- diffusion coefficient (cm2/year)
As shown in Fig.4, chloride ion diffusion coefficientin cold environment is much lower than 20Υ, about 4times. And seawater use makes chloride ion diffusion
coefficient lower than freshwater. It might be that initialchloride ion content induced by seawater decrease thedifference of chloride ion concentration between mortar
and exposure environment. In cold environment, thedifference of coefficient between freshwater andseawater is small.

Fig.6 Arrhenius plot and activation energy(Oxygen permeability)
This value is relatively higher than previous study(15Υ-35Υ) [2] but roughly match. Arrhenius law was
also able to be applied in case of anti-washout concretein cold environment.

5. Corrosion behavior of anti-washout concrete
mixed with seawater in cold environment

Fig.4 Cl- diffusion coefficient

Until the end of exposure, corrosion current density (Icorr)
values of every specimen are lower than 0.2μA/cm2(value of threshold of corrosion start proposed by CEB).
Fig.7 shows the corrosion rate at the end of exposure.
This figure indicates the difference of temperature is verylarge compared with difference of mixing water. BFC
and FA mix shows the higher value than OPC.

4.2 O2 permeability
To clarify oxygen permeability, limit current density
obtained from cathodic polarization curve of steel bar inmortar specimens. Oxygen permeability is obtained fromthe Faraday’s law (Eq.4).
(Eq.4)
J = - i lim nF
Note: J : Oxygen permeability (mol /cm2࣭sec),
i lim : Limit current density (μA /cm2),
n : Number of electron exchanged (= 4),
F : Faraday constant (= 96500 C/mol)
As shown Fig.5, oxygen permeability in cold wasvery low compared with 20Υ. Then cold environment
contribute to delay the steel bar corrosion.
In OPC case, seawater use makes oxygen
permeability high, but in BFC and FA case, seawater use
makes oxygen permeability low. This trend is the same
between 20Υ and 0Υ.

Fig.7 Comparison of corrosion rate (5month)
To clarify this result, chloride ion content on steel bar
and in mortar matrix was compared in Fig.8. According
to the correlation between Icorr and chloride content on
steel bar was higher than and in mortar matrix.
While the casting of anti-washout concrete, arranged
steel bars are dunk into seawater directly. So, muchchloride ion is on the surface of steel bar.
Average of chloride content on steel bar (about
13μg/cm2) was not changed in each mix proportion.
This is why difference of corrosion behavior between
freshwater mixed and seawater mixed was small.

Fig.5 Oxygen permeability
To check the effect of temperature to oxygen
permeability, Arrhenius plot shown in Fig.6 was made.
From the slope of plot, activation energy was obtainedwith using (Eq.5).

Fig.8 Comparison of Cl- effect (on steel vs. in mortar)127

mixed. But, lifetime of propagation period between
freshwater mixed and seawater mixed is small.
If more lifetimes are demanded, by changing thecement type from OPC to BFS or FA mix in the mix
proportion design propagation period can be longer asshown Table.2, or W/C decreasing because of oxygen
permeability decreasing.

To check the effect of temperature to corrosion rate,
Arrhenius plot shown in Fig.9 was made. From the slopeof plot, activation energy was obtained with using (Eq.5).

Table.2 Result of the lifetime prediction(Cover depth = 70mm)
Fig.9 Arrhenius plot and activation energy(Corrosion current density)㻲㼞㼑㼟㼔㼣㼍㼠㼑㼞㻌㼙㼕㼤㼑㼐㻵㼚㼏㼡㼎㼍㼠㼕㼛㼚As shown in Fig.10, the activation energy of oxygen
permeability and corrosion rate is almost the same. This
indicates, corrosion rate reaction is oxygen rate-limiting.

㻿㼑㼍㼣㼍㼠㼑㼞㻌㻹㼕㼤㼑㼐㻼㼞㼛㼜㼑㼞㼓㼍㼠㼕㼛㼚 㻵㼚㼏㻌㻗㻌㻼㼞㼛㻵㼚㼏㼡㼎㼍㼠㼕㼛㼚㻼㼞㼛㼜㼑㼞㼓㼍㼠㼕㼛㼚 㻵㼚㼏㻌㻗㻌㻼㼞㼛㻻㻼㻯㻌㻞㻜䉝

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㻲㻭㻌㻜䉝

㻝㻠㻚㻡

㻝㻠㻚㻤

7. Conclusion
In this thesis, the performance (compressive strength andconsistency) and durability against chloride attack areclarified. From the experiments, conclusions are obtained
as follows;
1. Even in cold environment, anti-washout mortar
has enough strength (over 20N/mm2). Because
seawater use promotes strength development,
required day for remold of seawater mixed ismore 3 days faster than freshwater mixed.
2. For consistency, both of cold environment andseawater use makes consistency bad. However,with increasing water content about 24kg/m3,
the same consistency of freshwater mixed in20Υ can be obtained in case of seawater mixedin 0Υ.
3. Difference of corrosion rate after 5 months
exposure between freshwater mixed andseawater mixed was small, because steel bar is
exposed to seawater while the casting in case ofanti-washout concrete.
4. Lifetime of seawater mixed anti-washout RC is32.5 years in cold environment. Especially,
difference of lifetime of propagation period
between freshwater mixed and seawater mixed
is small.
From these results, seawater can be used as mixing
water of anti-washout concrete in cold environment.

Fig.10 Comparison of activation energy

6. Lifetime prediction of anti-washout RC
mixed with seawater in cold environment
As shown in Fig.11, deterioration of RC divided somestage. And critical factor is different in each period. In
this study, lifetime is defined the sum of incubation
period and propagation period.

Fig.11 Concept of RC lifetime in term of corrosion
Incubation period is controlled by chloride ion
diffusion. For calculation of lifetime of this period,
chloride ion diffusivity coefficient which obtained in
section 4.1 and chloride ion concentration around thesteel bar obtained in chapter 5 are used. In this study,
corrosion threshold chloride ion content was not obtained,so latter value was evaluated on the safe side.
Propagation period is controlled by oxygen
permeability. For calculation of lifetime of this period,
amount of corrosion product for cracking proposed by
Yokozeki[3], and corrosion rate during propagation
period which is calculated by oxygen permeability
obtained from section 4.2 and relationship between
oxygen permeability and corrosion rate[4].
Table.2 shows the result of lifetime calculation. As aresult, lifetime of seawater mixed anti-washout RC is32.5 years in cold environment. This is enough long touse. Because of initial Chloride ion content, incubation
period of seawater mixed is shorter than freshwater

8. Reference[1] Takahiro Nishida, “Influence of Temperature on
Deterioration Process of Reinforced Concrete Members
Due to Steel Corrosion, Doctoral thesis, 2006
[2] C. L. Page et al., “Kinetics of oxygen diffusion in
hardened cement pastes”, Journal of Materials Science22, pp.942-946, 1987
[3] K. Yokozeki et al., “A Rational Model to Predict
Service Life of RC Structures in Marine Environment”,
Forth CANMET/ACI International Conference on
Durability of Concrete 1, 1997[4] S. Maruyama, “Influence of Temperature and Cement
on Corrosion Rate of Steel Bar under Carbonation”,
Master thesis of Tokyo Institute of Technology, 2004 (inJapanese)128

Design and control of switching regulator for energy harvesting considering
output characteristic of generator
Student Number: 11M18048

Name: Daisuke IWASAKI

Supervisor: Kunio TAKAHASHIⓎ㟁ᶵ䛾ฟຊ≉ᛶ䜢⪃៖䛧䛯䜶䝛䝹䜼䞊䝝䞊䝧䝇䝔䜱䞁䜾⏝䝇䜲䝑䝏䞁䜾䝺䜼䝳䝺䞊䝍䛾タィ䛚䜘䜃ไᚚ᪉ἲ㻌ᒾᓮ ኱㍜ᮏㄽᩥ䛷䛿㟁ຊ※䛻䠍䠌V ௨ୗ䛾 DC 䛸 AC 䛾Ⓨ㟁ᶵ䜢⏝䛔䚸୍ᐃ䛾ᅇ㌿㏿ᗘ䛻䛚䛡䜛㟁ὶ䛸㟁ᅽ≉ᛶ䜢ᐇ㦂ⓗ䛻ィ䛩䜛䛣䛸䛻䜘䜚Ⓨ㟁ᶵ䛾᭱኱ฟຊⅬ䛾᥎ᐃ䜢⾜䛳䛯䚹᭦䛻䠈➼౯ᅇ㊰䜢ᥦ᱌䛧䠈Ⓨ㟁ᶵෆ㒊䛾᢬ᢠ䛸䜲䞁䝎䜽䝍䞁䝇䜢ᐇ㦂್䛛䜙᥎ᐃ䛩䜛䛣䛸䛷䜶䝛䝹䜼䞊䝝䞊䝧䝇䝔䜱䞁䜾⏝᪼ᅽᆺ䝇䜲䝑䝏䞁䜾䝺䜼䝳䝺䞊䝍䛾タィ᪉ἲ䛸ไᚚ᪉ἲ䛻䛴䛔䛶᳨ウ䛧䛯䚹

1Introduction

Figure 1 shows the wind generator system for energy
harvesting. The wind power can be converted into

Today the technology of energy harvesting is in themechanical power by the turbine blade, and then into

spotlight. For example there are tiny power sources

electric power by the generator. Since the generated

surrounding us likeelectric power still has low voltage, a boost switchingsolar power under weak light ora wind power in the breeze.

However these power

regulator is required to enable the storage of thesources can’t be used directly because their output are

generated electric power. The boost switchingsmall or influenced by environmental variation. On

regulator can control the switching period and duty

the while the total amount of these energies in theratio to boost up the output voltage of the generator

world

and

can

cover

our

demandonelectricity

chargetoastoragedevice

with

high

[2]

consumption including tiny energy sources. The

charging-efficiency .

technology of energy harvesting is to be possible to

The turbine blade and generator have their own

use this tiny energy.

output characteristic, such as the maximum power

We can’t apply existing wind power generator

point (MPP) of operation. Therefore,system for energy harvesting. One of the reasons is

maximum power point (Maximum Power pointthat it needs large energy to generate electric power intracking, MPPT) is important to raise system’shigh efficiency. Sensing wind speed or wind direction,

charging-efficiency.
‫ݒ‬and controlling the blades may consume larger energy
than power generation of energy harvesting

[1]

.

߱

Other reason is that power sources of energyܶharvesting are small and unstable. Existing large-scale‫ܫ‬wind power generation system is connected to theܸelectricity network. But in the case of energy

tracking theWind speed
Angular
Velocity
TorqueOutput current
Output voltage

harvesting it is difficult to connect directly. When weuse energy harvesting it is need to control its output or

Fig.2 Characteristic of each element

charge to storage device in high efficiency.
Figure 2 shows that the characteristics of the turbine
blade and the generator determine the system’s
characteristic. To gain high efficient system for energy
harvesting it is needed to measure each characteristic
Fig.1 System for energy harvesting by wind powerand determine optimum method of design and control.

129

2

Equivalent circuit model

Figures 4 and 5 show the experimental set up to
examine the I-V characteristics of the DC generator
and AC generator, respectively.

Fig.4 Experimental device with Canon DN22M
Fig.3 Equivalent circuit model and output wave
Figure 3 shows the equivalent circuit of a generator
connected to a load (ܼ) [2]. The generator consists of a
DC voltage supply (ܸ୔ୈ ), an AC voltage supple ofsine wave (ܸ୔୅  ߱‫)ݐ‬, an internal resistance (ܴ୔ ) and

Fig.5 Experimental device with Shimano

an internal inductance (‫) ୔ܮ‬. Base on the circuit theory,

DH-2N30JC

the output current ݅ሺ‫ݐ‬ሻ is obtained as݅ൌܸ୔୅ඥሺ‫߱ ୔ܮ‬ሻଶ ൅ ሺܴ୔ ൅ ܼሻଶሺ߱‫ ݐ‬െ ߶ሻܸ୔ୈ൅ܴ୔ ൅ ܼ߶ ൌ ିଵ

㻌 The generators are driven by a servo motor(Oriental motor NexBL BMU series) via a gear or belt
(1)

at a constant rotational speed. For the AC generator,
shown in Fig.5, a bridge diode were used to rectify
generator’s output voltage. A variable resistor is used‫߱ ୔ܮ‬
ܴ୔ ൅ ܼ

as a load and its across voltage is measured by an(2)

oscilloscope (Tektronix TDS2024B).

Consequently, the output voltage across Z is obtained

Figure 6 shows the measurement results of the load’sascurrent and voltage while changing the load’s

ܸ୧୬ ൌ

ܸ୔୅ ܼ

ඥሺ‫ܮ‬௉ ߱ሻଶ ൅ ሺܴ୔ ൅ ܼሻଶܸ୔ୈ ܼ൅ܴ୔ ൅ ܼ

ሺ߱‫ ݐ‬െ ߶ሻ

resistance and rotational speed.
As shown in Fig. 6, the I-V characteristics of the DC
generator has a linear relation, where as that of the AC(3)generator is curved when larger current is output.

Where, ܸ୔୅ , ܸ୔ୈ , ܴ୔ , ‫ ୔ܮ‬need to beexperimentally estimated for several angularfrequency (߱).

3

Measurement of
I-V characteristics of generation

Two types of generators, such as DC generators

Fig.6 I-V characteristics of generators(Canon DN22M-12) and AC generator (Shimano
DH-2N30JC) are targeted to use in this study. Their

Therefore, comparing between the two generators, aoutput characteristics, i.e. I-V characteristic, internal

wide range of the current and voltage of the DC

resistances and inductances are estimated fromgenerator can be considered as the adaptable range forexperiments as follow.

the equivalent circuit model. On the other hand, only apart of the I-V characteristic of the AC generator is

3.1

I-V characteristics

able to be approximated to use as the equivalentcircuit model. . Consequently, when the generator’s130rotational speed is slow, a wide range of the measured

selected. When the rotational speed is low theI-V characteristics can be used to estimate the internal

waveform of the output voltage fall into disorder andresistance of the generators, while only a part of the

it is difficult to approximate to sine wave. The

I-V characteristics within the range of high speed andequivalent circuit model of generators can’t apply forsmall output voltage is able to use.

these conditions.
Figure 8 shows the relationship of the generators’

3.2

Estimation of Internal resistancerotational speed and output voltage’s main frequency.
When the DC generator (canon DN22M) rotate slowly,

In a part of adaptable range to equivalent circuit

the main frequency are not linear because output wave

model the slope of the measured I-V characteristics

isshows the internal resistance of the generators.

generator’s internal brush.

disorder. Output wave may be influenced by

Fig.7 Internal residence of generators

Fig.9 Internal inductance of generators

From Fig.7 the values of internal resistance are

Therefore, the generator’s internal inductance which

estimated as shown in table 1, DC is within 20%, AC

is estimated by equivalent circuit model are notis within 31% of variation.

constant, as shown in table 2.

Table.1 The average value of internal resistance

3.3

Canon DN22M

Shimano DH-2N30JC

17.83ȳ

8.81ȳ

Table.2 The value of internal inductance
Canon DN22M

Shimano DH-2N30JC

Mini

0.010H

0.083H

Max

0.156H

0.119H

Estimation of Internal inductance
Table 2 shows the value of internal resistance and㻌 The frequency of the measured output current andinductance of the generators which is rotating are

output voltage are determined by fast Fourier

estimated. And the method of designing switchingtransform (FFT).

regulator which is connected with generator is
proposed.

4

Control of switching regulator

Using the circuit of switching regulator withgenerator which is designed by above method.

Fig.8 Main frequency of the generators’ output
voltages
However, in spite of higher order frequency of theoutput voltage, only the dominant frequency were

Fig.10 Experimental setup and circuit131㻌 Power source is the DC generator (Canon DN22M)
because its adaptable range is wide. The output
voltage of power source is boosted by switchingregulator and the capacitor (EPCOS 10000 Ɋ F) ischarged. Output voltage and current are measured by
the oscilloscope (Tektronix DPO2024B) and thecapacitor’s voltage is measured by oscilloscope(Tektronix TDS2012). Switching period and duty ratio
is controlled by the function generator (WAVE

Fig.12 Power source’s characteristic and input

FACTORY WF1943).

characteristic

The switching duty ratio can controlled the boosted
voltage and the range of power source’s output swing.
It is considered that if the range of output swing
included the maximum power point the speed ofcharging capacitor will be fast.

Fig.13 Switching regulator’s input power
According Fig.13 the power source’s output power ismaximum when output voltage and current include
the power sources maximum power point. It isnecessary that switching duty ratio is controlled to
track the maximum power point.

5

Conclusions

In this study the method of design and control for the
purpose of charging capacitor using switchingregulator with generator are proposed. The range thatthe equivalent circuit model can adopt is disclosed.
Furthermore the internal resistance and inductancewhen generator is rotating are estimated.

It is useful

to design switching regulator with generator. Next
using switching regulator and capacitor theFig.11 Input current and voltage of Power Source

generator’s output can be boosted and charged in the(Duty ratio 90%, 80%, 70%, 60%)

condition of various switching duty ratio. From thisanalysis it is important to control switching duty ratio

Figure11 shows that switching regulator’s input

which is able to swing output within the range ofvoltage are getting increased and input current are

power source’s maximum power point.

getting decreased. Input voltage is almost zero when
FET is ON. On the other hand input voltage is boosted

References

and capacitor is charged when FET is OFF.

[1] “A Neural Network Based MPPT Controller for Variable Speed
Wind Energy Conversion Systems” 8th Mediterranean Conference
on Power Generation, Transmission, Distribution and Energy
Conversion MEDPOWER 2012[2] 䇾Control Method of Boost Switching Regulator for Generatorswith Voltage Variation” Y. Honda, Master’s thesis of Tokyo Instituteof technology, 2012

Figure12 shows the relationship between generator’s
I-V characteristic and input I-V characteristic. The
average point of input I-V characteristics are nearlyover the generator’s I-V characteristic.

132

D EVELOPMENT OF DIRECTIONAL CHANNEL SOUNDER USING USRP AND GNU
RADIOStudent Number: 14M18218

Name: Tianyang Min

Supervisor: Jun-ichi TAKADA൚༻ιϑτ΢ΤΞແઢ‫ػ‬Λ༻͍ͨࢦ޲ੑి೾ଌఆ૷ஔͷ։ൃᮗɹఱ༶ແઢ௨৴ʹ͓͍ͯ఻ൖ࿏ಛੑ͸γεςϜઃ‫ܭ‬Λߦ͏ͨΊͷॏཁͳ৘ใͰ͋Δɽૹ৴৴߸͸఻ൖ࿏Λ௨ͯ͠‫཭ڑ‬‫ݮ‬ਰ͞ΕΔ͹͔ΓͰ͸ͳ͘ɼ൓ࣹ΍ճંɼࢄཚͳͲʹΑΓଟॏ೾ͷӨ‫ڹ‬Λड͚Δɽಛʹɼ౸དྷ೾֯౓෼෍ಛੑ͸
MIMO ఻ૹධՁΛߦ͏ࡍʹॏཁͳࢦඪͰ͋Γɼۙ೥੝Μʹ఻ൖ࿏ଌఆ͕ߦΘΕ͍ͯΔɽ͜ΕΒͷಛੑΛଌఆ͢Δ
ͨΊʹߴ෼ղೳͳࢦ޲ੑ఻ൖ࿏ଌఆ૷ஔ͕ඞཁͱͳΔɽ‫ࡏݱ‬ɼ఻ൖ࿏ଌఆʹ༻͍ΒΕΔଌఆ૷ஔ͸ߴՁ͔ͭେ‫ܕ‬Ͱ͋Γɼ఻ൖ࿏ଌఆ࣮‫ݧ‬͸ඇৗʹߴ͍අ༻͕͔͔Γɼརศੑ΋௿͍ͱ͍͏໰୊͕͋Δɽͦ͜Ͱຊ‫Ͱڀݚ‬͸൚༻ιϑτ΢ΣΞແઢ‫ػ‬ʢUniversal Software Radio Platform) Λ༻͍ͯࢦ޲ੑ఻ൖ࿏ଌఆ૷ஔΛ։ൃ͠ɼͦͷଥ౰ੑʹؔ͢Δ‫ূݕ‬Λߦͬͨɽຊ૷ஔ͸ɼ൚༻ͷଌఆ૷ஔʹൺ΂ͯɼ҆Ձ͔ͭখ‫͋Ͱܕ‬Γɼ௨ৗͷઐ༻૷ஔʹൺ΂ͯɼRF प೾਺͕બ୒ՄೳͰ͋Δ͜ͱ͔Βॊೈੑ͕༏Ε͍ͯΔɽ

1Introduction

thesis abstract presents a development of low-cost andcompact directional channel sounder that is able to conveniently and accurately measuring the real propagation
channel characteristics. By conducting a comparison indoor channel sounding measurement with VNA, the performance of this work is validated that it can achieve almost the same accuracy as VNA for measuring directional
property of the channel. Moreover, this kind of compact and inexpensive channel sounder is expected to be
applied for distributed network channel sounding, which
requires multiple tranceivers to be allocated to measure
the distributed channel in a large scale. Also, with increase of transmission interferences caused by small aircraft (drone) recently, radio propagation channel in the
air is necessarily to be clariﬁed. The compact channelsounder is expected to be mounted on a drone, and allocates the mobile stations(MS) in the air so as to measureand investigate the propagation channel above the ground.

Channel sounding is to measure the property of radio propagation channel, and have long held importancefor wireless communication system design. Recently, thedirectional property of the channel has been recognized
as an important factor when evaluating the propagation
channel. The knowledge of it can be applied to spatial diversity in multi-input-multi-output (MIMO) wireless network.
To measure the directional channel characteristics, theraytracing simulation software (e.g., raplab) has become
popular among researchers, which can predict the mainline of sight (LoS) path, and major non-line of sight NLoS
specular reﬂected paths. However, a lot of comparison results showed that there still exists a lot of discrepancies
between measurement results and raytracing simulation
prediction. These kinds of discrepancies are consideredas the contribution of scattering phenomenon (such as diffuse scattering) on rough scattering surface, and these diffused components which raytracing simulation can ʟt predict is regarded as important factor that is expected to be
utilized in MIMO system. In particular, for higher radio
wave frequency, it is easier to interact with small scattering objects and more dense multipath components (DMC)
is expected to be observed. The phenomenon mentioned
above is very difﬁcult to be reproduced by raytracing simulation. Alternatively, Vector network analyzerʢVNA)
can also be used to conduct channel sounding measurement so as to save the development cost and additional
calibration workload. However, VNA is designed thatboth transmitter and receiver modules are combined inside one equipment body, it has the limitation that it canbe utilized only in short distance measurement environment (indoor environment). Because transmitter and receiver are separately set far away, it ʟs very difﬁcult touse VNA for outdoor measurement. So in reality, channelsounder is necessarily to be designed to conduct outdoor
channel sounding measurement. But development of conventional directional channel sounder is very high-cost,
and the channel sounder is also huge size, inconvenientand not ﬂexible for RF frequency.
Considering all these respects mentioned above, this2

Structure of channel sounder

2.1 Universal software radio platform

Figure 1: Universal Software Radio Platform

As shown in Figure 1, USRP is comprised of a motherboard and a daughterboard. The complex baseband signals transfered from the host PC are transformed to intermediate frequency (IF) complex signals at near DC bydigital-up-converter (DUC) after the processing of interpolation, then IF digital signals are converted into analog
signals by using high speed DA converter with 16-bit resolution and 400MS/s sampling rate. Finally, the IF analog
signals are streamed into RF up-converter circuits. On theother hand, at the receiver side, the RF received signals
are down-converted into IF complex signals at near DC
by RF down-converter circuits. Then, IF complex signals
are sampled by AD converter with 14-bit resolution and133

100MS/s sampling rate, and converted again into complex
baseband signals by using digital-down-converter (DDC)
inside the FPGA .
2.2 The detail conﬁguration
Figure 2 shows the main structure of channel sounder.
The transmit signal was stored in Tx PC, inversely thereceived signal was stored in Rx PC. Baseband Tx signal is upconverted in FPGA and stream through DAC to
be converted into Analog signal. Finally the IF signal ismodulated into RF signal by QAM modulation. For receiver side, the received RF signal is demodulated on thedaughterboard, then the IF signal is digitalized by ADC
and downconverted inside the FPGA into baseband signal. A common external rubidium clock is connected in
between transmitter and receiver for frequency synchronization and Pulse-Per-Second (PPS) signal is connected
to Tx/RX FPGA ʟs clock to trigger the transmitting andreceiver at the same timing so as to realize timing synchronization.

Figure 3: T-junction DUT

Figure 4: Comparison of impulse response obtained by developed system and vector analyzer (reference)

In order to measure the directional property of propagation channel, array antenna is supposed to be used todetect the angle of departure (AoD) and angle of arrival(AoA) from the multipath. Thus, the phase coherence ofthe channel sounder is necessarily to be evaluated. A threehours phase coherence test was conducted, the results areshown in Figure 5, the phase changes between -6 and 7
degree, and remains relatively stable at -5 degree.

Figure 2: USRP channel sounder structure

2.3

Transmit signal

In this work, a multitone signal was used as a transmit
signal. The multitone signal can be represented as
N/2
X1
exp(j2πk∆F t + jφk ).
s(t) = √N k=−N/2(1)

where ∆F means the subcarrier spacing, N denotes thenumber of the subcarriers. Similarly to orthogonal frequency division multiplexing (OFDM), multitone signal
is advantageous over the signal carrier pseudo random(PN) sequence from the view point of the spectrum efﬁciency although peak-to-average power ratio (PAPR) is
higher. A method to minimize PAPR of multitone signal
is by introducing the Newman phase condition φk [2].
2.4

Performance of USRP channel sounder

The performance of developed USRP channel sounderis evaluated by conducting a T-junction DUT test [1]. As
shown in Figure 3, the edge A is connected to transmitter, the edge B is connected to receiver, and the edge C is
kept open. At the same time, the vector network analyzer
was also used to measure the DUT characteristics for reference. The comparison results of channel impulse response is shown in Figure 4. There are two peaks that canbe observed. The ﬁrst peak represent the direct path, andthe second peak represent the reﬂected path from openedge C with a delayed time. There is good agreement between the channel impulse response of DUT measured by
VNA and USRP channel sounder that validated the good
performance of developed channel sounder.

Figure 5: Phase coherence test

2.5 Extension to directional channel sounder
Figure 6 shows the detail structure of directional channel sounder. The Tx/Rx antennas are ﬁxed on the rotators that are controlled by rotation control PC throughCOM port. The USRP Tx/Rx are controlled by Tx/Rx
PC by using TCPIP connection. Also, the Tx/Rx PC areconnected with rotation control PC respectively by using
TCPIP connection. The Tx PC is set as client, rotation
control PC and Rx PC are set as server. Once the Tx
PC trigger the Tx USRP to start transmitting signals, the134whole system will start working automatically.
The antenna (shown in Figure 7) of the directional
channel sounder is a self-fabricated standard patch antenna that designed by using a commercial electromagnetic simulator (CST). The center frequency of thisantenna is 2.4GHz, gain is 5.08dBi and Half power
beamwidth is 94.40 .

P(φ, f ) = [A(φ, f ) · e j

2πfrcosφc, A(((φ − ∆η), f ) · e j, ...., A((φ − (L − 1 )∆η), f ) · e j

2πfrcos(φ−(L−1 )∆η)c

(3)Here, ∆η = 2π
L . L denotes the number of virtual arrayelements. P(φ, f ) denotes the array response of the virtual array, A(φ, f ) denotes the radiation pattern of single
element of virtual array. φ is the azimuth rotation angle.

We assume the propagation channel characteristics remain unchanged during the whole measurement, and rotate the Tx/Rx antennas over 3600 degree and acquire thetransfer function at each azimuth angle. The antennas
that mounted on a tripod are connected to USRP channelsounder. The local oscillator of the USRP sounder is synchronized with a 10MHz output from external rubidium
clock. The height of tripod is 1.3 meters that considers
almost the same height when mobile phone users usingphones. The angle for each rotation is 100 that is calculated based on the wavelength of transmit signal. Considering that the beamwidth of the array need to be sharp,
the number of virtual array elements is set as 36. Theinterval distance between two elements is set as half radio wave length. The frequency is 2.4GHz (wave lengthis 3 × 108 /2.4 × 109 = 0.125m), so the radius can becalculated as 0.125 ÷ (10 ÷ 180 × π) = 0.358m.

3

Indoor measurement campaign

We conducted an indoor channel sounding measurement by using the developed directional USRP channel sounder. At the same time, we measured the identical measurement environment by using VNA (Rohde
Shwartz ZVA 40) to compare and evaluate the performance of directional USRP channel sounder. The conﬁguration of the VNA measurement is the same with the
USRP channel sounder.
The measurement was conducted in the laboratory experiment room in Tokyo Institute of Technology South6 building (Figure 8 ). There are a lot of ﬁxtures likewooden desks, steel shelves (ﬁlled with objects), steel
carts, and small objects like measurement equipments.
The major scattering objects can be considered as building wall, ﬂoor, pillars, and steel shelves.
The Table 1 presents the parameter settings of USRP
sounder. For VNA, the sweeping tones number is set as
128, the bandwidth is 12.5MHz, the center frequency is
2.4GHz, and the transmitter power is set as 5 dBm.

Figure 6: Structure of directional channel sounder

Figure 7: Self-fabricated patch antenna

The virtual array response is obtained based on thesimulation results. To get the rotated radiation pattern foreach single virtual element, The phase of the single antenna is rotated for each element position. The mode vector of the array response of uniform circular array (UCA)
with radius r and element number L isα(φ, f ) = [e j2 πfrcosφc, ej

2 πfrcos(φ−∆η)c, ...., e j

2 πfrcos(φ−(L−1 )∆η)c

2πfrcos(φ−∆η)cFigure 8: Top view of measurement environment
Table 1: Parameters setting of USRP channel sounder]T(2)
Then, the rotated single virtual array element is multipliedwith the element of mode vector shown as below
135

Carrier frequency
Sampling rate
Bandwidth
Transmit signal
Number of tones
Tone spacing
Delay resolution
Transmit power

2.4 GHz
25 MSps
12.5M HzMultitone
128
97.656 kHz
80 ns
5 dBm

]T

4

Figure 9: The double angular power spectrum measured by
USRP

Data analysis

A transfer function matrix was deﬁned with the Tx andRx antenna pointing at i-th and j-th angle, be Hijk . With
the inverse fourier transform, the total channel impulse
response can be calculated discretely as
k=1

hij [n] =

1 X
j2πkn
|ξW [k]Hij exp(
)
N
N

(4)

N

where N denotes the total number of tones of multitone
signals. W [k] denotes a window function to reduce a sidelobe level, ξ = Pk=1NW [k] .
NTo estimate the angular properties (e.g. AoD, AoA) of
the channel, channel estimation algorithm is usually used
to perform this estimation. In this work, beamforming algorithm was used to estimate angular parameters. If wedeﬁne ΩR = (φR , θR ), and the ΩT = (φT , θT ), then
only considering the vertical polarization, the the power
spectral density function of AoD and AoA for a subcarrier k is calculated as:aH (ΩR , k ) · HVV (k ) · a∗T (ΩT , k )Y (ΩR , ΩT , k) = R
kaR (ΩR , k )k2F kaT (ΩT , k )k2F
(5)Then, the power spectral density function in delay domain
can be obtained by inverse Fourier Transform.

Figure 10: The double angular power spectrum measured by
VNA

N −1

y(ΩR , ΩT , n) =

5

j2πkn
1 X
)
Y (ΩR , ΩT , k )exp(
N
N
k
(6)
Figure 11: The track of major propagation paths

Measurement results

6

By integrating the power of this complex amplitude
function over delay domain, the double angular power
spectrum can be obtained. The comparison of double angular spectrum in azimuth between USRP and VNA ispresented in Figure 9 and Figure 10. The clusters detected by VNA and USRP have a good agreement thatvalidates the good performance of developed directional
USRP channel sounder. There are four major clusters thatare detected. The cluster marked by yellow circle is the
LoS cluster which is the direct transmission (yellow line
in Figure 11 )between transmitter and receiver. The cluster marked by red circles represents the propagation of
signal that had one reﬂection on the door side wall (redline). The clusters marked by green circle represents the
propagation of signal that had one reﬂection on the front
side wall (green line). Finally the cluster marked by purple circle represents the propagation of signal that had one
reﬂection on the back side wall (purple line).

Conclusion

This thesis abstract presents the development of directional channel sounder using USRP and GNU radio. The
performance of the developed channel sounder was validated by conducting a comparison indoor channel sounding measurement between USRP and VNA. The results
showed that the angular position of the clusters that detected by developed USRP channel sounder and VNA
have a good agreement. For future work, this USRP
sounder is expected to conduct measurement campaign
simultaneously with 60GHz channel sounder in identical environment to clarify the difference of propagation mechanism between UHF radiowave and mili-meter
wave.

References[1] T. Min, MɽKim,J. Takada, “Development of MIMO Channel Sounder using GNU Radio/USRP,” IEICE Technical Report
SR2014-3, pp. 15–21, May. 2014.
[2] MɽKim,J. Takada, Y. Konishi, “Novel Scalable MIMO Channel Sounding Technique and Measurement Accuracy Evaluation
With Transceiver Impairments,” IEEE Trans. Instrum.Meas, vol.
61, no.12, pp. 3185–3197, Dec. 2012.
[3] D. Mass, M. H.Firooz, J. Zhang, N. Patwari and K.Kasera,“Channel sounding for the masses: low complexity GNU
802.11b Channel Impulse Response Estimation,” IEEE Trans.
Wireless Commun, vol. 11, no.1, pp. 1–8, Jan. 2012.

136

LIFETIME PREDICTION OF REINFORCED SEAWATER CONCRETE MIXED WITH FLY ASH
WHICH WERE PRODUCED IN JAPAN OR PHILIPPINES AGAINST CHLORIDE ATTACK

Student Number: 14M18260 Name: Cheng YI Supervisor: Nobuaki OTSUKI

ሷᐖ࡟࠾ࡅࡿ᪥ᮏཬࡧࣇ࢕ࣜࣆ࡛ࣥ⏘ฟࡉࢀࡓࣇࣛ࢖࢔ࢵࢩࣗࢆ⏝࠸ࡓᾏỈ⦎ࡾ㕲➽ࢥࣥࢡ࣮ࣜࢺࡢᑑ࿨ண᫆ ᡂᮏ◊✲࡛ࡣ㸪ῐỈࡢ⠇⣙࡜⏘ᴗ๪⏘≀ࡢ᭷ຠ฼⏝ࡢほⅬ࠿ࡽ㸪⦎ΰࡐỈ࡟ᾏỈ㸪ΰ࿴ᮦ࡟᪥ᮏཬࡧࣇ࢕ࣜ
ࣆ࡛ࣥ⏘ฟࡉࢀࡓࣇࣛ࢖࢔ࢵࢩࣗ(௨ୗ FA)ࢆ౑⏝ࡋࡓ㕲➽ࢥࣥࢡ࣮ࣜࢺࡢ≀㉁㏱㐣ᛶࡸෆ㒊㗰ᮦࡢ⭉㣗ᣲືࢆ᫂ࡽ࠿࡟ࡋ㸪ሷᐖ࡟ᑐࡍࡿᑑ࿨㸦㕲➽⭉㣗࡟ࡼࡾࢥࣥࢡ࣮ࣜࢺ࡟ࡦࡧ๭ࢀࡀ⏕ࡌࡿࡲ࡛ࡢᮇ㛫㸧ࢆᐃ㔞ⓗ࡟ホ౯ࡋࡓ㸬ࡑࡢ⤖ᯝ㸪ᾏỈ⦎ࡾࡢᑑ࿨ࡣῐỈ⦎ࡾࡢ 60%⛬ᗘ࡜࡞ࡿࡶࡢࡢ㸪FA ࢆ 30%⨨᥮ࡍࢀࡤᾏỈ⦎ࡾ࡛ࡶ⣙ 30 ᖺࡢᑑ࿨࡜࡞ࡗࡓ㸬ࡲࡓ㸪ࣇ࢕ࣜࣆࣥ⏘ FA ࢆ౑⏝ࡋࡓ᪉ࡀෆ㒊㗰ᮦࡢ⭉㣗᢬ᢠᛶࡀ㧗࠿ࡗࡓ㸬

moist air for 7 days. These specimens were casted in Tokyo.
Specimens mixed with PF were exposed in 30°C natural
seawater after cured in 30°C moist air for 1 day. These
specimens were casted in Manila. Second one was prism shapemortar specimen embedded with streel bar shown as Fig.2. This
type was used for investigation of corrosion current density,
corrosion threshold Cl- content, and O2 permeability throughaccelerated deterioration test. Specimens were exposed in 50°C,
3 % NaCl condition after 7days cured in 20°C or 40°C water.
The same water used for the mixing was used for curing. Totally,
16 patterns of specimen were casted as shown in Tab.1.

1. Introduction
While water shortage is concerned, using seawater as concretematerial in Japan and Philippines where are surrounded byocean, is considered. Also, using FA is considered partly because
of increasing coal power. However, the usages of seawater andFA as concrete material haven’t been investigated enough.
Considering this background, following objectives have beenset.
1. Clarify the material properties and corrosion behavior ofreinforced concrete (RC) mixed with seawater and FA.
2. Predict quantitative lifetime of RC mixed with seawater andFA against chloride attack.
2. Experimental works
2.1 Materials
Freshwater, artificial seawater (ASTMD1141-98(2003), Cl1.98%), and natural seawater (Cl- 2.06%) were used as mixing
water. Land sand and land gravel were used as aggregate.
SR235 (φ13mm×10cm) was used as reinforcement. Ordinary
Portland cement (OPC) and OPC replaced with FA(replacement ratio is 10%, 30%, 50% and 70 %) were used.
Two types of FA include FA produced in Japan (JF) and FA
produced in Philippines (PF), were used.
2.2 Specimens
In this study, two types of specimen were used. First one was
non-reinforced cylinder concrete specimen shown as Fig.1. This
type was used for investigation of Cl- diffusion coefficient after
exposed in seawater for 4 month. Specimens mixed with OPC
were exposed in 20°C artificial seawater after cured in 20°C
moist air for 5 days. Specimens mixed with JF were exposed in
20°C or 40°C artificial seawater after cured in 20°C or 40°C

Fig.1. Concrete specimen
Fig2. Mortar specimen
Tab.1 Mix proportions of specimens137

by corrosion rate which is estimated from oxygen permeability3),
the duration that amount of corrosion products reach the amountfor cracking is estimated as propagation period.
3 Experimental results

2.3 Measurement items and its methods(1) Cl- diffusion coefficient
It is calculated from Fick’s diffusion equation shown asequation 1. Measurement method is conformed to Japan Societyof Civil Engineers (JSCE)-G 572-2010.
δc / δt = DΔ 2c Eq.1
(c: amount of Cl- content (kg/m3), t: exposure time (year), D:
Cl- diffusion coefficient (cm2/year))(2) Oxygen permeability
It is calculated by substitute limit current density (Ilim) obtained
from cathodic polarization curve in equation 2. Investigation was
conducted when the age of specimen is 140 days.
δQ / δt = 㸫Ilim / nF Eq.2
(δQ / δt: Oxygen permeability (mol/cm2/sec ), Ilim: limit current
density (A/cm2), n: atomic value (=4), Faraday constant(96500c/mol)).
(3) Corrosion current density
Corrosion current density is evaluated by using equation 3. The
polarization resistance of steel embedded in mortar is evaluatedbased on AC-impedance method using two patterns frequency(10 kHz 10mV, 10 mHz 10mV).
Icorr = K / (Rct × S) Eq.3
(Icorr: Corrosion current density (µA/cm2), Rct: Polarization
resistance (Ω), S: Surface area of steel bar (40.82cm2),
K : Stern-Geary constant (0.0209V))(4) Corrosion threshold Cl- content
Investigation was conducted when Icorr of specimen hasreached 0.2 μA / cm2 according to CEB Working Party V/41).
Measurement method is conformed to JCI-SC45.
2.4 How to predict lifetime of RC against chloride attack

Fig.4 Result of Cl- diffusion coefficient 1

Fig.5 Result of Cl- diffusion coefficient 2

Fig. 6 Result of oxygen permeability 1

Fig.3 Definition of lifetime
In this study, lifetime of RC against chloride attack is definedas sum of incubation and propagation period which is a
performance based design as shown in Fig.3. For the predictionof incubation period, covering depth, initial Cl- content, surface
Cl- content, Cl- diffusion coefficient, and corrosion threshold Clcontent are required. Inputting covering depth, Cl- diffusion
coefficient, initial Cl- content, and surface Cl- content to Fick’s
diffusion law, duration that Cl- content in covering depth reaches
the corrosion threshold Cl- content is estimated as incubation
period. For the prediction of propagation period, the corrosion
rate and the critical amount of corrosion for cracking are
required. Dividing the critical amount of corrosion for cracking2)

Fig. 7 Result of oxygen permeability 2

Fig. 8 Result of corrosion current density 1
1383.1 Material properties(1) Cl- diffusion coefficientFig.4 and Fig.5 show the measured result of Cl- diffusion
coefficient. Focus on mixing water, Cl- diffusion coefficient ofspecimens mixed with seawater is smaller than that mixed withfreshwater. It might be because initial Cl- content induced byseawater has reduced concentration gradient of Cl- .
Focus on FA replacement ratio, Cl- diffusion coefficient is thesmallest when FA replacement ratio is 30%. It might be because
concrete is the most tense when FA replacement is 30%.
Additionally, the value of FA50 (replace OPC with 50% FA) isalmost same or lower than OPC. So in a view point of “not todegrade performance of concrete”, the maximum FA
replacement ratio is 50%.
Focus on curing and exposure temperature, Cl- diffusion
coefficient of specimens cured and exposed in 40°C are muchsmaller than that cured and exposed in 20°C. It might bebecause higher the temperature, faster the reaction of FA.
Focus on FA type, Cl- diffusion coefficient of specimen mixed
with JF is lower than that mixed with PF when FA replacement
ratio is 30%. However, it is reversed when FA replacement ratio
exceeds 50%. It might be because of the difference of curing
period and temperature. It might be that the impact of curing
period is dominant when FA replacement ratio is lower than
30% while the impact of temperature is dominant when FA
replacement ratio exceeds 50%.
(2) Oxygen permeability
Fig.6 and Fig.7 show the measured result of oxygen
permeability. Focus on mixing water, oxygen permeability of
specimens mixed with seawater is almost same as that mixed
with freshwater when FA replacement ratio is less than 30%.
However, the value of specimens mixed with seawater is about
twice as that mixed with freshwater when FA replacement ratio
exceeds 50%. It might be because of the difference of pore size
distribution.
Focus on FA replacement ratio, oxygen permeability decreases
up to FA replacement ratio is 30% or 50%. It might be because
FA has made the concrete tense.
Focus on curing temperature, oxygen permeability of
specimens cured in 40°C is much lower than that cure in 20 °C.
It might be because high temperature curing has accelerated thereaction of FA.
Focus on FA type, oxygen permeability of specimens mixed
with PF is lower than that mixed with JF especially when FA
replacement ratio is 50%. It might be because PF has higher
reactivity than JF due to its high content of CaO. CaO produces
Ca(OH)2 through hydration which is necessary for the reactionof FA.
Focus on temperature at measurement, oxygen permeability
becomes 2 to 3 times when the temperature rises 10°C (fromFig. 9 Result of corrosion current density 2

Fig. 10 Result of corrosion threshold Cl- content

Fig.11 Result of incubation period (Cover depth is 70mm)

Fig.12 Result of propagation period (Cover depth is 70mm)

Fig.13 Result of lifetime of RC (Cover depth is 70mm)
139with seawater is about 60% of that mixed with freshwater.
Focus on FA replacement ratio, it shows the longest lifetime
when FA replacement ratio is 30% or 50%. The longest lifetimeof RC mixed with seawater is about 30 years when FA (JF)replacement ratio is 30%. Focus on type of FA, the lifetime of
RC mixed with PF tends to be shorter than that mixed with JF.
However, the life time of RC mixed with PF is shortly estimatedsince the curing period of concrete mixed with PF is one day
and mortar mixed with PF is cured in 20°C. If the concrete has
been cured in 7 days and mortar has been cured in 30°C, its
lifetime would be much longer.
Conclusions
Experimental works were conducted regarding the influencesof mixing water (freshwater and seawater), FA replacement ratio
to OPC (0%, 10%, 30%, 50% and 70%), type of FA, (JF andPF) and curing or exposure temperature (20°C, 30°C and 40°C)
on the material properties and corrosion behavior of RC. Also,
the lifetime which is a performance based design includes
incubation and propagation periods (duration until crack occurs)of RC against chloride attack were estimated. As results, thefollowing were found:
1) Using seawater as mixing water instead of freshwater can
reduce Cl- diffusion. Replacing FA to OPC from 10% to 50%
can reduce Cl- diffusion and oxygen permeability. Using PF can
reduce more Cl- diffusion coefficient and oxygen permeability
than JF. Higher the curing temperature, lower the Cl- diffusion
and oxygen permeability. In case using PF, corrosion current
density tends to be lower than in case using JF. It means
corrosion resistance of PF tends to be higher than JF.
2) Life time of RC mixed with seawater is about 60% of that
mixed with freshwater. Replacing OPC with FA from 10% to
50% can extend the lifetime of RC. Especially, RC mixed withseawater shows the longest lifetime (about 30 years) when FA(JF) replacement ratio is 30%. If there were enough curing
period (7days) and temperature (30°C), RC mixed with PF
would has shown longer lifetime than that mixed with JF.

20°C to 30°C). The results obtained from the measurement
conducted in 30°C were used to predict propagation period of
RC because air temperature in Philippines is 30°C.
(3) Corrosion current density (Icorr)
Fig.8 shows the measured result of time-dependent Icorr change.
The period until Icorr reaches the threshold (0.2μA/cm2) iscompared in this figure. Focus on mixing water, the period of
specimens mixed with seawater is shorter than that mixed withfreshwater. Focus on FA replacement ratio, the period is thelongest when FA replacement ratio is 30%.
Fig.9 shows the measured result of Icorr when the ages of
specimens are 91 days (84 days exposed in 50°C, 3% NaClcondition). It is for the comparison between specimens mixed
with JF and that mixed with PF. It can be said that Icorr of
specimens mixed with PF tend to be lower than that mixed with
JF. Therefore, it can be said that the corrosion resistance ofspecimens mixed with PF are tend to be higher than that mixed
with JF.
(4) Corrosion threshold Cl- content
Fig.10 shows the measured result of corrosion threshold Clcontent. It can be said that corrosion threshold Cl- content
decreases significantly when FA replacement ratio is higher than30%. It might be because of the low pH due to lack of OPC.
Since corrosion threshold Cl- content of specimens mixed withPF has not been investigated, corrosion threshold Cl- content ofspecimens mixed with PF is considered the same as that mixed
with JF.
3.2 Lifetime prediction of RC against chloride attack(1) Incubation period
Fig.11 shows the predicted result of incubation period of RC
against chloride attack. Focus on mixing water, the incubation
period of RC mixed with seawater is about half of that mixed
with freshwater. Focus on FA replacement ratio, it shows thelongest period when FA replacement ratio is 30%. Focus on typeof FA, the period of RC mixed with PF tends to be shorter thanthat mixed with JF.
(2) Propagation period
Fig.12 shows the predicted result of propagation period of RC
against chloride attack. Focus on mixing water, the propagation
period of RC mixed with seawater is almost same as that mixed
with fresh water up to FA replacement ratio is 30%. However,the period of RC mixed with seawater is much shorter than that
mixed with freshwater when FA replacement ratio exceeds 50%.
Focus on FA replacement ratio, it shows the longest period when
FA replacement ratio is 30% or 50%. Focus on type of FA, theperiod of RC mixed with PF tends to be shorter than that mixed
with JF.
(3) Lifetime
Fig13 shows the predicted result of lifetime of RC against
chloride attack. Focus on mixing water, lifetime of RC mixed

References
1) CEB Working Party V/4.1 (1997). Strategies for testingand Assessment of Concrete Structure Affected byReinforcement Corrosion (draft 4), BBRI-CSTC-WTCB
2) K. Yokozeki et al., (1997). "A Rational Model to Predict
Service Life of RC Structures in Marine Environment", ForthCANMET/ACI International Conference on Durability ofConcrete 13) S. Maruyama, (2004), "Influence of Temperature and Cement
on Corrosion Rate of Steel Bar under Carbonation", Masterthesis of Tokyo Institute of Technology (in Japanese)140Effect of tangential force on adhesion between an elastic beam and a rigid bodyStudent number: 14M18075 ɹ Name: Shin SHIMAHARA ɹ Supervisor: Kunio TAKAHASHI

ਫฏྗ͕஄ੑྊͱ߶ମฏ໘ؒʹಇ͘‫ڽ‬ணྗʹ‫͢΅ٴ‬Ө‫ڹ‬ౡ‫৿ݪ‬஄ੑྊଆ໘͕߶ମฏ໘ʹ‫ڽ‬ண͢Δࡍʹൃੜ͢Δ‫ڽ‬ணྗʹਫฏྗ͕‫͢΅ٴ‬Ө‫ڹ‬Λٞ࿦ͨ͠ɻཧ࿦ղੳͰ
͸ઢ‫ܗ‬ྊཧ࿦Λ༻͍ͯਫฏྗ͕‫ڽ‬ணྗʹ‫͢΅ٴ‬Ө‫ڹ‬Λ‫ͨ͠ࢉܭ‬ɻ࣮‫Ͱݧ‬͸ήϧ੡ͷྊͱΨϥε੡ͷ‫ج‬൫͕઀৮͢Δࡍʹൃੜ͢ΔྗΛ 6 ࣠ྗ֮ηϯαΛ༻͍ͯଌఆͨ͠ɻ͜ΕʹΑΓ஄ੑྊଆ໘‫ڽ‬ணϞσϧʹ͓͍ͯ΋ਫฏྗΛ‫ڽ‬ணྗͷ੍‫͍༻ʹޚ‬Δ͜ͱ͕ग़དྷΔ͜ͱ͕໌Β͔ʹͳͬͨɻ·ͨɺ‫ڽ‬ணྗͷ੍‫ޚ‬Λߦ͏ࡍʹ͸ྊͷҾ্͖͛֯౓Λߟྀ͢Δ͜ͱ͕‫ٻ‬ΊΒΕΔ͜ͱΛࣔࠦͨ͠ɻಉ࣌ʹઢ‫ܗ‬ྊཧ࿦Λ༻͍ͨཧ࿦ղੳ͕࣮‫݁ݧ‬ՌͱҰக͠ͳ͍͜ͱΛࣔͨ͠ɻ͜ΕΒͷ݁Ռ͔Βྊͷ‫ڽ‬ணϞσϧΛվྑ͢ΔඞཁੑΛࣔͨ͠ɻ

1

stiffness of a hair, a single spatula is assumed to be an elas-

Introduction

Geckos are well known for its ability to run rapidly on walls

tic beam in the previous study[13]. Adhesion between side

and ceilings. This means that geckos realize robust adhe- surface of an elastic beam and a rigid body is discussed ussion, easy detachment and compliance with surface rough- ing linear beam theory. Tangential force is ignored in theness. The key to these features is geckos’ micro-nano hair discussion in spite of its importance for control of grip-andstructure, which consists of setae and spatulae, on their foot[1].release. Therefore the effect of tangential force on adhesion
A single seta is approximately 100µm in length. The tip of between side surface of the elastic beam and the rigid bodysetae branches to hundreds of splatulae. A single spatula has not been clariﬁed.
In the present study, theoretical analysis using linear

is approximately 200nm in length[2]. The micro-nano hairstructure deforms and absorbs surface roughness to gener- beam theory and experiment are carried out to clarify theate robust adhesion. Also they can be detached from the effect of tangential force on adhesion between side surface
surface in several tens of nanoseconds because of the hairs’ of an elastic beam and a rigid body.
structural feature.

2 Theoretical analysis

Because of these features, gecko-inspired adhesive, which
imitate the micro-nano hair structure to realize gecko-like

2.1 Model of adhesion between side surface
of an elastic beam and a rigid body

behavior, is expected to be utilized for devices like wallThe effect of tangential force on adhesion between side sur-climbing robots[3, 4, 5, 6].
Regarding adhesion of the micro-nano hair structure,
it is known that tangential force affects adhesion[7, 8, 9,10]. A single seta exhibits a different tribological response

face of an elastic beam and a rigid body is analyzed using
linear beam theory. The elastic beam, which has bending
stiffness EI and the dimension of length L, thickness H anddepending on direction of parallel drag. When a seta is width W, contacts to the rigid body at angle θ (Fig.1(a)).
dragged against setal curvature, repulsive normal force is During the contact, normal force Fn and tangential force
measured. In contrast, when dragged along setal curvature,

Ft are produced. It is assumed that the tangential force Ftadhesive normal force is measured. Gecko-inspired devices contributes only for bending moment within non-adhesion
utilize this phenomenon for grip-and-release control[4, 6]. area of the beam. Therefore, shear stress induced by the◦
In some theoretical studies, a single spatula is assumed tangential force is ignored. θ < 20 [14] and H/L ≪ 1 areto be an elastic tape and the pull-off force is discussed us- assumed. Because of these assumptions, deformation of theing Kendall peel model[11] for thin adhesive tape[1, 9]. beam can be explained by linear beam theory. Of course,
Kendall’s theory considers a surface energy term, a poten- the assumption about the tangential force obviously contial energy term and an elastic energy term due to extension tradicts the reality. However, theoretical analysis includof the ﬁlm. However, the effect of bending stiffness on ad-

ing tangential force is important for understanding of adhe-hesion is also important[12]. In order to consider bending sion control. Accordingly, this assumption is regard as valid141

temporarily. Validity of this assumption is discussed based bending moment and doesn’t contribute for elongation of
on experimental result later.

the beam, the elongation elastic energy is obtained asŨelongation = 0(3)Using work of adhesion ∆γ, the surface energy is obtainedas˜Ũsurface = −∆γ̃(1 − l)(4)∆γ
where work of adhesion is normalized as ∆γ̃ = 6EI/WL
2.

Fig. 1: Contact between side surface of an elastic beam anda rigid body with an angle θ.

From Eq.(1) and the length l obtained by the minimum
point of the total energy, the relation between the displacement and the normal force with various tangential force is

2.2 Force between elastic beam and rigid body obtained as shown in Fig.2.
As for the calculation, x−y axis is ﬁxed as shown in Fig.1(b).
Deformation of the non-adhesion area 0 < x < l is obtained using linear beam theory. By considering the relation
among the displacement d, the deformation and the force,the normal force is obtained asp
 p
F̃t 2 3F̃t sinh 2 3F̃t l˜
F̃n =
p
 p

 p

2 − 2 cosh 2 3F̃t l˜ + 2 3F̃t l˜ sinh 2 3F̃t l˜
 p





1 − cosh 2 3F̃t l˜
× d̃ − p p tan θ
2 3F̃t sinh 2 3F̃t l˜(1)

where force F, the displacement and the length of the nonFig. 2: Relation between the normalized displacementd ˜lFadhesion area l are normalized as F̃ = 12EI/L
2 , d̃ = L , l = L .
d̃/ tan θ and the normalized normal force F̃n with various
The adhesion area is equilibrium at the lowest energy tangential force.
condition. Therefore the length of the non-adhesion areal̃ can be obtained by considering total energy of the sys-

2.3 Effect of tangential force on adhesiontem. The total energy of the system consists elastic energy
caused by bending Uelastic , elastic energy caused by elonga- Adhesion force is the minimum value of the normal force.
The relation between the tangential force and the adhesiontion Uand surface energy U.
elongation

surfaceforce with various work of adhesion is shown in Fig.3.

The bending elastic energy is obtained as
Ũbend =

F̃n2

F̃t
tan θ
p
 p
 2 1+
2˜F̃n
4 3F̃t sinh 2 3F̃t l


3 Experiment!

The experimental system is shown in Fig.4. The measurement of the force between an elastic beam and a substrate

!
!
!
!
q
q
× cosh 2 3F̃t l˜ − 1 sinh 2 3F̃t l˜ − 2 3F̃t l˜q

was carried out by controlling displacement of the elastic
beam. In this experiment, the normal force and the tangen-

!
!
! 2
q
q
q
 tial force between the beam and the substrate (rigid body)F̃+ sinh 2 3F̃t l˜ cosh 2 3F̃t l˜ + 2 3F̃t l˜ t2 tan2 θ were measured simultaneously by 6-axis force sensor underF̃n(2) the quasi-static condition. As for the beam, shock absorbing
U
where energy U is normalized as Ũ = 6EI/L. Because it

gel CRG-T2505 (Tanak Co.,Ltd) was used. The beam wasis assumed that the tangential force contributes only for the 15mm in length, 15mm in width and 5mm in thickness. The
Young’s module of the beam was measured using tensile142

testing machine for elastic range and obtained 2.3 × 105 Pa.
As for the substrate, a glass plate was used.
An example of the experimental results is shown in Fig.6.
Fig.6 shows transition of the force during the experiment.
At the start of the experiment, the edge of the beam contacted to the substrate. Then, the beam was moved down
toward the substrate. This phase is called loading process(Fig.5(a)). After the loading process ended at the point A,
it was lifted up. This phase is called unloading process(Fig.5(b)). The adhesion force, which is the minimum normal force measured during the unloading process, was measured at the point B. Finally, the separation occurred at theFig. 3: Relation between the normalized tangential force F̃t
and the normalized adhesion forceF̃ad with various work ofadhesion.

point C.

Fig. 6: Transition of the force during the experiment.
Fig. 4: Experimental system for the measurement of tangential force and normal force between an beam and a glass 4 Discussion
substrate.
The relation between the adhesion force and the tangential
force at the point B is shown in Fig.7. Fig.7 shows thattensile tangential force strengthens the adhesion force andcompressing tangential force weakens the adhesion force.
This tendency indicates that the tangential force can be utilized for adhesion control. It is remarkable that there aresome points where the adhesion force is repulsive. Geckoinspired adhesive which utilized the model of adhesion between side surface of the elastic beam and the rigid bodycan realize easy detachment by controlling adhesion applying the tangential force.
Fig.7 also shows that the theoretical analysis using linear beam theory is invalid for estimation of the effect of thetangential force on adhesion. Therefore the adhesion modelof the beam has to be modiﬁed.
Fig. 5: Schematic illustration of the experimental process.

Pull off angle dependence on the tangential force andthe adhesion force is shown in Fig.8. Fig.2 shows that there143

is the optimal pull off angle to maximize and minimize the rigid body. In order to maximize the variation of the adhetangential force. Because it is important to maximize the sion force, the optimal pull off angle has to be considered.
variation of the adhesion force for grip-and-release con- The experimental result also shows that the theoretical analtrol of gecko-inspired devices, consideration for the opti- ysis using linear beam theory is invalid for discussion about
mal pull off angle is important for adhesion control using the effect of the tangential force on adhesion. Therefore thethe tangential force.

adhesion model of the beam has to be modiﬁed.
References

Fig. 7: Relation between the tangential force and the adhesion force.

Fig. 8: Pull off angle ϕ dependence on the tangential force
and the adhesion force
5 Conclusion
The effect of the tangential force on adhesion between sidesurface of the elastic beam and the rigid body is theoretically discussed using linear beam theory. The measurement[1] Chen, B., et al., ”Hierarchical modelling of attachment
and detachment mechanisms of gecko toe adhesion”,
Proceedings of the Royal Society A, 464, pp. 16391652, 2008.
[2] Gao, H., et al., ”Mechanics of hierarchical adhesion
structures of geckos”, Mechanics of Materials, 37, pp.
275-285, 2005.
[3] Geim A. K., et al., ”Microfabricated adhesive mimicking gecko foot-hair”, Nature Materials 2, 461-463,
2003[4] Santos, D., et al., ”Directional adhesion for climbing:theoretical and practical considerations”, Journal of
Adhesion Science and Technology, 21, pp. 1317-1341,
2007.
[5] Jeonga, H. E., et al., ”A nontransferring dry adhesivewith hierarchical polymer nanohairs”, PNAS vol.106
no.14, 5639-5644, 2009[6] Kim, S., et al., ”Smooth Vertical Surface Climbing
With Directional Adhesion”, IEEE Transactions on
Robotics vol.24 no.1, pp. 65-74, 2008.
[7] Autumn, K., et al., ”Adhesive force of a single gecko
foot-hair”, Nature 405, 681-685, 2000.
[8] Takahashi, K., et al., ”Gecko’s foot hair structure andtheir ability to hang from rough surfaces and movequickly”, International Journal of Adhesion and Adhesives 26, pp. 639-643, 2006.
[9] Autumn, k., et al., ”Frictional adhesion: a new angle
on gecko attachment”, The Journal of Experimental
Biology 209, pp. 3569-3579, 2006.
[10] Zhao, B., et al., ”Adhesion and Friction Force Coupling of Gecko Setal Arrays:Implications for Structured Adhesive Surfaces”, Langmuir, 24(4), pp. 15171524, 2008.
[11] Kendall, K., ”Thin-ﬁlm peeling -the elastic term”,
Journal of Physics D : Applied Physics, 8, pp. 14491452, 1975.
[12] Sauer, R. A., ”The peeling behavior of thin ﬁlms withﬁnite bending stiffness and the implications on gecko
adhesion”, The Journal of Adhesion, 87, pp. 624-643,
2011.
[13] Y. Sekiguchi, et al., ”Adhesion between side surface
of an elastic beam and ﬂat surface of a rigid body”,
Journal of Adhesion Science and Technology, Vol. 26,
No. 23, pp. 2615-2626, 2012[14] ಸྑହ඙, ”େม‫ܗ‬Λߟྀͨ͠஄ੑྊͱ߶ମؒͷ‫ڽ‬ண
”, 2014 ೥ਫ਼ີ޻ֶձय़‫ق‬େձߨԋ࿦จू, No.J13,
735-736, 2014of the force between the elastic beam and the substrate iscarried out by controlling displacement of the elastic beam.
In the experiment, the normal force and the tangential force
between the beam and the substrate are measured simultaneously by 6-axis force sensor. It is shown by the experimental result that tangential force can be utilized for control of the adhesion force between the elastic beam and the144

Development of Simultaneous Measurement System of MIMO
Channel Response and Body Motion for Study of Dynamic
WBAN Channel
Student Number: 14M18230

Name: Gaochao Chen

Supervisor: Jun-ichi Takada

ಈతແઢϘσΟΤϦΞωοτϫʔΫνϟϯωϧ‫ڀݚ‬ͷͨΊͷ
MIMO νϟϯωϧԠ౴ͱਓମಈ࡞ಉ࣌ଌఆ૷ஔͷ։ൃ
௠ɹߴ௒
WBAN ͷνϟϯωϧ͸ɺΞϯςφɺਓମɺपғ‫Ͳͳڥ؀‬ɺ༷ʑͳཁҼʹӨ‫͞ڹ‬ΕͯɺͦΕΒͷӨ‫ڹ‬ཁૉ͕ෳ߹
తʹνϟϯωϧԠ౴ͩͱଌఆ͞ΕΔ͔Βɺ؆୯ʹ෼཭͢͜ͱ͕Ͱ͖ͳ͍ɻಈతͳ WBAN νϟϯωϧ͸ WBAN ͷγεςϜΛઃ‫͍͓ͯʹܭ‬ඇৗʹॏཁͳ৘ใʹͳΔ͔ΒɺγϛϡϨʔγϣϯϕʔεͷνϟϯωϧ෼ੳ͕ෆՄܽͰ͋
Δɻຊ࿦จͰɺMIMO νϟϯωϧԠ౴ͱਓମಈ࡞Λಉ࣌ʹଌఆ͢ΔγεςϜͷ։ൃΛ௨ͯ͡ɺଌఆ݁ՌͱγϛϡϨʔγϣϯ݁Ռͷൺֱ͕Ͱ͖ΔΑ͏ʹͳΔɻ‫ڀݚ‬͸কདྷ WBAN ͷಈతͳνϟϯωϧͷ‫ʹڀݚ‬໾ཱͭɻ

1

Introduction

ous measurement system of MIMO channel responses andbody motion is developed for study of dynamic WBAN
channel. The developed system can improve the accuracy and efﬁciency of comparison between measurement
and simulation, and contribute to understand the inﬂuential factors such as antenna position and orientation.
The research ﬂowchat is shown in Fig 1. In measurement, a 3x3 MIMO channel sounder is utilized to obtain measured MIMO channel responses of WBAN. During measurement, a motion capture device, Kinect, is utilized to capture body motion. The captured body motion
data are utilized to generate body data and antenna data
through avatar generation procedure. Then the generated
body data and antenna data are imported to a electromagnetic simulator in [3] for channel simulation. Finally thesimulated channels are compared to the measured channels.

Wireless body area network (WBAN) is a network
which consists of several sensor/actuator nodes inside,on or around human body [1]. It is expected to be
widely utilized in medicine, health-care, sports training
and other ﬁelds. Dynamic behavior of WBAN channel
is important information for design and characterizationof WBAN system. However, in measurement, the inﬂuences of various factors, such as antenna type, position,orientation, electromagnetic interaction, surrounding environment, human motion as well as scattering from bodysurface itself are simultaneously measured as channel responses and difﬁcult to separate each other. Furthermore,
the disparity of those inﬂuential factors leads to difﬁcultyof comparison among those measurement results. Considering the difﬁculty of body motion repeatability in measurement, simulation is a useful, potential tool for WBAN
channel analysis, while the reliability of simulation is stillquestioned without comparison to measurement.
A comparison between measurement and simulation ispresented in [2]. The avatar for simulation is generated
in animation software, Poser 6. In order to match the human motion and motion rhythm as same as the avatar in
Poser, 60 times of measurements are repeated to ﬁnd thebest the matching. Also video record is utilized to help
placing antenna and ﬁnding best matching. The average
procedure time for one frame is approximately 2 days.
The challenges of above comparison system are: ﬁrstly, it
costs long time to match body motion in measurement andavatar motion in simulation; secondly, the antenna mismatch (including antenna position and orientation) cannot
be avoided; thirdly, it is difﬁcult to extend this avatar motion to other body motion. Therefore, a more convenientcomparison system is required. In this paper, a simultane-

Figure 1: Research ﬂowchat

1
145

2

Experiment

2.1

3x3 MIMO channel sounderlink, the motion of walking and sitting-standing are done.
Therefore, totally 8 measurements are conducted. The antenna orientation setup is shown in right part of Fig 3.
Transmitter 1 and receiver 1 are perpendicular to bodysurface, transmitter 2 and receiver 2 are parallel to bodysurface and oriented horizontally, and transmitter 3 andreceiver 3 are parallel to body surface and oriented vertically. Measurement setup for channel sounder is listed on
Table 1.

A 3x3 MIMO channel sounder is developed for channel measurement. The structure of the sounder is shownin Fig 2. It consists of two dielectric resonator antennas (MIMO DRA) [4, 5] and a multi-port oscilloscope.
In transmitter side, a micro controller controlled switch
switches 3 ports of MIMO DRA (transmitter) sequentially. This is called time division multiplexing (TDM)
scheme. In receiver side, 3 ports of MIMO DRA (receiver) are connected to the multi-port oscilloscope respectively, to receive the signals. Ampliﬁers are inserted
between oscilloscope and receiver to guarantee the measurement range. By applying TDM in transmitter side, a
3x3 MIMO channel is obtained. The performance of thesounder is also evaluated. An attenuator is inserted between port1 in transmitter side and port 1 in receiver side,
instead of antennas as a channel simulator, to evaluate themeasurement range. The measurement result shows thatit can measure the pathgain down to -80 dB with error ofless than 2 dB. Also an attenuator of 40 dB is inserted into
each port pair (Rx1-Tx1, Rx2-Tx1,...,Rx3-Tx3), to obtain
the disparity in different port pairs. The measurement result shows their disparities are less than 1 dB after calibration. More information about the construction as well as
performance evaluation of the developed sounder can befound in [6].

Figure 3: Antenna setup in measurement

3 Simulation
3.1 Avatar generation procedure
The avatar generation procedure is shown in Fig 4. A motion capture device, Kinect, which is shown in upper leftpart of Fig 4, captures body posture by tracing 20 joints
data with frame rate of 30 fps. Then the traced joints data
are utilized to create a motion data ﬁle, biovision hierarchy ﬁle (.bvh). .bvh ﬁle is a ﬁle which designs body motion with scheme of skeleton. Upper right part of Fig 4shows one frame of the generated .bvh ﬁle. Then the generated .bvh ﬁle is imported into animation software, Poser
7, to activate the pre-built avatar. The avatar will do thebody motion deﬁned by the .bvh ﬁle, as shown in lower
left part of Fig 4. The center of gravity of red diamondshows the location of antenna and two diagonals of the reddiamond show orientation of the antennas which are parallel to body surface. The cross product of these two diagonals is calculated to obtain the orientation of the antenna
which is perpendicular to body surface. Then two types ofwavefront.obj (.obj) ﬁles (containing diamond and excluding diamond) are exported from Poser 7 in order to obtainantenna data through comparison. Next, the exported .obj
ﬁle which excludes diamond is voxelized by an application, binvox.exe [7], generating a .binvox ﬁle, shown inlower right part of Fig 4. Finally, the generated .binvox
ﬁle are utilized to convert the voxelized body motion intoa digital data ﬁle (body data) for numerical simulation.

Figure 2: The structure of the MIMO channel sounder

2.2

Measurement
Table 1: Measurement setup

Item
Frequency
Frame rate
Sampling rate
Measurement time
Sample per frame

Parameter2.45 GHz
30 fps6.25 Giga sample/s
15 s
20000

3.2 Electromagnetic simulator [3]An electromagnetic simulator in [3] is utilized for channel simulation. In this simulator, ﬁnite-difference time domain (FDTD) method is utilized. The simulation setup islisted on Table 2. The simulation domain is 1.5 m×2.3
m ×1.5 m, sufﬁcient to cover human motion. And cellsize of 0.005 m is much less than wave length of 2.45
GHz (corresponding to 0.1225 m). In the simulation, both

The measurement is conducted in an anechoic chamber
to alleviate the reﬂection due to surround environments.
The transmitter is placed on navel and receivers are allocated on chest, head, hand and upper leg respectively,
which is shown in left part of Fig 3. For each antenna
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Figure 5: Concept of simulation domain and procedure

different channels, their performance of comparison are
different. The gap between strongest channel and weakest channel is larger than 30 dB, which can be conﬁrmed
both in measurement and simulation. This phenomenon
indicates that polarization combination is an important
inﬂuential factor to dynamic WBAN channel. Channelsof Rx1-Tx1, Rx1-Tx3, Rx3-Tx1 and Rx3-Tx3 have relatively stronger mean values and lower standard deviation.
These phenomenons indicate that these channels are lessinﬂuenced by body motion. While the gap between simulation and measurement in channels of Rx2-Tx1 and Rx2Tx3 are extremely high. The standard deviations in simulation are much higher than them in measurement. In order to understand the reason, inﬂuential factors of antenna
in simulation are investigated.

Figure 4: Avatar generation proceduretransmitter and receiver are utilized as inﬁnitesimal small
dipole antennas. The concept of simulation domain andprocedure can be found in Fig 5. Firstly, the transmitter is oriented to X-orientation, then the generated electromagnetic ﬁeld in the location of receiver are recorded.
Secondly, the same procedure are done by orienting thetransmitter to Y-orientation and Z-orientation respectively.
Next, the obtained electric ﬁelds are converted to received
power with antenna data. Finally, the pathgain is obtainedwith the ratio of received power and transmitted power.
Table 2: Simulation setup

Parameter
Simulation domain
Cell size
Boundary
Feed current I
Frequency
Dieletric constant of body
Conductivity of bodyValue
300 × 460 × 300(1.5 m×2.3 m×1.5 m)
0.005 m10-layer PML
1.0 A
CW 2.45 GHz
53.57
1.81 S/m

Figure 6: Comparison between measurement and simulation,antenna link of navel-chest, walking motion

4.2 Inﬂuential factors discussion in simulation

4

Comparison

4.1

Comparison between measurement andsimulation

Due to the mismatch of real human body shape in measurement and avatar shape in simulation, antenna position
mismatch and orientation mismatch may happen in comparison. Since the speciﬁc antenna orientation in measurement is difﬁcult to obtain, the inﬂuences of antenna
position mismatch and orientation mismatch to dynamic
WBAN channels are investigated through simulation. In
order to simplify the analysis of antenna position mismatch, the transmitter is moved along each axis in an-

Comparison between measurement and simulation forantenna link of navel-chest, walking motion is shown in
Fig 6. The statistic values of mean and standard deviation are utilized for comparison, and listed on Table 3. In
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Table 3: Statistic values of comparison between measurement
and simulation, antenna link of navel-chest, walking
motionChannelRx1-Tx1
Rx2-Tx1
Rx3-Tx1
Rx1-Tx2
Rx2-Tx2
Rx3-Tx2
Rx1-Tx3
Rx2-Tx3
Rx3-Tx3

Measurement
Mean [dB] Std-30.982.04-40.683.04-48.32
3.77-44.44
7.46-47.61
5.48-61.006.87-27.81
2.90-37.55
2.83-46.51
2.47

is more absorbed by body. Also polarization combination
is another inﬂuential factor to dynamic WBAN channel.
In channels of Rx2-Tx2 and Rx3-Tx2, the transmitter andreceiver are both parallel to body surface, while due todifferent polarization combination, their performance arequite different.

Simulation
Mean[dB] Std-32.71
0.95-58.259.16-42.32
1.81-55.538.06-51.878.74-63.05
8.23-36.61
1.84-60.898.81-45.142.30

5 Conclusion & Future work
5.1 Conclusion
Dynamic WBAN channel is important information for
design and characterization of WBAN system. However,
the inﬂuential factors are integrally measured as channel
responses and difﬁcult to separate each other from measurement results. Furthermore, the difﬁculty of body motion repeatability in measurement leads to difﬁculty of dynamic WBAN channel analysis. Therefore, simulation
based analysis is required. Comparison system of measurement and simulation is necessary to understand thereliability of simulation. There are several drawbacks inmotion design and antenna placement in the comparison
which is introduced in [2].
Therefore, a simultaneous measurement system of
MIMO channel responses and body motion is developed
for study of dynamic WBAN channel. The developed system can contribute to investigation of the inﬂuential factors such as antenna position and orientation, body motion.

Figure 7: Antenna setup for inﬂuential factors investigation

tenna’s local coordinate with resolution of 2 cm. For same
reason, the receiver is rotated around each axis with resolution of 5 degree. The position setup for transmitter isshown in left part of Fig 7 and the orientation setup forreceiver is shown in right part of Fig 7.

5.2 Future work
In this system, antenna embedding channels are utilized.
In order to remove the inﬂuential factor of antenna type,antenna de-embedding should be considered. Also investigation of other antenna links and different body motions
should be evaluated for this system. Since MIMO channels are utilized in this measurement system, the related
research in diversity issue can be discussed.

References[1] A. Reichman and J. Takada, “Body Communications,” in Pervasive Mobile
and Ambient Wireless Communications - COST Action 2100, Roberto Verdone and Alberto Zanella, Eds, Springer-Verlag, 2012, ch.15, pp. 609-660.
[2] M. Gallo, P. S. Hall, Q. Bai, Y. I. Nechayev, C. C. Constaninou and M.
Bozzetti, “Simulation and Measurement of Dynamic On-Body Communication Channels,” IEEE Trans. Antennas Propag., vol. 59, no. 2, pp. 623-630,
Feb, 2011.

Figure 8: Inﬂuence of receiver rotation around ζ-axis[3] J. Naganawa, “Antenna De-embedding in Wireless Body Area Network
Channel,” Doctor Thesis, Tokyo Institute of Technology, Mar, 2015.

The inﬂuence of receiver rotation around ζ-axis is shownin Fig 8. Channels of Rx2-Tx1, Rx3-Tx2 and Rx2-Tx3
are sensitive to orientation mismatch of receiver which isrotated around ζ-axis. For orientation change of 10 degree, their channel strength difference can be greater than
20 dB. Other channels are not sensitive to orientation mismatch of receiver. This phenomenon indicates that in simulation, the orientation of antenna should be prudently designed. Similar results can be found in receiver rotatedaround χ-axis and υ-axis and transmitter moved along χaxis, υ-axis and ζ-axis. This is reasonable due to the antenna which is parallel to body surface, its radiation power[4] K. IshimiyaɼJɽLangbackaɼ ZɽYingɼ and JɽTakadaɼ“A Compact
MIMO DRA Antenna,” 2008 International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, pp. 286-289, Mar. 2008.
[5] K. IshimiyaɼZɽYingɼ and JɽTakadaɼ“A Compact MIMO DRA for802.11n Application,” 2008 IEEE International Symposium on Antennas andPropagation, July. 2008.
[6] G. Chen, J. Naganawa, M. Kim and J. Takada, “Development of a Tripolarized Dynamic Channel Sounder for Wireless Body Area Network,” 9thInternational Symposium on Medical ICT, Mar. 2015.
[7] URL: http://www.cs.princeton.edu/ min/binvox/

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