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

TRIDE-2016-01

May 9, 2016

Abstracts of Master Theses
Presented in February 2015

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, 2014 and February 12, 2015,
respectively. This technical report consists of the abstracts of those theses.

TECHNICAL REPORT OF INTERNATIONAL DEVELOPMENTENGINEERING
TRIDE-2016-01
TABLE OF CONTENTS(COMPLETING IN SEPTEMBER 2014)
ARCHING EFFECT IN UNDERCUT SLOPE WITH PLANES OF DISCONTINUITY............................................................................... THANACHOTE TECHAWONGSAKORN 1

FACTORS AFFECTING ON INFORMATION AND COMMUNICATIONTECHNOLOGY (ICT), INTEGRATION OF PRIMARY SCHOOL TEACHERS IN
MONGOLIA................................................................................................. OYUN TSOGTSAIKHAN 5

THE STUDY OF ECONOMIC DEVELOPMENT EFFECT ON FLOOD DAMAGE................................................................................................. TARINEE THONGCHAROEN 9STUDY OF PROPAGATION MECHANISMS IN INDOOR MULTIPATH CHANNELS
AT 11 GHZ................................................................................................. KHAGENDRA BELBASE 13

CITIZEN-CENTRIC PERSPECTIVE ON THE ADOPTION OF E-GOVERNMENT IN
THE PHILIPPINES................................................................................................. ALDWIN URBINA 17

IMAGE CODING BY ORIENTATION ADAPTIVE TRANSFORM WITH
PREDICTION................................................................................... BANSIL GAUDENCIO JR. GAMBOA 21

PERFORMANCE EVALUATION OF PUBLIC BUS NETWORK IN SHIRAZ CITY,
IRAN................................................................................................. MARYAM FEREIDOONY 25

PORTFOLIO MANAGEMENT FOR TRANSPORTATION INFRASTRUCTURE
DEVELOPMENT PROJECTS IN THAILAND............................................................................ PITCHAYA CHUNGSAWANANT 29

PERFORMANCE ANALYSIS OF OPEN-LOOP MIMO SYSTEM UNDER RICIANFADING CHANNEL................................................................................................. NOMIN MAITSETSEG 33(COMPLETING IN MARCH 2015)
WAVELET BASED IMAGE CODING VIA LINEAR PREDICTION AND

CLUSTERING....................................................................................................... SHIYU ZHOU 37

11 GHZ INDOOR MIMO CHANNEL PARAMETER ESTIMATION USINGGRADIENT METHOD....................................................................................................... SHIZHI ZHANG 41

HUMAN BODY SHADOWING EFFECTS ON INDOOR MILLIMETER-WAVE
PROPAGATION CHANNEL CHARACTERISTICS....................................................................................................... HUNG PHAM KINH 45

DEPTH IMAGE GENERATION BY IMAGE SEGMENTATION....................................................................................................... SOICHI UCHIYAMA 49

HYBRID IMAGE COMPRESSION BY USING VECTOR QUANTIZATION (VQ) AND
VECTOR-EMBEDDED KARHUNEN-LOEVE TRANSFORM (VEKLT)....................................................................................................... KIUNG PARK 53

ELECTROMAGNETIC WAVE SCATTERING SIMULATION USING PHYSICALOPTICS ON POINT CLOUD DATA....................................................................................................... HIROKI OHARA 57

FINANCIAL ANALYSIS OF RESIDENTIAL ENERGY STORAGE SYSTEMS WITH
EMPHASIS ON ELECTRICITY CONSUMPTION PATTERNS....................................................................................................... TEPPEI KATATANI 61

SOLVENT EXTRACTION OF RARE EARTH METALS BY A CONTINUOUS
STIRRED VESSEL......................................................................................... TAKEHIKO YAZAKI 65

FE-CE CATALYSTS FOR N2O DECOMPOSITION....................................................................................................... HUITING WANG 69

AN EMPIRICAL ANALYSIS OF DISCLOSURE PATTERNS OF ENVIRONMENTAL
INFORMATION BY JAPANESE ENTERPRISE....................................................................................................... RYOYA SUEHARA 73

EVALUATION OF DURABILITY OF PRESTRESSED CONCRETE BYINVESTIGATING BEAM SPECIMENS EXPOSED TO MARINE ENVIRONMENT
FOR 40 YEARS...................................................................................................... TAKUYA KURAMOCHI 77

RESEARCH ON MAXIMUM WIND SPEED RADIUS OF TYPHOON PASSING
THROUGH JAPANESE SOUTHERN OCEAN BASIN....................................................................................................... WENJIE WU 81

SEPARATING GLOBAL WARMING AND URBANIZATION EFFECTS TO
RAINFALL DURING SUMMER

....................................................................................................... NATSUMI KAWANO 85

DEVELOPMENT AND SUSTAINABLE MANAGEMENT OF CONSTRUCTION
AUTHORIZATION DATABASE FOR THE WORLD HERITAGE SITE: CASE OF
DEPARTMENT OF WORLD HERITAGE LUANG PRABANG, IN LAO P.D.R.
..................................................................................................... NATSUKO SAKANASHI 89

MODELING ADVECTION VELOCITY OF SURFACE TEMPERATURE
FOOTPRINTS....................................................................................................... EIJI IWATSUKA 93

ESTIMATION OF THE CONTAINED CO2 EMISSIONS FROM THE SUPPLY CHAIN
OF CHINESE MANUFACTURING INDUSTRY....................................................................................................... LI TIANYUE 97

CORROSION RESISTANCE OF STAINLESS STEEL IN CONCRETE AFTER LONGTERM EXPOSURE IN MARINE ENVIRONMENT....................................................................................................... TOMOHIRO NAGATA 101

DEVELOPMENT OF NUMERICAL MODEL EVALUATING GROUND SCOUR
BEHIND COASTAL DYKES DUE TO TSUNAMIS....................................................................................................... NAGASE SHOHEI 105

PHENOL REMOVAL BY ACTIVATED CARBON USING PHYSICAL ACTIVATIONDERIVED FROM EXHAUSTED COFFEE GROUNDS....................................................................................................... JUNKI IKUTAME 109

SYNTHESIS OF ZEOLITES FROM LAKE SLUDGE USING MICROWAVE HEATINGMETHOD AND THEIR APPLICATIONS AS ADSORBENT....................................................................................................... WENJING WANG 113

IMPACT ON DOMESTIC AIR CARGO DEMAND BY CHANGING AVIATIONCIRCUMSTANCES....................................................................................................... RYUUHEI KUDOU 117

SUGAR PRODUCTION FROM AGAR BY RECOMBINANT BREVIBACILLUS
CHOSHINENSIS.................................................................................................... SANOMI KASHIMURA 121

PROTOTYPE OF AN AUTONOMOUS ENERGY HARVESTING SYSTEM USINGBOOST CONVERTER....................................................................................................... RYUSHI ASAMI 125

ACCELERATION OF THE COMPOSTING BY INOCULATING STRAIN RB1 AND
CONTROLLING COMPOSTING TEMPERATURE....................................................................................................... HIDEHIRA HIRAI 129

MECHANISMS OF WASTEWATER TREATMENT PROCESS IN THE ANAEROBIC-AEROBIC REACTORS SYSTEM....................................................................................................... RYUTARO BESSHO 133

Arching effect in undercut slope with planes of discontinuity
Student Number: 12M51379 Name: Thanachote TECHAWONGSAKORN Supervisor: Thirapong Pipatpongsa୙㐃⥆㠃ࢆ᭷ࡍࡿᩳ㠃ἲᑼ࡟࠾ࡅࡿ࢔࣮ࢳస⏝ࢸࢳ࢛ࣕ࢘ࣥࢧࢥࣥ ࢱࢼࢳ࣮ࣙࢺ
Physical models of undercut slope with various patterns of discontinuous planes in different dip angles
and dip directions were undertaken to characterize arching effect for mining applications. Consequences ofsymmetrical and unsymmetrical patterns of faults resulted in differences of failure width and geometries ofcollapsed slope. Slopes with discontinuities failed at narrower undercut widths with different failure
mechanisms from those without discontinuities, confirming that faults potentially weaken slope. The
lowermost sliced portion of slope displaced towards the pit during excavation due to widening separation of
fault planes and eventually led to beam-like failure rather than arch-shaped detachment. This study indicatedthat arching effect is reduced in undercut slopes having discontinuous planes; hence, geological structures
should be considered in design criteria.

1Introduction

As a consequence of the excavation difficulty, thephysical model of undercut slope has been studied inorder to understand the failure mechanisms anddetermine the maximum excavated width. The detailsof undercut slope experiment will be clarified.
Previous research has convinced the mechanicalidealization by validating with the results of physicalmodels with sufficient reliability for practical designs[2]. However, the effects of faults existing at theactual site have not included in the previous study.
Therefore, slope models were improved by insertingin this study.
This research pays attention to the influence of
discontinuous planes on the failure mechanisms ofundercut slopes i.e. how discontinuous planes affectarch action of slope and maximum failure width.
Series of experiments were conducted by varying thealignment and dip angle of faults in the slope as well
as changing the excavated position.

The Mae Moh open-pit coal mine and Mae Mohlignite-fired thermal power plant located in Lampang
province, northern Thailand is one of the main sourceof energy for the generation of electricity throughout
the northern provinces of Thailand. Both mine andpower plant have been operated under Electricity
Generating Authority of Thailand (EGAT) since 1952.
The thermal power plant, which is the largest
lignite-fired power plant in Southeast Asia, provides
the total capacity of 2,400 Megawatts which
represents about 15% of the total electrical energy
demand in Thailand. The fuel used in electric
generation is supplied by an open-pit mine that canserve the stable and lowest fuel cost per unit forelectricity production in Thailand until 2028. The Mae
Moh mine is also the largest open-pit mine inSoutheast Asia. The pit has a maximum width about 4
km and a maximum length about 9.5 km. The minecurrently produces annual production about 15 million
tons, representing about 80% of the whole productionin Thailand. Clay seam in the shearing zone (seeFig.1) was observed in the site [1]. This clay seam
potentially causes a failure because of its low shear
strength, especially when excavating the slope without
prior removing upside unstable rock masses.
A

Cross section A-A’ of Unstable Area 4.1

2

According to the plastic limit of beam, stress
distribution on rectangular cross section with a widthb and a height h is shown in Fig. 2. The equilibrium of
between tension and compression yielding on tension
side and compression side is expressed by Eq.(1)where ߪ௖ is unconfined compressive strength,ߪ௧ = ܽߪ௖ is uniaxial tensile strength, ܽ = (1 െ‫)߶݊݅ݏ‬/(1 + ‫ )߶݊݅ݏ‬and ߶ is a friction angle ofmaterial.
(1)ܾ(݄ െ ‫ߪ)ݕ‬௖ = ܾ‫ߪݕ‬௧The neutral axis ‫ ݕ‬is obtained from Eq.(1):
݄
(2)
‫=ݕ‬
1+ܽ
Then plastic moment ‫ܯ‬௣ can be determined via
Eq.(3) where ܶ is a summation of tensile force acting
on the tension side:
݄ 1
ܽ
(3)
‫ܯ‬௣ = ܶ = ܾ݄ଶ
ߪ
2 2
1+ܽ ௖

A’

Unstable rock mass
Lignite layer

Potential failure plane(Shearing zone)

Theoretical background

Borehole NEI N29(Depth: 50m)

Figure 1: Cross-section of unstable slope of theMae Moh mine (Courtesy of EGAT)1dy

NeutralM axisMxh䎱
Rectangular beam

3

Stress distribution

y

ӻ

b

ӻ

Figure 5 shows the overview of undercut-slope
physical model. The model consists of 2 parts; i.e.
basal part and slope part. Both are made of moist
silica sand no.6. The dimension of basal part is 1.30 m
wide, 0.40 m long, and 0.06 m thick. It is laid on an
acrylic plate and laterally supported by the sand paper.
The dimension of slope part is 1.30 m wide, 0.80 mlong, and 0.06 m thick. It is laid on Teflon plate withan inclined angle 40o and laterally supported by thesand paper. In the slope part, pressure gauges are
installed to observe the changes of magnitude and to
depict the trajectories of the major principal stress.
After constructing the model, faults are inserted
into the slope part by using cutting blade in order tomake discontinuous planes. There are 3 series ofexperiments i.e. slope without fault, slope with 3
parallel faults at 15 cm, 25 cm, 35 cm from the toe ofslope (see Fig. 5, left), and slope with 3 slanted faults(see Fig. 5, right). In each series of experiment withfaults, there are 3 types of faults i.e. faults normal to
the slope plane, faults in vertical direction, and faults inhorizontal direction (see Fig. 6). Digital camera installed
parallel to the slope plane is used to capture a series ofphotos for GeoPIV analysis [3]. Moreover, high speed
camera is installed to observe the slow motion of
failure mechanisms.
After the preparation of physical model, the basal
part is excavated symmetrically from the center line
and gradually expanded in a symmetrical manner until
the failure is observed. However, some experiments
were excavated either from the left or right lateralsupport for observing the effects of excavated location andlateral constraints.

䎱
Plastic limit

Figure 2: Rectangular cross section analysis

Figure 3: Fixed-end beam with uniformly
distributed load
According the collapse mechanism of fixed-end beam
under uniformly distributed load as shown in Fig. 3,
the collapse load ‫ ݓ‬can be derived by the balance
between external and internal plastic works after
formation of three plastic hinges due to negative
bending moment at both ends and positive bending
moment at the middle of the beam:௟ଶ‫ܯ‬௣ 2ߠ + 2‫ܯ‬௣ ߠ = 2 න ‫ݔ݀ߠݔݓ‬଴‫ ݈ߠݓ‬ଶ4‫ܯ‬௣ ߠ =416‫ܯ‬௣
‫ =ݓ‬ଶ
݈Let ݈ = ‫ܤ‬௙ , hence:‫ܤ‬௙ = 4ඨ

‫ܯ‬௣
‫ݓ‬

Experimental methods

(4)
(5)
(6)

(7)

Next, uniformly distributed load ‫ ݓ‬is calculated bysubtracted the driving load due to cascading down
self-weight mass along the inclined plane by theinterface friction between the soil mass and the
bedding plane (see Fig. 4).
ܿ௜‫ ݏݐߛ = ݓ‬൬‫ ߙ݊݅ݏ‬െ ‫׎݊ܽݐ‬௜ ܿ‫ ߙݏ݋‬െ ൰
(8)
ߛ‫ݐ‬

Figure 5: Undercut slope physical model

40o

Figure 4: Idealization of discontinuous portion
failure mechanism

Figure 6: Different dip direction of faults
Table 1 shows the basic material parameters ofsilica sand no.6 which is used in the experiment.
Water content is checked and controlled at 10% before

Hence, the failure width ‫ܤ‬௙ under the beam-failureassumption is predicted by using Eqs.(3), (7), and (8).
2conducting the experiments. Bulk density is controlled
at 13.68 kN/m3 during constructing the model.
Interface friction angle and interface adhesion shownin the table are obtained from a series of slip test of
free-standing block of sand on the Teflon plate [4].
Table 1: Material parameters of silica sand no.6
Parameters
Value
Water content
10%Bulk density
13.68 kN/m3
Interface friction angle
21oInterface adhesion
0.15 kPa
Unconfined compressive strength1.59 kPa

4(a) Selected mesh of patches

Experimental results

Firstly, the effect of different dip angle of faults is
considered. The test with horizontal faults, which isthe smallest dip angle, resulted in the largest failure
width. On the other hand, the test with vertical faults,
which is the largest dip angle, resulted in the smallest
failure width.
Secondly, the effect of initial excavated location isconsidered. The tests which initial excavated location
started from either right or left lateral support resulted
in a larger failure width than that which initial
excavated location started from the center line.

(b) Displacement vectors

Table 2: Summary of failure width
Initial
Averageexcavated
failureTests
locationwidth (cm)No fault
Center
45.6
No fault
Right
50
3 normal parallel faults
Center
39.2
3 normal parallel faults
Right
55
3 vertical parallel faults
Center35
3 horizontal parallel faults
Center65
3 normal slanted faults
Center45
3 normal slanted faults
Right
92.5
3 normal slanted faults
Left
50
3 vertical slanted faults
Center35
3 horizontal slanted faults
Center55(c) Deviatoric strain contours
Figure 8: PIV analysis of test with 3 normal
parallel faults
Thirdly, as can be seen in Fig. 7, the separated
opening appeared at the lowest fault, and then thetension crack developed at the right and left of thebeam. After that, tension crack occurs at the bottom of
the beam. Next, considering PIV analysis as shown in
Figs. 8(a)-(c). In Figs. 8(c), dark area in contour ofdeviatoric strain represents an opening separation
along the discontinuous plane. This mechanism are
different from those observed in the test without fault[5]. Comparing the experimental result andidealization, the average failure width of the test with
3 normal parallel faults which initial excavatedlocation started from center line is 39.2 cm. And thefailure width calculated from beam-failure assumption
is 37.6 cm. Therefore, the theoretical prediction is inreasonably acceptable agreement with experimental
results.
Lastly, considering the result from pressure gauges,
Fig. 10 shows the graph plotted between the excavatedFigure 7: Failure mechanism of the test with 3
normal parallel faults

3

width and the soil pressure measurement of test 3 withnormal parallel faults which initial excavated location
started from center line. The red circle marks theswitch of stress trajectory from active condition topassive condition at the location of pressure P2 and P4.
In the same manner, the black circle marks the switchof stress trajectory at the location of pressure P5 andP6. Two vertical red lines indicate the first and thesecond failure. As can be seen in the graphs, before
failure is observed, the arch action tends to change
from active condition which minor principal stress is
continuous to passive condition which major principal
stress is continuous (see P4 which is plotted in yellow
line and P6 which is plotted in green line) [6].

slope which excavated location is near lateral support
is wider than those which excavated location is at thecenter line.
3) Pressure gauge is one of alternative
apparatus which can be used to monitor the failure. In
other words, the switch of trajectory of major
principal stress indicates the failure-prone condition.
4) For PIV analysis, the results can be used to
predict the shape of failure. In practice, despite prism
poles are used to monitor the displacement of slope atthe site instead of PIV technique, this study suggests
the suitable locations on the slope for monitoring or
warning purposes.

6[1] Pipatpongsa, T., M.H. Khosravi, and J. Takemura.
Physical modeling of arch action in undercut
slopes with actual engineering practice to Mae
Moh open-pit mine of Thailand. in The 18th
International Conference on Soil Mechanics andGeotechnical Engineering (ICSMGE18). 2013.
Paris, France.
[2] Khosravi, M.H., Arching effect in geomaterials
with applications to retaining walls and undercut
slopes, in Department of International
Development Engineering. 2012, Tokyo Instituteof Technology.
[3] White, D.J. and W.A. Take, GeoPIV: Particle
Image Velocimetry (PIV) software for use ingeotechnical testing. 2002.
[4] Techawongsakorn, T., et al., Slip mechanisms
and interface shear strength between moist silica
sand and acrylic plate, in The 47th Japan
National
Conferenceon
GeotechnicalEngineering. 2013, The Japanese GeotechnicalSociety: Toyama International Conference Center.
p. 895-896.
[5] Khosravi, M.H., et al., Arch action over an
excavated pit in a stable scarp investigated byphysical model tests. Soils and Foundations,2011. 51(4): p. 723-735.
[6] Pipatpongsa, T., et al., Slope failures along
oblique plane due to sequential removals ofpropping portion in physical model tests, in The
15th National Convention on Civil Engineering,Engineering Institute of Thailand. 2010: Ubon
Ratchathani University. p. p.135 CD-ROM.

Figure 9: Installed location of pressure gauges

Figure 10: Graph plots between Excavated widthand Pressure of the test with 3 normal parallel faults

5

References

Conclusions

The 1st series of undercut slope without fault was
conducted in order to compare the results with the 2nd
and 3rd series, namely, undercut slope with 3 paralleland 3 slanted faults, respectively. Some experiments
are chosen to repeatedly conduct to confirm thereproducibility of physical models. According to theresults from experiments, it can be concluded that,
1) Dip angle of fault affects to the stability ofslope i.e. slope with less dip angle of fault is morestable than the more one.
2) Excavated location and lateral supports also
affect to the stability of slope i.e. failure width of4

FACTORS AFFECTING INFORMATION COMMUNICATION TECHNOLOGY(ICT) INTEGRATION OF PRIMARY SCHOOL TEACHERS IN MONGOLIA
Student Number: 12M51480
Name: Tsogtsaikhan OYUNSupervisors: Shinobu YAMAGUCHI and Jun-Ichi TAKADA
There is no consolidated view on how to measure teachers’ effectiveness of ICT use in school settings should bemeasured. Modifying the performance standards created by the International Society for Technology in Education, thisstudy developed the instrument in Mongolian context together with Mongolian education experts. Using this instrument,
359 primary school teachers were sampled. Data was analyzed by exploratory factor analysis to find the distinct
construct, and multiple regression analysis to find the factor affecting teachers’ use of ICT in teaching andcommunication. The results found both teacher-level and school-level factors contributing to the integration of ICT in
the teaching process. These results highlight the importance of individual teachers and schools roles in ICT integration.

Given this background, the research objectives are
twofold: 1) to develop an instrument to measureteachers’ use of ICT in Mongolian context; and 2) toinvestigate factors affecting teachers’ use of ICT ofprimary school teachers in teaching and other activities
in Mongolia.

1 Introduction
Until 1990, the Mongolian education system was
widely acknowledged for providing universal accessfor its population that was both dispersed and mobile
as nomadic pastoralists. At the end of 1980s, the adultliteracy rate marked 99% [1].
During the 1990s, Mongolia went through thedecentralization process and shifted to the marketoriented economy. During this period, every sector was
facing difficulties. In education sector, the budget was
decreased and the literacy rate was dropped due to theincreased number of school dropouts and inadequate
school infrastructure [2].
Since then, Mongolian education reform took
place with several new legal acts, such as New
Education Law (1993, 2002) and New Educational
Standard (2005). Primary and secondary education
experienced a major transition from 10-year school
system to 12-year school system. The current 12-year
education system consists of primary education of six
years and children starting at the age of six, which
expands the length of primary schooling and increasesrole of the teachers [3].
Besides the education reform, the Mongolian
Government has been providing better ICT
infrastructure to offer more opportunities for national
development. Master Plan on Education (2006) defines
ICT as the most appropriate tool for improving accessand quality of education. In this context, ICT is
identified as an important tool to promote teaching andclassroom management [4].
Currently in Mongolia, the primary and secondary
school teachers are expected to know how to integrate
ICT in school context. However, there is a limited
number of studies conducted to understand how theteachers are integrating ICT into their teaching andother school activities. The series of education reform,
particularly emphasizing the importance of thedevelopment of teaching skills, include ICT use ineducation. The development of proper instrument isnecessary to determine the current status of teaching in
integrating ICT in their teaching. However, there is no
standardized instrument to measure the level of suchskills in Mongolian education context.

2 Literature Review
Measuring teachers’ use of ICT were often focused onteachers’ frequency of using ICT [5][6][7]. ICT
integration in this study is also referring to teachers’
ICT use in school settings. More specifically, itmeasures how frequent teachers using ICT in theirteaching and other school activities.

2.1 Instruments to measure teachers’ ICT
integration
Several standards and instruments have been used tomeasure teachers’ ICT integration. For example, theNational Educational Technology Standards for
Teachers (NETS-T) developed by the International
Society for Technology in Education (ISTE) is widelyadopted in the North America as indicators for teachers’
proﬁciency in ICT [8]. It consists of six standard areasand 23 performance indicators in forming a structure
and boundary in evaluating teachers’ proﬁciency in
ICT use in schools.
Both developed and developing countries haveadopted the NETS-T in their national technology plans,certiﬁcation, licensure, curriculum plans or assessment
plans including Norway, Costa Rica, Malaysia, Japan,
Korea, Australia, the Philippines, China and Turkey.
Using the performance standards, Hsu’s study
developed equivalent ICT integration scales andvalidated with large number of samples [9]. However,there is no standardized measurement being applied indeveloping countries.

2.2 Factors
affectingintegrationteachers

ICT

Following the framework used by Tondeur [10], schoollevel factors are categorized into contextual andcultural variables, and teacher level factors are
categorized into structural and cultural variables.

572% response rate. Finally, 359 questionnaires wereconsidered valid for data analysis.

Contextual school characteristics consist of thecharacteristics such as school infrastructure and school
size while cultural school characteristics include
leadership and school support. Structural teacher
characteristics are fixed information such as teachers’
gender and years of teaching. Cultural teacher
characteristics include teachers’ positive perception
related to use of technology, computer attitudes andtraining attendances.

3 Methodology

Figure 1: Data collection sites in Mongolia
Source: Created by author

The data collection took place between January 2013
and June 2014 including three field visits in Mongolia.
Both quantitative and qualitative data collection andanalysis were employed.

In order to supplement qualitative analysis, thefocus group discussion and interview with a total of 13
teachers and education experts were conducted inTokyo and Ulaanbaatar in 2014.

3.1 Research process

Table 1: The instrument (Section 3 of the survey)

To achieve two research objectives mentioned earlier,
three research questions are identified.
1) What is the ICT integration measurement applicablein the Mongolian context?
2) Which school characteristics are associated withprimary school teachers’ ICT integration in Mongolia?
3) Which teacher characteristics are associated withprimary school teachers’ ICT integration in Mongolia?
In order to develop an instrument to measureteachers’ ICT integration, three steps are followed: 1)
selection of ICT integration framework, 2)
identification of potential ICT integration frameworkfor Mongolia, and 3) localization of ICT integration
framework in Mongolian context.
As for the first step, this study modified NETS-T
to be applied in Mongolian context with the following
reason: 1) its popularly used in both developed anddeveloping countries; 2) multiple cases using NETS-T
in Asia; and 3) validation with large samples ofteachers in Taiwan.
For the second step, Hsu’s study was carefully
analyzed to identify categories applicable to measure
ICT skills of primary school teachers in Mongolia. As
a result, the study identified three categories that
Mongolian primary schools teachers are currently
using ICT. These three categories are the areas of 1)
lesson preparation, 2) communication, and 3) selfdevelopment activities.
As the third step, the original question items from
Hsu’s study were analyzed and pilot tested with 22
Mongolian education experts. The draft instrument wasrevised and survey questionnaire was produced (table1).
The newly developed instrument was used for datacollection to answer research question 2 and 3.

Lesson preparation:

1.
2.
3.

4.

Selfdevelopment

Communication

1.
2.
3.
4.
1.
2.
3.
4.

Question Items
Use the Internet to search for information as
supplementary teaching and learning material
Use the computer to create teaching material,class demonstrations and exam questions
Use Power point and other presentation software
to create presentation
to present teachingmaterial in class
Use the Scratch and other software to record, edit
sound or music in my teaching material
Use e-mail or chat to communicate with students
and parents for collaboration and to give advice
Use e-mail or chat to communicate with otherteachers
Use e-mail or chat to communicate with ECD
methodologist
Use e-mail or chat to communicate withprofessors and experts
Spent time to learn and practice ICT skills
Use online courses for professional development
Use VCDs and DVDs for self-development
Working by team to learn ICT skills

Note: 4-point-Likert scales (1=never use to 4=very often)
Source: developed by author based on Hsu’s study

3.3 Data analysis
3.3.1 Quantitative analysis
The quantitative analysis includes three components.
First, descriptive analysis was conducted to illustrate
the demographics of teachers and school characteristicsof the data set. Second, exploratory factor analysis was
conducted to verify whether the new instrument ismeasuring the intended constructs. Third, multiple
regression analysis was utilized to find the factors
affecting the ICT integration scales of primary school
teachers.

3.2 Data collection
The survey questionnaire consists of three sections: 1)
teacher background information (18 items); 2) school
background information (17 items); and 3) use of ICT(12 items). A total of 571 questionnaires were
distributed to three different regions reflecting regional
diversity in Mongolia from August to September, 2013.
A total of 413 questionnaires were returned, yielding

3.3.2 Qualitative analysis
The qualitative analysis using face-to-face focus group
discussions and individual interviews with education
experts and primary school teachers were used toverify and explain the results of quantitative analysis.

6

IT usage in teaching increases by 0.170 scale score.
Other influencing factors include teachers’ attendance
for training (T) and frequency of using computer per
week (F). This model explains 31.7% of the varianceof IT usage scale of primary school teachers in
Mongolia.

4 Findings
4.1 Descriptive statistics
As for the basic demographics, 93% of the respondents
were female teachers. 40.1% falls into the age between
31 and 40. 40.4% of the teachers have 1 to 10 years ofteaching experience. 68.5% have bachelor’s degrees
and 9.2% obtain master’s degrees. Regarding thecomputer ownership, while 78% of the teachers havetheir own computer, only 23% have an access tointernet at home.
As for school characteristics, 56% of the schools
are located in soum centers, while 21% are located inaimag centers and 23% in Ulaanbaatar city. 57% of theschools are connected to the internet, but only 10.6%
have an access to internet at classroom level. Most
teachers do not own computer at school (72.1%) and
38.5% have an access to internet at teacher
development center at school.

ITUS=0.626+0.17(DL1)+0.342(SSp)+0.233(P)+0.132(F)+0.139(T)(1)

Equation (2) shows that primary school teachers’
use of ICT for communication (CS) is predicted by six
determinants, namely, school location, access tointernet at school, access to internet at home, schoolsupport, years of teaching and teachers’ title. First, if aschool is located in Ulaanbaatar (DL2), the frequency
of using ICT for communication increases by 0.366
scale score. Second, if a school has internet connection(IS), teachers’ frequency of using ICT for
communication increases by 0.338 scale score. Third,
if the teacher has the internet connection at home (IH),
frequency of using ICT for communication increases
by 0.276 scale score. Other factors influencing onteachers’ use of ICT for communication include schoolsupport (SSp), years of teaching (YOT) and teachers’
title (DT1). It was found that both years of teaching
and teachers’ title are negatively associating withteachers’ use of ICT for communication. The second
model explains 41.1% of the variance ofcommunication scale of primary school teachers in
Mongolia.

4.2 Exploratory factor analysis
The exploratory factor analysis was conducted toverify the newly developed scales. The Kaiser-MeyerOlkin measure (KMO-test) was used to determine
whether question items are categorized into distinct
constructs. The result, KMO=.870, is consideredadequate, indicating its value greater than 0.5. Further,
the factor extraction method was conducted todetermine how many constructs are to be identified.
The result shows that only two constructs are retained,
which satisfied Eigen value greater than 1. Finally,factor rotation method was used to interpret newly
identified scales. As a result, original three scales(lesson preparation, communication, and selfdevelopment) were re-categorized into two new scales.
The focus group discussion was organized to
discuss the new constructs. They were named as 1) IT
usage scale, and 2) Communication scale. These two
new scales were utilized for the further analysis

CS=0.713+0.366(DL2)+0.338(IS)+0.249(SSp)-0.21(YOT)-0.159(DT1)+0.276(IH)(2)

All the findings from the multiple regression
analysis were discussed with interview with Mongolianteachers.

5 Discussion
The study confirmed that nine factors areassociated with teachers’ ICT integration in Mongolia.
As for the school level factors, school location, accessto the internet at school and school support were foundto have significant association. Among these
influencing factors, the analysis found the schoolsupport is the strongest determinant for both teachers’
use of ICT for teaching and communication. Thismeans if the school promotes integrating ICT into theirschool activities, teachers are likely to adopt newmodes of ICT. The qualitative analysis also confirmedthat teachers believe school support is the mostimportant factor for their ICT integration, which
supports the current literature [11].
In Mongolia, soum center schools are considered
rural while aimag center and Ulaanbaatar city schools
are considered urban. The study confirmed that urban
school teachers have more frequent use of ICT for theirteaching and communication activities, which is argued
by current discussion [12].
As for the teacher level factors, teachers’ positive
perception, training attendance, frequency of using
computer per week, access to the internet at home,

4.3 Multiple regression analysis
The multiple regressions analysis was used to identify
the factors affecting teachers’ use of ICT for teaching
and communication. Fourteen independent variables
were chosen based on literature review and interview
with Mongolian teachers.
The result of the multiple linear regressions on the
primary school teachers’ IT usage and communication
scales are follows:
Equation (1) depicts that the use of ICT forteaching (ITUS) is predicted by five determinants,
namely, school support, positive perception, school
location, training and frequency of using computer per
week. First, the stronger the level of teachers’
agreement that school is supporting the use of ICT(SSp), teachers’ frequency of IT usage in teaching
increases by 0.342 scale score. Second, if the teachers
have higher perception (P) on IT usage in teaching,
their frequency IT usage in teaching increases by 0.233
scale score. Further, if a school is located in aimag
center or in Ulaanbaatar (DL1), teachers’ frequency of

7

found to be significant factors influencing teachers’ useof ICT.
These findings have important policy implications
for local government and school administrators todevelop more effective ICT implementation andteacher training at school level in Mongolia.

teachers’ years of teaching and teachers’ title were
found to have significant association with teachers’
ICT integration in Mongolia.
The study also confirmed positive perception ofteachers increases the use of ICT for teaching, which
supports current literature [13].
The findings of this study supports the literature[14] indicating the importance of the teacher training.
It found that the more frequent teacher attends ICT
related training, the more frequent they use ICT inteaching. In Mongolia, training is the only way thatteachers can learn about how to utilize ICT into theirteaching and school works once they graduate fromcollege.
Study also confirmed that access to internet bothat school and home have a significant association withteachers’ frequency of using ICT for communication,
which is argued by multiple literature [7][10]. In
Mongolia, not every teacher is able to utilize Internet atschool or home for finding information andcommunicating. Qualitative data illustrated that, thelimited access to internet via computer has been greatly
improved with a smart phone usage in Mongolia, andthus, the role of smart phone can be the further area ofinvestigation.
The study confirmed previous studies that teacherswith greater seniority and more years of experience
seemed to be slower in learning communication tools
or making multimedia lesson materials [7][15]. In
Mongolia, the younger teachers are able to learn how
to use ICT into their teaching when they are studying atcolleges and universities. But the teachers, who havemore teaching experiences and higher titles, could notlearn these skills during their college education in the
past.
Other factors such as teachers’ gender, school size,and computer ownership were not making difference
for teachers’ ICT integration in Mongolia, and thus,
further investigation is required.

References[1] Gita Steiner ̺ Khamsi & Ines Stolpe (2004).
Decentralization and recentralization reform in Mongolia:
tracing the swing of the pendulum. Comparative Education,
40:1, 29-53[2] Sukhbaatar, J., Yamaguchi, S., Takada, J.,R. Bat-Erdene, L.,
Orgilmaa. (2012). Sustainable use of ICT for teacher
training in primary schools in Mongolia-Phase 2, Finalreport submitted to Japanese Ministry of Education, Culture,
Sport, Science and Technology, Japan.
[3] Onodera, J., Yamaguchi, S., (2011). ICT use in primaryschools: Comparative analysis of five rural provinces in
Mongolia. Conference Proceedings, 55th Annual
Conference on Comparative and International Education
Society, Montreal, Canada.
[4] Ministry of Education and Science (MECS), (2012). Masterplan to develop education of Mongolian in 2006-2015.
From:
http://www.mecs.gov.mn/data/lavlah/master_paln/education/EDU_MP_mon.pdf[5] Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht,
J. (2008). Identifying discriminating variables between
teachers who fully integrate computers and teachers withlimited integration. Computers & Education, 51(4), 1523–
1537.
[6] Becker, H. J. (2001). How are teachers using computers in
instruction? American Educational Research Association,
Seattle, WA.
[7] Hsu Shihkuan and Kuan Ping-Yin. (2013). The impact ofmultilevel factors on technology integration: the case of
Taiwanese grade 1–9 teachers and schools. Education Tech
Research Dev 61:25–50, DOI 10.1007/s11423-012-9269-y[8] ISTE. (2000). National education technology standards andperformance indicators for teachers (NETS). From:
http://www.iste.org/docs/pdfs/nets_for_teachers_2000.pdf?
sfvrsn=2[9] Hsu.S. (2010). Developing a scale for teacher integration of
information and communication technology in grades 1–9.
Journal of Computer Assisted Learning 26(3):175–189.
doi:10.1111/j.1365-2729.2010.00348.x[10] Tondeur, J., Valcke, M., & van Braak, J. (2008). A
multidimensional approach to determinants of computer use
in primary education: Teacher and school characteristics.
Journal of Computer Assisted Learning, 24(6), 494–506.
[11] Dawson, C., & Rakes, G. C. (2003). The inﬂuence ofprincipals’ technology training on integration of technology
into schools. Journal of Research on Technology in
Education, 36(1), 29–49.
[12] Toprakci, E. (2006). Obstacles at integration of schools into
information and communication technologies by taking into
consideration the opinions of the teachers and principals of
primary and secondary schools in Turkey. Journal of
Instructional Science and Technology (e-JIST), 9(1), 1-16.
[13] Van Braak, J., Tondeur, J., & Valcke, M. (2004).
Explaining different types of computer use among primaryschool teachers. European Journal of Psychology of
Education, 19(4), 407–422.
[14] Davis, N., Preston, C., & Sahin, I. (2009). Training teachers
to use new technologies impacts multiple ecologies:
Evidence from a National Initiative. British Journal of
Educational Technology, 40(5), 861–878.
[15] Inan, F. A., & Lowther, D. L. (2010). Factors affecting
technology integration in K-12 classrooms: A path model.
Educational Technology Research and Development, 58(2),137–154.

6 Conclusion
The study contributed in developing the instrument tomeasure how teachers use the ICT to enhance their ICT
integration in teaching in Mongolian context. It also
investigated the teacher level and school level factors
affecting frequency of teachers using ICT for teaching
and communication activities.
The instrument developed in this study utilized
Hsu’s ICT integration scales and localized, identifying
two scales, namely IT usage (measuring frequency ofusing ICT for teaching) and communication(measuring
frequency
of
using
ICT
for
communication).
The study found that six teacher level factors andthree school level factors are affecting teachers’ ICT
integration scales. For Mongolian primary school
teachers, positive perception on ICT use in teachingand school support are found to be important factors
increasing the frequency of ICT use in school settings.
With the rapid infrastructure development, influence ofInternet connections both at schools and homes are8THE STUDY OF ECONOMIC DEVELOPMENT EFFECT ON FLOOD DAMAGE
Student Number: 12M5356

Name: Tarinee Thongcharoen

Supervisor: Naoya ABE

Floods, among all types of natural disasters, are the most common types and the leading cause of economic damages andfatalities. In reality, physical, economic, social and political factors determine vulnerability of population and their capacity to copewith and recover from natural hazards. The reduction in the risk of floods can be undertaken by reducing these vulnerabilities. The firststep to reduce vulnerability is to identify them. This study proposed an Economic and Social Vulnerability Index involving three
approaches, which consist of several combinations of economic and social development indicators, to investigate the relationshipbetween economic and social development and flood damages. The proposed index can be used to determine vulnerability status of aparticular country and for international benchmarking on vulnerability. The study aims to describe the impact of economic and social
development of countries on flood damages in terms of fatalities and economic loss and identify the most vulnerable group of countries
based on the proposed index.
The vulnerability perspective regarding risks of floods

1. Introduction
Nowadays, there are increasing concerns toward

assumes that damages from floods will be experienced when adisasters and their destructive effects. This may be due to thedisaster strikes an underprivileged population. A goodfrequent occurrences and overwhelmingly devastating impacts

combination of preparation, prevention and structuralof disasters that many countries suffered in the last decademeasures before, during and after floods are necessary for(EM-DAT, 2013). Flood is one of the most prevalent anddamage mitigation and prevention. However, the necessary

problematic natural disasters. The number of floods that

degrees of measures vary for different countries as the degrees

occurred during 2004-2012 was observed to be increasingof vulnerability among countries are at different degrees. It is

sharply throughout every region around the world. Regarding

essential for governments, policy makers and individuals to begeographic regions, Asia suffers the highest loss from floods

aware of their respective risk levels and acknowledge theiramong all regions.

own vulnerability and capability to be able to adapt and adopt

Flood damages across countries with different

appropriate measures against floods. Moreover, international

income levels, according to the World Bank’s definition,

cooperation and exchanges of knowledge and technologies

account for different proportions of the total damages fromamong countries with similar situations and capabilities canfloods (Figure 1). For high-income countries, while damages

lead to more efficient development of flood awareness andin terms of fatality are very low, 25% of the total damages inprevention policy.

terms of direct economic loss is still accounted for. The uppermiddle income countries suffered the most in terms of both
Fatality

number of people affected and direct economic loss fromNumber of people affectedflooding. The sum of the damages suffered by these countries

Direct economic loss

accounted for over 50% of the total direct economic loss.

Number of disaster

These differences in the amount of damages suggest that not

0

only geographical differences but also levels of economic
Lowdevelopment have an effect on the amount of damages fromfloods.

Lower-middle

20

40

60

Upper-middle

80

100

High

Figure 1: The percentage of total damages from flood during
Vulnerability is the degree to which a population,

2004-2012 according to country’s income level (Source: EM-

individual or organization is unable to anticipate, cope with,DAT version 12.07, 2013)

resist and recover from the impacts of disasters (WHO, 2002).

9

2. Objectives of the study

the assumption that the effect of floods will eventually result

The study has two objectives, as stated below:

in fatality or economic loss. If there were high occurrences ofa) To examine which economic and social indicators best

floods and each flood resulted in small amount of damages,

explain which countries are most likely to suffer higher

the final value of the damages would be high. However, if a

economic losses from flood and identify the correlation thatcountry experienced flood several times but those floods

exist between said indicators and economic loss from flood.

yielded no damages, the occurrence of floods will be neglectedb) To develop a standardized data index that factor in key

since they result no damage in term of economic loss andeconomic and social vulnerabilities from floods and identify

fatality.

the groups of countries, which experience similar impacts in

3.2 Economic and Social Development Vulnerability Index

terms of economic loss from flood.

Vulnerability is understood as the degree to whicha country or a community is likely to experience harm due to

3. Research Methodology

exposure to a hazard. In actuality, vulnerability depends on

3.1 Fatality and percentage of economic loss from flood toseveral factors.

Under the assumption that wealthy people will

GDP as a proxy for flood damages
In this study, two dimensions of damages due tohave more capability to cope with emergency event like flood

flooding were considered: fatality and percentage of economic

and other disaster due to higher income, good quality of life

loss from flood to GDP. The selection criteria of the twoand better education (Baritto. F. 2008, Banuri. S. 2005,

dimensions are discussed in the succeeding paragraphs.

Schumacher. I. & Strobl. E. 2011), three aspects of economic

Once flood strikes a community, the mostand social vulnerabilities was considered: economic resilience,

destructive consequence of the disaster result in loss of life, or

distribution of wealth, human health and well-being. A set ofin other words, loss in human resources because the loss of life

indicators proposed as indirect measures of economic andcannot be recovered. Other consequences would result insocial vulnerabilities are compared against the relative

economic loss, which can be recovered depending on themagnitude of economic losses to GDP and fatality. The key

capacity of the community. However, the same amount of

indicators include the following: Gross saving per capita,

direct economic loss from flood can have different extent of

Human Development Index (HDI), Income distribution index

consequences because of size of economies. For example,(Gini). In order to investigate the effect of education levels onflooding in Guyana in 2006 affected the country badly and had

flood damages, three approaches to compute the index werechain consequences to several sectors in the country.

proposed. With the assumption that educated populations are

According to EM-DAT (2013), the economic loss by flood

more capable of disseminating information and preparing theaccounted for 11.85% of GDP in that year and it took therisk prevention measures against disaster, approach 1, 2 andcountry to recover for several months to get back to the same

approach 3 were created to see the effect of higher education

economic level as before the event. As another example,

level on figures of damages. Approach 2 and 3 composed ofAustria experienced approximately similar amount of

the same 3 proxy indicators for each aspect like those ineconomic loss from flood in 2009 with that in Guyana in 2006.

approach 1 with extra indicators on secondary and tertiary

Even though the level of loss was high, it only accounted for

education level accordingly. The indicators for each approach

0.05% of Austria’s’ GDP and have lower effect to the overall

are shown in Table 1.

economy of the country.
Both fatality and percentage of economic loss to
GDP are summed up for every event within a year. By doing
this, the effect of occurrence was indirectly considered with

10

Table 1 List of Key Indicators for computing index
Approach

4. Data
Two data sets were used in this study. The first

Indicators

1

Gini

HDI

Gross saving

included information on flood characteristics. The data was

2

Gini

HDI

Gross saving Secondary

obtained from the OFDA/CRED International Data Base. The

3

Gini

HDI

Gross saving Secondary Tertiary

original data includes 1,518 observations during 2004-2012
periods. In order to analyze the data on a per country and3.3 Methodology Framework.
The objectives and methodologies used in thisyearly basis, the data was rearranged. Data on economic losses

study are summarized in Figure 2. First, methodology forfrom events that occurred in the same year within the same

computing economic and social vulnerability index values

country are combined together and set as a new observation.

adoptedThe final number of observations considered, then, wasfrom theinequality-adjusted HDI

methodology

proposed by the United Nations Development Program

reduced to 736 events.

Each country’s economic and social(UNDP) in 2010. Second, the data analysis employed principle

development data were collected from several sources.

correspondence analysis (PCA) to assign a weighted linear

In order to compute the index, all data must be

combination to the original indicators (shown in Table 1) inavailable. Nonetheless, some countries were not included inorder to make a comparison with the results from economic

the database for various reasons, mainly for theirand social vulnerability index

unavailability of certain crucial data. For some countries, data

Third, to choose the best approach for creating anfor economic and social indicators are available in some years

index, which has the most power to explain the effect ofbut missing in other years. When considered available data of

economic loss and fatality. Pearson's correlation was used togross saving from 2004-2012, values of the same country

show the relationship between the amount of damages andchanged rapidly even between consecutive years. This lead toIndex values and figures of damages and component scores inthe difficulties in predicting or assuming the trend of thethis case the percentage of economic loss. Finally, cluster

indicator and would be inappropriate to treat the missing year

analysis (CA) was used in order to identify the group ofdata with the data from the available year. This also applied tocountries with similar levels of vulnerability and extent ofother indicators as well. To avoid this bias, the year which isdamages. The most vulnerable group identified by using

richest in terms of data availability were used for further

cluster analysis was found to be associated with 2 conditions.

analysis. Hence, the analysis only considered flood events inFirst, the group of countries had the lowest average level ofyear 2004.

index value or component score. Second, the group with the

5. Conclusions and Recommendations

lowest score also had the highest average percentage of

The damages from floods and economic and social

economic loss to GDP.
Figure 2: Methodology framework and result from eachindicators for countries, which floods struck in 2004, haveprocedure

been examined.
a) The results showed that, among the three proposed
approaches, the composition of five economic and social
development indicators (i.e., Human Development Index,
income distribution index, gross saving per capita, secondary
education and tertiary education level enrolments) was
considered to be the best approach to identify countries withhigh flood vulnerability in terms of economic loss with the11highest correlation coefficient when compared to othersocial damages due to the disaster event tend to be difficult andapproaches (Table 2). The negative coefficients represent thattime-consuming, and often resulting in the annual-basis data

as index values or component scores increase, the figures ofwith uncertainty. As a result, the group of vulnerable countries

damages decrease. The results suggest that higher levels ofaccording to economic and social vulnerability index, in thiseducation (i.e., which reflects awareness level of disasterstudy, is only a snapshot of time (2004) and may not fully

preparedness) correlate to lower damages from flood.

represent previous trends or on-going evolutions of certainb) The result shows that current vulnerability to flood in term

dimensions. Nonetheless, due to rapid changing on the trendof economic and social development in Bangladesh, Bhutan,of flood occurrence and severity, it is more appropriate to rely

India, Indonesia, Iraq and Tajikistan is profound.

on the most recent available data to reflect the present

Regardingfatality, Nepal, China. Bangladesh and India seem to have a

condition for future study.

unique characteristic in the extent of damage and difference

Third, the percentage of economic loss to floodlevel of economic and social development.

was used as a proxy for country’s flood sensitivity and to
explain the extent of which economic loss from flood affected

Table 2: The comparison of Pearson correlation coefficientsthat country economic. However, there are many aspects offor Index and Component with figures of damages

economic activities that are not included in the measurementof GDP. The result may not be accurate for some countries. For

Approach

Ecogdp*Fatality**

10.0137-0.18432-0.0502-0.17543-0.0739-0.19971-0.1181-0.17072-0.1429-0.22733-0.1442-0.2048

example, the Philippine have large remittance from aboard
PCA

Index

resulting the large number in GNI. Timor-Leste’s GNI also
higher than GDP because of international aid. Finally, theindex is intended to spatially identify economic and social
vulnerable populations and focus on the different effect fromlevel of education. However, direct linkages between disasters
and the economy and social development are complicated and*Ecogdp is the percentage of economic loss from flood to Gross Domestic
Product (GDP).

not clear. To investigate further relationship between economic**Fatality is the number of people killed by flood

loss from floods, geographic characteristics environment
conditions and other country characteristics, which can be

6. Limitations and Future Researcheffect by flood, should be consider.

While the study consist of important aspects ofReferences:economic and social vulnerability, it has also raised a number

EM-DAT the International Disaster Database (2013). Data version 12.07,

of potential directions for further research. First, limitation in

www.emdat.beWorld Bank (2014). Country and Lending Groups, http://data.worldbank.org/

data accessibility for countries with poor recording system canBaritto, F. (2008). Disasters, Vulnerability and resilience: A methodologicalframework from an economic perspective. ICTSD – UN-ISDR., Switzerland.

lead to miscalculation of the analysis. Data on least developed

Banuri, S. (2005). The Macroeconomic Impact of Natural Disasters. Term Paper

countries is very poor and these countries have been excluded

ECO 5322-01. Department of Economics. Richardson, Texas. December.

from the study. An effort should be done to improve available

Jonkman, S. N., & Kelman, I. (2005). An analysis of the causes andcircumstances of flood disaster deaths. Disasters, 29(1), 75–97

information on a predefined list of indicators.

Rygel, L., O’sullivan, D., & Yarnal, B. (2006). A Method for Constructing a
Social Vulnerability Index: An Application to Hurricane Storm Surges in a

Second, the results of the analysis in the study

Developed Country. Mitigation and Adaptation Strategies for Global Change,

relied on the quality of the data-set. In order to obtain the most11(3), 741–764.

precise result, the data had better be as much accurate as

Schumacher, I., & Strobl, E. (2011). Economic development and losses due tonatural disasters: The role of hazard exposure. Ecological Economics, 72, 97–

possible. While the occurrence of disaster can be recorded

105.

when the event happens, the measurement of economic andPlate, E. J. (2002). Flood risk and flood management. Journal of Hydrology,267(1-2), 2–11.

12

STUDY OF PROPAGATION MECHANISMS IN INDOOR MULTIPATH CHANNELS AT 11 GHz
Student Number: 12M51362

Name: BELBASE Khagendra

Supervisor: TAKADA Jun-ichi

11 GHzにおける屋内マルチパスチャネルの伝搬メカニズム
Abstract
This thesis investigates the dominant propagation mechanisms in indoor line-of-sight (LOS) environments with the helpof channel measurement and ray tracing simulation. Double-directional channel sounding was performed using 24×24
multiple-input multiple-output (MIMO) channel sounder at 11 GHz with 400 MHz bandwidth to get the MIMO channeltransfer function. Path parameters from ray tracing simulation and antenna array response were used to reconstruct the ray
tracing based channel transfer function. Beamforming was applied to the channel transfer function to get angle resolved
power spectrum. Then the angular and delay power spectra were used to identify the dominant propagation mechanisms in
considered environments. Along with LOS component, walls and window reflected paths were found to be the dominant
propagation mechanisms observed in both measurement and simulation. But ray tracing failed to predict the scattering
power from the long and thin metal objects such as window frame and metal pillar edge which are the significant objects
observed in measurement.

1

Introduction

With the ever growing demand of higher data rates inmobile systems, use of new transmission techniques andwider frequency bandwidth is necessary. However, thefrequency bands in the lower microwave range (below 6GHz) are already congested or have limited bandwidthavailable for further use. Therefore exploring the upper
frequency bands where the wider bandwidth is available isinevitable choice for the future high bit rate communication systems [1]. Along with the use of higher frequencies,
use of multiple antennas at both transmitter and receiver
popularly known as MIMO system can increase the data
rate significantly. Since the performance of MIMO systems highly depends on the directional and delay characteristics of the channel, study of the channel should not belimited to the study of path loss characteristics and moredetailed analysis on directions of arrival (DOA), directionof departure (DOD), and time delay of arrival of the multipath components is needed. These characteristics are dictated by the dominant propagation mechanisms in considered environments.
In wireless communication systems, propagation channel
is not the controllable phenomenon. But if the appropriate
channel models are known, the transceivers including antennas can be designed to exploit the channel to achieve
maximum data rate. Therefore this work is focused on the
study of wideband MIMO propagation channel at 11 GHz
in indoor environments. This frequency is of particular interest because of the availability of wider bandwidth, andthe higher path loss which is suitable for reducing the cellsize in future small cell mobile systems.
In order to study the dominant propagation mechanisms,
the measurement and ray tracing simulation are performedin this study. Ray-tracing algorithms are popular approaches for propagation prediction and modeling since
they can investigate most of the radio propagation mechanisms of wireless channels. But the ray-tracing algorithms do not include the non-specular scattering effects,
which are common in micro-cell and indoor environments.
Channel sounding measurements are the foundation for

Parameter

Value

Carrier Frequency
Signal Bandwidth
Delay resolution

11 GHz400 MHz2.5 nsTable 1: Specification of channelsounder

Figure 1: Channel
Sounderchannel modeling as they can represent the realistic environments. Although the they provide the most accuratecharacteristics of the channel, the task of channel sounding is very complex, costly and time consuming. So, theuse of propagation simulation tools is a very efficient way
to predict the channel for characterization.
The previous studies in outdoor microcell have shownthat the metallic objects with dimensions as small as tensof radio wavelengths can be the source of significant scattering [2, 3]. Also the double directional MIMO channelsounding data is used to identify the propagation mechanisms in indoor environments at 5.3 GHz [4]. Since thetype and size of objects in indoor environments are different from outdoors, dominant mechanisms are expected tobe different. At 11 GHz, scatterers as small as few tens ofcentimeters can have the significant effect in received multipath components. Therefore, the objective of this study
is to analyze and compare directional wideband channel
characteristics of measurement and simulation to find thesignificant discrepancies of mechanisms.

2

Methodology

2.1

Channel Sounding

Channel sounding refers to the task of measuring the properties of propagation channel. 24×24 MIMO channelsounder is used in this study [5], as shown in Fig. 1. Thissounder has a very high delay resolution of 2.5 ns, so has
the capability to distinguish multipath components withhigh accuracy. By using the uniform circular array (UCA)
antennas for beamforming, it can achieve the angular reso13

Path Loss [dB]100Free space
Co-polarization
Cross-polarization

90
80
70
60

Tx

100
90
80
70
Free space
Co-polarization
Cross-polarization

60
50

50
1

10
Radial Distance [m]Figure 2: Example of ray tracing output for Room B

110

Path Loss [dB]

110

Rx

1

10
Radial Distance [m]Figure 3: Measured path loss Figure 4: Simulated path loss
characteristics of Hall
characteristics of Hall
A
A

lution of 30 degrees. The specification of channel sounderare given in Table. 1.
2.1.1

surrounding scatterers. Since the material properties has avery big influence in the computed results, some trial simulations were done to select the material types to best fit
the measurement results. One sample ray tracing result for
Room B is shown in Fig. 2.

Description of the environment

The environment considered in this study is indoor line-ofsight (LOS) scenario. Two hall and room environments areinvestigated for the dominant propagation mechanisms.
One is a big hall (Hall A) of size 30 m × 10 m × 7 m
having concrete walls and large glass windows. The floor
is made of concrete and the ceiling is considered to be a
open space. Another is the room (Room B) of size 18 m ×
10 m × 3 m with concrete walls having metallic boards atthe end, metallic floor, and concrete ceiling. The windows
are covered with metallic blinders so can be considered tobe metallic. The room was made empty by moving all thefurniture in one side of the room. The detailed location,
shape and orientation of these rooms are described in [1].
2.2

3

To study path loss from the measurement, transmitter was
moved along a route and channel transfer function is measured at a fixed receiver. Antenna arrays having dual polarized i.e. vertically polarized (VP) and horizontally polarized (HP) antenna elements with omni-directional azimuth
radiation pattern with a gain of 4 dBi are used. Then thepath loss is calculated using the spatial averaging as mentioned in [1]. This averaging was performed over the distance of 0.5 m (20λ) to mitigate the effect of small scalefading. Ray tracing simulation was also done with moving transmitter and fixed receiver with the same locations
as in measurement. Small scale fading in ray tracing was
mitigated using the random phase approach as describedin [7]. This approach realized the spatial averaging considering that for a nearby locations, the magnitude of received
power is almost same but the phase is different based on
total path length traveled by a wave.
The measured and simulated path losses were then fitted
into the power-law model, which is a linear plot in log-log
scale where abscissa is distance and ordinate is path loss.
This linear model is represented by

Ray Tracing Simulation

Ray Tracing simulation is one of the widely used techniques for the prediction of site-specific propagation channel. It uses geometrical optics (GO) to trace the paths
taken by the electromagnetic waves when they travel fromtransmitter to the receiver in the given environment. In thisstudy, commercial ray tracing software RapLab [6], which
implements the image method of ray tracing to find the raypaths from transmitter to the receiver is used as a simulator. The building model, reconstructed from point clouddata of 3D measurement, was simplified further by removing the small objects which are difficult to handle by GO
based ray tracing simulators. In Hall A, some pieces offurniture were present while doing the measurement, butwere discarded in the simulation. In Room B, projectors
and LCD display screens which were fixed on ceiling werediscarded. The walls are modeled as concrete, windows as
glass plates, and metallic boards and pillars are modeled
as steel. The used values of relative permittivity (ǫr ) andthe conductivity (σ) for concrete and glass are shown in
Table 2. The conductivity of steel is set to 1.03 × 107
Siemens/meter. The simulator takes into account of thereflection and diffraction of the ray-paths based on the
conductivity and dielectric property of the walls and other

L[dB] = a · 10 log10 (d[m]) + b + X,

Rel. Permittivity (ǫr )Conductivity (σ) S/m

Concrete
Glass

6.76
5.0

2.3×10−3
1.0×10−12(1)where a and b denote the path loss exponent and the intercept at 1 m distance, respectively. And, X ∼ N(0, σ 2 )indicates a log-normally distributed random variable withzero mean and variance σ 2 . Here σ represents the standard
deviation. Fig. 3 and Fig. 4 show the path loss characteristics obtained from measurement and simulation respectively for the co-polarization and cross-polarization casesfor Hall A. Ray tracing simulation is found to have better
accuracy in predicting the path loss characteristics for copolarization case. Since the measurement antenna havecross polarization discrimination (XPD) value of about
16 dB, it cannot measure the cross-polarization power ifcross-polarization power ratio (XPR) is above this level.
From simulation XPR is found to be 30 dB, this means thelimitation of measurement antennas make it impossible toTable 2: Material parameters used in simulation
Material

Path Loss and Polarization Characteristics

14

discuss about the cross-polarization path loss. But the values of co-polarization power ratio (CPR) are showing thegood match with mean value near 0 dB in measurement
and simulation for both environments. This means the pathloss behavior of the channel is similar for vertically polarized and horizontally polarized waves.

4

Glass window
with metallic
framesTx

Identifcation of Dominant Propagation
Mechanisms

Metallic Pillar

To study the dominant propagation mechanisms andsite specific behavior, two-dimensional double-directional
measurement was conducted using uniform circular antenna array (UCA) to obtain the 24 × 24 MIMO channeltransfer function. Ray tracing based channel transfer function is reconstructed using the antenna calibration data andpath parameters obtained from the simulation. This reconstruction is necessary as the direct comparison of measurement and simulation results is not effective because of thedifferent resolution limits of these two approaches. The
results from ray tracing has infinitesimal resolution in angular and delay domain but the measured result’s resolution is limited bay system bandwidth and beamwidth of
the antenna used in channel measurement.
4.1

Rx
Concrete wall

Figure 5: Measured angular delay profile for Hall A

where af (ϕTx , f ) and af (ϕRx , f ) are transmitter and receiver array response in the azimuth plane, and Hf is thechannel transfer function constructed in (2), or the measured channel transfer function. The beamforming is applied in every 6◦ in the entire azimuth plane (from 0◦ to360◦ ) for co-elevation angle of 90◦ since the dominant
paths are coming from the azimuth plane because of theequal antenna heights at transmitter and receiver. Further
analysis of the channel and the identification of the dominant propagation mechanisms is done using this beamformed power spectrum.

Channel Transfer Function Reconstruction

The ray tracing based channel transfer function can be
written as [8]H(f ) =

L
X

4.3

l=1

(2)

where L is total number of ray-paths obtained from the
ray-tracing simulation, aR (ϕR,l , θR,l , f ) is the array response of receiving antenna array, which is the function of
azimuth (ϕR,l ), co-elevation (θR,l ) and frequency f of thel-th, and aT (ϕT,l , θT,l , f ) is the array response of transmit antenna array. The delay time τl corresponds to path
length of l-th path. H(f ) ∈ CMR ×MT , and MT , MR are
number of antenna elements at transmitter and receiverside in the measurement. The dual-polarized complex pathweight for l-th path is given in matrix form as
γVV,l γVH,lΓl =(3)∈ C2×2
γHV,l γHH,l(5)This is a double directional complex impulse response. To
calculate the transmitter side angular delay spectrum, thesquared amplitude of the impulse response is summed up
along the receiver azimuth for each delay unit. Mathematically,
PTAS (ϕTx , τ ) =where ∆ϕRx and BRx denote the resolution of antenna pattern data and half-power beam width respectively of the
beamformer at the receiver. Similarly we can calculate theangular delay spectrum at the receiver. The angular delay power spectra when overlapped with building model,
scatterers could be identified by tracing the rays from thelocation of transmitter and receiver as shown in Fig. 5.
From this figure, dominant paths in measurement were
found to be the LOS component and single bounce reflection from concrete wall, glass window, metallic pillar, andglass screen. Glass screen path could not be detected bysimulation because of small mismatch in the orientation ofglass screen in the simulation model. Since it is difficult
to find all the discrepancies from angular power spectra,
delay domain characteristics at the same point is analyzed
further with the help of delay power spectrum.

Beamforming

Beamforming or matched filtering of the reconstructedchannel transfer function using ray tracing and the measured channel transfer function is done to obtain the
double-directional power spectrum [9]. The angular power
spectrum from beamforming is calculated as follows:
PBF (ϕRx , ϕTx , f ) =∆ϕRx X|PADS (ϕRx , ϕTx , τ ) |2 (6)
BRx ϕ
Rx

where, γVV and γHH are the co-polarized path weights i.e.
when the transmitting and receiving antenna polarizations
are same. Similarly, γVH and γHV are the cross-polarized
path weights when the cross polarized antennas are used.
4.2

Angular Delay Power Spectrum

To study multipaths in double azimuth and delay domain,
angle resolved channel impulse response is obtained bytaking inverse discrete Fourier transform (IDFT) to thebeamformed power spectrum.

PADS (ϕRx , ϕTx , τ ) = IDFT PBF (ϕRx , ϕTx , f )aR,l (ϕR,l , θR,l , f ) Γl
aTT,l (ϕT,l , θT,l , f ) e−j2πf τl ,Glass screen∗aHf (ϕRx , f ) Hf af (ϕTx , f )2kaf (ϕRx , f ) k kaf (ϕTx , f ) k2
(4)

15

-70
Measured
Simulated

Path Gain [dB]

-75
-80
-85
-90
-95
-100

0

20

40

60

80 100
Delay [ns]

120

140

160

180

Figure 7: Steel pillar

Figure 6: Power Delay Spectrum from measurement andsimulation, Hall A

4.4

tribution in received multipath component in the measurement, but not in the ray tracing simulation. Also the polarization dependency of the scatterer’s orientation is examined. Using the measurement results, it is confirmedthat vertically oriented metallic objects scatter the power
when vertically polarized signal is incident and horizontal metal frames scatter the power for the horizontally polarized wave. Ray tracing could not include these effects
which limits the accuracy of the ray tracing simulation.
Use of physical optics approach to take into account of themetal scattering along with the geometrical optics assumption of ray tracing is expected to improve the accuracy ofthe simulation results.

Delay Power Spectrum

The delay power spectrum represents the average powerof the received signal along the delay. It is obtained bysumming up the squared amplitude of double directional
channel impulse response with respect to transmit and received azimuth. It is defined as∆ϕTx ∆ϕRx X X
PDS (τ ) =|PADS (ϕRx , ϕTx , τ ) |2
BTx BRx ϕ ϕ
Rx

Tx

(7)
Fig. 6 shows the delay power spectrum for the same Tx
Rx locations and it is representing the same multipath as
those depicted in Fig. 5. From this power spectrum, twopaths which arrive at the delay of 70 and 72 ns are foundto have major discrepancy with about 5 dB higher power
observed in measurement. The first path is coming fromthe metallic pillar and the second is coming from the window. These identified scatterers are shown in Fig. 7 andFig. 8. For the path coming from metal pillar, reflection
from flat face was dominant and diffraction from the vertical edge has negligible power in the ray tracing. But
from measurement, scattering from the edge appears to be
dominant and ray tracing fails to predict this power. For
the path arriving from window reflection, scattering frommetal frame is attributed to the additional power seen inmeasurement. Window is modeled as a glass in the beginning and model with metal frames is added to checkif ray tracing results could be improved. But it is foundthat ray tracing could not include this scattering even theframes are modeled. Another finding is that the horizontal
polarized signal was not showing the scattering from thatlocation, which shows the polarization dependency of thescattering objects. Similar analysis was done for Room
B and dominant propagation mechanisms were found tobe the LOS component, 4 single bounce reflections from
windows and metallic boards and two more double bounce
reflections from wall end metallic boards. Major scattering
objects causing discrepancies were identified as the vertical section of window frame for vertical polarization andtop horizontal frame for the horizontally polarized signals.

5

Figure 8: Metal frames inwindow

References[1] M. Kim, Y. Konishi, Y. Chang, and J. Takada, “Large Scale Parameters and Double-Directional Characterization of Indoor Wideband
Radio Multipath Channels at 11 GHz,” IEEE Trans. Antennas Propagat., vol. 62, no. 1, pp. 430–441, January 2014.
[2] M. Ghoraishi, J. Takada, and T. Imai, “Identification of Scattering
Objects in Microcell Urban Mobile Propagation Channel,” IEEE
Trans. Antennas Propag., vol. 54, no. 11, pp. 3473–3480, Nov. 2006.
[3] N. Lertsirisopon, G. S. Ching, M. Ghoraishi, J. Takada, I. Ida, andY. Oishi, “Investigation of non-specular scattering by comparing directional channel characteristics from microcell measurement andsimulation,” IET Microw. Antennas Propag., vol. 2, no. 8, pp. 913–
921, 2008.
[4] J. Poutanen, K. Haneda, J. Salmi, V.-M. Kolmonen, F. Tufvesson,
and P. Vainikainen, “Analysis of Radio Wave Scattering Processes
for Indoor MIMO Channel Models,” in Proc. IEEE Int. Symp. Personal, Indoor and Mobile Radio Communications, Tokyo, pp. 102–
106, 2009.
[5] Y. Konishi, M. Kim, Y. Chang, and J. Takada, “Versatile Radio
Channel Sounder for Double Directional and Multi-link MIMO
Channel Measurements at 11 GHz,” to be published on IEICE Trans.
Electron., vol. E97-C, no. 10, Oct. 2014.
[6] RapLab. [Online]. Available: http://network.kke.co.jp/products/raplab/.
[7] S. Takahashi and Y. Yamada, “Propagation-loss prediction using ray
tracing with a random-phase technique,” IEICE Trans. Fundamentals., vol. E81-A, no. 7, pp. 1445–1451, July 1998.
[8] H. Zhu, J. Takada, K. M. Araki, and T. Kobayashi, “A Ray-TracingBased Characterization and Verification of the Spatio-Temporal
Channel Model for Future Wideband Wireless Systems,” IEICE
Trans. Commun., vol. E84-B, no. 3, pp. 143–153, March 2001.
[9] S. Zhang, “Prediction of Indoor MIMO Channels for Specific Systems using Ray Tracing Simulation,” Bachelor’s thesis, Tokyo Institute of Technology, March, 2013.

Conclusion and Future WorkIn this work, it has been identified that window frames andmetal pillars in indoor environments have significant con16

CITIZEN-CENTRIC PERSPECTIVE ON THE ADOPTION OF E-GOVERNMENTIN THE PHILIPPINES
Student Number: 12M51474

Name: Aldwin Uy URBINA

Supervisor: Naoya ABE

ABSTRACT
Information and communications technologies (ICTs) and the Internet are widely used as strategic means to improvethe efficiency and effectiveness of governments and the accessibility of government information and services to citizens.
However, due to disparities in socio-demography, access to ICTs, Internet use as well as in the patterns of Internet use,
and awareness of available e-government services in the Philippines, the adoption of e-government by citizens is facedwith major challenges (e.g., inequitable access to information and government services, widening of the gap between
advantaged and disadvantaged groups). This study employed primary data collected through a national-scale survey
conducted as a result of collaboration with a private social research institution. Results of the study confirm that a large
portion of the population in the Philippines has not yet used or accessed government web portals or “online government
channels”. Furthermore, the study initially examined potential predictors of e-government adoption and eventually
uncovered which of the potential predictors have significant influence on the use of e-government services by
employing logistic regression analysis. This study intends to serve as a reference to policymakers and other relevant
stakeholders in the Philippines in appropriately addressing the gaps in the adoption of e-government by citizens.
1 INTRODUCTION
ICTs and the Internet have transformed traditional meansof communication and interaction among citizens. They
have also been widely used in the private/business sector
and the success of its utilization is extensively evidenced
at a worldwide scale. This has caused governments andpublic sector organisations around the globe to becomeaware of their potential and consequently utilize them;
thereby triggering investments into electronic services(Choudrie et al., 2004).
In general, the employment of ICTs and the Internetin the administration of government functions and in thedelivery of public services is described as electronic
government or e-government. E-government helps
governments achieve greater efficiency in performing
their administrative operations and improve public service
delivery by providing efficient, convenient and less costly
means in accessing government information and services.
However, critics claim that the development of electronic
public services has been primarily guided by supply side
factors and that technological possibilities rather than userneeds have determined the design of online public
services (Verdegem and Verleye 2009).
Studies on the adoption of e-government in literature
can be categorized into two groups, namely, the supplyside and the demand-side (Reddick 2005). The first groupof studies is concerned on the adoption of e-government
from the supply-side perspective, which deliberates
factors [e.g., information technology (IT) infrastructure,
financial resources for IT development, number of egovernment initiatives implemented] that are essential to
the government as the supplier of public services(Reddick, 2005). On the other hand, the second group ofstudies mainly focuses on the demand-side perspective of
e-government adoption, which explores factors that areimportant to the clients of the government or consumersof public services (Reddick 2005). This study is
associated with the latter group, which is the demand-sideof e-government adoption, particularly the use of online
government portals in accessing government informationand services by citizens.

1.1 Objectives of the Study
There is a threat of digital exclusion 1 with theimplementation of e-government, which can exacerbate
already-wide gaps between advantaged and disadvantaged
groups. Yet, interests of policymakers in e-government
have been directed toward the supply-side of electronic
public service delivery and consequently, there are much
fewer, if not lack of, data and investigations on the
demand side (or from the perspective of citizens). Hence,
this study aims to respond to the proposition for moredemand-side oriented assessments of e-government.
Specifically, the objectives of the study are (a) todetermine the current state of e-government adoption by
citizens in the Philippines; (b) to examine the factors
affecting the usage of online government portals or
websites; to identify the significant predictors of usage
and the barriers underlying the low usage of suchchannels; and (c) to increase the awareness andunderstanding of policymakers in government on thecurrent state of e-government adoption by citizens in thePhilippines and to present opportunities on how to resolve
prevailing issues identified in this study.
1.2 Significance of the Study
According to the United Nations (2012), there is nocomprehensive data available to assess citizen usages at
the global level (UNDESA, 2012). Furthermore, there has
not been any study or data on the adoption of egovernment by citizens in the Philippines to date. Thus,
this study intends to fill-in information gaps in the state ofe-government adoption in the Philippines and responds tothe advocacy for data initiatives and information sharing
regarding e-government adoption for international
benchmarking.
2 METHODOLOGYThis study employed primary data obtained from anational-scale survey conducted by the Social Weather

1

17

Digital exclusion generally refers to the marginalization of individuals who do
not have access to and cannot effectively use ICTs and the Internet.

Stations or SWS2 in March 2014 in the Philippines. The
multi-stage probability approach 3 was used for selecting
1,200 national representative samples. The survey data
was obtained through face-face interviews of voting age
adults (i.e., 18 years and above).

relationship between two variables. When the value of
OR is equal to 1, the predictor variable does not affect theoutcome; when OR > 1, a unit increase in the predictor
variable will increase the odds of the outcome by thevalue of OR; when OR < 1, a unit increase in thepredictor variable will decrease the odds of the outcomeby the value of OR.

Table 1 Summary of Key Survey Questionnaire Topics
Key Topics
Sociodemographic
Information
Ownership of
Computer andInternet Usage

Adoption of
E-Government

Perception of
E-Government

Details
Age, Gender, Educational Attainment, ,Employment Status, Socioeconomic Class,
Geographical Location, Type of Settlement (i.e.,
Urban or Rural)Household Ownership of Computer, Use or
Non-use of the Internet, Frequency of Internet
Use, Types and Number of Internet Activities
Undertaken
Awareness of Government Websites, Use or
Non-use of Government Websites, Government
Information or Service Accessed throughGovernment Websites, Satisfaction with
Government Information or Service Accessed
through Government Websites, Problems
Encountered in Using Government Websites,
Reasons for Not Using Government Websites
Willingness to Provide Personal Information on
Government Websites, Usefulness ofGovernment Websites

3 RESULTS AND DISCUSSION
A fully effective (i.e., all answers or responses to the setof questions in the survey questionnaire were consideredeffective) 100% response rate was achieved sincehousehold surveys were conducted through face-to-faceinterviews by SWS.
3.1 Disparities in Access to ICTs and the Internet
The examination of the national survey data shows thathousehold ownership of computer and Internet use are
generally low. Computer ownership in the household andInternet use tend to be higher in younger adults,
individuals with higher educational attainment,
individuals who belong to higher socioeconomic classes(or have higher incomes), and live in urban areas. They
are also slightly higher in individuals who are males,
employed and living in the National Capital Region(NCR) and Balance Luzon geographical areas.
Among Internet users, there are more individuals
who are using the Internet less often than every otherweek. Also, individuals who are males, belonging tohigher socioeconomic classes, living in urban areas and in
the capital (NCR) generally use the Internet relatively
more frequently. Moreover, Internet users visit social
networking sites more than undertaking any other Internet
activities (e.g., getting information on current events; buy
things; getting information on health or physical fitness;getting information on health topic that’s hard to talkabout; working on blog; using Twitter; playing online
games; and sharing artwork, photos, stories or videos).
However, most individuals undertake only few Internet
activities (1-3 activities). Relatedly, the indicator ‘breadthof Internet activities undertaken’ reflects the skills andexperience of individuals in using a number/variety ofavailable Internet applications.

The survey data were analyzed using logistic
regression analysis to investigate the extent of theassociation concerning the propensity to use egovernment services with respect to a set of explanatory
variables. The following equations (1) and (2) describe
the calculation for the probability of usage or non-usageof online government portals:ࡼሺ࢟ ൌ ࡶȁ࢞ሻ ൌ ࡼሺ࢟ ൌ ࡶȁ࢞૚ ǡ ࢞૛ ǡ ǥ ǡ ࢞࢑ ሻ(1)ࢋࢼ࢞ࡼሺ࢟ ൌ ࡶȁ࢞ሻ ൌൌ Ꮙሺࢼ࢞ሻ
(2)૚ାࢋࢼ࢞
where y is dependent variable; J is the discrete choice of a
respondent (e.g., 1=Yes; 0=No); x is the independent
matrix; β is the column vector for the obtainedcoefficients by the regression, which represents thechange in the natural logarithm of P(y=J|x) for each unitchange in x; and Ʌ = cumulative probability density
function (cdf), where the probability density function(pdf) is a logistic function.
To obtain the corresponding logit function, the above
equation (2) is transformed into equations (3), (4) and (5):࢒࢕ࢍ࢏࢚ሾࡼሺ࢟ ൌ ࡶȁ࢞ሻሿ ൌ ࢒࢔ ቂࡼሺ࢟ୀࡶȁ࢞ሻቃ૚ିࡼሺ࢟ୀࡶȁ࢞ሻࢋࢼ࢞ࢼ࢞࢒࢕ࢍ࢏࢚ሾࡼሺ࢟ ൌ ࡶȁ࢞ሻሿ ൌ ࢒࢔ ൥ ૚శࢋࢋࢼ࢞ ൩
૚ି

(3)
(4)

૚శࢋࢼ࢞

࢒࢕ࢍ࢏࢚ሾࡼሺ࢟ ൌ ࡶȁ࢞ሻሿ ൌ ࢼ૙ ൅ ࢼ૚ ࢞૚ ൅ ࢼ૛ ࢞૛ ൅ ‫ ڮ‬൅ ࢼ࢑ ࢞࢑ (5)The odds ratio (OR), which represents the constanteffect of a predictor x on the likelihood that one outcome
will occur, can be obtained fromࡻࡾ ൌ ࢋࢼ
(6)
The odds ratio is a measure of effect size andtherefore provides information on the strength of2

SWS is a private non-stock, nonprofit social research institution, which is
responsible for the conduct of the quarterly Social Weather Surveys, which aim
to provide an independent source of pertinent, accurate, timely and credible
data on Philippine economic and social conditions.

3

Sampling method that is done sequentially across different hierarchal levels, e.g.,
from country-level to provincial to municipal to village to household to selectionof household members.

Figure 1 Frequency of Internet Use
3.2 Attitudes Toward E-Government
With regard to the respondents’ attitudes toward egovernment, 61% of the 1,200 respondents are at least
somewhat willing to provide their personal information
on government websites. The respondents’ willingness toprovide personal information on government websites
suggests that they generally find e-government
trustworthy. On the other hand, 78% of the 1,200
respondents perceived government websites as useful in
18

supplementing traditional government services delivery
channels (e.g., personal visit, postal service, telephone).
Results of the respondents’ reactions toward their
willingness to provide their personal information on
government websites and their perception on theusefulness of government websites reflect their general
positive attitudes toward e-government.

4 EXPLORATORY DATA ANALYSISThe conceptual framework (Figure 5), derived fromliterature review and results obtained from the survey,
generally presents the distinct research elements
organized to exhibit the purported relationships that exist
between predictor and outcome variables of this research.
Awareness is a prerequisite to the adoption of egovernment. Accordingly, satisfaction with e-governmentis only manifested after adoption. These concepts havebeen regarded in the design of the generated logistic
regression models, particularly in the sample sizes
considered for each model (i.e., related to the number ofobservations).

Figures 2 & 3 ‘Willingness to Provide Personal
Information on Government Websites’ and ‘Perceived
Usefulness of Government Websites’
Awareness,
Adoptionand3.3 E-Government
Satisfaction
The awareness and usage of government websites (419
and 104, respectively, out of 1,200 total respondents) are
generally low. Consistent with the analysis on access to
ICTs and the Internet, awareness and usage ofgovernment websites tend to be higher in younger adults,
males, individuals with higher educational attainment,
employed, individuals belonging to higher socioeconomic
classes, living in urban areas and in the capital (NCR). On
the other hand, 83% of users of government websites areat least somewhat satisfied with the information or service
they recently accessed using government websites.
Results also show that the most commonly accessed
government information or services through government
websites are related with social security benefits,
employment or job application and health services. While
there are more e-government users that did not experienceany problems with government websites, those who
experienced problems mentioned technical failure in thewebsite (e.g., website is inaccessible or unavailable,
website contains broken links) and delayed response toinquiry or delivery of service as the two most common
problems. Furthermore, for the non-users of government
websites, the major reasons given by respondents for notusing government websites are: (i); lack of necessary
skills and experience in using the Internet (ii) lack ofawareness of available e-government services; (iii)
preference for traditional service delivery channels; and(iv) lack of convenient access to the Internet.

Figure 5 Conceptual Framework of the Study
4.1 Research Variables
Table 2 describes the dependent (outcome) andindependent (predictor) variables used for the data
analysis.
Table 2 Independent and Dependent Variables
Independent Variables· Age· Gender· Educational Attainment· Socioeconomic Status· Status of Employment· Geographic Location· Type of Settlement· Internet Use· Frequency of Internet Use· Breadth of Internet Use· Household Ownership of Computer· Trust in E-Government· Perceived Usefulness of EGovernment

Dependent Variables· Awareness of EGovernment· Adoption of EGovernment· Satisfaction with EGovernment

4.2 Logistic Regression Analysis
For the data analysis, logistic regression was employed to
identify the significant predictors of and their impacts on
the awareness and usage of and satisfaction with online
government portals or websites. A number of 13 potential
predictors were considered for the three logistic
regression models (i.e., awareness, adoption andsatisfaction models).
Based on the results of the logistic regression models,
both the awareness and adoption models are statisticallysignificant (given the resulting p-value <0.05 significancelevel associated with the likelihood ratio chi-squared test

Figure 4 Most Commonly Accessed E-Government Services

19with 13 degrees of freedom). On the other hand, thesatisfaction model was found to be not statisticallysignificant (p-value = 0.07, or not <0.05) thus the resultsof this model may not be interpreted. Results show that
there are statistically significant overall relationships
between the combination of independent and thedependent variables for the awareness and adoption
models. The variance inflation factor (VIF) for the
independent variables are within tolerable levels (ranges
from 1.10-5.11), thus multicollinearity was not a concern.

b) The combination of socio-demography, access to ICTs
and the Internet, and attitudes toward e-government
affect the adoption of e-government among citizens.
The significant barriers to e-government adoption by
citizens in the Philippines are the lack of awareness ofavailable e-government services, lack of access to ICTs,
lack of skills and experience in using the Internet andlow education level.
c) To overcome the barriers, it is recommended forpolicymakers to create policies that are conducive tothe acceptance of ICTs and the Internet. The lack ofaccess to ICTs and the lack of skills and experience inusing the Internet are normally inherent to
disadvantaged groups in the society since they are lesslikely to be exposed and interested to the use oftechnologies. These disadvantaged groups are usuallyassociated with individuals with low levels of income,low education level, who are unemployed, and who
lives in rural areas. Moreover, skills and experience inusing the Internet are mostly acquired by individuals
who have seen some value in the use of the technology.
Thus, specific educational campaigns on ICT andInternet use, and on creating incentives therewith (e.g.,
showcasing of local cultures, products and services forrural areas), intended for each of the distinguisheddisadvantaged groups may be undertaken to addresseach group’s specific needs. Furthermore, effective
information and education campaigns on available
online government services are equally crucial tofurther increase awareness and ultimately, the adoptionof e-government by citizens.

Table 3 Logistic Regression Results (Odds Ratios)
Predictor Variables

E-Government Outcome Variables
Awareness
Adoption
Satisfaction
1.00
0.96
1.06
0.91
1.10
1.14
0.80
1.12**
1.34**
1.02
1.06
0.97
0.98
1.88
2.12**
1.27
1.81
0.65
1.13
0.63
1.43
1.24
0.98
0.66
1.44
1.45
0.44
1.01
1.30**
1.43**

Age
Gender
Educational Attainment
Socioeconomic Status
Status of Employment
Geographic Location
Type of Settlement
Internet Use
Frequency of Internet Use
Breadth of Internet Use
Household Ownership of
0.97
ComputerTrust in E-Government
1.16
Perceived Usefulness of
1.82**
E-Government
No. of Observations
1,200
Maximum-likelihood-645.78
Estimation
Chi-squared, (13)261.07
Prob>Chi-Squared
0.00
Pseudo R-squared
0.17
Mean VIF
1.97** p-value is < .01 significance level

4.36**

6.79

0.83

4.71

3.73**

4.82

419

104

-138.89-40.32

191.80
0.00
0.41
1.90

21.18
0.07
0.21
1.38

From the results of the analysis, it was inferred thatthe predictor variable ‘status of employment’ has thehighest impact on e-government awareness (OR=2.12).
The variable ‘perceived usefulness of e-government’(OR=1.82) closely follows as the predictor with thesecond highest impact while variables ‘breadth of Internet
use’ and ‘educational attainment’ ranks as the third andfourth of having impacts (OR=1.30 and OR=1.12,
respectively) on e-government awareness. On the other
hand, predictor variable ‘household ownership ofcomputer’ has the highest impact on e-government
adoption (OR=4.36). The variable ‘perceived usefulnessof e-government’ (OR=3.73) closely follows as thepredictor with the second highest impact while variables‘breadth of Internet use’ and ‘educational attainment’ranks as the third and fourth of having impacts (OR=1.43
and OR=1.34, respectively) on e-government adoption. It
can be deduced that individuals who own and accesscomputers at their home, who perceive e-government asuseful, who are skilled in using the Internet and havegenerally higher educational attainment are most likely touse e-government services.

6 LIMITATIONS AND FUTURE RESEARCH
The unsuccessful attempt in obtaining significant resultsin the logistic regression model for e-government
satisfaction may be largely due to the relatively small
sample size (i.e., N=104) considered in this model.
Furthermore, there are several possible predictors thatwere not investigated (e.g., perceived ease of use andperceived risks of using online channels and channel
preferences of citizens, which were inferred in literature
to have effects on the usage of the Internet and egovernment services). Future research could embark on
investigating these aspects to create more robust
frameworks/models. Nevertheless, the large amount ofdata obtained from diverse study areas used in thisresearch strengthens the generalizability of the findings.
References
Belanger, France, and Lemuria Carter. "Trust and risk in e-government adoption." The
Journal of Strategic Information Systems 17, no. 2 (2008): 165-176.
Choudrie, Jyoti, et al. "Evaluating Global e-Government Sites: A View using Web
Diagnostic Tools." Electronic Journal of E-Government 2, no. 2 (2004): 105-114.
Horst, Mark, et al. "Perceived usefulness, personal experiences, risk perception and trust
as determinants of adoption of e-government services in The Netherlands."
Computers in Human Behavior 23, no. 4 (2007): 1838-1852.
Reddick, Christopher. "Citizen interaction with egovernment: From the streets toservers?" Government Information Quarterly 22, no. 1 (2005): 38-57.
Reddick, Christopher, and Michael Turner. "Channel choice and public service delivery in
Canada: Comparing e-government to traditional service delivery." Government
Information Quarterly 29 (2012): 1-11.
UNDESA, United Nations Department of Economic and Social Affairs. United Nations EGovernment Survey 2012. New York, United States of America: United Nations, 2012.
Verdegem, Pieter, and Gino Verleye. "User-centered E-Government in practice: A
comprehensive model for measuring user satisfaction." Government Information
Quarterly 26, no. 3 (2009): 487-497.
Wangpipatwong, Sivaporn, et al. "Understanding Citizen’s Continuance Intention to Use
E-Government Website: A Composite View of Technology Acceptance Model andComputer Self-efficacy." The Electronic Journal of E-Government 6, no. 1 (2008): 5564.

5 CONCLUSIONS AND RECOMMENDATIONSThe study has revealed the current state of adoption of
ICTs, the Internet and e-government by adult citizens in
the Philippines. It has also identified the significant
predictors and barriers of the usage of e-government
services in the country. Thus, the following conclusions
are drawn with respect to the objectives of the study:a) Large portion of the population in the Philippines has
not yet used or accessed e-government services.

20

Image Coding by Orientation Adaptive Transform with
Prediction
Student No: 12M51445

Name: Gaudencio G. Bansil, Jr.

Supervisor: Yukihiko Yamashita

A prediction method is proposed for image coding using the orientation adaptive vector
embedded Karhunen Loève Transform (OA-VEKLT). The encoding is done by determining
transform coefficients of the prediction target block. The coefficients are predicted by comparing the lower horizontal and right vertical regions of the neighboring blocks depending on theorientation. The difference between the quantized coefficients of the original image and thequantized predicted coefficients is coded by the Huffman-runlength code. Experimental results
are shown to evaluate the proposed method.

1

Introduction

bedded. These authors also designed the Orientation adaptive vector-embedded KLT (OAVEKLT) which can represent edges and lines,
and attempt to exploit directionality in image
compression.
OA-VEKLT, a variation of KLT, is developed to transform the image edges and lines tomanage the directionality in image compression [5].
In this paper, I propose a procedure ofpredictive coding for the OA-VEKLT (POAVEKLT). This paper presents the comparison
between the reconstructed images obtained byusing JPEG, OA-VEKLT, and POA-VEKLT
coders. The organization of this paper is asfollows. In Section 2, I review JPEG and
OA-VEKLT. In section 3, I present and formulate the procedure of predictive coding by
OA-VEKLT. In section 4, I present the experimental results and comparison of JPEG, OAVEKLT, and POA-VEKLT. Finally, I conclude
the structure in Section 5.

In this digital era, advancement in image andvideo technologies immensely changes the communication speed, data storage capacity, multimedia security (data encryption and decryption), better image and video quality, and international standards. Systems of image andvideo compressions are advancement that generates many applications that help the digitalworld.
The purpose of image compression is to create a system that will represent a specified
image by reducing redundant and irrelevant
data for diminishing storage, rapid transmission, and acceptable quality for its usage. Theinternational standard JPEG [1, 6] is a digital image compression that utilized the discretecosine transform (DCT) [2, 3]. It uses only one
transform during compression making it a nonadaptive transform coder [4]. The problem of
non-adaptive transform coder is the pronounce
loss of image visual artifacts on strong edges
and lines. In solving this problem, adaptive
transforms are created, which select an appropriate transform basis on the characteristics ofthe selected region. One of the transforms thatsome of the researches use is the KarhunenLoève Transform (KLT). The KLT is the transform that concentrates most of energy into thefirst few coefficients among all unitary transforms.

2

JPEG and OA-VEKLT

2.1

JPEG

In the input, 8×8 blocks of the source image
are grouped. They are shifted from unsigned
integers with range [0, 2P - 1] to signed integers[-2P-1 ,2P-1 -1], and input to the Forward DCT(FDCT). At the output from the decoder, theInverse DCT (IDCT) outputs 8×8 blocks toform the reconstructed image.
After processing the source image using the
FDCT, the data are quantized. The goal ofthis process is to remove information which is

Tanaka and Yamashita [5] proposed a procedure that uses a novel orthogonal transformcalled the vector embedded KLT (VEKLT),
which is derived from the correlation matrix inwhich an arbitrary orthonormal system is em-

21visually insignificant. Since quantization is agroup mapping, it is the main source of lossiness in DCT-based encoders. Quantization isdefined as rounding to the nearest integer ofthe division of each DCT coefficient by its related quantizer. In the decoder, dequantizationis the process wherein the normalization is removed by multiplying the related quantizer.
After the quantization process, the DC coefficient [position (0,0), the top left corner] isseparated from the other coefficients (63 AC
coefficients). The last processing stage of the
DCT-based encoder is the entropy coding. This
stage encodes the quantized DCT coefficientstightly based on the quantized data statisticalproperties.

fixed suboptimal KLT. For typical natural images, neighbouring pixels are strongly correlated (0.9 < ρ < 1). It has been shown that
the KLT U leads to the DCT as ρ → 1 [2, 3].
OA-VEKLT is an adaptive transform which
manages the orientation of a local region byusing the VEKLT. Suppose we create an orN
thonormal system {wi }L−1
i=0 in R , where L ≤N . We call a subspace spanned by {wi }L−1i=0 acharacteristic subspace and write it by W . We
introduce a matrix W such aswhere we assume rank(R) = N .
The matrix R is real and symmetric, hence
there exists eigenvalues [7] λ0 ≥ λ1 ≥ . . . ≥λN −1 > 0 and corresponding eigenvectors−1
u0 , . . . , uN −1 such that {ui }Nis an ori=0
N
thonormal basis of R . The KLT matrix U
is given by a matrix of order N whose columns
are the basis vectors ui , namely,

where I is the size of a block and onedimensional notation such that dl (p + qI) =dl (p, q). dl is extended from the discrete function to the continuous one, and rotated the coordinate (p, q) by the orientation θ. Then, theL−1rotated version {dθl }l=0 is obtained such that

W=

L−1
X

wi wTi .

(3)

i=1

In creating the OA-VEKLT, an orthonormal system for a characteristic subspace W
must be designed. The following method is
2.2 OA-VEKLTa characteristic subspace designed by Tanaka
KLT is a series to express a given random func- and Yamashita [5] for the OA-VEKLT.
Consider the subset {dl }L−1
tion. The orthogonal basis functions are obl=0 of the DCT batained as the eigenvectors of the correspond- sis [2] such that
ing auto-covariance matrix. Suppose we cre√
2
(2p + 1)lπ
ate N -dimensional vectors from a given imdl (p + qI) =Cl cos,I
2I
age by taking blocks of N pixels. Let f =[f (0), . . . , f (N − 1)]T be a vector of the origip, q = 0, . . . , I − 1(4)nal data samples RN . The correlation matrix
l = 0, . . . , L − 1, L ≤ I,
R with respect to f is given by(√1
l = 0,2C=lTotherwise,
1
R = Ef [ff ],(1)

U = [u0 u1 . . . uN −1 ]T .

dθl (p + qI) = dl (p′(θ), q′(θ)),(5)(2) whereThe transformed vector g = [g(0), . . . g(N −T1)] is obtained as g = Uf.
Generally, the KLT is considered as an impractical transform because it depends on input signals. Therefore, it is usual to use an appropriate correlation matrix such as the firstorder Markov model, that is, (R)ij = ρ|i−j| ,where ρ is the correlation coefficient between
adjacent pixels. The matrix R leads to the

p′(θ)
q′(θ)



=



cos θ
− sin θ

sin θ
cos θ



p − I/2
q − I/2



+



I/2
I/2



,

p, q = 0, . . . , I − 1.
(6)

Obviously, the dc vector d0 is immutable,that is, dθ0 = d0 . Since the set of vectors
L−1{dθl }l=0 may not be an orthonormal system,
they have to be orthonormalized in order toconstruct an orthogonal transform. For this22

purpose, the Gram-Schmidt process is performed. The orthonormalized vectors are chosen as an orthonormal basis of the characteristic subspace W , and obtain the characteristic
projector Wθ as we have seen in eq.(3).
OA-VEKLT is constructed by defining thecorrelation matrix R, which is the N × N sym- Figure 2: The encoding system for POAmetric matrix of a first-order Gauss-Markov VEKLTmodel given by
In the decoder of POA-VEKLT as shown√in
Figure3. The bit stream output of the en(p−p′)2 +(q−q′)2(R)p+qI,p′I+q′I = ρ,(7) coder is processed by the coefficient decoder.
It is then dequantized, inverse transformed andp, p′, q, q′ = 0, . . . , I − 1,
buffered. The coefficient is predicted and thenquantized. The sum of the quantized predicted
where ρ is the correlation coefficient. Using an
coefficient and the decoded quantized coeffiN ×N matrix Q such that Q = R - RW - WRcient is taken until the image is reproduced.
+ WRW, we obtain matrix Qθ with respectto θ.
The DCT basis functions and the OAVEKLT basis functions corresponding to a direction are shown in Figure 1.
Figure 3:
VEKLT

The decoding system for POA-

Now, I will explain the proposed prediction
method. Given an image block f and surrounding pixels g as shown in Figure 4. The direcFigure 1: DCT basis functions and OA- tion of the block is determine by OA-VEKLT. I
make a basis function vi on pixels surrounding
VEKLT basis functions, respectively
the prediction target block by extrapolating wi
If f has no direction, DCT is selected and run
length coding is in succession. If f has a direc3 Image coding with predic- tion, the true transform coefficient is given bytion by OA-VEKLT (POA- αi = hf, wi i, and αi is estimated by minimizing

VEKLT)g−nX2

α̂i vi→ min(8)Figure 2 illustrates the encoding system for
wrt n = 3 or 7.
POA-VEKLT. The 512 × 512 pgm format
image is process by DCT, and OA-VEKLTs
of 8 directions . The transformed data arequantized and hold to be reprocessed. The
quantized data will then be dequantized andbuffered. Coefficient will now be predicted andquantized afterwards. The difference between
preprocessed quantized data and the quantized predicted data (will be explained in deαi = hf, wi i
tail later) is calculated before sending it to thecoefficient coder to be delivered as bit stream Figure 4: Prediction coding of a block using
the surrounding pixels
data to the decoder.
i=023

Let α̂i be the solution of eq. (8). If the diIn coding of Barbara, the PSNR of POArection is horizontal or vertical, 8 coefficients VEKLT and OA-VEKLT have almost the same
are predicted and it is minimized with respect output and they have a little advantage comto α̂0 ∼ α̂7 . Otherwise, 4 coefficients are pare to JPEG as shown in Figure 6.
predicted and it is minimized with respect toα̂0 ∼ α̂3 . The difference of the quantized data,q(αi ) − q(α̂i ), is Huffman coded, where q(α) isa quantizing function.

4

Experimental Results

In this experiment, the coding and decodingof 512 × 512 portable gray map (pgm) format were performed by the predictive coding proposal technique. An example of a test
image Barbara is shown in Fig 5, wherein
the compression methods JPEG, OA-VEKLTand POA-VEKLT are numerically assessedby determining the peak signal-to-noise ratio(PSNR) and bits per pixel (bpp) of the compression methods.

Figure 7: PSNR and bpp plot of dollar.pgm for
JPEG, OA-VEKLT and POA-VEKLT coding
In coding of the test image dollar.pgm,
POA-VEKLT clearly outperforms the OAVEKLT and JPEG as shown in the coding output PSNR vs bpp in Figure 7.

5

Conclusion

In this paper, I proposed to apply predictive
coding to OA-VEKLT.
Based on the results, it can be concluded
that OA-VEKLT has a potential to become
better by using predictive coding. The resultsof dollar.pgm and Barbara.pgm compressedby POA-VEKLT outperforms OA-VEKLT and
JPEG in PSNR and bit-rate comparison.
The findings suggest that the predictive
coding approach has a possibility to improve
the OA-VEKLT.

Figure 5: Barbara 512 x 512 test image
2

255
PSNR is given by, P SN R = 10 log( MSE )where MSE is mean square error, M SE =N
1 P
|xi − yi |2 ; N is the total number of pixels,
N
i=1

xi is the pixel value of the original image andyi is the pixel value of the noisy approximation
F
image. Bits per pixel is determine by bpp = N,
where F is the size of the image in bits.

References[1] K.R. Rao and J.J.Hwang, “Techniques and Standards for Image,Video and Audio Coding’ ’, Prentice Hall, New Jersey, 1996.
[2] N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete Cosine Transform”, IEEE Trans. Computers, vol. COM-25, pp. 90-93, Jan.
1974.
[3] K. R. Rao and P. Yip, “Discrete Cosine Transform: Algorithms,
Advantages, Applications”, New York, NY: Academic Press, Inc,
1990.
[4] G.K. Wallace, “The JPEG Still Picture Compression Standard”,
Communications of the ACM 34, April 1991, pp. 31-44.
[5] T. Tanaka and Y. Yamashita, “Vector-Embedded Karhunen-LoèveTransform and its Application in Orientation Adaptive Coding of
Images”, IEICE Transmission Fundamentals, vol. E83-A, No. 6,
June 2000, pp.1257-1266.

Figure 6: PSNR and bpp plot of Barbara.pgm
for JPEG, OA-VEKLT and POA-VEKLT coding[6] S. David, “Data Compression”, Verlag, New York, Springer, 1998.
[7] S.J. Leon, “Linear Algebra with Applications”, Englewood Cliffs,
NJ, Prentice Hall, 1994.

24

PERFORMANCE EVALUATION OF PUBLIC BUS NETWORK IN SHIRAZ CITY, IRAN!
Student Number: 12M51675 Name: Maryam FEREIDOONY Supervisor: Shinya HANAOKA!
!
Public transport is one of the social facilities, which are provided with the aim of improving social welfare. However
providing efficient and effective public transport for the ever-increasing demand with the limited resource available is achallenge in the developing world. In this study Shiraz public bus transport service efficiency and effectiveness byusing output-oriented BCC model (VRS) of Data Envelopment Analysis (DEA) and GIS was evaluated. Among 61
selected bus lines, 9 lines identified as inefficient and ineffective lines. Spatial investigation of inefficient andineffectiveness lines was done by making digital map using GIS techniques, with focusing on land use, bus lines
density, bus station density and population distribution around each line. Further implications of this study are to assist
decision making of local government regarding improvement of bus public transport network.

1. Introduction

necessary. Shiraz city is selected for this study. In
recent years the city has expanded enormously andpaying more attention to public transportation is one ofimportant concern of local government. Now city has
two different structures, old and modern. The oldstructure consists of different gates and districts at
different times. There are several shortages ininfrastructural
servicesandefficientpublictransportation in both new and historical zone.
Three objectives of this study are as follow:
1-Comprehend the characteristics of bus transportation
passengers by conducting survey.
2-Evaluate the performance (Efficiency andEffectiveness) of Shiraz city bus lines by DEA.
3- Spatial investigation on inefficient andineffectiveness bus lines with focusing on land use,
bus lines density, bus stations density and population
distribution by GIS.
The main objective of this study is to evaluate Shirazcity public bus transport operation efficiency andservice effectiveness and make recommendations on
possible improvements on the network and operations.

Public transit is an integral and important componentof urban transportation network. Its advantages of
convenience, speediness and effectiveness are easier tobe given full play comparing to other traveling modes.
Currently, such problems as congestion and slow speedof bus vehicles and deficit of bus transit enterprises
emerge in many cities. This trend has stimulated muchresearch interest on the performance evaluation of
Public transport network (Taylou, Garrett, & Iseki,
2001; Murray, 2001; Tsamboulas, 2004)
However, much existing research concentrates on
evaluating the performance of overall operation
efficiency of the whole public transport network using
Data Envelopment Analysis (DEA). Little attention has
been given to the operation efficiency and service
effectiveness of single bus lines.
Performance measures are navigation tools that canhelp public transport authorities and city governments
determine where they want to go and how to getthere.it helps to identify potential problems and optimal
solutions (Dhingra, 2011).
Performance evaluation essentially should include
two aspects, namely, the operation efficiency from thetransit enterprises perspective and service effectivenessfrom the passenger’s perspective.
As for efficiency: The service authority normally aims
to minimize the operational cost without impeding thedaily travel demand of the passenger.
As for effectiveness: passenger should feel that buses
are available to meet their daily travel demand withlower cost.
Currently, public transit agencies are trying tooperate more efficiently and effectiveness and theyalso begun to focus more on service planning,attempting to analyze regional and local demographics
in relation to transit services (Sutton, 2005). Iran has
one of the highest urban population growth rates in theworld and today more than 69 percent of Iran’s
population is living in urban areas. In this time, public
transportation in Iran cities relies mainly on the bus
network and except capital Tehran the subway system
is not opened yet in most cities so evaluation theefficiency and effectiveness of public bus lines is

Figure 1.1 Framework25

n: Index of input, n=1….,Nm: Index of output, m=1,….,M
xnj: The nth input for jth DMU
ymj :The mth output for jth DMU:
um ,vn :Non negative weight for nth input and mth output.
!! : Effeciency/Effectivness ratio of DMUk .
The targeted DMU is designated as DMUk if
efficiency and effectiveness score (!) equal to 1 means
an efficient and effective line. if efficiency andeffectiveness score (!) between 0.6 and 1 means afairly efficient and fairly effective line and if efficiency
and effectiveness score (!) of less than 0.6 means andinefficient and ineffective line.

2. Methodology
2.1
Field Survey!
Surveys are one of the most effective ways ofcollecting data from current or prospective users(Sauro, 2013). Extensive surveys were carried out intwo different peak periods (7-9 AM and 5-9 PM) daily
over a two-weeks duration from 7 th to 21st of June
2014.
This survey was conduct to understand thecharacteristics of bus public transportation passengers
in Shiraz city. The questionnaire for passenger was
prepared to conduct at bus stops so it was made asshort as possible to obtain the required information in
the waiting time. It mainly included questions onsocio-economic characteristics of the customer, travel
characteristics, satisfaction level and expectation of thebus service. A total of 350 samples were collected atbus stops with the help of field assistants. Most of thesurvey was conducted at the 4 main bus terminals of
Shiraz city namely Namazy, Valiasr, Ghasrdasht andDastgheib terminals.

2.3 Selections of Input and Output Variables
The key affecting factors of operation efficiency are
the resources provided by bus transit enterprises while
key affecting factors of service effectiveness are
passengers’ service requirements (Guo, 2010). From
the operator’s perspective, the objective is to use as
few resources as possible and make as much profit as
possible. From the passengers’ perspective, bus lines
should meet the demand by providing cheap and direct
service to them. Table 2.1 displays the input-output
specifications for evaluation operational efficiency andservice effectiveness.

2.2 Data Envelopment Analysis
Data Envelopment Analysis (DEA) has been widelyused to measure the efficiencies and effectiveness of
public transit network (Zhu, 2003). DEA is a nonparametric approach and linear programming technique
to measure relative performance of a set of units called
Decision Making Units (DMUs).
In this study DMU is the term used to refer to bus
lines. DEA models can be classified based on theirorientation into two types: input and output-oriented
models. We adopted the output-oriented BCC model(Banker et al., 1984), as the overall objective of a bus
line is maximizing output (annual revenue and annual
passenger).
A reason to choose the BCC model is that it employs
a Variable Return to Scale (VRS) assumption, whichmeans that efficiency and effectiveness may increaseor decrease with a change in size in input or output(LaoLiu, 2009).
Mathematically, the output-oriented BCC model
can be written as"!
!
!!! !! !!"!!!!!!"#!!! !!!! ! ! !
!!!!!!!!!!!!!
!!! !! !!"!
!
!!! !! !!"!"#$%&'!!!"!!!! !
! ! !!!!!!! !!!!!!!!!
!!! !! !!"Table 2.1 Input and output variables for the DEA model
Performance DMU
Operation
Efficiency

Each
Bus
Lines

Service
Effectiveness

Each
Bus
Lines

Input variables
1. Line length (km)2. Number of vehicles
3. Frequency (per hour)
4. Number of bus stop (roundtrip)
1.Average traveling time (min)2.Vehicle-kilometers (per day)
3.Average distance of bus stops (m)Output variables
Annual revenue(million Rials)

Annual total
number ofpassengers

In this study efficiency and effectiveness of 61
public bus lines was evaluated. Required GIS data was
obtained from spatial planning department ofmunicipality and additional data was obtained fromtraffic organization and bus company in Shiraz City.
Value of line length, Number of bus stop, Average bus
stop distance obtained from GIS and Number ofvehicle, Frequency, Average travel time, VehicleKilometers, Annual revenue, Annual number ofpassengers obtained from traffic organization and bus
company in Shiraz city.
After applying DEA model for 61bus lines
efficiency and effectiveness level of each line iscalculated. GIS technique was used to visualize the
distribution of bus stops and lines, examine transit
service coverage and land use around ineffective andinefficient lines.

!

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

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

26

3. Case study
Shiraz is the fifth biggest city in Iran and is thecapital of Fars Province. It is located in the southwestof the country on the ‘Rudkhaneye Khoshk’ seasonalriver; 200Km from south seashore of Iran. It is built
over a green plain of the Zagros Mountains, 1500
meters above sea level with a moderate climate andregular seasons. Shiraz population was 1.3 million in2011.
According to an official survey of local government
the shares of the different modes of travel in this city
are as follows: private cars and taxis 66 percent, buses
19 percent and the other modes 15 percent which is anunsustainable trend in a longer term. Therefore, thepublic bus is the main mode of public transportation(Sharifi, 2011). This city consists of 4 bus zone.

Figure 4.1.2 Occupation

Figure 4.1.3 Reason for choosing bus

Passengers satisfaction`s level of offered service by
public bus company are low in case of crowdedness,
comfort, station status and waiting time as shown in
Figure 4.1.4.

Figure 3.1 Shiraz city bus lines

After finishing survey we evaluated efficiency andeffectiveness of 61 public bus lines of Shiraz city using
DEA. Required GIS data was obtained from the spatialplanning department of municipality and additional
data was obtained from traffic organization and bus
company in Shiraz City.

Figure 4.1.4 Passenger satisfaction

Passenger’s expectations are focused on ventilation
inside bus, station condition improvement, punctuality
and securities as shown in Figure 4.1.5.

4. Results
4.1 Survey Result
The public bus users are mainly young people andmain reason for using bus is cheep fare as shown in
Figure 4.1.1, Figure 4.1.2 and Figure 4.1.3.

Figure 4.1.1 AgeFigure 4.1.5 Passenger expectation27

4.2 DEA Model Result
The result in Table 4.2.1 indicate that out of the 61
lines, 14 bus lines are technically efficient, 16 bus lines
are fairly efficient, and 31 bus lines are consideredinefficient.

performers lines in Shiraz city and should be carefully
re-planned or even eliminated. In the next step digitalmaps was made with focusing on land use, bus lines
density, bus stations density and population
distribution around the inefficient and ineffective lines
to make recommendation to possible improvement on
the bus network and operations. Here we explain about
the possible reason for inefficiency and ineffectivenessof line No.46 in Zone4.

!

Figure 4.2.1 Population density & Land use around Line 46

Table 4.2.1 Operation efficiency result

The result of the DEA model is displayed in Table
4.4.2. 8 bus lines are technically effective, 15 bus lines
are fairly effective and 38 bus lines are consideredineffective.
Figure 4.2.2 Density of bus line & bus station around line 46

This bus line starts from Darvaze Kazerun terminal(high density area) in central part of Shiraz to
Fargangian neighborhood in west of Shiraz city. As we
can see in Figure 4.2.1 population density around thisline only at the beginning of the line is high. However
Fargangiyan neighborhood is one the new urban
development area in Shiraz city although this line isonly line which gives service to this area, there arevery low travel demand for this line. The originalalignment of the city bus lines was determined toprovide nearly high spatial coverage of the city, but notnecessarily based on the expectations of the bus passenger demands from/to the various zones.

!

5. Conclusion
In this study we have combined DEA and GIS toexamine the operational efficiency and service
effectiveness of a public transit. Results indicate thatamong 61 lines in whole city only 3 lines had highperformance and 9 lines from all 4 public bus zone andmainly from zone number 3 had the worse level of
performance.

Table 4.2.2 Service effectiveness result

Based on the scales of the efficiency andeffectiveness scores, the lines 31,51,70 are efficientand effective lines which means they have highperformance in Shiraz city and on the other hand thelines 16,22,35,36,46,48,62,72,78 are inefficient andineffective lines which means they are the worse

References
1

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models of estimating technicaland scale inefficiencies in data envelopment analysis.
Chu, X., G. J. Fielding, & B. W. Lamar. (1996). Measuring transit performance using dataenvelopment analysis.
3
Karlaftis, M. G .(2004). A DEA approach for evaluating the efficiency and effectiveness.
4
Yong, L. & L. Lin (2009). Performance evaluation of bus lines with data envelopment
analysis and geographic information systems.
2

28

PORTFOLIO MANAGEMENT FOR TRANSPORTATIONINFRASTRUCTURE DEVELOPMENT PROJECTS IN THAILAND
Student Number: 12M51451

Name: Pitchaya CHUNGSAWANANT

Supervisor: Shinya HANAOKA, Keisuke MATSUKAWA

Transport project selection is one of the most important planning activities encountered by a government. Thai government is now
facing with this problem because a lot of transport projects are proposed, but resources are limited; therefore, selection of an
appropriate group of projects to be implemented is needed. This paper proposes a comprehensive combination of methods to evaluate,
rank and select transport project portfolio. The main advantages of this combination are simple weight calculation, multi-criteria
evaluation with few information from experts and optimal selection condition. This combination is expected to support Thaigovernment to make effective decision in transport projects investment.

1. Introduction
Nowadays, in Thailand, poor quality of transport
infrastructure, high freight transport cost and high energy
consumption from transport sector can clearly be seen as
obstacles for development of the country; therefore, Thaicabinet decided, in January 2013, to propose 70 projects, then
53 projects still remained after pre-screening process by
Ministry of Transport, to improve transport infrastructures asshown in Table 1 so as to build up capacity for competitivenessin transportation system of the whole country with three main
objectives. 1.) Modal shift: Promote the modal shift from thehigher to lower transport cost (16 projects) 2.) Connectivity:
Develop transport infrastructure and multimodal facilities tosupport connectivity to the sub-region and AEC (28 projects)
and 3.) Mobility: Develop and upgrade transport facilities &infrastructures to increase mobility (9 projects).
Table 1: Proposed Thai transport projects
Mode of transportation
No. of projects
Rail transport
37
Road transport
10
Water transport
5
Border facilities
18
Total
70
After pre-screening
53
However, at this moment, Thai government is now facingwith problems because these 53 projects are proposed to beinvested, but budgets are limited. In this case, Project Portfolio
Management (PPM), which is a management activity to selectproject portfolio from available project proposals in order toachieve organization’s goal and objectives without exceeding
available resources or violating other constraints, is one
suitable tool that Thai government can apply in order to findoptimal project portfolio for investment.
The objective of this research is to propose a comprehensive
combination of methods which can be practically applied inorder to obtain optimal project portfolio for transport
infrastructure development projects in Thailand.
Research framework as shown in Figure 1 begins withdefining a set of possible investment projects and defining
criteria for transport project evaluation. After that, project data
can be prepared. All data are used as input into the combinationconsisting of (i) weight determination process by Proportion
method, (ii) multi criteria decision making (MCDM) process by
PROMETHEE II method and (iii) constraints consideration
process by PROMETHEE V method in order to evaluate, rank
and select optimal project portfolio.

Figure 1: Research framework

2. Methodology
The main methodology of this research focuses oncomprehensive
combinationofProportion
method,PROMETHEE II method and PROMETHEE V method fortransport project selection. Each method is explained as follow.

2.1 Proportion method
Proportion Method is a method developed by Aldian et al(2005) to determine objective and criteria weights in the
research framework. Stepwise procedure of Proportion method
can be explained as follow.
First is questionnaire design. The concept of questionnaire is“please rank the importance of the criteria and determine therelative importance between a given criterion and a lower rank
criterion”. Second, experts fill out the questionnaire, then rankof criteria and relative importance of each criterion in
proportion scale as shown in Table 2 will be obtained. Finally,
weight of each criterion is calculated by equation (1).
‫ݓ‬௜ ൌ σ௡௝ୀଵ ܽ௜௝㻌݊െͳሺ݊‫ ݔ‬ቀ
ቁሻ
ʹ

(1)

Where wi is weight of criterion i, n is No. of criteria, j iscriterion j, aij is proportion of criterion i to j.

29

3. Data preparation

Table 2: Proportion of criterion i and j and definition
Criterion i (%)
50
60
70
80
90

Criterion j (%)
50
40
30
20
10

Definition (i൒j)
Equally important
Moderately more important
Strongly more important
Very strongly more important
Extremely more important

3.1 A set of possible investment projects
From firstly proposed 70 projects, Ministry of transport hasdone pre-screening to be 53 projects and make assumption toassign each project to support only one objective. Then, due tothe availability of project information, 39 projects consisting of
13 projects which support objective 1 (Modal shift), 18 projects
which support objective 2 (Connectivity) and 8 projects which
support objective 3 (Mobility) have been selected withinformation shown in Table 3, 4 and 5 as follow.
Table 3: Possible investment Modal shift Projects
Modal shift Projects (13 projects selected)
Type of projects
No. of projects
Double track train
11
Sea port
2
Table 4: Possible investment Connectivity Projects
Connectivity Projects (18 projects selected)
Type of projects
No. of projects
Double track train
2
High speed train
4
Motorway3
Border and Customs station
9
Table 5: Possible investment Mobility Projects
Mobility Projects (8 projects selected)
Type of projects
No. of projects
MRT
8

Proportion method is applied in (i) weight determination
process to obtain weight of each objective and each criterion.
This weight will further be used as input for multi-criteriatransport project evaluation in (ii) MCDM process.

2.2 PROMETHEE II method
Preference Ranking Organization Method for Enrichment
Evaluation (PROMETHEE Method) developed by Brans(1982) is proposed. PROMETHEE II method is one of
PROMETHEE family of outranking methods for a set ofprojects to be ranked and selected among criteria. ThisPROMETHEE II method is selected to be used to evaluate andrank Thai transport projects based on multiple conflicting
criteria in (ii) MCDM process. Its stepwise procedure can be
described as follow.
First, pairwise comparisons are made by finding the difference
between evaluations of each pair of all projects on eachcriterion by equation (2). Next, find preference of alternativefrom equation (3).
dj(a,b) = gj(a) – gj(b)(2)
Pj(a,b) = Fj[dj(a,b)](3)
Where dj(a,b) is the difference between evaluations of projecta and b on each criterion j, P j(a,b) is preference of alternative ato b and F is preference function. Preference function is a tool
to translate the difference between the evaluations of two
projects into preference degree ranging from zero to one. In
practice, usual preference function is appropriate for qualitative
criteria with a small number of evaluation levels and linear
preference function is the best choice for quantitative criteria.
Then, global preference index, positive and negative
outranking flow can be calculated by equation (4), (5) and (6)respectively.
ߨሺܽǡ ܾሻ ൌ  σ௞௝ୀଵ ܲ௝ ሺܽǡ ܾሻ ‫ݓ‬௝(4)
ା‫ ׎‬ሺܽሻ ൌ ሺσ௫‫א‬஺ ߨሺܽǡ ‫ݔ‬ሻሻȀሺ݊ െ ͳሻ(5)‫ ି׎‬ሺܽሻ ൌ ሺσ௫‫א‬஺ ߨሺ‫ݔ‬ǡ ܽሻሻȀሺ݊ െ ͳሻ
(6)
Where ߨሺܽǡ ܾሻ is global preference index, ‫ݓ‬௝ is weight ofcriterion j, ‫׎‬ା ሺࢇሻ and ‫ ି׎‬ሺࢇሻ are positive and negative
outranking flow of project a.
Finally, net outranking flow can be calculated by equation (7).
This net outranking flow is the final indicator fromPROMETHEE II method to rank the projects in order ofpriority. The higher value of net outranking flow means thehigher in rank of the project.
‫׎‬ሺܽሻ ൌ  ‫׎‬ା ሺܽሻ െ  ‫ ି׎‬ሺܽሻ(7)
Where ‫׎‬ሺܽሻ denotes net outranking flow of project a.

3.2 Criteria set up and Project data
Nine criteria covering economic, environmental, social,technical and political aspects have particularly been set up for
projects in each group. Criteria No.1-8 which are budget,
project time, EIRR, energy cost saving, technical risk,
administrative risk, community risk and financial risk are used
to evaluate projects in all three groups. For ninth criterion,
logistics cost saving, quality of connectivity and travel timesaving is particularly assigned to assess modal shift projects,
connectivity projects and mobility projects respectively.
Most of project data in almost all criteria can be founddirectly from official organization except energy cost saving,
logistics cost saving, quality of connectivity and travel timesaving. Project data in these four criteria have to be estimatedbased on the assumption for unavailable data.

4. Results and Discussions
4.1 Weight of each objective and each criterion
All objectives and criteria are input of (i) weight
determination process by Proportion method. Questionnaire isdesigned in Microsoft Excel file, and sent to eight experts(Three senior governmental officers and five university
professors) by e-mail on May 1st, 2014. All responses from all
experts came back during May 10th – May 28th, 2014 by e-mail,so their subjective judgments which are rank and relative
importance of each objective and each criterion in proportion
scale can be collected for calculation. Results of weight of eachobjective and each criterion are shown in Table 6, 7, 8 and 9
respectively.

2.3 PROMETHEE V method

PROMETHEE V method developed by Brans and Mareschal(1992) takes net outranking flow of PROMETHEE II toconsider optimal selection condition and project constraints
with concept of 0-1 linear programming by equation (8).

Max Z = σ௔‫א‬஺ ‫׎‬ሺܽሻܺ௔

(8)

Where ܺ௔ is equal to 1 if project is selected and 0 otherwise.
30

Table 6: Weight of each objective of Thai transport projects
Objective
WeightObjective 1: Modal shift
0.3375
Objective 2: Connectivity
0.3708
Objective 3: Mobility
0.2917
Table 7: Weight of each criterion for Modal shift Projects
Criteria
WeightCriteria
WeightBudget0.1347Administrative risk

0.1017

Project time

0.1038Community risk

0.0938EIRR

0.1278Financial risk

0.1049Energy cost saving

0.1132

Logistics cost saving

0.1288

Technical risk

0.09133
Chiang Kong customs station
0.26904
Sadao customs station
0.21985
Nong Khai customs station
0.2065
6
Chiang Kong distribution center
0.2031
7Motorway: Chonburi - Mabtapud
0.1747
8
Aranyaprathet customs station
0.17359
Mae Sod customs station
0.088310
Mae Sai customs station
0.0876
11
Motorway: BangYai - Kanjanaburi
0.023712
Motorway: BangPaIn - Nakhonratchasima-0.0158
13
Double track: Denchai – Chiang Kong
-0.0535
14
Double track: BanPhai – NakhonPhanom-0.143715
High speed train: Bangkok - Rayong-0.441916
High speed train: Bangkok – PadangBesar-0.449617
High speed train: Bangkok - Nongkhai-0.511418
High speed train: Bangkok - Chiangmai-0.5434
Table 12: Rank of Mobility Projects
Rank
Description‫׎‬1
MRT Light Red Line0.2414
2
MRT Dark Red Line0.2172
3
MRT Dark Green Line
0.0815
4
MRT Blue Line
0.05835
MRT Purple Line
0.0288
6
Airport Rail Link
-0.1044
7
MRT Yellow Line
-0.1411
8
MRT Orange Line
-0.3817
These projects are ranked by net outranking flow (‫ )׎‬which isthe final result from PROMETHEE II method. The higher invalue of this ‫ ׎‬means the higher in ranking of the project.

Table 8: Weight of each criterion for Connectivity Projects
Criteria
WeightCriteria
WeightBudget0.1333Administrative risk

0.0920

Project time

0.1063

Community risk

0.0979EIRR

0.1278Financial risk

0.1226

Energy cost saving

0.0927

Quality of connectivity

0.1274

Technical risk

0.1000

Table 9: Weight of each criterion for Mobility Projects
Criteria
WeightCriteria
WeightBudget0.1247Administrative risk

0.0948

Project time

0.1035

Community risk

0.0885EIRR

0.1299Financial risk

0.1205

Energy cost saving

0.1160Travel time saving

0.1378

Technical risk

0.0844Meaning of weight of each objective or each criterion is theimportance of that objective or that criterion when comparedwith others in the group. Weight of each criterion will further
be used as one input into (ii) PROMETHEE II method of
MCDM process for multi-criteria project evaluation in the next
step of the combination.

4.3 Optimal portfolio of Thai transport projects

In final process of the combination, (iii) constraints
consideration process is implemented. At first, three project
implementation constraints are set up with concept of 1) budget
limit: Investment for each project group ൑ (objective
weight)x(allocated budget), 2) supporting all objectives: At
least one project from each group must be selected into
portfolio and 3) including compulsory projects: All compulsory
projects must be included in the portfolio. Compulsory projects
are defined by Thai government, and their details are shown in
Table 13.
Table 13: List of compulsory projects
Modal shift Projects
Project with rank 5, 6, 7, 8 and 9
Connectivity Projects Project with rank 6, 7 and 13
Mobility Projects
Project with rank 3, 4 and 5
Four scenarios of different sets of constraints are made as
follow. The first scenario considers all three constraints. Second,
third and fourth scenario considers only two constraints andignores one constraint. Apply PROMETHEE V with ‫ ׎‬andconstraints into Visual PROMETHEE software. Optimal
portfolio is obtained.

4.2 List of projects in order of priority
Project data, criteria, criteria weights and preference function
value are input. All input data are put into VISUALPROMETHEE software, and rank in order of priority by net
outranking flow is obtained as shown in Table 10, 11 and 12
respectively.
Table 10: Rank of Modal shift Projects
Rank
Description‫׎‬1
Pakbara Port
0.2724
2
Song Khla 2 Port
0.2045
3
Double track: PakNamPho - Denchai
0.0700
4
Double track: SuratThanee–PadangBesar
0.0576
5
Double track: LopBuri – PakNamPho
0.0571
6
Double track: NakhonPathom – HuaHin-0.0016
7
Double track: MabKraBao - Jira
-0.0253
8
Double track: Jira – KhonKaen-0.07129
Double track: PrajuabKirikan - Chumporn-0.0755
10
Double track: HuaHin- PrajuabKirikan-0.1121
11
Double track: Chumporn – SuratThanee-0.1175
12
Double track: Jira - Ubonratchathanee-0.1201
13
Double track: KhonKaen – NongKhai-0.1383
Table 11: Rank of Connectivity Projects
Rank
Description‫׎‬
1
Padang Besar customs station
0.4192
2
Mukdahan customs station
0.29404.3.1 Scenario 1: Considering all three constraints
All three constraints which are budget limit, supporting all
objectives and including compulsory projects are taken into
account in this scenario. Optimal portfolio for Thai transport
projects investment is obtained in Table 14.
Table 14: Optimal project portfolio for scenario 1
9 Modal shift Projects

31

Project with rank 1, 2, 3, 4, 5, 6, 7, 8 and 9
12 Connectivity Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 13
5 Mobility Projects
Project with rank 1, 2, 3, 4 and 5
Total: 26 projects

4.4 Conventional and Proposed combination
In this research, a combination of (i), (ii) and (iii) is proposed.
For conventional procedure, methodology (i) and (ii) can also
be done by Analytical Hierarchy Process known as AHP
method. Comparison of conventional and proposed
combination of (i) and (ii) are shown in Table 18.
Table 18: Comparison of conventional and proposed (i)+(ii)4.3.2 Scenario 2: Considering only constraint 1 and 2
Constraint 1 and 2 which are budget limit and supporting all
objectives are taken into account in this scenario. Optimal
portfolio is obtained in Table 15.
Table 15: Optimal project portfolio for scenario 2
5 Modal shift Projects
Project with rank 1, 2, 3, 4 and 5
11 Connectivity Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11
5 Mobility Projects
Project with rank 1, 2, 3, 4 and 5
Total: 21 projects

Conventional(AHP+AHP)(Proportion+PROMETHEE II)

Proposed

Weight calculation

ComplicatedSimpleInformationneeded from experts(No. of questions)

Numerous(i) = 108(ii) = 259

Few(i) = 108(ii) = 0

From Table 18, regarding weight calculation of conventional
procedure, it is complicated because of solving eigenvalue
problem with n x n matrix while the proposed combination cancalculate weight with arithmetic calculation. For information in
pairwise comparison needed from experts by questionnaire, it isclearly be seen that in (ii) of the proposed combination, no
information from experts to make pairwise comparison
between each pair of projects is needed. For this case, task ofexperts to make 259 pairwise comparisons of projects can be
reduced.

4.3.3 Scenario 3: Considering only constraints 1 and 3
Constraints 1 and 3 which are budget limit and including
compulsory projects are taken into account in this scenario.
Optimal portfolio is obtained in Table 16.
Table 16: Optimal project portfolio for scenario 3
9 Modal shift Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8 and 9
12 Connectivity Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 13
5 Mobility Projects
Project with rank 1, 2, 3, 4 and 5
Total: 26 projects

5. Conclusions
This study is based on project portfolio management topropose a combination of (i) weight determination process by
Proportion method, (ii) multi criteria decision making processby PROMETHEE II method and (iii) constraints consideration
process by PROMETHEE V method to define criteria weight,
evaluate and rank projects with various criteria and optimallyselect transport project portfolio for Thailand. From analysis offour scenarios of project implementation constraints, it can beunderstood that selecting project with positive net outranking
flow can effectively consider and fully cover budget constraint
issue, and it can be realized that compulsory project has strong
influence on portfolio selection. In addition, three connected
processes in the combination are carefully set up in appropriate
order to include necessary procedures for comprehensive
evaluation and selection. Due to advantages of this combinationin simple weight calculation, multi-criteria evaluation with few
information from experts and optimal selection condition, thiscombination is expected to support Thai government to makeeffective decision in transport project investment.

4.3.4 Scenario 4: Considering only constraints 2 and 3
Constraints 2 and 3 which are supporting all objectives andincluding compulsory projects are taken into account in this
scenario. Optimal portfolio is obtained in Table 17.
Table 17: Optimal project portfolio for scenario 4
9 Modal shift Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8 and 9
12 Connectivity Projects
Project with rank 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 13
5 Mobility Projects
Project with rank 1, 2, 3, 4 and 5
Total: 26 projects
From Table 14, 15, 16 and 17, it can be seen that basicallyproject with positive value of net outranking flow is selected
into portfolio according to optimal selection condition of
PROMETHEE V method. In addition, it can be noticed fromscenario 2 that when compulsory project constraint is notconsidered, projects with negative value of ‫ ׎‬disappeared; itmeans compulsory project constraint can have influence on
portfolio selection. Moreover, after considered at scenario 4,
although budget constraint is not considered, optimal project
portfolio is still the same. It means net outranking flow frommulti-criteria (including budget criterion) evaluation by
PROMETHEE II method has already consideredappropriateness of budget.

References[1] Abu-Taleb, Maher F., and Mareschal, B., Water resources planning in the
Middle East: application of the PROMETHEE V multicriteria method, European
Journal of Operational Research 81, 500 – 511, 1995.
[2] Aldian, A., and Taylor, Michael A.P., A Consistent Method to Determine
Flexible Criteria Weights for Multicriteria Transport Project Evaluation in
Developing Countries, Journal of the Eastern Asia Society for Transportation
Studies Vol. 6, 3948 – 3963, 2005.
[3] Archer, NP., and Ghasemzadeh, F., An integrated framework for project
portfolio selection, International Journal of Project Management Vol. 17, No. 4,
207 – 216, 1999.
[4] Behzadian, M., Kazemzadeh, R.B., Albadvi, A., and Aghdasi, M.,
PROMETHEE: A comprehensive literature review on methodologies andapplications, European Journal of Operational Research 200, 198 – 215, 2010.
[5] Brans, J.P., Vincke, Ph., and Mareschal, B., How to select and how to rank
projects: The PROMETHEE method, European Journal of Operational Research
24, 228 – 238, 1986.
[6] Macharis, C., Springael, J., De Brucker, K., and Verbeke, A., PROMETHEE
and AHP: The design of operational synergies in multicriteria analysis.
Strengthening PROMETHEE with ideas of AHP, European Journal ofOperational Research 153, 307 – 317, 2004.
[7] Shang, Jennifer S., Tjader, Y., and Ding, Y., A Unified Framework forMulticriteria Evaluation of Transportation Projects, IEEE Transactions on
Engineering Management Vol. 51, No. 3, 300 – 313, 2004.

32

PERFORMANCE ANALYSIS OF OPEN LOOP MIMO SYSTEMUNDER RICIAN FADING CHANNEL
Student Number: 12M51497

Name: MAITSETSEG Nomin

Supervisor: TAKADA Jun-ichi

ライスフェージングチャネルにおける開ループ型 MIMO システムの性能評価
In this work, the evaluation of the performance degradation due to the presence of line-of-sight (LOS) component foran open loop multiple input multiple output (MIMO) system under Rician fading channel is presented. The bit error rate
(BER) performance of 2 × 2 MIMO systems in terms of the Rician K factor for space time block coding (STBC) andspatial multiplexing schemes utilizing with minimum mean square error (MMSE) receiver are analyzed. Dual-polarized
diversity and multiplexing schemes are also evaluated to compare their performance in LOS MIMO environments. Monte
Carlo simulation technique is used to simulate the channels and show the validity of the performance analysis. The tradeoff
between multiplexing gain in dual-polarized multiplexing scheme and system robustness in STBC scheme is given in LOS
scenario.

1

Introduction

In mobile communication systems, use of multiple antennas at both the transmitter and receiver side has been gaining popularity in order to meet the increasing demand ofhigh data rate and efficient spectrum usage. Multiple input multiple output (MIMO) systems, arguably one of themost advantageous technologies in wireless communications, allows wireless systems in multipath environments
to achieve greater system reliability through diversity techniques, or achieve greater system capacity through spatial
multiplexing [1, 2].
The main mechanism that allows MIMO systems toachieve such improvements to the performance of wirelesssystems is the rich scattering environment of the channel.
This rich scattering environment produces multiple independent paths, which can be exploited to increase system
performance. Despite the fact that MIMO system requires
statistically uncorrelated, independent channels to achieve
the performance improvements using multiplexing and diversity techniques, this is not always the case in the realworld scenario.
The propagation environment and channel characteristics provide significant challenges for wireless communications systems. A typical MIMO system with Mt transmit and Mr receive antennas diagram is shown in Figure
1. If the line-of-sight (LOS) component is presented insuch environments, channels are more correlated, which
considerably degrades the performance of MIMO systems.
Due to this high correlation between channels, the channel
matrix becomes rank deficient, which reduces the degrees
of freedom of the channel. In a pure LOS environment,
the channel rank is equal to one (RH = 1).
Another factor affecting the performance of MIMO systems is the information about the channel that is known to
the transmitter. MIMO systems can be classified as closed
loop or open loop systems, depending on whether transmitter has feedback from receiver or not. In the closed
loop system, transmission scheme can be adaptive to thechannel state, but it should be fixed in the open loop system. Closed loop system outperforms open loop system,

Figure 1: A typical MIMO systembut closed loop system is not applicable for some scenarios such as one-way transmission or point to multipointtransmission.
There have been several works on Rician fading MIMO
channel and its performance. Most of them considered
the closed loop MIMO system under Rician fading channel, and achieved performance improvement using antenna spacing and beamforming techniques [3]. It was also
shown in [4], that the impact of LOS, and Rician K factor is different for spatial multiplexing gain and diversity
gain. While the increase in K factor reduces the multiplexing gain, diversity gain is less affected by Rician K factor.
In [5], it was suggested that by employing co-located orthogonally polarized antennas, decorrelation between antennas was achieved. There is also a limited number ofstudies that have addressed the polarization to achieve performance improvement in MIMO system such as [6, 7].
Dual-polarized MIMO system has received attention recently due to its space and cost advantages, and potential
to recover the multiplexing gain in LOS MIMO scenarios.
The main objective of this study is to evaluate and compare the performance of co-polarized and dual-polarized
open loop MIMO transmission schemes under Rician fading channel to identify system performance dependence on
Rician K factor.
33

2Channel Modeling

Rician distribution is used to describe fading condition
when there is a significant LOS component arriving at thereceiver in addition to the multipath components modeledby Rayleigh fading. Rician fading channel model includes
Rayleigh fading channel model as a special case.
Rician channel is modeled as the sum of a fixed component and a multipath fading component and is describedby Eq. (1).

H=

r

K
HLOS +
1+K

r

1
HNLOS ,
1+K

Figure 2: Uniform linear array configuration(1)

where H is the channel matrix, HLOS and HNLOS are thechannel response matrices for LOS component and multipath fading component, respectively. K is the Rician K
factor of the channel, defined as the ratio of the power inthe LOS component of the channel to the power in themultipath fading component. The total transmitted power
is normalized to unity, due to open loop MIMO system.
Therefore the normalization of the multipath fading component of the channel matrix is given as:E[h2nlos:mr ,mt ] = 1.

(2)

We have HNLOS (mt = 1, 2, mr = 1, 2), and hnlos denotes the channel gain between mt th transmit antenna andmr th receive antenna. HNLOS is assumed to be modeled
as independent and identically distributed (i.i.d.) complex
Gaussian with a zero mean and variance σ 2 .
In the case where K is sufficiently small, the channel correlation is reduced. When K is equal zero (K = 0), there
is no LOS part and degree of freedom is equal to two with
two transmit and two receive antennas. In this case, it ispossible to achieve full multiplexing gain.
The LOS components are modeled using deterministic
physical channel models assuming uniform linear array(ULA) configuration aligned along φ = 0(x) direction [8].
The channel matrix HLOS for LOS path which has fixed
entries with unit power (|hlos:mr ,mt |2 = 1) is expressed as:
HLOS = ar (φr )aHt (φt ),

Figure 3: Dual-polarized MIMO configuration

χc is the inverse of the antenna cross polarization discrimination (XPD) which is the ratio of the co-polarized
and cross polarized component. XPD characterizes theantenna sensitivity to the polarization. Multipath fading
component HNLOS is given as:
HPNLOS = X ⊙ HNLOS ,(6)

where X is dependent on the polarization scheme. For
well separated and uncorrelated antennas, X is given by:

√ 
χµ
1
√
X= √
,
(7)
χ
µ

(3)

where at and ar are the array response vectors. φt andφr are Angle of Departure (AOD) and Angle of Arrival(AOA) respectively. In this work, AOD and AOA are assumed as 90◦ as shown in Figure 2. With larger K, thechannel can be assumed as pure LOS and NLOS part isignored.
Performance of the dual-polarized MIMO multiplexing
and diversity schemes’ is analyzed in terms of Rician K
factor in this work as well. Dual-polarized 2 × 2 MIMO
configuration is given in Figure 3. In [6], simplified Rician
channel model for dual-polarized MIMO system is given
as:
r
r
K
1
H=
Xc HLOS +Xc HPNLOS , (4)1+K
1+K

where µ and χ are the co-polarization power ratio (CPR)and cross polarization power ratio (XPR), respectively. In
this work to show the coupling between antennas, µ is assumed as equal to one. It is noted that the HLOS is only
affected by the antenna depolarization [6], with the resulting LOS component equal to HLOS = ar (θr )aHt (θt ).

1 1
Eq. (4) becomes identical to Eq. (1) when Xc =
1 1


1 1
and X =, so Eq. (4) can be also used for co1 1
polarized antenna case.

where Xc is the antenna depolarization matrix expressed
as:

√ 
1
χc
Xc = √.
(5)χc1

Diversity technique is based on the principle of exploiting
the multiple independent paths of the signals arriving at
the receiver. From these multiple copies of the same transmitted signal, the probability that the received signal from34

3

MIMO Transmission schemes

3.1

Transmit Diversity Scheme

all paths experience a deep fade is reduced, which leads toan improvement of the slope of the BER curve [2]. Simply, it is for combating system performance degradation
due to fading channels. In this research, 2 × 2 space time
block coding (STBC) scheme, known as Alamouti scheme
is employed [9]. This is due to the low computational complexity of the scheme, as well as channel state information
feedback is not needed to the transmitter, which satisfies
the open loop MIMO condition. Since open loop MIMO
system is assumed, the energy is divided evenly between
the transmitters. Signal to noise ratio (SNR) at the receiverof STBC scheme is expressed as:
γ=

Mr
X

(|hmr 1 |2 + |hmr 2 |2 )mr =1

Eb /N0,2(8)

Figure 4: Semi-analytical BER performance evaluation process

where γ is the received SNR at the receiver and Eb /N0
is the SNR per bit. By using this SNR, analytical BER is
estimated as follows:1
Pb = erfc(γ).
(9)
2

10
10

Spatial Multiplexing Scheme

BER

3.2

10

Transmitting independent signals from each transmit antenna at the same time using the same frequency is the coreidea of spatial multiplexing [1]. In spatially multiplexed
MIMO system, signal detection at the receiver is a complex operation. Due to its low computational complexity
and advantages over the simpler zero forcing (ZF) linear
receiver, the minimum mean square error (MMSE) linear
receiver is considered in this work [2]. MMSE receiverminimizes the BER and can mitigate the multistream interference with less noise enhancement within the linear
receivers. Similar to the STBC scheme, total power of thesystem is normalized to unity and equal amounts of power
is assigned to each stream. Signal to interference and noise
ratio (SINR) at the mt th stream of 2 × 2 spatial multiplexing MIMO system is estimated as:
γ=

1
− 1,
−1
Eb
HH + I
)
(N
H
m
,m
M
r
t
t
0

10
10

4

1
erfc(γ).
2co-polarized STBC
dual-polarized STBCAWGN (Theoretical)

-1

-2

-3

-4

-5

-6

10
-10

0

10
20
K(dB, Rician Factor)

30

Figure 5: BER of 2 × 2 MIMO STBC schemes

4.1

Performance Evaluation and Comparison between Co-polarized and Dual-polarized STBC
Schemes

In Figure 5, the BER performance of co-polarized STBC
and dual-polarized STBC is given. It is noted that an increase in Rician K factor has less impact on performanceof STBC scheme as shown in Figure 5, since the fading is
reduced. When Rician factor K is large, the BER performance of co-polarized STBC scheme converges to AWGN
case. There is power loss in dual-polarized STBC scheme
compared to co-polarized STBC scheme. This decrease inperformance is because of the reduction in received power
due to the XPD.

(10)

where γ is the SINR for the mt th stream, IMt is the Mt ×
Mt identity matrix, and mt = mr . Analytical BER is thencalculated by using this estimated SINR.
Pb,mt =

10

0

(11)

Performance Evaluation

Due to the LOS environment, performance degradation
may occur. In order to overcome this issue, a dualpolarized MIMO system was suggested in [6]. The performance evaluation and comparison is performed on the copolarized and dual-polarized MIMO system in order to determine the performance trend in LOS scenario for MIMO
transmission schemes. Monte Carlo simulation technique
is used to simulate the channels in order to validate thoseanalytical results and BER is estimated analytically. The
BER performance evaluation process is illustrated in Figure 4. BER of each transmission schemes is estimated asa function of Rician K factor when Eb /N0 is equal to 10
dB.

4.2

Performance Evaluation and Comparison between Co-polarized and Dual-polarized Spatial
Multiplexing Schemes

In Figure 6, the BER performance of co-polarized anddual-polarized spatial multiplexing schemes is given. In
contrast to STBC scheme, BER performance is degradedin spatial multiplexing scheme with increasing Rician K
factor as shown in Figure 6. When Rician K factor islarge, we can not separate the stream in the co-polarized
spatial multiplexing scheme due to the rank deficiency ofthe channel.
However, by using dual-polarized multiplexing transmis35

-2

10

• 2 × 2 STBC scheme (co-polarized)-310• 2 × 2 Spatial Multiplexing scheme (co-polarized)

-4

10

-5

10

co-polarized SM
dual-polarized SM
AWGN (Theoretical)• 2 × 2 Dual-polarized STBC scheme• 2 × 2 Dual-polarized Spatial Multiplexing scheme

-6

10
-10

0

10
20
K(dB,Rician Factor)

STBC scheme performance improves with the presenceof a LOS component and channel robustness is increased.
In contrast, the poor spatial multiplexing effect is observed
despite having a high SNR per bit where the MIMO channel is dominated by the LOS component.
The multiplexing gain is restored with an increase in
Rician K factor in the dual-polarized MIMO spatial
multiplexing scheme, thus, the performance gain was
directly related with XPD factor. However in low Rician
K factor scenario, the performance is less than the
co-polarized spatial multiplexing case. In STBC scheme,
co-polarized MIMO configuration should be preferredover dual-polarized MIMO since power loss due topolarization occurs in dual-polarized channel. In the caseof multiplexing scheme, dual-polarized MIMO system
performance is improved in LOS scenario because of
its advantage of complete separation of the stream. The
tradeoff between dual-polarized spatial multiplexing and
STBC scheme should be considered in terms of systemrobustness.

30

Figure 6: BER of 2 × 2 MIMO spatial multiplexing schemession, the multiplexing gain is recovered and the BER performance is improved. Similar to the co-polarized STBC
scheme, the BER performance converges to AWGN channel with higher value of Rician K factor. In lower scenario
of Rician K factor, the performance of dual-polarized
spatial multiplexing scheme is less than the co-polarized
spatial multiplexing scheme. It can be concluded thatwith strong XPD, performance improvement is achievedin LOS MIMO scenario by using dual-polarized antennaconfiguration.
4.3

Performance Evaluation and Comparison between STBC and Spatial Multiplexing Schemes

In large Rician K factor scenario, two streams can be separated in dual-polarized spatial multiplexing scheme. However, when we compare the performance of co-polarized
STBC and dual-polarized spatial multiplexing scheme,
there is a tradeoff between system robustness and multiplexing gain as shown in Figure 7. Even though the multiplexing gain is recovered in dual-polarized MIMO system,
the system is more robust in co-polarized STBC scheme.

References[1] G. J. Foschini and M. J. Gans, “On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas,”
Wireless Personal Communications, vol. 6, pp. 311–335, 1998.
[2] A.Paulraj, R.Nabar, and D.Gore, Introduction to Space Time Wireless Communications. Cambridge University Press, 2003.
[3] K.Kobayashi, T.Ohtsuki, and T.Kaneko, “Precoding for MIMO
Sytems in Line-Of-Sight Environment,” IEEE GLOBECOM 2007,
2007.

0

10

[4] R.Nabar, H.Bolcskei, and A.J.Paulraj, “Outage Properties of SpaceTime Block Codes in Correlated Rayleigh or Rician Fading Environment ,” IEEE ICASSP, vol. 1, pp. 2381–2384, 2002.

-1

10[5] R.Nabar, H.Bolcskei, V.Erceg, D.Gesbert, and A.J.Paulraj, “Performance of Multiantenna Signaling Techniques in the Presenceof Polarization Diversity,” IEEE Transaction on Signal Processing,vol. 50, no. 2, pp. 2553–2562, 2002.

-2

10

BER

Discussion and Conclusion

In this thesis, performance evaluation of 2 × 2 open
loop MIMO system under Rician fading channel was performed in terms of BER for the following transmission
schemes:

-1

10

BER

5

0

10

-3

10

[6] C.Oestges, “Propagation Channel, Capacity and Error Probability
with Dual-Polarized Wireless Transmissions,” Indian Journal of Radio and Space Physics, vol. 36, pp. 411–417, 2007.

-4

10

-5

10

-6

10
-10

STBC
Dual Polarized STBC
SM
Dual Polarized SM
0

10
20
K(dB, Rician Factor)[7] J.Han, J.Baek, and J.Seo, “MIMO OFDM Transceivers with DualPolarized Division Multiplexing and Diversity for Mutimedia Broadcasting Services,” IEEE Transaction on Broadcasting, vol. 59, no. 1,
pp. 174–182, 2013.

30[8] F.Bohagen, P.Orten, and G.E.Oien, “On Spherical vs. Plane Wave
Modeling of Line of Sight MIMO Channels,” IEEE Transaction on
Communications, vol. 57, no. 3, pp. 841–849, 2006.

Figure 7: BER of 2 × 2 MIMO transmission schemes[9] S.Alamouti, “A Simple Transmit Diversity Technique for Wireless Communications,” IEEE J.Sel. Areas Commun., vol. 16, no. 8,
pp. 1451–1458, 1998.

36

Wavelet Based Image Coding
via Linear Prediction and Clustering
Student Number：13M18221

Name：Shiyu Zhou Super visor：Yukihiko YAMASHITA

線形予測とクラスタリングを用いた

ウェーブレット画像符号化に関する研究
周施雨

JPEG に代表されるブロック単位の静止画像圧縮方式には, 復号した画像にブロック歪が現われる問題が生じる。JPEG2000 は離散ウェーブレット変換（DWT）を利用することによってこの問題を解決した。本研
究はウェーブレット変換による画像符号化の高度化を目的とし, 「クラスタリングによりクラス分けした線

形予測によって、ウェーブレット変換係数を予測する画像符号化」を提案し, 計算機実験によって, 提案手法の有効性を確かめる。

1. INTRODUCTION
Image coding methods have been invented to reduce
irrelevance and redundancy of digital image data in order to store or transmit the data in an efficient form
for web usage and so on. The block transform based(a) Verticalimage coding such as JPEG is successfully used forthese purposes. However, JPEG suffers from a problem called the block distortion caused by its blockwise(b) Horizontal

Fig. 1. Coefficients of DWT decompositiontransformation. The wavelet based image coding suchas JPEG2000 has solved the problem by using the discrete wavelet transform (DWT) [1,2,3].
Prediction is another key technology to enhance
the performance of image coding. By sending only(a) First stage(b) Multi-stage

Fig. 2. Coefficients of 2D-DWT decomposition

the difference between the original and its predictedvalues, the data size can be reduced. However, inthe case of applying prediction to the 2-dimensional
discrete wavelet transform (2D-DWT) that is used in
JPEG2000, a distance of several pixels between theof the proposed framework.

target pixel we want to predict and decoded pixels we
can use, makes precise prediction to be difficult.

2. WAVELET BASED IMAGE CODING WITH
PREDICTION

To solve this problem, in this paper, we propose to
introduce prediction into the 1-dimensional transformpart of 2D-DWT. We developed a linear prediction

2.1. 2D-DWT
Daubechies proposed DWT for multiresolution sig-

method with clustering, which does not generate anyside information, to predict the detailed (high fre-nal analysis [1]. Its analysis process is as follows. An
input signal is split by a low and a high frequency passquency) coefficients by using the approximated (lowfrequency) coefficients and the restored components.
We also conducted experiments to show the advantage

filters. Both outputs are downsampled by 1/2 and theapproximated (low-frequency) and the detailed (highfrequency) coefficients are obtained respectively. Iter-37

atively, the approximated coefficients are filtered anddownsampled as input signals. Its synthesis process isalmost the opposite of the analysis process.

row from top to buttom in CLIm . CLLm and the rows
in CLIm above the target one can be decoded. If we
can predict the target row in CLIm , the predicted values

Since an image is a 2D signal, 2D-DWT is used
for image coding. To explain 2D-DWT, we define
the following notations. Let M be the number ofof CLHm in the row can be obtained by DWT. Since
the target row is adjacent to the above rows, the target
pixels we want to predict and the reference pixels we
can use are adjacent. This short distance makes theprediction more precise. Then, the data size is reduced

stages of 2D-DWT. At m-th stage of 2D-DWT, CXYm
(X, Y = L, H, I) denotes the set of transformed coeffi-by sending not the quantized values of CLHm but thequantized difference between their true and estimatedcients, where X and Y specify the type of coefficientsof horizontal and vertical transforms, respectively.
L and H indicate approximated and detailed coeffi-values.
The reason why we predict not CLHm but CLIm is that
CLIm is similar to a normal image so that prediction

cients, and I indicates “not transformed” ones. For
example, CHL2 denotes horizontal detail and vertical
approximate coefficients as the results of second stage

is easy. The prediction scheme can also be applied todecomposing CILm into CLLm and CHLm .

2D-DWT.
We assume that horizontal DWT is performed after vertical transform, provided that the order of transforms is commutative. First, an input image is split
into the sets of vertical approximated and detailed coefficients CIL1 and CIH1 . Fig. 1 (a) illustrates the arrangement of the coefficients. CIL1 and CIH1 are respectively split into the sets of horizontal approximatedcoefficients, CLL1 and CLH1 , and the sets of detailed
coefficients CHL1 and CHH1 . We have 4 types of co-Fig. 3. Block diagram of encoding process of a stage

efficients totally for a stage of 2D-DWT. Fig. 2 (a) illustrates the arrangement of the coefficients. We iteratively decompose CLL(m−1) into CLLm , CLHm , CHL1 ,Let PXYm and RXYm be the sets of the predicted andand CHHm m = 1, 2, . . . , M (Fig. 2 (b)).

the decoded coefficients of CXYm , respectively. We denote the difference between CXYm and PXYm by DXYm .

When we combine prediction with the DWT decomposition, it has to be based on the values of decodable

The fundimental process of encoding is shown in Fig.3.
And the algorithm is shown in Algorithm 1.

pixels. In order to obtain a coefficient by DWT, weneed several pixel values around the corresponding position of the coefficient. Furthermore, in order to obtain

2.3. Encoding of coefficients
We use the SPIHT encoder with quantization to en-a pixel value by inverse DWT (IDWT) for decoding,
we need several coefficients around the pixel. Thus,

code coefficients [4]. For the prediction, we have to
fix the quality of a decoded image, quantization has tothe prediction of 2D-DWT coefficients has been considered to be difficult because we have to predict thevalues of pixels that are apart from the decodable pix-

be introduced. An arithmetic coder is applied to the
outputs of SPIHT.

els by not less than several pixels.
3. PREDICTION
Here, we explain the proposed prediction algorithm

2.2. Coding scheme with prediction

when CILm on the k-th row is going to be predicted. We
intend to use RILm in the region above the k-th row andHere, we describe the proposed coding scheme using
the DWT decomposition with prediction in detail.

RLLm . Let us consider that the pixel value of CILm at(x, k) is the target to be predict. We use linear prediction with clustering for the purpose. We extract a block

Consider we decompose CLIm into CLLm and CLHm
and predict CLHm . The decomposition is done row by

38

Algorithm 1 2D-DWT with prediction
Require: An input image CLL0 and the total DWTstage M.
for m ← 1 to M do
Obtain CLLm , CLHm , CHLm , and CHHm from
CLL(m−1) by 2D-DWT.
end for
Output the quantized CLLM , dequantize it, and store

Fig. 4. Data for prediction (nX = 5, nY = 3)

the result into RLLM .
for m ← M to 1 dofor k ← 0 to NY,m − 1 do

is given by

Make PILm on the k-th row by using RLLm and
RILm on the 0, 1, . . . , (k − 1)-th lines.

hacmax , ũi +Make PHLm on the k-th row by DWT of PILm .
Make DHLm on the k-th row subtracting PHLm
from CHLm .

at odd x. Therefore we separately make two sets of gc
and ac (c = 1, 2, . . . , C) for even x and for odd x.
Here, the linear prediction coefficient vectors ac canbe obtained as follows.

Add the dequantized values and PHLm on the kth, and store the results into RHLm on the k-throw.

ac = (U cU Tc )−1U c vc .

Obtain RILm on the k-th row by IDWT.
end for(3)cwhere Jc and {ûc, j , v̂c, j }Jj=1
be the number and theset of normalized learning data in cluster c. U is an

Similarly, obtain DLHm and RLHm .
Output the quantized values of DLHm , DHLm , andCHHm .

(nX nY , Jc )-matrix, and U = (ûc,1 , ûc,2 , . . . , ûc,Jc ). V
is a Jc -dimensional vector, which describes the set ofpredicted value. v = (v̂c,1 , v̂c,2 , . . . , v̂c,Jc )T .

Calculate RLL(m−1) by 2D-IDWT.
end for

4. EXPERIMENTAL RESULTS

data of width nX and height nY from LILm and RILm .
The horizontal center on the bottom row of the block

In this experiment, we compare codings with andwithout clustered linear prediction. We encode and de-is aligned to (x, k). The bottom and the other rows are
extracted from LILm and RILm as shown in Fig. 4. The

code the luminance part of the standard images “Lena”,“Barbara”, and “Intersection” shown in Fig. 5 (a), (b),data is vectorized to an (nX nY )-dimensional vector u.
We subtract the average of ū from u and define ũ.
1
ū
nX nY

(2)

Because of downsampling of DWT, the properties of
LILm of pixels are very different between at even x andQuantize DHLm on the k-th row and dequantize
the result.

ũ = u −

1
ū
nX nY

and (c). “N2” in standard color image data (SCID)
database shown in Fig. 5 (d) is used as a learning data
to obtain ac and gc . We let the number of cluster C =(1)

128, the block width nX = 5, and its height nY = 3.

and divide it by the standard deviation of u. The results
is denoted by û. If the standard deviation is smaller
than a threshold, the data is eliminated from data. Next,

Fig. 6 shows their rate distortion curves, decribe bit
rate vs. PSNR (peak signal noise ratio). We can see
coding with the proposed prediction scheme outper-

we make C clusters by the k-means method [5] using
the normalized vectors, and obtain the centroid vec-

forms that without prediction for three images. Especially, improvements in ’Intersection’ is larger thantors gc (c = 1, 2, . . . , C) and linear prediction coefficient vectors ac . We find c that maximizes |hũ, gc i|. It
is denoted by cmax . The predicted value PILm at (x, k)

those of ’Lena’ and ’Barbara’. This is because theproperties of ’Intersection’ and ’N2’ are similar andthey have many straight edges.

39

(a) Lena(b) Barbara(c) Intersection(d) N2

34

34

38

33

33

37

32

32

36

31

31

35
34

PSNR

39

PSNR

PSNR

Fig. 5. Standard images and 1D-DWT decomposition

30
29

33

29

28

With prediction
Without prediction

32

28

With prediction
Without prediction

27
0.2

0.3

0.4

0.5
Bit rate

0.6

0.7

0.8

30

With prediction
Without prediction

27
0.2

0.3

0.4

(a) Lena

0.5
Bit rate

0.6

0.7

0.8

0.2

0.3

(b) Barbara

0.4

0.5
Bit rate

0.6

0.7

0.8

(c) Intersection

Fig. 6. Rate distortion curve
can be applied to improve prediction. Applying thisscheme to moving image coding is a very interesting
theme.

As for the visual quality, both methods provided almost the same results since the quantized differences
are sent to the decoder.
Although our prediction method is primitive and its
computational complexity is high, we can show possibilities of the proposed coding scheme using linear

6. REFERENCES[1] I. Daubechies, “Ten Lectures on Wavelets,” Society for Industrial and Applied Mathematics,

prediciton with clustering. It should be emphasizedagain that the proposed scheme does not need any side

Philadelphia, 1992

information. By a more precise and efficient prediction algorithm, we will improve the coding efficiency.
Furthermore, by applying a more sophisticated coding[2] M. Vetterli, J. Kovaevi, “Wavelets and Subband
Coding,” PrenticeHall, Englewood Cliffs, 1995.

method of coefficients, the proposed scheme can be
more efficient. For example, it can be made by modify-[3] Geoffrey M. Davis, Aria Nosratinia, “Waveletbased Image Coding: An Overview,” IEEE

ing EBCOT (Embedded Block Coding with Optimized
Truncation).

Trans. on Circuits and Systems for Video Technology, vol . no. 6, pp.243-250, June, 2002.
[4] Pearlman, William A. “A new fast and efficient
image codec based on set partitioning in hierar-

5. CONCLUSIONS

chical trees,” IEEE Transactions on Circuits andSystems for Video Technology 6 (3): 243250.
doi:10.1109/76.499834. ISSN 1051-8215, June

In this paper, we proposed an wavelet image coding scheme using linear prediction and clustering. The
proposed scheme does not need any side information.
We conducted an experiment of still image coding to

1996.

show advantages of the proposed scheme. For future
works, we have to research on a more precise and efficient prediction method. Super-resolution techniques[5] J.A. Hartigan (1975), “Clustering algorithms,”
John Wiley ;Sons, Inc.

40

11 GHz Indoor MIMO Channel Parameter Estimation Using Gradient
MethodStudent Number: 13M18244

Name: Shizhi ZHANG

Supervisor: Jun-ichi TAKADA

勾配法を用いた 11 GHz 帯における屋内 MIMO チャネルパラメータ推定
チョウ

カツシ

本論文では，平面を仮定したマルチパスチャネルモデルをベースとそた最尤推定法を用いて高周波帯無線チャネルのパラメータ推定を提案した．最尤推定を最適化する際共役勾配法とグローバルサーチ法の両方を用いたハイブリッド
手法で実行した．本来サーチベース法の精度を勾配法で改善すると同時に，勾配法の初期値問題をサーチベース法で定められるメリットが考えられる．

1

Introduction

2

Nowadays, the growing demand for higher data rates
and higher transmission efficiency requires us to optimize the existing transmission system. For this purpose the propagation channel characteristic is dominant in the system design. In the same time, the concepts of the future wireless communication are highfrequency, wide band, MIMO, which can increase thechannel capacity. After the stage of system design, we
have to make a standard for this service. So that wehave launched the massive channel sounding measurement campaign to analysis the propagation environment and help the standardize the system. The channel propagation measurement has been done in the indoor environment and outdoor environment, and theoutput of the measurement is the channel transfer function. However, the transmission environment always
complicates the wireless channel, such as the shadowing and fading issues. For this reason, the channel
model is necessary to model the channel characteristic.
Generally, the multi-path model is widely utilized inchannel analysis based on the approximation of plane
wave in far field. This model consists of the parametersof paths (ex. direction angles, gain, delay, etc). There
are some measurement campaign proceed the research
into high frequency propagation channel (11 GHz inthis paper). For the objective of model the high frequency channel and extract the parameters from thechannel, the investigation of path parameter set becomes more important. There are several approaches
for the investigation, among those the most popular
one is the maximum likelihood estimation [6]. For the
optimization of the likelihood function during the estimation in high parameter domain case, one method
called SAGE [5] reduces the computational complexity compared to the standard traditional algorithm by
optimizing the parameter domain respectively.
However, the convergence speed of SAGE is stillslow and in the measurement campaign of [1] [2], theantenna radiation pattern measured has a poor resolution which will lead large errors during global searching. In order to overcome these problems and improve
the accuracy of the estimation as well, this paper focuses on the conjugate gradient method which can realize a higher accuracy and fast convergence.

41

Signal Model and MLE Estimator

The electromagnetic wave can be approximated as plane
wave in far field, then well known formulation of the
wideband MIMO channel signal model [6] is
HMIMO (f ) =L
XaRx (ϕRx,l , ϑRx,l , f )Γl aT
Tx (ϕTx,l , ϑTx , f )l=1· exp(−j2πf τl )(1)

, where Γ is the complex polarimetric amplitude matrixof the lth of L paths, aTx , aRx are the antenna pattern
vectors, ϕTx , ϑTx , ϕRx , ϑRx denote the azimuth andco-elevation direction of departure (DoD) and direction of arrival (DoA), f means the frequency and τ
is the propagation delay, (·)H denotes the conjugatetranspose.
When the transfer signal is impulse signal, (1) becomes the channel impulse response, so
x = s(θ) + n,(2)

where x is the received signal, s(θ) = vec{HMIMO }is the impulse response, θ denotes the parameter set(e.g. DoD, DoA, delay) and n is the noise vector. Here
we rely on the assumption that the noise is i.i.d. zeromean Gaussian process, so that we have the probability
density function!
PN
2
1
i=1 ||xi − s(θ)i ||
, (3)
exp −
p(x, θ) =(πσ 2 )N
σ2
where N is the size of the x. (3) can be also called likelihood function [7] according to the estimation theory.
During the estimation, the likelihood function need tobe maximized. In other word, (4) needs to be minimized by the optimized parameter set θ.
L = θ̂ = arg min(x − s(θ))H (x − s(θ)).

(4)

θ

3

Comparison among Estimation
Algorithms

In order to implement the optimization in (4), generally, there are two considerable approaches, which aresearch based approach and gradient based approach.

Table 1: Optimization Scheme Comparison
Optimization scheme
Global based
Gradient based
Newton method

Accuracy
Large error
Good
Very Good

Table 2: System Setup
Parameter
Value

Speed
Slow
Fast
Fast

Starting from the global search, which search all
sample points in all parameter domains, there are many
search based methods, such as bi-section search, SAGE
(Space Alternative Generalization Estimation Maximization), etc. The merits of these search based approaches are the simple algorithm, simple flow, on theother hand the demerits are the intersection errors andthe computational complexity.
Compared to these search based methods, gradient
based approach is an analytical search method, which
can reach a local peak by less iterations. Because thedominant idea of gradient based methods is consider
the parameter space as a terrain in 3D space, the trial
point will fall down towards the local peak like theinfluence of the gravity. However the calculation of
the gradient is sometimes another critical problem because the complicated function formula. Some popularmethod like Newton-Raphson method, Quasi-Newton
method, gradient method, conjugate gradient method,
Lagrange method for conditional optimization problems, etc. Newton methods have the problem to calculate the Hessian matrix [4] which will also be a problem in some certain problems. The brief comparison
among these optimization scheme is shown as Tab. 1.
We can see that the gradient based method and Newton method have good accuracy, while they suffer fromthe initialization problem the most of the cases.
In this research the hybrid of the rough global searchand conjugate gradient method is proposed. This proposal solved not only the large error and the slow calculation speed of the the global search but the initialization problem in the conjugate gradient method byeach other.

4

Conjugate Gradient Method

The global search will omit the algorithm of global
search but focus on the conjugate gradient method.
From (4), the function L is that we want to optimize,θ is the parameter set to search. We take the Taylor
expansion at θ = α up to the first order like∂L(θ1 − α1 )∂θ1 θ=α∂L+··· +(θn − αn ) + higherorder.
∂θn θ=α(5)

L(θ) = L(α) +We rewrite the second term in the right hand sideas
L(θ) = L(α) + h∇L|θ=α , (θ − α)i,(6)where ha, bi is the inner product of vector a and vectorb, ∇A denotes the gradient of a scalar field A. In 2D
42

Central frequency

11 GHzBandwidth

400 MHzNo. of subcarriers

2,048

Antenna

24×24 dual polarized micro-strip

parameter space case, the gradient express the perpendicular direction to the contour line of the function. In
normal gradient method (maximum gradient method),
we search the minimum point in the direction of ∇L
iteratively. Due to ∇L(θ k+1 ) ⊥ ∇L(θ k+1 ), the algorithm [8] requires infinite iterations to reach the local
peak theoretically. For the accelerate the algorithm, werevise the search direction by the independence of thevector subspace [8] together with the Fletcher-Reeves’
equation and the estimation flow is shown as followed.
Conjugate Gradient Method Algorithm (NonLinear) [8]
1. Set an initial value θ 1 , the initial search
direction s1 = −∇L(θ 1 ) and initialize theiteration number k = 1
2. Set the break condition: If ∇L(θ k ) issmall then stop the iteration
3. Search through the sk direction and findthe value αk that minimize the function
4. Renew the point as θ k+1 = θ k + αk sk
5. Calculate λk =||∇L(θ k+1 )||2||∇L(θ k )||2

6. Calculate the new search direction using
λk as sk+1 = −∇Lθ k+1 + λk sk
7. Incrementation k = k + 1
8. Go to 2

5

Estimation Flow Chart and Implementation

5.1

Estimation Setup and Flow Chart

Recalling the measurement campaign [2], Table 2 describes the setup of the measurement and the estimation algorithm. Based on such setup, wireless channel
knowledge has been estimated and the comparison between the estimation result and the measurement isgoing to be discussed.
The estimation flow chart is shown as Fig. 1
For the purpose of validation of the algorithm, atest simulation will be set up. We will construct a test
channel and estimation the path parameters using the
proposed approach. In this moment, the simulation
setup will be introduced.
We generate the test channel with 6 paths. We use
these path information to reconstruct the test channel

−5

x 10
2.5

180

120

60
1.5
Level

DoA in azimuth [Deg]

2

0
1
−60

0.5

−120

−180
−180

−120

−60
0
60
DoD in azimuth [Deg]

120

180

Figure 2: Test Channel with 6 Paths Preset−40

Test Chennel
Estimated Value

Gain [dB]−60

Figure 1: Flow chart of the estimation algorithm

−80
−100

whose double direction beamforming is shown as Fig. 2.
This channel is an ideal channel that doesn’t have theinfluence of the noise. Additionally, we have set all coelevation angle as 90 degree for the omission of itself.

5.2

−120
0

5.12

Figure 3: Delay Domain Search Result

Implementation

The Simulation has been done under the test channel
constructed above, and here will show the demonstrateof the estimation. Regarded to the flow chart (Fig. 1),
the demonstration will also be decided into 3 parts.
Delay domain search We get the channel impulse
response by taking the inverse Fourier transformto the channel matrix and do the peak searching as shown in Fig. 3. After finishing the delay
domain peak search, we continue the estimation
procedure by estimating the path one by one.
Angular maxima Search the angular maxima at thedelay detected in the delay domain search. For
the initialization of the conjugate gradient method,
we submit the delay into global search, to findthe nearest grid point beside the real peak. The
result is shown in Fig. 4. After the global search,
we submit the output into the conjugate gradientmethod to refine the peak position. Generally, 2
to 3 iterations took during the conjugate gradientmethod because of the low dimension.
When the angular maxima is detected, we submit
the directional angle to a matched filter likeER (φR )HETT (φT ),Γ=||ER (φR )||2 ||ET (φT )||2

1.28
2.56
3.84
Delay [micro sec]

(7)43

where ET (φT ), ER (φR ) is the array response in
the transmit side and receive side respectively
and H is the current channel.
Γ = E†R (φR )Hsingle (E†T (φT ))T ,(8)

is another calculation approach using the pseudoinverse (·)† is also considerable but not efficient.
Subtraction of path Since we have finish detecting
all the parameters, we subtract the path from thechannel and repeat the iteration again. From thechannel model, we can just take the difference between the current channel and the one path channel, but in case of the residual power is small,
we should not take such path into account, so
we first judge the break condition, calculate theresidual power if the residual power is above a
acceptable level, we implement the subtraction.

6

Evaluation and Summary

6.1

Evaluation of the algorithm

In order to evaluate the accuracy of the channel estimation algorithm, the best way is to show the residual

ration stage of the estimation.
The estimation approach is based on the maximum
likelihood estimation was utilized in this research. For
the optimization of the likelihood function the hybridof rough global search and the conjugate gradient method
was proposed and implemented. The accuracy of thismethod has been evaluated by the residual power after
each iteration.
However due to the test channel is in ideal condition, the feasibility in analyzing the real measurement
data will be the future topic of this research.

−5

x 10
2.5

180
Estimated Points
120

DoA in azimuth [Deg]

2
60
Level

1.5
0
1
−60

0.5

−120

References

−180
−180

−120

−60
0
60
DoD in azimuth [Deg]120[1] Minseok Kim, Jun-ichi Takada, Yohei Konishi,“Novel Scalable MIMO Channel Sounding Technique and Measurement Accuracy Evaluation
With Transceiver Impairments,” IEEE Trans. on
Instrum. and Meas., Vol. 61, No. 12, Dec. 2012.

180

Residual power

Figure 4: Angular Profile Estimated Result[2] Yuyuan Chang, Yohei Konishi, Minseok Kin(Tokyo Inst. of Tech.), Jiyun Shen, Yasuhiro Oda(NTT DOCOMO), Jun-ichi Takada (Tokyo Inst.
of Tech.), “Analysis of Field Measurement with
Wideband MIMO Sounder at 11 GHz Frequency,”
IEICE Trans. Antennas Propagat., vol. 112, no.
384, AP2012-153, pp. 167-172, Jan. 2013.

0.4

0.2

[3] C.-F. Yang, B,-C. Wu, and C.-J. Ko, “A raytracing method for modeling indoor wave propagation and penetration,” IEICE Trans. Antennas
Propagat., Vol. 6, pp. 907-919, June 1998.

0
1

2

3
4
Number of path

5[4] Jonas Medbo, Fredrid Harrysson, “Efficiency and
Accuracy Enhanced Super Resolved Channel Estimation,” 6th EuCAP, Prague, Czech, 2012.

6

Figure 5: Residual Power [Percentage]

power asRES =||Hmeas − HMIMO ||||Hmeas ||(9)

Figure. 5 shows the decrease of the residual power by
the detection number of paths. The residual converges
to zero after the final path detection, and it also provedthat the algorithm has a good accuracy. However, thetest channel has so many ideal points, when the objective channel becomes a real measurement data, someestimation coefficients may need to be considered torevise.
As a result, the estimation algorithm can detect all
the path parameters correctly (with a residual power of1 %). However, there is no noise influence in the test
channel, in reality, the noise and the real measured
channel data is highly expect to be taken into account.

6.2

Summary

In this abstract, the communication background has
been introduced. From the background, it led us tomodel the high frequency propagation channel for theinvestigation of the channel characteristic. The wideband channel model has been introduced at the prepa44[5] B. H. Fleury, M. Tschudin, R. Heddergott, D.
Dahlhaus, K. Ingeman Pedersen, “Channel Parameter Estimation in Mobile Radio Environment
Using the SAGE Algorithm,” IEEE Journal on
Selected Areas in Commu., Vol. 17, No. 3, pp. 434450, March 1999.
[6] Andreas Richiter, “Estimation of Radio Channel
Parameters: Models and Algorithms,” Doctoral
Thesis Ilmenau University of Technology, 2005.
[7] Steven M. Kay, “Fundamentals of Statistical Signal Processing: Estimation Theory,” PrenticeHall PTR, 1993.
[8] Yukihiko Yamasita, “Lecture handout for Mathematics and Statistics for International Development Engineering,” Dept. of International Development Engineering, Tokyo Institute of Engineering, 2013.
[9] Yang Miao, Jun-ichi Takada, “Pattern Reconstruction for Deviated AUT in Spherical Measurement by Using Spherical Waves,” IEICE Trans.
on Commu., E97.B(1), pp. 105-113, 2014.
[10] M. Steinbauer, A. Molisch, and E. Bonek, “Thedouble-directional radio channel,” IEEE Antennas Propagat. Mag., vol. 43, no. 4, pp. 51-63, Aug.
2001.

HUMAN BODY SHADOWING EFFECTS ON INDOORMILLIMETER-WAVE PROPAGATION CHANNEL CHARACTERISTICS
Student Number: 13M18267

Name: Hung Pham Kinh

Supervisor: Jun-ichi TAKADAϛϦ೾԰಺఻ൖνϟωϧಛੑ΁ͷਓମःณͷӨ‫ڹ‬ϑΝϜɹΩϯɹϑϯҠಈ௨৴ͷരൃతී‫ʹٴ‬൐͍ɺΪΨϏοτ‫ڃ‬ͷ௨৴଎౓ͷແઢΞΫηεγεςϜͷधཁ͕ߴ·ͬͯ͘Δɻ ͜ͷ໰୊Λղܾ͢ΔͨΊʹɺ޿ଳҬΛ࢖༻Ͱ͖ΔϛϦ೾ʹ஫໨͕ू·͍ͬͯΔɻ ϛϦ೾͸ɺࣗ༝ۭؒ఻ൖଛࣦ΋ःณ࣌ͷճંଛࣦ΋େ͖͍ͨΊɺ԰಺ͳͲͷ୹‫཭ڑ‬ͷ‫Ͱڥ؀‬ར༻͞ΕΔɻ԰಺‫͚͓ʹڥ؀‬Δ఻ൖύεͷಈతͳःณ͸ओʹาߦऀͷҠಈʹΑͬͯੜ͡Δͱߟ͑ΒΕΔɻϛϦ೾԰಺఻ൖνϟωϧʹ͓͍ͯɺ ૹड৴Ξϯςφ͕੩ࢭͯ͠
͍Δ৔߹ͷ఻ൖύεͷาߦऀःณʹؔ͢ΔϞσϧԽ͕ใࠂ͞Ε͍ͯΔɻຊ‫Ͱڀݚ‬͸Ϣʔβ୺຤͕Ҡಈ͢Δ৔߹΁ͷ֦ு͢Δํ๏ʹ͍ͭͯ‫ݕ‬౼ͨ͠ɻ1ĂƐĞ^ƚĂƚŝŽŶ

Introduction

Recently, with the explosive spread of mobile communication, the demand for wireless access systems that have
speed of gigabit level have been increasing. In order tomeet this demand, increasing signal bandwidth is a useful
method. However, the low frequency bands have been already occupied by many communication systems. Fromthese situations, millimeter- wave that has high frequency
to allow to transmit large bandwidth and therefore capable of high data rate communication, are being developed.
The next generation communication systems are also expected to use both low frequency band and high frequency
band simultaneously [1], where high frequency band is a
important key to increase the data transmission rate.
Due to large propagation loss, millimeter-wave(mmWave) bands are expected to be used mostly in theindoor environment where the shadowing effects on propagation path by human bodies is very signiﬁcant. Related
to the effects of human bodies on indoor mmWave propagation channel, there are some researches which evaluated the effects when the user terminal is static [2] [3],however the model for dynamic user terminal, has notbeen reported.
From these situations, this research aims to evaluate
the effects of human bodies on indoor mmWave propagation channel when user terminal is dynamic through simulation. The main tasks of this study are to extend theexisting model for the static user terminal to the dynamic
user terminal. In this study, in order to evaluate the effect of human bodies on the whole channel, we divide
the whole channel into many paths, evaluate effects oneach path, and then sum them up to ﬁnd the effects on thewhole channel.

2hƐĞƌdĞƌŵŝŶĂůߠ௜ ߠ௥ZĞĨůĞĐƚŝŶŐ^ƵƌĨĂĐĞ

Z/

Figure 1: Image approach

The basic theory of this method is divide whole channel into many paths and then estimate the path based on
information from base station, user terminal and propagation environment. In this study, the image principle based
ray-tracing method in which a ray is assumed to reﬂect at
reﬂecting surface with a reﬂection angle equal to the incidence angle. Fig.1 represents the simple illustration ofimage approach.
2.1 Path Gain Calculation
The path gain here is calculated as the path gain of only
propagation channel without gain of antenna. The pathgain of each path is calculated as summation of free spacepath gain, reﬂection gain and shadowing gain. The equation for path gain of each path and for all paths are as
follows.
λ 2Y
)
Ri,k
(1)
P G1,i = (4πli
P G2,iP Gi
P Gtotalk(−15×N )/10

= 10
= P G1,i + P G2,i
X
=
P Gi

(2)
(3)
(4)

i

Here, P G1,i is summation of free space gain and reﬂection gain, P G2,i is path shadowing gain and P Gi ispath gain of each path. i is index of propagation path, andRi,k is the k-th reﬂection coefﬁcient of the i-th path, λis wavelength, and li is the path length of the i-th path.
Shadowing gain of each human body is -15 dB which isbased on measurement data of [6]. N is the number ofhuman bodies shadow a propagation path and N can becalculated as follows [3]1(mwl)N × exp(−mwl)(5)pN (N ) =N!

Ray Tracing Method

In indoor communications, because there always exists
many scatterers in propagation environment, not only thepath transmitted directly from base station to user terminal,but also multipaths due to reﬂection and scatteringfrom obstacles. The propagation path here is deﬁned as
the smallest entity in order to describe the radio channel. In order to ﬁnd the path traveled from base station
to the user terminal, ray-tracing method is a commonlyused method.
45

pN (N ) is probability N human bodies shadow path l.
The reﬂection coefﬁcient is calculated by using Fresnel
equation as follows [4]

Rk

R⊥

=

=

q
n12 cos θi − n212 − sin θi 2qn12 cos θi + n212 − sin θi 2qcos θi − n212 − sin θi 2q,cos θi + n212 − sin θi 2

1" !

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

1"

!

!

(6)

Figure 2: Two states Markov model&ŽƌWĂƚŚ݅(7)

Where Rk is the parallel component, R⊥ is perpendicularqǫcomponent and n12 =ǫ0 is refractive index between

two materials. In this study, simulation is performed in
the cubic room where the wall is assumed to be made by
concrete. The refractive index used for simulation is frommeasurement data of [5].
2.2

!

3.1

Basic Theory

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3.2 Path Shadowing Probability
When human bodies exist in propagation environment,
the path shadowing can be calculated as.

[3] showed that a human body can be modeled as a cylinder. In this study, human body is model as a verticallyoriented cylinder in a rectangular coordinate system. The
radius r and the height h describe the dimension of cylinder. In this study, r and h are assumed as the average sizeof Asian, and take values of 0.2 m and 1.7 m respectively.
The motion of human body is assumed to be random
where at any timing, human bodies can move in all directions with the same probability, and after ﬁnishing simulation the density probability of human in room is identicalfor all positions.

Path Shadowing State Transition Probability (PSSTP) ModeldŝŵŝŶŐ

Figure 3: Data processing

Human model3^ŚĂĚŽǁŝŶŐƵƌĂƚŝŽŶ‫ܦ‬௜ଵ^ŚĂĚŽǁŝŶŐ/ŶƚĞƌǀĂů‫ܫ‬௜ଵP (l) = 1 − exp(−mwl)(8)Where m is the population density [person/m2 ], w is thewidth of human body model [m], l is the length of horizontal portion of propagation path [m] which is lower thanthe human height. From this equation, we can see thatwhen the propagation paths, human density and width ofhuman body model are known the shadowing probabilityof them can be obtained. As explained, the path shadowing probability P can be obtained from path information,
human body size and population density, so that if weknow the the relationship between PSSTP and the shadowing probability we can obtain the shadowing condition
without calculating intersection between path and humanbodies. Similar with [3], in this study, the Monte Carlosimulation is taken to estimate the PSSTP of each path.
The simulation procedures are as follows. Firstly, moveuser terminal and human bodies, use ray-tracing method
to calculate the propagation path, their path length, thenusing equation (8) to calculate path shadowing probability. Calculate the intersection between propagation paths
and human bodies. These intersection represents whether
paths are shadowed or not. Finally, from the shadowing
probability information, calculate the average Pbg , Pgb .
Fig.3 represents data processing to obtain Pbg , Pgb fromsimulation data.
The obtained data from simulation includes path
length, shadowing probability and shadowing conditionof each path at each timing. The length of duration whenshadowing condition is 0 (un-shadowed state) is shadowing interval, length of duration when shadowing conditionis larger than 0 (1,2,..., shadowed state) is shadowing duration. After calculating all shadowing interval and shadowing duration, by using following equations, we can obtain average of P , Pbg and Pgb . By plotting the results,
we can obtain their relationship.

Generally, to know whether propagation path is shadowedby human bodies or not, we have to know whether thepath is intersected by human bodies or not. However,such works are not simple because they take much time
to compute. On the other hand, [3] proposed approaches
to obtain the shadowing condition of propagation path byusing two states Markov model when we know PSSTP
in the case user terminal is static. Fig.2 represents the
PSSTP in two states Markov model.
The shadowing events of each path are burst because
once path is shadowed by a human body, it takes time
to change to un-shadowed condition. It is similar for the
opposite side. In this ﬁgure, Pgb represents the PSSTP
from un-shadowed state (good state) to shadowed state(bad state) and Pbg represents the sate transition probability from shadowed state to un-shadowed state. From
this ﬁgure, we can see that at any timing, when we know
the shadowing condition (shadowed or un-shadowed) ofcurrent timing and the PSSTP, we can estimate the shadowing condition of path in the next timing.
Even for the case when user terminal is dynamic thepath shadowing events are burst, so the two state Markov
model can be adopted. The most important thing of thisapproach is how to calculate the PSSTP of each paths.
The following section will explain the procedures of it.
46Ͳ ^ĞƚƉŽƐŝƚŝŽŶŽĨďĂƐĞƐƚĂƚŝŽŶ͕ŝŶŝƚŝĂůƉŽƐŝƚŝŽŶŽĨƵƐĞƌƚĞƌŵŝŶĂůͲ hƐĞƌĂǇͲƚƌĂĐŝŶŐŵĞƚŚŽĚƚŽĨŝŶĚƉƌŽƉĂŐĂƚŝŽŶƉĂƚŚƐ͕ƉĂƚŚƐůĞŶŐƚŚͲ ^ĞƚŝŶŝƚŝĂůƐŚĂĚŽǁŝŶŐĐŽŶĚŝƚŝŽŶŽĨĞĂĐŚƉĂƚŚͲ ĂůĐƵůĂƚĞƉĂƚŚŐĂŝŶŽĨĞĂĐŚƉĂƚŚĂŶĚǁŚŽůĞĐŚĂŶŶĞů

Table 1: Simulation parameter setting

Parameter
Population density
Human body height
Human body radius
Room size (x, y, z)
Base station position (x, y)Antenna height
Maximum reﬂection number
Refractive index
Signal frequency
Movement speed
Distance of each step
Simulation steps

Value
0.07-0.24
1.7 m
0.2 m
(6, 9, 2.7) m
Center of room
1m
5
2.55-j0.084
60 GHz
1 m/s
0.1 m
25000

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Figure 5: Path gain calculation procedure!"#$"%&''()*+,!

!
Figure 6: Path gain calculated directly

It means the PSSTP is not signiﬁcantly inﬂuenced by user
terminal movement, but depends on only path shadowing
probability.

Figure 4: PSSTP

4
Pbg,i

=

Pgb,i

=

Pi

=

mean(

1
P

1
P
mean( j Dij )
X
mean(
Pik )

In order to verify the PSSTP model, the path gain obtained from that model is compared to the path gain ofwhole channel obtained by calculating directly throughray-tracing simulation.

(9)

j Iij )

(10)

4.1 Path Gain Calculation(11)

From the obtained relationship between path shadowing
probability and PSSTP, the path gain of channel can becalculated through simulation as in Fig5.
The procedures to calculate path gain directly by using intersection information obtained through ray-tracing
method is same with the procedures explained above,
however the shadowing condition is calculated directlybased on information of intersection between human bodies and propagation paths.

k

3.3

Simulation Setting

The parameter for simulation can be seen as in table 1
3.4

Path Gain Comparison

Simulation Results

From the simulation, the relationship between path shadowing probability and PSSTP is plotted as shown in Fig.4.
In this ﬁgure, results for the simulation where the user terminal is dynamic is compared to the existing model where
the user terminal is static.
In this ﬁgure, horizontal axis denotes the average ofpath shadowing probability, vertical axis denotes the average of PSSTP. From this ﬁgure, the behavior of the PSSTP
can be completely explained just by using path shadowing
probability. When path shadowing probability is small,shadowing condition tends to move from un-shadowed
state to shadowed state. On the opposite side, the similar
behavior can also be observed. Also, the results for dynamic case can be well ﬁtted by using the existing model.

4.2 Results Comparison
As seen from Fig.6 and Fig.7, at lower population density (m = 0.07, 0.13) of the room, the proposed PSSTP
model can predict the path gain well. However, at higher
population density (m = 0.18, 0.24) the proposed model
could not predict well. The reason can be considered hereis, in this study all propagation paths are assumed to be independent so the path shadowing probability of them are
not dependent each other. However, in fact once a path is47

Figure 9: Error duration
Figure 7: Path gain calculated by using PSSTP model

In the error free duration results, the maximum lengthis several hundred seconds. In the error duration results,
the maximum duration for all case is only several seconds,
and in both 4 cases mostly 90% of them is shorter than 1s.
It means that the error free duration is much larger thaterror duration therefore mmWave communication can beused in indoor situations.

6

Conclusion

In this thesis, the PSSTP model was extended to the dynamic user terminal case from the existing model. The
simulation results show that the PSSTP is not signiﬁcantly
inﬂuenced by user terminal movement, it depends on only
the path shadowing probability. On other hand, the results of path gain show that the proposed model can predict the path gain well at lower population density, but theaccuracy still must be considered, particularly at higher
population density. Finally, the results of error duration
behavior show that although mmWave is easily shadowedby human bodies, it still can be used in indoor situations.
The study still has some limitations, such as the propagation environment and human movement model are simple, the effects of user who handles user terminal are notconsidered. These issues will be considered in future.

Figure 8: Error free duration

shadowed, the paths close to it also have high probability to be shadowed. In other words, between propagation
paths there is correlation related to path shadowing probability. This issues must be considered in the future work.

5 Error Duration Behavior
When a channel is affected by an obstacle, the path gain ofchannel will be decreased, and when path gain becomes
smaller than a value called threshold, transmission between base station and user terminal cannot performed.
In communication system, it is important to know the behavior of such characteristic. In this study, the error duration when path gain is smaller than threshold, and error
free duration when path gain is higher than threshold areused to evaluate effects of human bodies on propagation
channel. From CDF plot of path gain in Fig.6, we can see,the minimum level of path gain when it is not shadowedby any human body is about -80 dB, and when channel
is affected by human bodies, path gain changes mostly
from -80 dB to -90 dB. Therefore, we assume that when
path gain of channel in smaller than -85 dB, the transmission between base station and user terminal is can not performed, and when path gain of channel is higher than -85
dB, transmission can be still performed. Because the accuracy of the path gain calculated by using PSSTP modelis still not good, we use the path gain calculated directlyfrom ray-tracing simulation to evaluate the error duration
behavior of channel. Figure 8 and 9 show the results forerror duration and error free duration.

References[1] MiWEBA,ʠ http://www.miweba.eu/ ʡ[2] K. Sato, T. Manabe, ʠ Estimation of propagationpath visibility for indoor wireless LAN system under shadowing condition by human bodies ʡ, IEEE
VTC 98, Ottawa, Canada, vol. 3, pp. 2109-2113,
May 1998.
[3] I. Kashiwagi, T. Taga, T. Imai,ʠ A Path Shadowing
Model focused on the Effects of Human Activities
in Indoor Environments, ʡIEEE VTC 2007 fall, pp.
889-893, Oct. 2007.
[4] A. Goldsmith, Wireless Communication, Cambridge University Press, pp. 42, 2005
[5] K. Sato, H. Kozima, H. Masuzawa, T. Manabe, T.
Ihara, Y. Kasashima, K. Yamaki, ʠ Measurementsof reﬂection characteristics and refractive indices ofinterior construction materials in millimeter-wave
hands, ʡ in Proc. 45th IEEE Veh. Technol. Conf.,
Chicago, IL, Jul. 26-28, 1995

48

[6] Ճ౻໌ਓɺਅು෢࢚ɺ
ʠ 60 GHz ଳແઢ఻ૹ࿏ʹ͓
͚ΔਓମʹΑΔःณͷӨ‫ڹ‬ɺʡɹ 1997 ਐֶ૯߹େ
ձɺB-1-33ɺp. 33ɺʢ1997ʣ

Depth Image Generation by Image Segmentation
Student Numberɿ13M18043

NameɿSoichi Uchiyama

Super visorɿYukihiko YAMASHITA

Abstract‫ࡏݱ‬ҠಈϩϘοτ͸ɼଟ਺ͷߴՁͳηϯαʔΛ༻͍ͯࣗಈ૸ߦΛ࣮‫͍ͯ͠ݱ‬ΔɽͦͷͨΊՁ͕֨ߴ͘ར
༻Ͱ͖Δ৔໘͕‫ݶ‬ΒΕ͍ͯΔɽͦ͜Ͱຊ‫Ͱڀݚ‬͸ɼKINECT ͱ͍͏҆ՁͳήʔϜ༻ͷ 2 ࣍‫ݩ‬ը૾ͱ‫཭ڑ‬ը૾ΛऔಘͰ͖ΔηϯαʔΛར༻͠ɼ࣮࣌ؒͰ 3 ࣍‫ۭؒݩ‬Λೝࣝ͢Δํ๏ΛఏҊ͢Δɽฏ໘ͷ࿪Έɾ෺ମ‫ܗ‬ঢ়ͷม‫ܗ‬ɾ‫཭ڑ‬ը૾ͷܽଛͳͲ KINECT ๊͕͑Δ໰୊ͷதͰɼΑΓྑ͍ 3 ࣍‫ۭؒݩ‬ͷ෮‫ݩ‬ͷͨΊʹɼ·ͣը૾ྖҬ෼ׂΛ༻͍ͯ 2 ࣍‫ݩ‬ը૾Λ෼ׂ͢Δɽͦͯ͠ɼ2 ࣍‫ݩ‬ը૾ͱ‫཭ڑ‬ը૾ͱରԠͤͯ͞‫཭ڑ‬ը૾Λ෼ׂ͢Δɽ࠷‫ʹޙ‬ɼฏ໘ͱ൑ผ͞ΕͨྖҬʹରͯ͠࠷దͳฏ໘Λਪఆͯ͠ 3 ࣍‫ۭؒݩ‬ͷ෮‫ݩ‬Λߦ͏ɽຊ‫ڀݚ‬Ͱ͸‫ʹݧ࣮ػࢉܭ‬ΑΓɼఏҊख๏ͷ༗༻ੑΛ͔֬ΊΔɽ

1

Introduction

2 Generation method of distance image
In recent years, mobile robots realize automatic trav-

eling with combined information obtained from GPS,KINECT consists of a color image sensor, a distancea laser range finder, and a stereoscopic video camera.
image sensor, four-microphone array. Its specifications
However, installing those sensors makes their price higher.
are as shown in Table. 1.
Therefore, reconstruction of three-dimensional space bymatching features extracted from the two-dimensional
Table. 1: The specifications of KINECT
images has been attempted. However, it doesn’t work
well. Then, by the two-dimensional image matching it
Depth acquisition system projector-camera system
is considered to be difficult to construct a system that
Depth resolution
640x480px
supports the automatic traveling. The point cloud data
RGB resolution
1600x1200px
generation for the three-dimensional space reconstruction from a distance image has also been attempted.
Frame rate
60fps(max)But, with the increase the number of points, the required
The imaging range
0.8ʙ3.5m
computational load for matching increases. And it will
The spatial resolution
x, y direction 3mm
go away from the real time processing.
at the point of 2mz direction 1cm
Therefore, I focused on an inexpensive sensor called
KINECT which can obtain a two-dimensional image
Acquisition of the distance image is done by the projectorand a distance image for gaming machine. After it was
released in November 2010, the researches using KINECT camera system as shown in Fig.1.
have developed open-source drivers and various applications. When software development kit is publishedfrom Microsoft in June 2011, KINECT is not limited to
the game field but has been attempted to utilize in various fields such as medical and human flow measurement.
In this study, I propose a method to recognize thethree-dimensional space for a mobile robot by using
KINECT. I show the validity of the proposed methodby experiment results.
Fig. 1: Acquisition of the distance image

1
49

Projecting a known light pattern to objects in threedimensional space, KINECT can acquire the distanceimage by observing the projected pattern which shifts
according to the distance. The pseudo-random pattern
is used in order to recognize the position of each point.
Each point of the virtual image of the projection sideand the observed image of the light-receiving side areassociated with each other as shown in Fig.2 by virtueof epipolar line as shown in Fig.3.
In the light pattern, by changing the size and shapeof the spots for each area, KINECT obtains distanceinformation which is accurate with respect to the near
distance, and rough with respect to far distance.

is an image illustrated by Fig.5.

Fig. 4: Generation of pseudo-random patternFig. 5: Generated pseudo-random patternFig. 2: Triangulation
From the distance image measured by KINECT, we
can see the following problems.
In Fig.6, the wall and the floor are should be plane,
but they are wavy. And some shapes such as the handare distorted. This phenomenon is also found in threedimensional reconstruction based on the point cloud.
Also, in Fig.7, the region in the red circle where thepattern is not projected because of the shadow of an object and the region in the blue circle where the pattern
can not be obserbed exist.
Fig. 3: Epipolar geometry

All 3D points within the field of view of a camera isincluded in the epipolar plane intersecting the respective
image epipolar line. If a point in an image is given, thepoint corresponding in the other image should be on thecorresponding epipolar line. This is referred to as epipolar constraint. By the epipolar constraint, 2-dimensional
search for a matching point becomes one-dimensional
search, and it is possible to discard mismatched points.
Pseudo-random pattern that KINECT irradiates is generated as Fig.4. The generated pseudo-random pattern
250

Fig. 6: Distort image by KINECT

4 Three-dimensional space recognition experiment
In order to verify the proposed method, I carried out
computer experiments. Then, I show the results of thecomputer experiments.

Fig. 7: Regions of shadow and occlusion

3

The proposed method

To solve problems described in Chapter 2, I propose
a new method for the distance image generation by using the two-dimensional image. First, I detect edges
from the two-dimensional image. Then, I divide thethe two-dimensional image into several regions by using the edge information. Finally, I estimate a plane to
fit the point group of the target region.
For edge detection, I use the Canny edge detector.
The feature of this algorithm is to make contours fromcandidates of edges. Contour is formed by applying ahysteresis threshold to the pixel. This means that there
are two thresholds, upper and lower limits. If the pixel
has a gradient greater than the upper threshold, the pixel
is joined to contour. If the gradient of the pixel is smaller
than the lower threshold, the pixel is discarded. In thecase that the gradient of the pixel is between the thresholds, only if the pixel is connected to a pixel that has
a gradient greater than the upper threshold, the pixel isjoined to contour.
For image segmentation, I perform labeling on theimage using the extracted edges. Then, in order to simplify and visualize, I cut out the largest region as thetarget region in the divided image by labeling.
For plane estimation, I use the RANSAC algorithm.
RANSAC is an abbreviation of RANDdom Sample Consensus. First, RANSAC generates many planes for setsof randomly extracted sample points. This algorithm selects the plane that fits all sample points the most. Thisalgorithm is robust when there is an outlier on the measured value.

Fig. 8: Image of an environment

Fig. 9: Result of Canny edge detector

Fig. 10: Target region

351

necessary to use parameters to extract more edges. But
if many edges are detected, trade-off between processing time and performance will arise for image segmentation.
Also, a raster scan is not efficient for real-time performance. Therefore, rather than searching by a raster
scan, probabilistic Hough transform will divide image
more accurately, and real-time performance is expected
to be improved.
My proposed method has restored the distance information in the regions of shadow and occlusion. However, those regions may not be truly a plane. By preparing the image channel for reliability, it would be possible for the mobile robot to handle properly the restored
distance information.

Fig. 11: Result of plane estimation

5 Conclusion
In this study, I propose a new method to recognize
the three-dimensional space for a mobile robot by using
KINECT. I showed the proposed method restored thedepth information of the region that can not be observed
because of shadow and occlusion by experiments. In order to improve the plane extraction method in my proFig. 12: Distance image by KINECT
posed method, we have to investigate the following researches: to optimize the parameters of the Canny edge
Fig.8 shows an environment including a regions that detector, to detect lines more efficiently by probabilistic
can not be observed because of shadow and occlusion. Hough transform, and to make the measurement method
Fig.9 shows the result of the Canny edge detector. Fig.10 of reliability for estimation.
shows the target region which has the largest area basedon the labeling information. Fig.11 shows the estimatedplane image derived from the RANSAC algorithm to Reference
the target region. Fig.12 shows the original distance im- [1] Microsoft, "Kinect", http://www.xbox.com/ja-JP/kinectɽ
age.
[2] Gary Bradski and Adrian Kaebler, "Detailed explanation
In the proposed method, I used the Canny edge deOpenCV Image processing and recognition that uses a
computer vision library", Ltd. O’Reilly Japan, 2009ɽ
tector. Then, I labeled the two-dimensional image by
raster scan, and divided into several regions. After that [3] IMURA Masataka,"imura :products :Labelingclass",
http://oshiro.bpe.es.osakaI estimated the plane by using the RANSAC algorithmu.ac.jp/people/staff/imura/products/labelingɽ
to adapt to the depth image corresponding to the target
region of the two-dimensional image. Compere with [4] Murakami Shinnichi, "Image processing engineering",
Tokyo Denki University Press,1996ɽ
Fig.11 and Fig.12, the proposed method restored thedepth information of the region that can not be observed [5] Tanijiri Houju, "KINECT sensor image processing programming", Ltd. cut system,2011ɽ
because of shadow and occlusion. I can confirm the ef"OpenCV
2.2
C
Reference",
fectiveness of the proposed method by the computer ex- [6] opencv.jp,
http://opencv.jp/opencv-2svn/c
ɽ
periment.
Since canny edge detector is a powerful edge detector, it detects only strong edges with the recommended
parameters, and weak edges are lost. Therefore, it is452Hybrid image compression by using vector quantization (VQ) and vector-embedded
Karhunen-Loève transform (VEKLT)Studuent Number: 13M18250, Name: Kiung Park, Supervisor: Yukihiko Yamashita

ベクトル量子 化 (VQ) と ベ ク ト ル 埋 め 込 み KLT (VEKLT) に よ る ハ イ ブ リッド 画 像 圧縮朴 基雄
本論文では，ベクトル量子化と二つの直交変換，離散コサイン変換 (DCT)，ベクトル埋め込みKLTを組み合わせ，新しいブロック変換に基づく画像圧縮法を提案する．まず，入力画像を8×8のブロックに分割し，原ブロッ
クとベクトル量子化されたブロックの差分をVEKLTで変換する．同時に原ブロックをDCTで変換する．その後，各ブロックに対して算術符号長の比較により，DCTまたはVEKLTの結果を選択し，その選択結果を基に二値イン
デックス画像を作る．そして，二値インデックス画像を四分木分割し，多様なサイズのDCTを実行する．計算機実験を行い，提案方法がピーク信号対雑音比 (PSNR) と視覚評価，両面でJPEGより高い性能を持つことを示す．
In this paper, a new block-transform-based image compression scheme is proposed by combining VQ and two
transformations, DCT and VEKLT. To compress high details
efficiently, the difference between an original block and its
vector-quantized block is transformed by VEKLT which isa kind of KLT to introduce a local statistics. In the method[5], the rotated DCT basis is embedded into an orthogonaltransformation matrix which is calculated from a correlation
matrix such as the first-order Markov model. In this paper,
the vector for DC component and the selected representative
vector by VQ are embedded into the orthogonal transformation matrix which is calculated from the corresponding
trained covariance matrix. The representative vectors for
VQ are made by two steps. First, the candidates for the
representative vectors are sampled from each training image(BSDS500 [9]) by the Gaussian mixture model (GMM) to
use the probability distribution [10]. Then, the final 4096
representative vectors are extracted by k-means clusteringfrom the groups of sampled candidates. To adopt variableblock size DCT for compressing low details efficiently,
quadtree decomposition is performed on the binary index
image that indicates the category of each block (DCTand VEKLT). The binary index image is made from the
VQ coefficients which are calculated by the inner productbetween a normalized input vector and a corresponding
representative vector. Thus, there is no side-information for
quadtree because its information is dependent on the VQ
coefficients. Finally, the VQ indexes are coded by Huffman
coding because the number of symbols is very large, and thescalar-quantized coefficients (DCT, VEKLT, VQ) are codedby the arithmetic coding based on the basic algorithm [11].

I. I NTRODUCTION
JPEG is an international standard for still image compression. Even if it has weak points such as blocking
distortion and image quality degradation in high details, it
has been used widely because its algorithm is simple andfast, and the compression ratio is enough. Quantization usedin JPEG is scalar quantization. Shannon’s rate-distortion
theory indicates that a better performance can be achievedby coding vectors instead of scalars [1], [2]. Thus, if we
introduce VQ into JPEG, we can expect a better compression
performance.
Although the block prediction method is a mainstream
for reducing spatial redundancy in recent years, VQ wasstudied popularly in the past [3]. Especially, the general
concept of this paper is motivated by the method [4]. It
proposes a hybrid image coding scheme combining VQ and
DCT with variable block segmentation by quadtree. The
residual image is segmented with a quadtree into square
blocks whose size depends on their classes (low details, highdetails, random textures) of the region according to a roughqualitative appraisal of its local perceptual importance andcoding difficulty. In this paper, low details belong to DCT
category and high details belong to VEKLT [5] category for
reducing their coding difficulty.
In recent years, the hybrid image coding scheme combining VQ and transformation is proposed [2], [6], [7],[8]. In the method [2], VQ and the existing image coding
scheme such as JPEG or JPEG2000 can be combined andthe VQ indexes do not have to be coded because they are
obtained from the upsampled image of a decoded downsampled and distorted image. In the method [7], VQ is
performed on a sparse representation which is obtained bycompressed sensing theory in the wavelet domain. In themethod [6], [8], VQ is performed on a truncated vector
from DCT coefficients. The indexes and the codebook whichis generated from an input by using k-means clustering
algorithm are transmitted.

The rest of this paper is organized as follows. In Section
2, the proposed scheme is explained by a block diagram.
In Sections 3∼5, some key techniques of the proposed
scheme are presented. In Section 6, the experimental results
demonstrate the advantages of the proposed scheme. Finally,
Section 7 concludes this paper.

53

has not been developed yet, the basic arithmetic coding
algorithm is used [11]. The sign and the absolute valueof a coefficient sequence are arithmetic-coded because thehistogram distribution of symbols is usually symmetric.
The frequency of symbols is exponential-Golomb-coded
for the decoder side. The difference of DC coefficients
between current and previous blocks is coded to eliminate
the spatial correlation of neighbor. The AC coefficients arecoded after zigzag-scan with the symbol of end-of-block(EOB). The VEKLT coefficients and EOB are coded without
any scanning order because they are already arranged by
the descending eigenvalue order. The VQ coefficients arecoded without calculating the difference because their spatial
correlation is small. The VQ indexes are Huffman-coded byusing the frequency of symbols estimated from the training
images [9] because the number of symbols is relatively large
in comparison with the length of sequence.

II. P ROPOSED METHOD
The proposed encoder is illustrated in Fig. 1. It can be
explained by 4 steps.

Figure 1.

Proposed encoder

D. Composition of output
Finally, the encoder outputs the array of unsigned 16 bits
which consists of the header and the codes of revised VQ
indexes, VQ coefficients, VEKLT coefficients, DC coefficients, and AC coefficients from DCT.

A. Vector quantization
After an input image is split into 8×8 blocks, each
8×8 block is vertically scanned, normalized and vectorquantized by using squared Euclidean distance. Vector quantizer outputs a VQ index and a VQ coefficient which is
calculated by the inner product between the input vector andthe selected representative vector. VEKLT is performed byusing the prepared transformation matrix corresponding tothe VQ index. The transformed coefficients by VEKLT are
scalar-quantized and arithmetic-coded, and parallelly DCT is
performed on the original blocks, and the scalar-quantized
AC coefficients are also arithmetic-coded. After comparing
the code-length of AC coefficients from DCT and the sumof code-lengths of VEKLT coefficients, VQ coefficient, and
VQ index, the shorter one is selected. If DCT is selected,
its VQ index is deleted and its VQ coefficient is changed tozero. Then, the binary index image which indicates whether
VEKLT or DCT is selected is constructed from the revised
VQ coefficients.

III. I MPLEMENTATION OF VEKLTLet f i be the vertically scanned i-th block of image, an
input can be written as
f i = hf i , ΦDC iΦDC + hf i , Φi,AC iΦi,AC .
By normalizing f i by L2 norm, Φi,AC is obtained. Then,the representative vector Φ̂k,V Q nearest to Φi,AC , where kis the VQ index of f i is selected by vector quantizer. In
case of 8×8 block size, the dimension of f i is 64. BecauseΦDC and Φ̂k,V Q are perpendicular, f i can be expanded as
f i = hf i , ΦDC iΦDC + hf i , Φ̂k,V Q iΦ̂k,V Q +61
X

hf i , Φkj iΦkj .

j=0

where Φkj (j = 0, 1, · · · , 61) are perpendicular to bothΦDC and Φ̂k,V Q .
The remaining basis vectors Φkj (j = 0, 1, · · · , 61)
are obtained by VEKLT. Let Rk be the covariance matrix
corresponding to the VQ index k and Wk = ΦDC ΦT
DC +TΦ̂k,V Q Φ̂k,V Q , the symmetric matrix Qk is defined by

B. Transformation
Quadtree decomposition is performed on the binary index
image whose size is one-eighth of the input because it issplit into 8×8 blocks. The quadtree data structure for thebinary index image decomposes a 22 × 22 image block
into a 3-level hierarchy, where all blocks at level n havesize 2n × 2n , 0 ≤ n ≤ 2 [12]. It means that the maximum
block size for DCT is 32×32. Then, a transformation among
VEKLT (8×8) and DCT (8×8, 16×16, 32×32) is performed
according to the quadtree decomposed binary index image.

Q k = Rk − R k W k − W k Rk + W k Rk W k .
Φkj (j = 0, . . . , 61) are given by the eigenvectors of Qk
corresponding to nonzero eigenvalues. The transformation
matrix Tk is given by

C. Entropy coding
There is no scalar quantization table which is used in
JPEG. All coefficients are scalar-quantized equally. An entropy coder which is optimized in the proposed method

Tk = [Φk0 , . . . , Φk61 ]T .
By operating Tk on f i , the 62 VEKLT coefficients are
obtained.

54

Figure 2.

Test images. From left to right : ’Airplane’, ’Lena’, ’Mandrill’, ’Peppers’, ’kodim03’, ’kodim05’, ’kodim19’, ’kodim23’, ’Fruits’, ’Outdoor’

IV. B INARY INDEX IMAGE

prepared training images (BSDS500) into the encoder, thecovariance matrix Rk is obtained from the residual signals
calculated by f i − hf i , ΦDC iΦDC − hf i , Φ̂k,V Q iΦ̂k,V Q ,where f i have the same VQ index k. Finally, the 4096
VEKLT transformation matrixes are calculated by Section
3 because the number of representative vectors is 4096.

The concept of the binary index image is very simple.
That is just a binary expression of the scalar-quantized VQ
coefficients which is reshaped into two dimensional array,
whose size is one-eighth of the original image by assigning
non-zero and zero values into false and true respectively.
Because the value of VQ coefficient is revised by zero if
DCT is selected, true means that the block is low detailand it is more efficiently decorrelated by DCT than VQ andVEKLT. On the other hand, false means that the block ishigh detail and it is more efficiently decorrelated by VQ andVEKLT than DCT. Quadtree decomposition is performed on
the binary index image to adopt variable block size DCT forcompressing low detail efficiently. At the decoder side, sideinformation for quadtree is not needed because the binary
index image is dependent on the VQ coefficients. Fig. 3
shows an example of the binary index image.

(a)

(b)

VI. E XPERIMENTAL RESULTS
All experiments are performed on a PC with Intel Corei7-3970X 3.50GHz processor. In the proposed method, boththe encoder and decoder are written by MATLAB and
MATLAB coder which is an application for compiling mfile to C-code is used for reducing computation time at foror if sentences. The ten test images in Fig. 2 are used
to demonstrate the performance of the proposed method.
The objective and perceptual qualities are evaluated by
PSNR and reconstructed images respectively. We compare
the performances of the proposed method and JPEG (IJG
library with arithmetic option) [13]. Table. I shows the PSNR
comparison. Fig. 4 (a) shows the rate-distortion relations.
They show that the proposed method outperforms JPEG witha gain in PSNR of around 0.5∼3.0 dB except ’Mandrill’
because the fur of ’Mandrill’ is difficult to be decorrelated.
Especially, the proposed method performs better in PSNR
if the bpp increases because the ratio of blocks which areprocessed by VQ and VEKLT increases (Fig. 4). Fig. 5
shows that the visual quality of the proposed method isbetter than that of JPEG at high details. The feature of center
wood grain is well maintained in the proposed method. Fig.
6 shows that the elapsed time of the proposed method is
slower than that of JPEG. However, it is faster than theconventional VQ [8].

(c)Figure 3. (a) : Binary index image of ’Lena’, (b) : Quadtree decomposed(a), and (c) : Binary index image modified from (b) by assigning false intotrue on (a)V. C ONSTRUCTION OF REPRESENTATIVE VECTORS AND
CORRESPONDING VEKLT TRANSFORMATION MATRIXES

A. Representative vectors

Table IPSNR (dB) PERFORMANCE COMPARISONFirst, the 500 training images from BSDS500 [9] areprepared. Then, the normalized input vectors are obtained
from each training image. GMM is applied to them byusing EM algorithm with initial values obtained by k-means.
Because the mean vector has the highest probability in eachcluster, it appears often in images. Thus, the 1000 mean
vectors are primarily sampled from each training image.
After collecting them, the 500000 candidates are obtained
for the representative vectors. Finally, the 4096 vectors are
extracted from the candidates by using k-means and they arenormalized again. The final 4096 vectors are stored in the
representative database for VQ.

Test image
Airplane (512×512)

Mandrill (512×512)

kodim03 (768×512)

kodim19 (512×768)

Fruits (2560×2048)

bpp
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0

Proposed
31.87
35.99
40.08
22.57
24.80
28.25
34.71
38.70
43.48
29.57
32.37
36.66
33.47
36.67
40.38

JPEG
31.13
34.89
38.86
22.46
24.50
27.05
33.45
36.41
40.45
28.66
31.54
34.97
32.64
35.20
38.63

Test image
Lena (512×512)

Peppers (512×512)

kodim05 (768×512)

kodim23 (768×512)

Outdoor (2048×2560)

bpp
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0
0.25
0.5
1.0

Proposed
33.10
36.20
39.09
32.49
34.70
37.04
24.12
27.43
31.85
37.05
40.45
43.50
25.12
28.86
33.87

JPEG
31.95
35.09
38.13
31.54
34.13
36.38
23.78
26.11
29.63
35.18
38.69
42.13
24.57
27.19
31.25

VII. C ONCLUSION
This paper proposes a new block-transform-based image
compression scheme by combining VQ and two transformations, DCT and VEKLT. Experimental results demonstrate

B. VEKLT transformation matrixes
Because VEKLT is a kind of KLT, a covariance matrix isneeded to make a transformation matrix. After inputting the55

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(a) Left : Result of ’kodim03’, Right : Result of ’kodim05’(a)(b) Left : Result of ’kodim03’, Right : Result of ’kodim05’
Figure 4. (a) Rate-distortion relations (X-axis : bpp, Y-axis : PSNR (dB)),(b) VEKLT ratio in all 8×8 blocks (X-axis : bpp, Y-axis : VEKLT ratio)

the advantages of the proposed techniques. Its PSNR outperforms JPEG and its visual quality is better than thatof JPEG at high details. However, there are still many
aspects to be investigated in the future, such as reducing
computation time, introducing block prediction, replacing
DCT with more efficient transformation such as lappedbiorthogonal transform (LBT), efficient scanning method
in 16×16, 32×32 blocks and entropy coding, the approximation method of a true covariance matrix, the optimum
number of the representative vectors, their mining method
and so on.

(b)

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Models,” IEEE Transactions on Speech and Audio Processing, vol. 8, no. 4, pp.
385-401, Jul. 2000.

(c)
Figure 5. (a) : Cropped original ’kodim03’, (b) : Cropped ’kodim03’
reconstructed by the proposed method (bpp : 0.25), and (c) : Cropped’kodim03’ reconstructed by JPEG (bpp : 0.25)(a) Result of ”Lena”(b) Result of ”Outdoor”

Figure 6. Comparison of elapsed time (X-axis : bpp, Y-axis : elapsed time
(s))

56

Electromagnetic Wave Scattering Simulation Using
Physical Optics on Point Cloud Data
Student Number: 13M18050

Name: Hiroki OHARA Supervisor: Jun-ichi TAKADA఺‫ʹ܈‬ΑΔ෺ཧޫֶ๏Λ༻͍ͨి࣓೾ࢄཚγϛϡϨʔγϣϯֶ੶൪߸: 13M18050

ࢯ໊: খ‫و޺ ݪ‬ࢦಋ‫׭ڭ‬: ߴా ५Ұి࣓೾ࢄཚ໰୊ʹ͓͍ͯɼࢄཚ෺͕೾௕ΑΓे෼େ͖͍৔߹ɼϚΫε΢ΣϧํఔࣜΛ௚઀ղ͘‫ີݫ‬ղ๏ʹൺ΂ɼࢄཚ෺‫ܗ‬ঢ়ʹର͢ΔదԠੑɼ‫ؒ࣌ࢉܭͼٴ‬ͷ఺Ͱ༗རͰ͋Δ෺ཧޫֶ๏ (PO) ͕‫ͳྗڧ‬γϛϡϨʔγϣϯख๏ͱͯ͠ར༻
͞Ε͍ͯΔɽ·ͨɼߴਫ਼౓ͳి࣓քγϛϡϨʔγϣϯΛߦ͏ͨΊʹ͸ɼର৅ͱͳΔࢄཚ෺ͷ‫ܗ‬ঢ়Λਖ਼֬ʹϞσϦϯά
͢Δඞཁ͕͋Δɽۙ೥ɼ෺ମͷ̏࣍‫ܗݩ‬ঢ়ΛϞσϧԽ͢ΔͨΊʹɼϨʔβεΩϟφΛ༻͍ͯେྔͷ఺‫܈‬Λऔಘ͠ɼ‫ܗ‬ঢ়Λਖ਼֬ʹද‫͢ݱ‬Δख๏͕஫໨͞Ε͍ͯΔɽຊ࿦จͰ͸ɼ͜ͷٕज़Λ PO ΁Ԡ༻͢Δํ๏ʹ͍ͭͯ‫ݕ‬౼Λߦͬͨɽ

1

Introduction

In order to increase the performance of future wirelesssystems, it is necessary to model the propagation channel more accurately. In the actual wireless communication environments, it is necessary to analyze not only
the direct wave from the transmitter to the receiver
but also the scattered wave from a scatterer. Since theback scattering affects wireless communication usually,
the analysis of back scattering is one of the importantstudies for electromagnetic wave scattering problems.
As the method to analyze the electromagnetic wave
scattering, there exist some simulation methods suchas Method of Moment (MoM), Finite-Difference TimeDomain method (FDTD), Ray Tracing method (RT),and Physical Optics (PO). PO is one of the high frequency approximation methods, which gives a good accuracy for obstacles which are large enough comparedto the wavelength around back scattering region.
On the other hand, it is necessary to create thegeometrical model of scatterers accurately when performing electromagnetic wave scattering simulations.
Usually 3D CAD is used to create models. However, it
is difficult for 3D CAD to deal with models of complex
objects. Recently, complex model construction using
point clouds has been gaining attention due to its accuracy and speed for arbitrary shape [1]. Firstly, point
clouds are measured using a laser scanner, and then amesh generation method is used to transform the point
cloud into a 3D model of the object.
In addition, the most important factor in electromagnetic wave scattering simulation is the relation between accuracy and computation time. Since there isa trade-off between the simulation accuracy and thecomputation time due to the number of meshes of themodel, the suitable simulation parameters should be
discussed.
In this study, electromagnetic wave scattering simulation using PO on point cloud data is proposed assuming a sphere which can be solved analytically forverification. In order to reduce computational loadof the simulation, Quadric Edge Collapse Decimation(QECD) which is a mesh reduction method is applied,
and the trade-off between simulation accuracy and computation time was examined using Radar Cross Section(RCS) and Root Mean Square Error (RMSE) based on
the mesh area which is normalized to the wavelength.
57

Finally, from this trade-off analysis, the appropriate
mesh size for simulations is determined, and the simulation accuracy based on the appropriate mesh size iscompared to the exact solution which can be analytically solved.

2

Physical optics

PO assumes that the surface electric current is equal totwice of the incident magnetic field on the illuminatedpart of the scatterer when the surface is a Perfect Electric Conductor (PEC). On the other hand, the surface
electric current on the shadow part is assumed to be
zero. In general, PO can accurately calculate the scattered field on the illuminated part, however, the error
occurs on the shadow part. The surface electric current
is defined as follows [2]2n̂ × Hi
Illuminated part
Js =
,
(1)
0
Shadow part
where Hi is the incident magnetic field and n̂ is thenormal vector from the surface. In order to calculate
the scattered field for the mesh model, the following
equation is used [2]ZZ− jkZ0 −jkre
J s ejk·g dsp ,(2)
E s (r, θ, φ) =4πr
Awhere r is the distance from the origin to the observation point Q, k is the wave number, Z0 is the characteristic impedance of free space, θ and φ are the elevation
and azimuth angle of the observation point, A is theilluminated part of the surface, dsP is the differential
area of the surface, and g is the inner product of theintegral point and the observation point as shown in
Fig. 1. In order to evaluate the scattered field, RCS is
used as follows
σ = lim 4πR2
R→∞

|Es |2,|Ei |2(3)where Es is the scattered electric field, Ei is the incident electric field, R is the distance between observation point and scatterer.

3

Modeling

3.1

Point cloud data

Before performing electromagnetic wave scattering simulations using the point cloud of actual environments,

Fig. 1: Coordinate system
Fig. 3: Mesh data

0.5

z [m]

0.25
0
-0.25
-0.5
0.5 0.25

y [m]0 -0.25-0.5 -0.25(b) After(a) Before
Fig. 4: Quadric Edge Collapse Decimation

0.25 0.5

0x [m]Fig. 2: Point cloud data
it is necessary to verify the validity of the simulation
using a simple model. The point cloud data in thisstudy is shown in Fig. 2. This data was created byusing Matlab, assuming that the data is obtained bya laser scanner. For example, this model has 14281
points on the surface.

3.2

Mesh

Figure 3 shows the mesh model from Fig. 2. In orderto create the mesh from point cloud, Marching cubes
algorithm is used to form the surface for 3D object[3]. Firstly, the volume enclosing the whole object isdivided into many small cubes called voxels. Then, all
the voxel vertices which exist inside the object are set
a value of 1, and all vertices are set to 0. There exist 15
possible mesh patterns depending on the combinationof vertices. These mesh patterns are applied to thevertices determined in the previous step to create afinal 3D mesh surface. By using this technique, it ispossible to create a surface from point cloud data. As
a result, the surface of this model is composed of 65308
meshes.

4

Mesh reduction

4.1

Quadric edge collapse decimation

The model which is created by marching cubes has
the large number of meshes on the surface because themodel refers to a large amount of points. In addition,
when the voxel becomes small, the number of meshes
increases compared to this model. If this model is applied to PO simulation directly, the simulation accuracy becomes high. However, the computation time
becomes very long due to the large number of meshes.
Therefore, there exists a trade-off between the simula58

tion accuracy and the computation time. In order toimprove this problem, QECD which is one of the meshreduction methods is introduced here. Figure 4 shows
the image of this algorithm [4].

4.2Normalized mesh area

In this study, mesh reduction rate is assigned to 40%
and QECD is repeated 10 times. Then, the size ofeach mesh on the surface becomes large. In order toevaluate the change, normalized mesh area A′ which
is normalized to wavelength is introduced here. The
definition is indicated by the following equation [5]Pml=1 ∆sl
′
,
(4)
A =
mλ2
where m is the number of meshes, λ is wavelength and∆sl is each differential mesh area of the surface. The
result is shown in Fig. 5. Every time QECD is applied,normalized mesh area becomes large.

5

Simulation result

5.1

Trade off between accuracy and computation time

In order to examine the appropriate model, the trade
off between simulation accuracy and computation time
is discussed. Low RMSE and short computation timeis desirable for simulations. RMSE is calculated byusing the following equation based on the simulation
parameters shown in Table 1, and the incident wave iscoming from +z to −z
v
u
N
u1X
(σPO (θ) − σExact (θ))2 ,(5)
σ̄(θ) = tN
θ=0where σPO is RCS of PO simulation, σExact is RCSof exact solution, N is the total number of observation points. When the electric field is perpendicular to

RMSE

0.4
0.2
0
0

2
4
6
8
The number of mesh reduction

6.5
6.0

1000

5.5

750

5.0

500

4.5

250

4.0
0
0
8
3
4
0
8
1
3
4
0
8
87 .473 .285 .171 .103 .061 .037 .022 .013 .008 .004
7
.
0
0
0
0
0
0
0
0
0
0
′0

10

A

Fig. 5: Change of normalized mesh area(a) TM component

5.0

RMSE

Table 1: Simulation parameter
Item
Value
Frequency f [GHz]3.0
Wavelength λ [m]0.1
Radius a = 5λ [m]0.5Incident angle θi [◦ ]0
Incident angle φi [◦ ]0Observation angle θ [◦ ]0 ≤ θ ≤ 360
Step angle ∆θ [◦ ]1
Relative permittivity εr
5.6 − j0.1
Relative permittivity µr
1.0
Surface impedance Zs [Ω]50Incident planez−x
TE componentz−y
TM component
z−x

1500

4.5

RMSE (TE-z)
Computation time 1250

4.0

1000

3.5

750

3.0

500

2.5

250

Computation time [s]

A′

0.6

1500

RMSE (TM-z)
Computation time 1250

Computation time [s]

7.0

0.8

2.0
0
70 738 853 714 030 618 371 223 134 080 048
8
0.7 0.4 0.2 0.1 0.1 0.0 ′ 0.0 0.0 0.0 0.0 0.0A(b) TE component

Fig. 6: Relation between RMSE and computation time

0.5

the incident plane, this is called TE component, andwhen the magnetic field is perpendicular to the incident plane, this is called TM component. In TM component and TE component, RMSE converges around
4.5 and 2.7 as shown in Fig. 6, respectively. From theseresults, it is found that the normalized mesh area sizeof 0.0371 results in the best trade-off between accuracy
and computation time. In the following simulations,this appropriate mesh size is utilized and the model isshown in Fig. 7.

5.2

Simulation accuracy for appropriate model

The simulation results for appropriate model are shownin Fig. 8 to 10. The results are plotted to dBsm unit toevaluate the detail of the accuracy between PO simulation and exact solution by using the following equation.
RCS[dBsm] = 10 log10 σ(θ)(6)

The simulations are implemented when the surface is
PEC and non-PEC which has dielectric constant or
surface impedance. Firstly, when the surface is PEC,
PO simulation and exact solution are in good agreement around back scattering regions which are 0◦ ≤
θ ≤ 90◦ and 270◦ ≤ θ ≤ 360◦ .
Secondly, the relative permittivity and permeability shown in Table 1 are utilized for non-PEC surface,assuming concrete [6]. By using these parameters, thereflection coefficient is calculated and applied to surface electric current. Although the attenuation of PO
simulation is increased in TM component, both resultsin TE component are in good agreement around theback scattering region shown in Fig. 9.
59

z [m]

0.25
0
-0.25
-0.5
0.5 0.25

y [m]0 -0.25-0.5 -0.25

00.25 0.5

x [m]Fig. 7: appropriate model
Finally, the surface impedance shown in Table 1 isutilized for non-PEC surface. The reflection coefficientis also calculated by using the surface impedance, andapplied to surface electric current as well. Although
RCS of PO simulation in TM component is lower thanthat of exact solution, both results in TE component
are almost identical each other around back scattering
region shown in Fig. 10.
However, there exist errors in non PEC model.
Error in the back scattering region is probably due
to the fact that the effect of the reflection coefficienton the surface magnetic current was not considered in
the simulations. In addition, in the simulations for the
non-PEC case, wave penetration is not considered, andthis probably explains the larger error in the forward
scattering region which is 90◦ ≤ θ ≤ 270◦ .

6

Conclusions

A modelling method for complex objects using point
cloud data was introduced. The point cloud data was

RCS [dBsm]

θ [deg](a) TM component(a) TM component

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360

θ [deg]θ [deg](b) TE component(b) TE component

RCS [dBsm]

Fig. 8: Surface : PEC

Fig. 10: Surface : Impedance
but there were errors for the non-PEC sphere case.
Next, the proposed simulation method will be appliedon point cloud data of actual environments obtainedusing a laser scanner.

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360

References[1] ੕‫ڼ‬, ࢁా‫ߒو‬, “ϨʔβεΩϟφʹΑΔϨϯδը૾ॲཧ,” ౦‫ػిژ‬େֶग़൛‫ہ‬, 2013.
[2] F. Chatzigeorgiadis and D. Jenn, “A MATLAB
Physical-Optics RCS Prediction Code,” IEEE Antennas & Propagation Magazine, Vol. 46, No. 4,
pp. 137-139, 2004.

θ [deg](a) TM component

RCS [dBsm]

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360

θ [deg]

RCS [dBsm]

RCS [dBsm]
RCS [dBsm]

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360

35
30
Exact solution
25
PO simulation
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
0 30 60 90 120 150 180 210 240 270 300 330 360[3] W. Lorensen and H. Cline, “Marching Cubes:AHigh Resolution 3D Surface Construction Algorithm,” Computer Graphics, Vol. 21, No. 4, pp.
163-169, 1987.
[4] M. Garland and P. S. Heckbert, “Surface simplification using quadric error metrics, ʡ in Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, ser.
SIGGRAPH ʟ97. New York, NY, USA:ACM
Press/Addison-Wesley Publishing Co., pp. 209216, 1997.

θ [deg](b) TE component

Fig. 9: Surface : Dielectrictransformed into a 3D mesh by using marching cubes
mesh generation algorithm and QECD was applied toreduce number of meshes. Then, the trade-off between
accuracy and computation time was examined, and theappropriate mesh size was determined. Finally, comparison between PO simulation and the exact solution
was conducted on PEC and non-PEC spheres. Results shows good agreement for the PEC sphere case,60[5] N. Lertsirisopon, M. Ghoraishi, G. S. Ching, J.
Takada and T. Imai, “Modeling of Selected Street
Scatterer Objects Using Physical Optics Approximation,” ి‫ֶؾ‬ձ‫ܭ‬ଌ‫ڀݚ‬ձࢿྉ, 2005.
[6] ా໺Ҫ३Ұ, ઒੉ɹོ࣏, “మ‫ے‬ίϯΫϦʔτนͷి࣓೾γʔϧυಛੑʹؔ͢Δ‫ڀݚ‬ʵಛఆप೾਺ͷి࣓೾γʔϧυํ๏ͷ‫ݕ‬౼ʵ, ʡ౦‫ݐٸ‬ઃٕज़‫ॴڀݚ‬ใ, No. 37, 2011.

Financial Analysis of Residential Energy Storage Systems with Emphasis on
Electricity Consumption Patterns
Student Number: 12M18040

Name: Teppei KATATANISupervisor: Naoya ABEᐙᗞࡢ㟁ຊᾘ㈝ࣃࢱ࣮ࣥ࡟╔┠ࡋࡓ⵳㟁ụࡢ཰┈ᛶศᯒ∦㇂ 㕲ᖹ෌⏕ྍ⬟࢚ࢿࣝࢠ࣮ࡢᬑཬࡀ㐍ࡴ୰ࠊ⣔⤫Ᏻᐃ໬ᑐ⟇ࡢᚲせᛶࡀቑࡋ࡚ࡁ࡚࠾ࡾࠊ⵳㟁ụࡣ෌⏕ྍ⬟࢚ࢿࣝࢠ࣮ࡢฟຊᏳᐃ໬ࡸ㟁ຊ㟂せࡢᖹ‽໬ࢆᐇ⌧ࡉࡏࡿᢏ⾡࡜ࡋ࡚ὀ┠ࢆ㞟ࡵ࡚࠸ࡿࠋᮏ◊✲࡛ࡣࠊᑟධࡀጞࡲࡗ࡚࠸ࡿᐙᗞ⏝⵳㟁ụࡢ཰┈ᛶ࡟タ⨨⪅ࡢ㟁ຊᾘ㈝ࣃ
ࢱ࣮ࣥࡀᙳ㡪ࢆ୚࠼ࡿ࡜⪃࠼ࠊୡᖏࡢከᵝᛶࢆ⪃៖ࡋࡓ」ᩘࡢ㟁ຊᾘ㈝ࣃࢱ࣮ࣥࢆ⏕ᡂࡋࠊ⵳㟁ụࡢ཰┈ᛶࢆศᯒ㺃ホ౯ࡋࡓࠋ

1. Introduction
not yetRenewable energy sources (RESs), such as photovoltaic (PV), wind
power, geothermal, hydro power and biomass, have been developed inorder to shift from a heavy dependency on fossil fuels and to reducecarbon emission. In Japan, the introduction of a feed-in tariff (FiT)
legislation, which has provided attractive price menus to renewableenergy producers and the households with PV system in certain fixed
period of time, stimulated further diffusion of RESs since 2012.
Although RESs are carbon free power sources during their energy
production processes, their power supply is intermittent, especially PV
and wind power because their power output is dependent on the climate,solar radiation and wind speed. Higher penetration of these unstable
generations causes problems on the power grid. In Japan, five out of ten
regional utility companies have announced that they intend to cease
purchasing electricity from RES generations in 2014, because of theirinsufficient balancing capability [1].
Energy storage system (ESS) is one of the options to maintaingrid stability as a countermeasure against the volatility of RES. In the
demand side, or consumer side, installation of ESS has started to spread
after the Great East Japan Earthquake, and it has been expected to be
adopted widely as renewables diffuse. However, the profitability ofresidential ESS, a critical driver of adoption, is unclear, even though thenecessity of ESS has been rapidly increasing.

already installed

To reduce electricity bill
To be environmentallyfriendly
For self-consumption
For the emergency use
0%

20%

40%

60%

Figure 1. The aims of adopting residential ESS (multiple
responses allowed)
The questionnaire asked the owners of ESS (256 persons) about
their reasons to adopt and usage of ESS, and asked the PV users (863persons) about their perception to adopt ESS.
Almost 76% of the PV users who do not own ESS, answered thatthey have recognized the availability of residential ESS. However, about
43% of the PV users answered that they have no intention to adopt theresidential ESS, and only 24% of users intend to install the ESS atpresent. Figure 1 describes the answers to the question which asked theperson who owns or intends to install ESS about the reasons or the
purposes to install ESS. In addition, the ESS owners answered that they
are using or own ESS for an emergency use (38%), to save on electricity
bill (38%), and to consume the electricity from PV by themselves (24%).
This results confirm that the financial aspect is the main concern for the
adoption, specifically for those who have an interest to install ESS,
though it is found that current adopters are using ESS not only for
maximizing its financial benefit.
We applied the logistic regression model to investigate the factorsthat influence on the adoption and the intention to adopt ESS. The result
tells that the income level positively affects to the adoption of ESS, andrecognition of ESS affects to the intention to adopt ESS positively, which
implies that consumers have positive perception toward residential ESS.
The model did not confirm the influence of satisfaction with PV systemon the intention to install ESS due to the low significance level.
The questionnaire also revealed that more than 60% of the PV
users intend to increase PV surplus, and the revenue, by saving and/or
shifting electricity consumption. However, almost 30% of respondents
did not answer their monthly amount of electricity consumption.
The results of the questionnaire survey are referred in following
simulations for the electricity consumption and the financial benefit ofresidential PV and ESS.

2. Literature Reviews
Existing research found that the economics of residential energy systems,
e.g. PV, combined heat and power system, are influenced by theelectricity consumption patterns in household [2, 3]. Although several
research estimates the financial benefit to adopt residential ESS, they
applied a few numbers or standardized pattern of electricity consumption[4, 5]. Hence, to evaluate the financial benefit of ESS, this research
investigated the profitability of ESS, specifically lithium-ion batteries,
with emphasis on the diversity of electricity consumption pattern mainly
determined by life schedules of households, under current and future
conditions.

3. Consumers’ Perception toward ESS
This research conducted a web-based questionnaire survey to examine
consumers’ perception toward the residential ESS and reveal actual
usage of ESS. We send the questionnaire to the person who owns
residential PV and/or ESS. The survey was conducted from 22 to 25 ofDecember 2014 and collected 1,119 responses, which include the ownersof PV (863 responses), ESS (83 responses), and both of them (173
responses).

61

Table 1. Hypothetical seven household types

4. Simulation of Electricity Consumption
The present study simulated electricity consumption patterns under theassumption that the profit of ESS is also affected by those patterns. To
incorporate the diversity of household types and their electricity
consumption patterns, we simulated daily activity patterns of residents
in households with time use survey data [6]. Figure 2 shows the flow ofsimulation model. Japanese Statistics Bureau has been conducting thetime-use survey every five years since 1976. It measured the daily
patterns and time allocation of life activities, and opens the data to the
public [7]. In the result of latest survey, carried out in 2011, participation
rate of 20 activities in 15-min scale average spend time and standard
error ratio of each activity in weekday and Sunday were used in thesimulation model. Table 1 describes seven hypothetical household types
considered in our study. On behalf of occupant’s life stages, the data ofaged 35 – 44, aged 70 – 75, and aged 10 – 14 was obtained for young
couples, elderly couples and children, respectively.
First, the schedule of being awake and the period of time at homefor each occupant are estimated, and the activities at home aredetermined afterwards. Second, the possession of electrical appliances,e.g. TV, a washing machine, is estimated by the ownership ratio for eachtype of household. The attributes of appliances, power consumption andstandby power, for example, are presumed homogeneous across thehouseholds. Third, calculate the electricity consumption associated withthe activities by the probability of operation, quoted from the previous
research [6]. Fourth, the power consumptions of heating, cooling, andlighting are calculated. The usage of heating and cooling is supposed toarise from the state of occupants and the air temperature.
The model simulated 10,000 households for each of the sevenhousehold type. Figure 3 indicates their distributions of annual electricity
consumption as the result of the simulation.

Household# of person

Household type

A
B
C
D
E
F
G

2
2
2
3
3
4
4

Young couple (double income)
Young couple
Elderly couple
Household A + a child
Household B + a child
Household A + two children
Household B + two children

5. Financial Analysis of Residential PV and ESS
5.1. The Role of Residential ESS
ESS owners can potentially reduce their electricity bill if they choosesome appropriate menus of electricity rate offered by utility companies(load-shifting). They also can consume less electricity from the power
grid by increasing electricity consumption from residential PV systems(i.e., self-consumption), besides using ESS for an emergency. To
maximize financial benefit, the operation pattern of ESS changes by the
relationship among electricity retail prices, a price household pays to use
electricity, and tariffs, a price of PV generation which is purchased byutility companies. We construct three different scenarios of tariff toinvestigate the profitability of residential PV system and ESS, which are;37 yen/kWh, 24 yen/kWh, and 10 yen/kWh.
Scenario 1. (37 yen/kWh)37 yen/kWh is the current tariff ensured by FiT for 10 years inJapan. Since this value is higher than the retail price in daytime and night,
the owner maximize the benefit by selling PV surplus. Therefore, ESS is
used to charge cheaper night electricity and discharge it during daytime.
Scenario 2. (24 yen/kWh)
If a FiT tariff is higher than the retail electricity price during thenight but lower than that during the daytime, we assumed that the owner
tried to charge PV surplus to ESS. However, the priority is still charging
ESS at night, because that earns more profit.
Scenario 3. (10 yen/kWh)In this case, a FiT tariff is even lower than the retail price at night,
it is assumed that the owner intend to utilize PV generation by using ESS.
Thus, the amount of PV surplus sold to the utility companies became
smaller. Germany has been facing nearly the same situation thathouseholds introduce ESS to consume more electricity from PV insteadpurchasing from the grid.
Figure 4 describes an example of daily electricity consumption,
PV generation, and charging/discharging patterns respectively with acase of electricity price menu, which offers cheaper retail electricity
during the night (21:00 – 9:00) in the case of Scenario 1.

Density

0.3

A
E

B
F

C
G

D

0.2

0.1

0
2,600

Figure 2. The flow of estimating electricity consumption in eachhousehold3,200 3,800 4,400 5,000 5,600 6,200
Annual Electricity Consumption [kWh]6,800

Figure 3. Distribution of estimated electricity consumption in eachhousehold type (A - G)62

Figure 4. Daily patterns of electricity consumption, PV generation,and charging/discharging of ESS in a household (Scenario 1)

5.2. Simulation of Charging and Discharging Pattern
To estimate the charging and discharging patterns of ESS in households,
we developed a simulation model. The input parameters for the modelare hourly residential PV generation, hourly electricity consumption,
which is estimated above, and characteristics of PV and ESS.
Hourly PV generation is estimated by an equation below,‫ܧ‬௣ ൌ ‫ ܪ‬ൈ ‫ ܭ‬ൈ ܲ ൊ ܵ(eq.1)

where, ‫ܧ‬௣ is the hourly electricity production from PV system (kWh),‫ܪ‬is the hourly solar radiation on the PV panel (kWh/m2), ‫ܭ‬is the factorof loss, including heat loss and conversion efficiency of power module,
ܲis the capacity of PV system (kW), and ܵis the standard intensity ofsolar radiation which is defined as 1 kW/m2 by JIS C8918. The data ofhourly solar radiation is collected from METPV-11 [8], the database of
NEDO. Reflecting national average PV capacity, 4 kW system isassumed to be mounted in household. The parameters of residential PV
system is summarized in Table 2.
Besides estimated electricity consumption patterns of householdby this study, the actual data which had been measured in 40 households
from 2002 to 2005 by the Architectural Institute of Japan, is used toestimate the profitability of ESS and PV.
As a residential ESS, this study focused a lithium-ion battery,which is currently used widely not only for the household but also forelectric vehicles, because of its high energy density. The main parametersof ESS are summarized in Table 2. “Capacity” represents how muchelectricity it can charge in total, and “Power output” does how muchelectricity it can charge in a moment. Hence, ESS can charge only 1 kWhin an hour if the power output is 1 kW. Lifetime of ESS is generally
expressed by the number of charging and discharging cycles. Suppose
one cycle per a day, 6,000 cycles indicates 15 years as a lifetime of ESS.
With this model, by simulating hourly charging and discharging
patterns to estimate the profitability of residential ESS (Figure 5), thedifference of ESS operation pattern between households are purely
caused by the difference of electricity consumption pattern.

Figure 5. The flow of simulating charging and discharging patternof ESS (Scenario 1)

5.3. Estimating the Profit of ESS and PV
The owner of both PV and ESS receives the financial benefit that consistof revenue, the profit of selling PV surplus, and saving, the difference ofelectricity bill before and after installing PV and ESS. The annual net
benefit is calculated as follows,ܵܽ‫݃݊݅ݒ‬௧ ൌ ‫ܤܧ‬௪Ȁ௢ǡ௧ െ ‫ܤܧ‬௪Ȁǡ௧

(eq.4)(eq.3)

where, ܰ‫ܤ‬௧  is the net benefit in the year ‫( ݐ‬yen), ‫ܧ‬௣ is the hourly
surplus electricity production of PV system (kWh), ‫ܤܧ‬௪Ȁ௢ is the annual
electricity bill without PV and ESS, and ‫ܤܧ‬௪Ȁ is the electricity bill with
PV and ESS.
Figures 6 to 9 show the result of the estimation of the annual profit
that the owner could receive. Under the current tariff situation, 37
yen/kWh, variability of annual profit of PV across the household type islow. However, the variability becomes quite high if the tariff became
lower, 10 yen/kWh. In addition, Figures 6 and 7 imply that the electricity
consumption patterns influence on the financial benefit of PV. When thetariff is 10 yen/kWh, the expected profit is estimated higher for thehousehold of elderly couples than for the household of young couple(double income) with a child, even their estimated annual consumption
patterns are quite similar. Moreover, the estimated annual net profit ofTable 2. Parameters of PV and ESS
4 kW
20 years
75%
30°
0° (South)

ESS (Lithium-ion battery)
Capacity
Power output
Lifetime cycles
State of charge
Efficiency(eq.2)

ܴ݁‫݁ݑ݊݁ݒ‬௧ ൌ ܶܽ‫݂݂݅ݎ‬௧ ൈ ‫ܧ‬௦ǡ௧PV
Capacity
Lifetime
The factor of losses
Inclination
Orientationܰ‫ܤ‬௧ ൌ ܴ݁‫݁ݑ݊݁ݒ‬௧ ൅ ܵܽ‫݃݊݅ݒ‬௧

5 kWh
1 kW
6,000 cycles
10% - 90%90%63

elderly couple is higher than that of young couple (double income) withtwo children, even the annual electricity consumption is lower. This
results are mainly affected by the electricity use during the daytime in
weekday, due to the difference of average time spending at home (e.g.
almost 12 hours for a male who is working and 19 hours for an elderly
male in average).
Figures 8 and 9 show the annual profit of ESS. In the case, 37
yen/kWh as the tariff, the variance of annual profit for the households
whose electricity consumption are relatively low is high. Furthermore, it
is found that the distribution of the financial benefit of ESS become
wider, if a FiT tariff become lower.

Density

0.8

0

௧ୀଵ

15,00025,00035,00045,000

0.5
A
C
E
G

0.4
Densityܰ‫ܤ‬௧ൌͲሺͳ ൅ ‫ݎ‬ሻ௧ିଵ

5,000

Annual net profit [yen]Internal Rate of Return (IRR), which represents the profitability ofinvestments, is also estimated for each result of the simulation. The
equation estimating IRR is,‫ ܴܴܫ‬ൌ ‫ݎ‬ǣ െ ‫ܥ‬଴ ൅ ෍

0.4

B
D
F

0.2

5.4. Results of Financial Analysis

்

A
C
E
G

0.6

0.3

B
D
F

0.2
0.1
0

5,000

15,00025,00035,00045,000

Annual net profit [yen](eq.5)

Figures 8 (upper) Figure 9 (lower). The annual net profit to adopt
ESS with the tariff 37 yen/kWh (upper), and 10 yen/kWh (lower)

where, ‫ܥ‬଴ is the initial cost, equals a system cost of PV and ESS, ܶisthe certain period of time, equals a lifetime of PV and ESS in this study.
The system cost of PV is assumed 375,000 yen/kWh, 1.5 million
yen for 4 kWh system, and the cost of ESS is assumed 200,000 yen/kWh,
1 million yen for 5 kWh system.
Estimated IRR for adopting the ESS is never beyond zero with theassumed capital cost, which indicates installing ESS has no merit fromthe financial aspect at present. However, considering to install ESS and
PV together, estimated IRR suggested the owner could receive thepayback, even it is lower than the IRR for the case to install only PV
system. The result of annual profit and IRR for the 40 households in real
has the same trend as the result for simulated households. Moreover, theestimated IRR has similar distribution of the annual profit. Hence, theIRR of ESS has a high variance.
This simulation analysis found that the average self-consumption
rate is 33.8% without ESS in 70,000 households, and this number
increases to 53.7% with ESS and lower tariff, which reduce the effect of
unstable power output on the grid.

6. Conclusion
This study analyzed the financial benefit of residential PV and ESS withthe hypothesis that the benefit could vary by the combination ofhousehold electricity consumption patterns and levels of FiT tariff. We
found that the electricity consumption pattern certainly influenced on the
profit rather than the amount of the consumption. Although theprofitability of ESS is currently estimated as quite low due to theintensive capital cost, this result, supplemented by the result of webbases survey results, suggests that consumers should measure their own
electricity consumption pattern would be crucial in advance to confirm
whether or not the adoption would be beneficial.
The study also confirms the possible advantages of residential ESS
to reduce the amount of electricity from PV, which concerns to cause anegative effect on the power grid, if an enough incentive to consume PV
electricity.
REFERENCES[1] ⤒῭⏘ᴗ┬. (2014). ⥲ྜ㈨※࢚ࢿࣝࢠ࣮ㄪᰝ఍ ┬࢚ࢿࣝࢠ࣮࣭᪂࢚

Density

0.4
0.3
0.2

A

B

C

D

E

F

ࢿࣝࢠ࣮ศ⛉఍ ᪂࢚ࢿࣝࢠ࣮ᑠጤဨ఍ (➨ 4 ᅇ).
[2] ⸨ᮏ༟ஓ, ᒣཱྀᐜᖹ, ᒸᮧ᭸, ୗ⏣ྜྷஅ. (2011)㸬వ๫㟁ຊᅛᐃ౯᱁㈙ྲྀ㺃඲㔞㈙ྲྀࡢࡶ࡜࡛ࡢኴ㝧ගⓎ㟁ࡢ⤒῭ᛶẚ㍑࡜ᬑཬᙳ㡪ホ౯. ࢚Gࢿࣝࢠ࣮㺃㈨※Ꮫ఍ㄽᩥㄅ, 32(3).

0.1[3] ᶫᮏඞᕭ, 㧗ᶫᚭ, 㰻ᕝ㊰அ. (2004). ࢚ࢿࣝࢠ࣮ࢳ࢙࣮ࣥホ౯ᡭἲࡢ0
145,000㐺⏝ -ࢥ࣮ࢪ࢙ࢿ࡜ࣄ࣮ࢺ࣏ࣥࣉࡢẚ㍑᳨ウ-. 㟁ຊ୰ኸ◊✲ᡤ ◊✲150,000

155,000

160,000

ሗ࿌᭩, W03007.

Annual net profit [yen][4] ኱ᕊⱥኴ㑻, ᑠᯘᗈṊ, ὸ฼┿ᏹ, ୖ㔝๛. (2013). PV タ⨨㟂せᐙ࡟࠾ࡅࡿ⵳㟁ụࡢ⤒῭ᛶホ౯㸫⵳㟁ụタ⨨࡟ࡼࡾ⤒῭࣓ࣜࢵࢺࡀ⏕ࡌࡿ

0.5
Density

0.4
0.3
0.2

᮲௳ࡢ᳨ウ㸫. 㟁ຊ୰ኸ◊✲ᡤ ◊✲ሗ࿌᭩, R12018.

A

B

C

D

[5] J. Hoppmann, J. Vollanda, T. S. Schmidta, V. H. Hoffmann. (2014).

E

F

The economic viability of battery storage for residential solarGphotovoltaic systems – A review and a simulation model. Renewable
and Sustainable Energy Reviews, 39, 1101–1118.

0.1
0[6] すᑿ೺୍㑻, ὸ㔝ᾈᚿ. (2006). ୡᖏࡢከᵝᛶࢆ⪃៖ࡋࡓᐙᗞ㒊㛛࢚ࢿ
50,000 60,000 70,000 80,000 90,000 100,000 110,000ࣝࢠ࣮㟂せ⏕ᡂࢶ࣮ࣝࡢ㛤Ⓨ. 㟁ຊ୰ኸ◊✲ᡤ ◊✲ሗ࿌᭩, Y05008.

Annual net profit [yen][7] Japanese Statistics Bureau (⥲ົ┬⤫ィᒁ). (2011). ᖹᡂ 23 ᖺᗘ ♫఍⏕άᇶᮏㄪᰝ.
[8] NEDO. ᖺ㛫᫬ู᪥ᑕ㔞ࢹ࣮ࢱ࣮࣋ࢫ METPV-11

Figures 6 (upper) and 7 (lower). The annual net profit to adopt PVwith the tariff 37 yen/kWh (upper), and 10 yen/kWh (lower)

64

Solvent Extraction of Rare Earth Metals by a Continuous Stirred Vessel
Student Number: 13M18184

Name: Takehiko YAZAKI

Supervisor: Ryuichi EGASHIRA

㐃⥆ᘧᨩᢾᵴࢆ⏝࠸ࡓᕼᅵ㢮㔠ᒓࡢ⁐፹ᢳฟ▮ᓮ ೺ᙪ
ࡲࡎPC-88Aࢆᢳฟ๣࡜ࡋ࡚ሷ㓟Ỉ⁐ᾮ-ࢣࣟࢩࣥ㛫࡟࠾ࡅࡿࢿ࢜ࢪ࣒ࠊࢪࢫࣉࣟࢩ࣒࢘ࠊ㕲ࡢᾮᾮᖹ⾮ࢆᐇ ࡋࠊࡇࢀࡽࡢᢳฟᖹ⾮ᐃᩘࢆồࡵࡓࠋࡘ࠸࡛ྠᵝࡢ≀㉁⣔࡟࠾࠸࡚ཎᩱỈ┦ࢆ㐃⥆┦㸪⁐፹Ἔ┦ࢆศᩓ┦࡜ࡋ࡚ᶆ‽ᆺࡢᨩᢾᵴ
࡟ࡼࡿ㔠ᒓࡢ㐃⥆ᢳฟࢆ⾜ࡗࡓࠋᨩᢾ㏿ᗘࡢቑຍࡸỈ┦ὶ㔞ࡢῶᑡ࡟క࠸ᾮᾮ᥋ゐ⏺㠃✚ࡣቑຍࡋࠊᢳฟ㏿ᗘࡶቑຍࡋ
ࡓࠋࡲࡓྛ㔠ᒓࡢ≀㉁⛣ືಀᩘࢆồࡵࠊศ㓄ẚࡀ኱ࡁ࠸᮲௳࡛ࡣỈ┦(㐃⥆┦)ഃࡢ≀㉁⛣ື᢬ᢠࡀᨭ㓄ⓗ࡛࠶ࡿࡇ࡜ࢆ
☜ㄆࡋࡓࠋ

determined by ICP-AES (SPS7800, SII Nano
Technology) and pH meter (Horiba, F-52), respectively.
2.2.Continuous extraction
The experimental conditions of the continuous
extraction with stirred vessel are listed in Table 3. The
material systems were the same as used in the extraction
equilibrium measurements as Table 1.
An experimental apparatus of the continuous
extraction operation is schematically shown in Fig.1. A
standard type stirred vessel of 0.08 m inner diameter andheight with baffles was used as a contactor. A six flat
blade paddle impeller of 0.05 m diameter was equipped
at the center of the vessel. The volume as a reactor inside
the vessel is ܸ ൌ ͵Ǥͺͷ ൈ ͳͲି଺ m3. The stirred vessel
was immersed in the constant temperature bath to keep
the temperature of the liquids in the vessel constant.
After the aqueous feed phase was provided to fill up thevessel, the supply of the organic solvent phase and thestirring were started to begin the continuous run with oil
in water (O/W) type dispersion. While the feed andsolvent phases separately entered through the inlets atthe bottom of the vessel, the raffinate and extract phases
after the extraction was obtained together through the
top outlet. The flow rates of the feed and solvent phases
were monitored by rotameters, and adjusted to proper
values by valves. The flow rates at exit were estimatedby measuring the volumes of the phases over the defined
period. The obtained raffinate solutions were analyzed
in the same way as used in the measurements ofextraction equilibrium to determine the metal
concentrations and pH.
The liquid-liquid dispersion in the stirred vessel,
which should affect mass transfer, was also studied interms of the drop size and the holdup of the dispersed
phase. The drop diameters of the organic dispersed
phase were measured as follows: a portion of O/W typeliquid-liquid dispersion in the vessel was sampledduring the run into an appropriate aqueous hydrophilic
surfactant solution prepared in a pipette to avoid thecoalescence of the organic phase drops; this surfactant
solution containing the drops was microphotographed;and the numbers of the drops of the respective diameter
ranges were counted from among the 200 ~ 300 dropson the photograph to estimate the mean diameter and the
distribution. At the end of the experimental run, thevalves at the inlets of the vessel were closed
simultaneously and the volumes of the liquid phases
retained in the vessel were measured. From these

1.Introduction
Rare earth metals are industrially useful and applied toadvanced technologies in many fields. Since these metals
are found in resource as a mixture, they should be
separated into each other. Solvent extraction is presently
one of the commercialized techniques for the separation,
and a number of fundamental studies of solvent extraction
for rare earth metal separation have been conducted, inwhich the reaction equilibrium of respective rare earth
metals with various kinds of extractants were mainly
discussed. For the practical use of this method on industrial
scale, the multi-stage stirred vessel is commonly utilized
as a contacting equipment. So the information of extraction
rate is necessary for effective operation, however, only few
studies of the extraction rate of rare earth metals using
continuous stirred vessel have been reported.
In this work, the extraction rates of metal ions bystirring vessel were studied, in which neodymium (Nd),dysprosium (Dy) and iron (Fe) were selected as model
metals of light rare earth, heavy rare earth and impurity
metals occurring in separation process of rare earth metals
respectively. Firstly the extraction equilibrium of metals
was confirmed in a batch-wise run. In the second, thecontinuous extractions of metals with stirred vessel werecarried out under various experimental conditions, and theeffects of these conditions on liquid-liquid dispersion,
extraction rate and yield were studied. Finally, the masstransfer coefficients of the metal ions were estimated basedon these experimental results, and the contribution of thelocal mass transfer resistances to the overall resistance wasstudied.
2.Experimental
2.1.Extraction equilibrium
The material systems and experimental conditions
are shown in Tables 1 and 2. Chloride hexahydrate of
Nd, Dy and Fe were used as a metal ion sources. 2Ethylhexyl phosphonic acid mono-2-ethylhexyl ester(PC-88A) was used as an extractant, without further
purification. This extractant was diluted by kerosene to
be an organic phase. Hydrochloric acid was used toadjust pH in the aqueous feed solution.
In batch-wise extraction, the specified amounts offeed and solvent phases in a flask of 5.0 × 10−5 m3 wereshaken in a constant temperature bath at 298 K for 12hours to be equilibrated and these liquid phases wereseparated by separating funnel. The metal
concentrations and pH in the aqueous phases were65

against ‫ ݌‬ୣ୯ . The values of the left side in Eq. (4)increased in the order of Nd, Dy and Fe. The plots for eachmetal ion increased linearly with ‫ ݌‬ୣ୯ with slope 3. In
these measurement ranges, the extraction reaction
followed Eq. (1). Then ‫ܭ‬ୣ୶ǡ୑ of each metal ion was
estimated by the intercept of the lines, drawn in Fig. 2 assolid lines, and these values are listed in Table 2. That of
Fe was the largest, that is, Fe should be extracted most,
followed by Dy and Nd.

volumes, the holdup of the dispersed phase, the volume
ratio of dispersed phase relative to total liquid in thevessel, was determined.

Fig.1 Schematic diagram of experimental system for continuous
extraction
Table 1 Material system

Solute in aqueous phase
Extractant
Aqueous phase
Organic phase

NdCl3 6H2O
DyCl3 6H2O
FeCl3 6H2O
PC-88A
Aqueous solution of HCl
Kerosene solution of PC-88A

Table 2 Experimental conditions of batch-wise extraction

Concentration of metal ininitial aqueous phase, ‫୑ܥ‬ǡୟ୯ǡ଴ [kmol/m3]
Concentration of PC-88A in
initial organic phase, ‫ୖܥ‬ୌଶǡ୭୰୥ǡ଴ [kmol/m3]‫ ݌‬of initial aqueous phase, ‫ ݌‬଴
Temperature, ܶ [K]Volume of aqueous phase, ܸୟ୯ [m3]Volume of organic phase, ܸ୭୰୥ [m3]Fig. 2 Relation among pH of aqueous raffinate, distribution ratio andconcentration of extractant of organic extractant.

5.0×10-3

Table 5 Experimental extraction equilibrium constants, ‫ܭ‬ୣ୶ǡ୑

0.25
ͲǤͳ̱͵ǤͲ (Nd)െͲǤ͵̱͵ǤͲ (Dy)െͲǤ͵̱ͲǤ͸ (Fe)298
2.0×10-6
2.0×10-6

Metal
Neodymium (Nd)
Dysprosium (Dy)Iron (Fe)3.2. Continuous extraction
3.2.1. Liquid-liquid dispersion
In all experiments of continuous extraction, the system
attained the steady state within 5 minutes and the liquidliquid extraction could be stably operated. The volume
flow rates of inlet and outlet solutions were same in allcases, and the volume flow rates of aqueous and organic
phases could be represented as ܳୟ୯ ൌ ܳୟ୯ǡ୧୬ ൌ ܳୟ୯ǡ୭୳୲ aand ܳ୭୰୥ ൌ ܳ୭୰୥ǡ୧୬ ൌ ܳ୭୰୥ǡ୭୳୲ , respectively.
The holdup of the dispersed phase to the total volume,‫׎‬, sauter mean diameter of the dispersed drops, d32, andthe specific interfacial area, ܽ, were estimated as,(5)‫ ׎‬ൌ ܸୢ Ȁܸ݀ଷଶ ൌ σ௟ ݈݀ ଷ Ȁ݈݀ ଶ
 (6)
(7)
ܽ ൌ ͸‫׎‬Ȁ݀ଷଶwhere Vd, V and d represent the volume of dispersed phase,
total volume, and diameter of dispersed drops. Figure 3
shows the effects of stirring velocity, n, and ܳୟ୯ on ‫׎‬.
The ‫ ׎‬increased as ݊ increased and ܳୟ୯ decreased. In
the lower range of ݊, the mixing was insufficient to cause
low ‫ ׎‬. As n increased, the flow pattern in the vessel
approached to the perfect mixing condition and ‫׎‬attained the ratio of the organic flow rate relative to thetotal flow rate: 0.25 (=ܳ୭୰୥ Ȁሺܳୟ୯ ൅ ܳ୭୰୥ ሻ). Figure 4 showsthe effects of ݊ and ܳୟ୯ on sauter mean diameter ofdispersed phase, ݀ଷଶ at fixed ܳୟ୯ . The ݀ଷଶ decreasedas ݊ or ܳୟ୯ increased. These tendencies were reported
in the previous studies of liquid-liquid dispersion [1].
Figure 5 shows the effects of ݊ and ܳୟ୯ on ܽ. The ܽ
increased as ݊ increased, and was less influenced by
ܳୟ୯ , since d32 decreased as n and Qaq increased, but ‫׎‬decreased as Qaq increased. The metal species or ‫ ݌‬inthe aqueous solution had little effect on ‫׎‬, ݀ଷଶ and a.

Table 3 Experimental conditions of continuous extraction

Concentration of metal inaqueous feed, ‫୑ܥ‬ǡୟ୯ǡ୧୬ [kmol/m3]
Concentration of PC-88A in
organic solvent, ‫ୖܥ‬ୌଶǡ୭୰୥ǡ୧୬ [kmol/m3]‫ ݌‬of aqueous feed, ‫ ݌‬୧୬
Temperature, ܶ [K]Volume flow rate of
aqueous feed, ܳୟ୯ǡ୧୬ [m3/h]
Volume flow rate oforganic solvent, ܳ୭୰୥ǡ୧୬ [m3/h]
Stirring velocity,݊ [1/h]5.0×10-30.25ͳǤʹ̱ʹǤͷ (Nd)ͲǤ͸̱ͳǤͺ (Dy)ͲǤ͵ (Fe)
298

6.3×10-3~13.0×10-3
3.0×10-6
1.8×104~5.7×104

3.Results and Discussion
3.1. Extraction equilibrium
The extraction reaction of trivalent lanthanides and iron
ion with PC-88A is generally expressed as(1) ଷା ൅ ͵ሺ ሻଶ ֖  ଷ ሺ ሻଷ ൅ ͵ ା
where  ଷା and ሺ ሻଶ represent the metal ion and PC88A dimer, respectively, and symbols with overbar
express species in organic phase. The reaction equilibrium
constant, ‫ܭ‬ୣ୶ǡ୑ , is thus, described as,‫ܭ‬ୣ୶ǡ୑ ൌయ஼౉ǡ౥౨ౝǡ౛౧ ஼ౄశǡ౗౧ǡ౛౧య஼౉ǡ౗౧ǡ౛౧ ஼౎ౄమǡ౥౨ౝǡ౛౧ൌ ݉୑య஼ౄశǡ౗౧ǡ౛౧య஼౎ౄమǡ౥౨ౝǡ౛౧The distribution ratio, ݉୑ , was defined as,݉୑  ൌ ஼౉ǡ౥౨ౝǡ౛౧஼౉ǡ౗౧ǡ౛౧

Extraction equilibrium constant, ‫ܭ‬ୣ୶ǡ୑
1.8×10-2
1.9
5.0×102(2)(3)

The arrangement of this equation leads to,݉୑ െ ͵‫ୖܥ‬ୌଶǡ୭୰୥ǡୣ୯ ൌ ͵‫ ݌‬ୣ୯ ൅ ‫ܭ‬ୣ୶ǡ୑
(4)
The metal and extractant concentrations in the organic
phases were estimated by the material balance
relationships with the metal concentration in the aqueous
phase. Figure 2 shows the plots of the left side in Eq. (4)

66

ܻ୑ ൌ

ொ౥౨ౝǡ౥౫౪ ஼౉ǡ౥౨ౝǡ౥౫౪ொ౥౨ౝǡ౟౤ ஼౉ǡ౥౨ౝǡ౟౤

(9)Figures 8 and 9 show the effects of the operating
conditions on ܻ୑ . ܻ୑ increased with an increase in n or‫ ݌‬୧୬ because ܴ୑ increased as these values. The YM
decreased as ܳୟ୯ increased because the residence time ofsolution decreased.

Fig. 3 (a) The effect of stirring velocity on holdup. Conditions:
Qaq=9.0×10-3 m3/h, pHin=2.0 (Nd), pHin=1.0 (Dy), pHin=0.3 (Fe). (b)The effect of volume flow rate of aqueous phase on holdup. Conditions:
n=2.4×104 1/h (Nd,Dy), n=3.6×104 1/h (Fe), pHin=1.2~2.0 (Nd),pHin=0.6~1.8 (Dy) , pHin=0.3 (Fe)Fig. 6 (a) Effect of stirring velocity on the extraction rate. Conditions:
Qaq=9.0×10-3 m3/h, pHin=2.0 (Nd), pHin=1.0 (Dy), pHin=0.3 (Fe), (b)Effect of volume flow rate of aqueous solution on the extraction rate or
yield of several metals. Conditions: n=2.4×104 1/h (Nd,Dy), n=3.6×104
1/h (Fe), pHin=2.0 (Nd), pHin=1.0 (Dy), pHin=0.3 (Fe)Fig. 4 (a) The effect of stirring velocity on the mean drop diameter ofdispersed phase. Conditions: Qaq=9.0×10-3 m3/h, pHin=1.2~2.0 (Nd),pHin=0.6~1.8 (Dy), pHin=0.3 (Fe) . (b) The effect of volume flow rate of
aqueous phase on the mean drop diameter of dispersed phase. Conditions:
n=2.4×104 1/h (Nd,Dy), n=3.6×104 1/h (Fe), pHin=1.2~2.0 (Nd),pHin=0.6~1.8 (Dy), pHin=0.3 (Fe)Fig. 7 Effect of ‫ ݌‬in the aqueous feed on the extraction rate.
Conditions: n=2.4×104 1/h, Qaq=9.0×10-3 m3/h.

Fig. 5 (a) The effect of stirring velocity on the specific interfacial area.
Conditions: Qaq=9.0×10-3 m3/h, pHin=1.2~2.0 (Nd), pHin=0.6~1.8 (Dy),
pHin=0.3 (Fe) . (b) The effect of volume flow rate of aqueous phase on
the specific interfacial area. Conditions: n=2.4×104 1/h (Nd,Dy),
n=3.6×104 1/h (Fe), pHin=1.2~2.0 (Nd), pHin=0.6~1.8 (Dy), pHin=0.3 (Fe)Fig. 8 (a) Effect of stirring velocity on the yield. Conditions: Qaq=9.0×
10-3 m3/h, pHin=2.0 (Nd), pHin=1.0 (Dy), pHin=0.3 (Fe), (b) Effect ofvolume flow rate of aqueous solution on the yield. Conditions:
n=2.4×104 1/h (Nd,Dy), n=3.6×104 1/h (Fe), pHin=2.0 (Nd), pHin=1.0(Dy), pHin=0.3 (Fe)3.2.2. Extraction rate
Extraction rate of metal M, ܴ୑ , is expressed as,ܴ୑ ൌ ܳ୭୰୥ǡ୭୳୲ ‫୑ܥ‬ǡ୭୰୥ǡ୭୳୲ െ ܳ୭୰୥ǡ୧୬ ‫୑ܥ‬ǡ୭୰୥ǡ୧୬
(8)
Figure 6 shows the effect of ݊ and ܳୟ୯ on ܴ୑ . The
ܴ୑ s of Dy and Nd increased as ݊ and ܳୟ୯ . As mentioned
above, ܽ increased with ݊ to enhance the extraction.
While ܳୟ୯ had little influence on ܽ, ‫୑ܥ‬ǡୟ୯ǡ୭୳୲ increased
with ܳୟ୯ to make ‫୑ܥ‬ǡ୭୰୥ǡ୭୳୲ larger, as a consequencecausing larger ܴ୑ . The ܴ୑ of Fe decreased as ܳୟ୯ .
These results indicate that the extraction rates of Dy or Nd,
and Fe were controlled by the diffusion and chemical
reaction steps, respectively, as reported in the previous
study[2,3]. Figure 7 shows the effect of ‫ ݌‬in the
aqueous feed, pHin, on ܴ୑ . The ܴ୑ s of Dy and Nd
increased with ‫ ݌‬୧୬ because the extraction reaction
should be enhanced with an increase in ‫ ݌‬୧୬ , as shown in
Fig. 2.
3.2.3. Yield
Yield of metal M, ܻ୑ is defined as,

Fig. 9 Effect of ‫ ݌‬in the aqueous feed on the yield. Conditions:
n=2.4×104 1/h, Qaq=9.0×10-3 m3/h.

3.2.4. Experimental mass transfer coefficients
Overall mass transfer coefficients based on the aqueous
phase, ‫୑ܭ‬ǡୟ୯, was defined as,(10)ܴ୑ ൌ ‫୑ܭ‬ǡୟ୯ ൫‫୑ܥ‬ǡୟ୯ǡ୭୳୲ െ ‫୑ܥ‬ǡ୭୰୥ǡ୭୳୲ Ȁ݉୑ ൯ܸܽ67

Here ݉୑  and KM,aq were expressed as,

݉୑ ൌ
ଵ

௄౉ǡ౗౧

య
௄౛౮ǡ౉ ஼౎ౄమǡ౥౨ౝǡ౥౫౪ൌయ஼ౄశǡ౗౧ǡ౥౫౪
ଵ
ଵ

௞౉ǡ౗౧

൅

measurement range. This estimation method might beapplicable for the design of the extraction vessel for the
separation of rare earth metals.

(11)
(12)

௠౉ ௞౉ǡ౥౨ౝ

Table 6 Local mass transfer coefficientwhere kM,aq and kM,org represent the local mass transfer
coefficients in the aqueous and organic phase,respectively. Here it was assumed ݇୑ǡୟ୯ and ݇୑ǡ୭୰୥were constant and independent of the operating conditions
and metal ion spiceis. Then both coefficients were decided
by fitting with all experimental results, and the obtained
values were listed in Table 6. The effects of ݉୑ on‫୑ܭ‬ǡୟ୯ is shown in Fig. 10. The solid line is drawn by Eq.
(12), and the dotted lines show ‫୑ܭ‬ǡୟ୯ when the masstransfer resistance lies only in either phase. In the range oflarge ݉୑ , the diffusion in the aqueous phase was ratedetermined, while in the range of small ݉୑ , the diffusionin the organic phase was rate-determined.

(a)Experimental(b)Theoritical

Density of aqueous phase, ߩୟ୯ [kg/m3]
Viscosity of aqueous phase, ߤୟ୯ [mPa/s]
Diffusion coefficient in aqueous phase, ‫ܦ‬ୟ୯ [m2/s]

ଵ

997
0.89
6.2×10-10

Table 8 Material properties for calculation theoretical ݇୑ǡ୭୰୥

Molar weight [kg/mol]
Viscosity [mPa/s]

PC-88A
0.31
35

Kerosene
0.28
2.3

4.Conclusion
In the case of batch-wise extraction, metals were largely
extracted in large ‫ ݌‬of aqueous phase, and extraction
equilibrium constant could be determined for each metals.
In the case of continuous extraction, interfacial area
increased by increase of stirring velocity and decreased byincrease of volume flow rate of aqueous solution. Rare
earth metals largely extracted by increase of contact
interfacial area and driving force for diffusion, on the other
hand extraction of Fe could be accelerated by only increaseof contact interfacial area. Extraction rate of rare earth
metals is determined by diffusion in both aqueous andorganic phase, while the extraction rate of Fe is seemed to
be determined by chemical reaction. The experimental
local mass transfer coefficients based on experimental
results are almost good agreed with theoretical calculated
values.

3.2.5. Theoretical mass transfer coefficients
The mass transfer coefficients in the dispersed drops
and continuous phase were estimated by the following
general simple models. The local mass transfer
coefficients in the continuous and dispersed phases were
estimated by the Ranz-Marshall [4] and penetration
models [4], expressed as,(13)݄ܵ ൌ ʹǤͲ ൅ ͲǤ͸ܴ݁ ଵȀଶ ܵܿଵȀଷ଺݇୑ǡ୭୰୥
4.0×10-3
5.4×10-3

Table 7 Material properties for calculation theoretical ݇୑ǡୟ୯

Fig. 10 Relation among distribution ratio and overall mass transfer
coefficients based on aqueous phase

ௗ

݇୑ǡୟ୯
7.0×10-1
1.5×10-1

௟మ గమ ஽௧(14)݇ୢ ൌ െ  ቄ మ σஶ௟ ௟మ  ቀെ ௗ మ ቁቅଷ௧గwhere ݄ܵǡ ܴ݁ and ܵܿ are Sherwood, Reynolds andSchmidt numbers, and ‫ ܦ‬is the diffusion coefficient in
the organic phase, calculated by the Wilke-chang equation
[4], respectively. The diffusion coefficient of lanthanum inthe aqueous phase [5] and the properties of water at 298K
are shown in Table 7. The relative velocity of the dispersed
drops in the continuous phase was calculated as terminal
velocity in gravity field [6]. The association parameter in
Wilke-chang equation was set as 1~2, and molecularvolume was ͵ʹʹ ൈ ͳͲି଺ ଷ Ȁ , cited from theprevious study [7]. The properties of the organic phaseused for the estimation were listed in Table 8. The
theoretical local mass transfer coefficients obtained bythese eqations were listed in Table 6 together with theresults experimentally obtained. The respective kM,aqs andkM,orgs experimentally and theoretically obtained were
almost same as each other. Then the estimation could fully
express the extraction rate of these rare earth metals in this68

Nomenclature‫ܥ‬: concentration [kmol/m3], ݉: distribution ratio [-], ܸ:
volume ݊: stirring velocity [1/h], ܳ: volume flow rate
[kmol/m3], ܴ: extraction rate [kmol/h], ܻ: yield [-], ‫ܭ‬:overall mass transfer coefficient [m/h], ݇ = local masstransfer coefficient [m/h], ݀ଷଶ =sauter mean diameter ofdispersed phase [m], ‫׎‬: dispersed phase holdup [-], ܽ:
specific interfacial area [m2/m3], ‫ܭ‬ୣ୶ : extraction
equilibrium constant [-]<Subscripts>
0: initial state, eq: equilibrium state, aq: aqueous phase,org: organic phase, in: inlet solution, out: outlet solution,
d: dispersed phase,M: Metal, RH2: extractant as dimer,
H+: hydrogen ion, Nd: Neodymium, Dy: Dysprosium, Fe:
Iron
References[1] Robert E. Treybal., LIQUID EXTRACTION 2nd edition, (1963)[2] K. Takahashi, et.al, Chemical Engineering Science, 57, 469 (2002)[3] Hideo Matsuyama, et.al, Hydrometallurgy, 24, 37 (1990)[4] ᇛሯ ṇ, ໬Ꮫᢏ⾡⪅ࡢࡓࡵࡢ⛣ື㏿ᗘㄽ, 275 (1966)[5] E.L. Clussler, Diffuison 3rd edition, 162 (2002)[6] ⸨⏣ 㔜ᩥ, ໬ᏛᕤᏛ₇⩦ ➨2∧, 182 (1979)[7] Eiji Kamio et.al, Chemical journal, 139, 93 (2008)

Fe-Ce catalysts for N2O decomposition
Student number: 13M18190

Name: Huiting WANG

Supervisor: Hirofumi HINODE

Fe-Ce \`4'f.Z]T<-_ 

 @UY7DOPdW[(MRQOL ;F  N2O -_!SRFe-Ce\`4!EBRJ0G/1IGc^N2O -_5`4H9!b*c^A)`4c^=GCeO2 Xg/f]?N`4H93.!Cac^`4&Fe2O3/CeO2(1:5)`4>h`4H9!Vf] 20N2O  450   100% N2O -_!e:f]!6,N2O -_K8hK+"$# 
2.1 Catalyst preparation

1 Introduction

2.1.1 Manual mixing method Fe-Ce catalysts were prepared by manual mixing
method. Fe2O3 (Kanto Chemical Co., Inc.) and three
types of CeO2 (JRC-CEO-1, JRC-CEO-2, JRC-CEO-3,
Japan Reference Catalyst) were used.
 Fe2O3 and CeO2 were mixed manually in a mortar
using small amount of ethanol. The mixture was thencalcined at 400 for 5 h under air flow.
 In order to reduce the pressure drop, the catalysts
were pelletized, crushed and sieved into 0.71 mm to
1.00 mm particles prior to activity test.

Nitrous oxide (N2O) is one of the powerfulgreenhouse gases with a life time of about 150 years
in the atmosphere, and a net greenhouse effect about
310 times and 21 times of the Global Warming
Potential per molecule of CO2 and CH4, respectively[1]. Moreover, N2O is also identified as a contributor
to the destruction of ozone layer in the stratosphere.
The abatement of N2O emissions from industrial
plants (adipic acid production, combustion process,etc.) is probably the most feasible by implementing
catalytic processes [2]. Therefore, it is important to
study

the

decomposition

of

N 2O

from

the

environmental point of view.

2.1.2 Impregnation method

Various catalysts have been studied for N2O
Catalysts were also prepared by impregnation

decomposition. Catalysts containing iron oxides, arethe subject of studies as potential N2O decomposition

method.

catalyst. Combination of CeO2 with other metal

JRC-CEO-2, JRC-CEO-3, Japan Reference Catalyst)

oxides often affects the mobility of oxygen on theirwere used as supports. The characteristics of eachsurfaces, thus in some cases, modifies the element’s

CeO2 were shown in Table 1. The precursor of metal

redox ability and catalytic performance [3].

is Fe(NO3)3%9H2O (Kanto Chemical Co., Inc)

In

our

laboratory,

Fe/TiO2

catalysts

Three

types

of

CeO2

(JRC-CEO-1,

using!"#$%&'(')%*+',-.%'/0%1'23',4%'%5.%627%3,'

impregnation method and TiO2-CeO2 catalysts using
manual mixing method for N2O decomposition haveCeO2 type

been studied [4-5]. The addition of CeO2 improved thecatalytic activity of TiO2. Considering both of theresearches, in this work, the decomposition of N2O
over Fe-Ce catalysts was investigated.

2 Experimental
1
69Specific surfacearea(m2/g)

Purity[%]

JRC-CEO-1

156.9

99.99

JRC-CEO-2

123.1

99.97

JRC-CEO-3

81.4

99.97

In preparation, CeO2 was impregnated in an

Samples were mixed in weight ratio of 1:1, 1:2 andaqueous solution of precursor. Then, the solution

1:5. Horizontal axis indicates reaction temperature andmixture was stirred at room temperature for one day

vertical axis indicates N2O conversion.

followed by drying up for about 12 h at 60. After
that, the sample was calcined for 5 h under air flow. In
order to reduce the pressure drop, the sample was
finally pelletized, crushed and sieved into 0.71 mm to
1.00 mm particles, the same as manual mixing
method.
The following nomenclatures for the catalyst
samples are used. Catalysts prepared by manual
mixing method are written as Fe2O3-CeO2(Y:Z)
where Y:Z represents the weight ratio between Fe2O3
and CeO2. Catalysts prepared by impregnation method
are written as Fe2O3  /CeO2X(Y:Z), where X
829&'(')","$-,2:'":,2;2,-'<=':","$-0,0'.6%."6%1'#-'7"3/"$'represents the type of CeO2 and Y:Z means the weight725239'7%,4<1'=<6'>+*'1%:<7.<02,2<3'ratio between Fe2O3 and CeO2.

Fe2O3 catalyst decomposed 96% N2O at 650.

2.2 Catalytic activity experiment The catalytic reaction was carried out in a

Fe2O3 -CeO2 (1:1), Fe2O3 -CeO2 (1:1), Fe2O3

fixed-bed flow reactor under atmospheric pressure.

activity, 100% N2O conversion were achieved at

The reactant gas was prepared by mixing N2O, O2 and

600. Among the 5 catalysts, Fe2O3-CeO2(1:1)

He as a balance gas. The mixed gas of 1000 ppm N2O,

catalyst showed slightly higher activity compared to

0%/5% O2 and He was fed to the catalyst at a flow

other catalysts. These results showed that manual

rate which corresponded to a space velocity of 16000

mixing method did not change the activity of CeO 2

-CeO2(1:1) and CeO2 catalysts showed similar

-1

much.

h .
 Gas chromatography (GC323w; GL Science Co.,columns) was used to analyze N2O.

3.2 Catalytic activity of catalysts preparedby impregnation method

2.3 Catalyst characterization

Figure 2 shows the catalytic activity results ofwith Porapak N, Porapak Q and Molecular Sieve 13X

Fe2O3  /CeO2  catalysts with different ratios ofCharacterizations of the catalysts were performed

Fe2O3 and CeO2, prepared by impregnation method.

by TG-DTA (TG8120), XRD (MultiFlex), Nitrogen

Samples were prepared in weight ratio of 1:1, 1:2 andAdsorption (Autosorb1) and SEM (JSM-5310LV)

1:5. Horizontal axis indicates reaction temperature,-EDS (JED-2140).

and vertical axis indicates N2O conversion.
Fe2O3 catalyst decomposed 98% N2O at 650.
100% N2O conversion were achieved at 500 by

3. Results and discussion

Fe2O3  /CeO2  (1:1) and Fe2O3  /CeO2  (1:2)

3.1 Catalytic activity of catalysts preparedby manual mixing method

best activity among the samples showed in Figure 2,

catalysts. Fe2O3/CeO2(1:5) catalyst showed the
100% N2O conversion was achieved at 450. Mixed
oxide catalysts showed higher activity than pure oxide

Figure 1 shows the catalytic activity results ofcatalysts of Fe2O3 and CeO2. Moreover, comparing

Fe2O3  -CeO2  catalysts with different ratios of

the results of Figure 1 and Figure 2, it was found that

Fe2O3 and CeO2, prepared by manual mixing method.
270

catalysts prepared by impregnation method showed

3.4 The influence of oxygen on catalytic
activity for N2O decomposition

higher catalytic activity than catalysts prepared bymanual mixing method.

Figure 4 shows the catalytic activity results of
Fe2O3/CeO2(1:5) catalyst, which performed thebest catalytic activity among all the catalysts
discussed. Activity tests were implemented in thepresence or absence of oxygen (5%).

829&'+')","$-,2:'":,2;2,-'<=':","$-0,0'.6%."6%1'#-'
27.6%93",2<3'7%,4<1'=<6'>+*'1%:<7.<02,2<3'

3.3 Catalytic activity of catalysts withdifferent types of CeO2
829&'A')","$-,2:'":,2;2,-'<='8%+*?B)%*+C(DEF':","$-0,'=<6'>+*'1%:<7.<02,2<3'23',4%'.6%0%3:%'<6'"#0%3:%'<='<5-9%3'

It is shown that the conversion temperature of N2O
was shifted to higher temperature in the presence ofoxygen. This result indicated that the catalytic activity
was inhibited by the presence of oxygen. The O2
could compete with the N2O in the process ofadsorption on active site, reducing the contact of N2O
with the active site; thus reducing the effectiveness ofN2O degradation. [6].
829&'?')","$-,2:'":,2;2,-'<=':","$-0,0'@2,4'12==%6%3,',-.%0'

3.5 XRD results<=')%*+'=<6'>+*'1%:<7.<02,2<3'

3.5.1 XRD patterns
Figure 3 shows the catalytic activity results of Fe2O3
/CeO2(1:5) catalysts with different types of CeO2.

Figure 5 and Figure 6 show the results of XRD

Three types of CeO2 (JRC-CEO-1, JRC-CEO-2,

analysis of catalysts with different ratios of Fe2O3 and

JRC-CEO-3 from Japan Reference catalyst) were used.

CeO2, prepared by manual mixing method andHorizontal axis indicates reaction temperature, andimpregnation method, respectively. Both Fe2O3 andvertical axis indicates N2O conversion.

CeO2 peaks were detected from the XRD patterns.
Increased intensity was observed with increased

N2O began to decomposed from 350, 100% N2O

content of Fe2O3 and CeO2, respectively.

decomposition were achieved at 450 for all thethree catalysts. Three catalysts showed similar
catalytic activity.
371

3.5.2 Crystallite size
Table. 2 Crystallite size of catalysts
Catalyst
Crystallite
Crystallite
size Fe2O3
size CeO2[nm][nm]
Fe2O3
59.6-

829&'E'GHI'.",,%630'<=':","$-0,0'.6%."6%1'#-'7"3/"$'725239'7%,4<1'

Fe2O3

26.1-

Fe2O3-CeO2(1:1)

41.7

7.9

Fe2O3-CeO2(1:2)

41.7

8.7

Fe2O3-CeO2(1:5)

34.8

8.5

Fe2O3/CeO2(1:1)

20.9

7.7

Fe2O3/CeO2(1:2)

18.1

7.9

Fe2O3/CeO2(1:5)

10.2

8.5

CeO2

-

9.5

Fe2O3/CeO2(1:5)

7.6

7.3

Fe2O3/CeO2(1:5)

8.9

10.5

 It could be considered that the catalyst with smaller
crystallite size showed higher catalytic activity,
because small crystallite size could increase the active
site exposed to N2O.

4. Conclusions
829&'J'GHI'.",,%630'<=':","$-0,0'.6%."6%1'#-'

Catalysts prepared by impregnation method showed

27.6%93",2<3'7%,4<1'higher catalytic activity than catalysts prepared bymanual mixing method. Catalysts prepared byimpregnation method showed higher catalytic activity
than pure oxides of Fe2O3 and CeO2. Catalysts withdifferent types of CeO2 showed similar catalytic
activity. Fe2O3 /CeO2 (1:5) catalyst showed thebest activity among all the catalysts which have been
discussed in this research, 100% N2O decomposition
was achieved at 450. By introducing oxygen, thecatalytic activity was inhibited and 100% N2O
decomposition was achieved at higher temperature.
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] S. Imamura, M. Shono, N. Okamoto, R. Hamada,
S. Ishida, Appl. Catal. A 142 (1996) 279[4] K. Yanagida, Master thesis, Tokyo Institute ofTechnoligy(2006)[5] Y. Fukuchi, Bachelor thesis, Tokyo Institute ofTechnology(2012)[6] S. Suarez, M. Yates, A.L. Petre, J.A. Martin, P.
Avila and J. Blanco, Appl. Catal. B64 (2006) 302

829&'K'GHI'.",,%630'<=':","$-0,0'@2,4'12==%6%3,',-.%0'<=')%*+' Figure 7 shows the XRD patterns of Fe2O3 /CeO2(1:5) catalysts with different types of CeO2.
CeO2 peaks became sharper in the sequence of Fe2O3
 /CeO2  (1:5), Fe2O3  /CeO2  (1:5), Fe2O3 /CeO2(1:5).
472

An Empirical Analysis of Disclosure Patterns of Environmental
Information by Japanese Enterprises
Student Number: 13M18110 Name: Ryoya SUEHARA Supervisor: Naoya ABE
本邦企業の環境情報開示実態の実証分析
末原 諒矢
本論では，近年の環境意識のグローバル化，市民団体の発言力の高まりを受けて変化した企業の環境情報開示について調べた．企業は，報告義務の遵守，自主的開示，質問書に対する返答の 3 つの義務を負っていると言われる．
本論では，本邦企業の情報開示について，判別分析，ロジスティック回帰分析を用いて実証分析した．結果的に，本邦企業の前年回答状況，企業機規模，グローバル度，レポート発行経験と質問書への回答状況との関連を確認した．

1

Introduction

disclose their information by investors. Firms usually
receive one questionnaire a year from Carbon Disclosure Project (CDP), the first NGO that aim to disclose
environment-related information to investors. CDP is
supported by the investors and governments, and provide environmental information to the investors, at thesame time, give the risk analysis to large companies(See Figure2).
In Japan, there is a small SRI market accounting for
0.1% of world’s SRI market, fewer on-demand disclosure with 47% of response rate compared to 71% (UK)and 69% (US). However, there is high recognition ofvoluntary disclosure because of the government’s encouragement.

Corporate activities have been significantly internationalized with liberalization of finance, relief of trade
regulation, and progress of information and communication technology since 1990s. Environmental issues
are now needed to be considered more at the global
level. Elkington coined the triple bottom line in 1994,
meaning that companies should consider three aspects:
financial, social and environmental. Moreover, Clean
Development Mechanism is defined in the Kyoto Protocol in 1997, firms environmental activities could also
affect their financial performance in the future. Thus,
Carbon disclosure is getting more and more importantfor both firms and investors.
There are three types of disclosures: mandatory disclosure, voluntary disclosure, and on-demand disclosure. Mandatory disclosure in Japan is provided by
Law Concerning the Promotion of the Measures to
Cope with Global Warming. 11,086 firms reported theemissions by their corporate activities.
Voluntary disclosure is a disclosure through sustainability reports usually displayed on a firm’s web site.
Firms may disclose their non-financial information by
publishing a sustainability report following the reporting guidelines. Global Reporting Initiative is one of
the most widely used international disclosure guidelines (See Figure1).
On-demand disclosure is information provision thatmust be prepared by firms when they are asked to

6+$$"122

Objectives

Unlike the other countries, Japanese firms are encouraged to disclose voluntarily by the government. This
seems to give a different perception of firms to ondemand disclosure. In order to differentiate the threepatterns of disclosure and observe the on-demand disclosure response tendency of Japanese firms, this study
aims to:
1. To investigate how Japanese firms recognizes thethree different disclosure patterns and;2. To identify who disclose and why they disclose theenvironmental information by means of
on-demand disclosure.

/-",%:($"12'&?
3&'2'%2'0(8@/:3A/+'>(-'&(

7!4;D/+'>(-'&(

8"+(-&#(&,;/$$"-,<%2%,%)(

1()(%-23
4()'5&

8E8:-","2"<1')C7%&%<=)')

B
B!"#$%&'()/"0(1&#(&2):%2'=(>8/+'>(-'&(

#(
0()2

*&+(),"-)&2*&0"-#%,'"&./(),'"&&%'-(!%->"&4')2<")/-(:-"F(2,7?!4:@3&

3&4"1#%2'"&56+)2%'&%,'-'2781($"1298!6:8
1($"1298;&0'1"&#(&2%-81($"12

3&0()2"1)!"#$%&'()*&0"-#%,'"&

B;3"-,7,(-#7'&+(),#(&,
6"&57,(-#7'&+(),#(&,
8"+(-&#(&,
797:(&)'"&7777;"2'%<<=71()$"&)'><(7*&+(),'&57?;1*@*+,-'.

Figure 1: Links around GRI

Figure 2: Links around CDP
73
1

97A&+'-"&#(&,
97;"2'%<
978"+(-&%&2(

3

Hypotheses based on the liter- Economic sectors
ature review
Matsumura et al. (2014) reported the difference of dis-closure among sectors. In Japan, telecommunication
service sector and materials sector disclosed the information the most. Firms in the telecommunication service only consume electricity and it seem to be easy forthem to measure and to disclose. On the other hand,the firms that are energy and chemical intensive would
have higher pressure especially from the investors. ReSequencesponding status would be different among sectors, rePublishing sustainability report has been a common flecting the characteristics of the sectors.
means for firms to manage their reputation since 1997.
H5 . Responding status is different from sector
On the other hand, reflecting the shallow understandto sector.
ing of SRI, CDP is not well know in Japan. Therefore, this study predicts that firms would more likely
to firstly try to publish sustainability report with refer- Report publish experience
ring a guideline and then adjust the report to answer
Ministry of the Environment has strongly encouraged
CDP’s questionnaire afterward.
firms to understand environmental issues since Kyoto Protocol in 1997. Japanese firms have disclosed
H1 . Firms are more likely to publish atheir non-financial information voluntarily by publishsustainability report before answering CDP’s
ing sustainability reports and putting it on their web
questionnaire.
sites. Firms that have published sustainability reports
are more likely to have higher awareness of environResponse status in previous year
ment and have knowledge of reporting.
Stanny et al. (2008) reported that the likelihood of disH6 . Firms that have disclosed voluntarily areclosure is higher for firms that have disclosed before.
more likely to respond.
The firms’ reputation by investors would be worse because of non-disclosure and the firms would even beconsidered as unable to achieve their emission reduc- 4
Methodology
tion target if the firm do not disclose. Also, the firmsthat has disclosed would learn what to measure or how H1 is discussed by using the response time after theto measure, in order to answer the questionnaire.
recognition of sustainability report and CDP, assuming
the base year as a year when firms recognize them first.
H2 . Firms responding to the CDP2013 are1997 is set for sustainability report, and years whenmore likely to respond to the CDP2014 .
firms receive the CDP questionnaire first for CDP inorder to see how differently firms respond.
H2 to H6 are tested by using the following binary
Total asset
logistic model.
The association between size of firms and disclosure
is reported in many literatures (Stanny, 2013, Luo etProb(DISC) = β0 + β1 CDP2013 AQ + β2 SIZE
al., 2012, Matsumura et al., 2014, etc.) Larger firms+ β3 TFSALEP + β4 REPORTEXP
could maintain good reputation by answering the ques+ β(5−13) INDUSTRIES + ε (eq.1)
tionnaire since they receive more media attention thansmaller ones. Moreover, larger firms have more finan- Where
cial/human resources and better governance than the DISC is one if the firm answered the CDP2014 quessmaller ones. Hence, investors concerning SRI would tionnaire and zero otherwise;
be interested in firms whose activities are stable in CDP2014 is one if the firm answered CDP2 014 and 0
terms of environment, social, and governance.
otherwise;This study set the following six hypotheses based on
the relevant literature review. H1 is set to clarify theobjective 1, and H2 to H6 are set to clarify the objective 2.

SIZE is the natural logarithm of total assets at the endof fiscal year 2013;
TFSALEP is foreign sales as percent of net sales forfiscal year 2013 net sales;
REPORTEXP is one if the firm have published sustainability report at least once and 0 otherwise;
INDUSTRIES are consumer discretionary, consumer
staples, energy, financials, health care, industries, information technology, materials, and telecommunication services. These are variables, which are one if thefirm operates in this GICS 1 economic sector and zeroH3 . Larger firms are more likely to respond.

Foreign sales proportion
Global firms doing their business in different countries with different environmental regulations and theirawareness of environmental risks and opportunities are
more likely to respond the questionnaire. Global firms
would recognize the opportunity to enter bigger carbon
emission trading market.
H4 . Global firms are more likely to respond.

1 Global Industry Classification Standard.

74
2

Table 1: Descriptive and correlation statistics.
Panel A. Descriptive statistics.
Variables

1st Quantile

Median3rd Quantile

Std. Dev.

CDP2013 AQ
0.44
SIZE
5.94
TFSALEP
0.27
REPORTEXP
0.62
Panel B. Correlation statistics.

0.00
5.50
0.00
0.00

0.00
5.84
0.19
1.00

1.00
6.34
0.51
1.00

0.50
0.66
0.29
0.49

CDP2013 AQ

SIZE

1
0.33***
0.28***
0.48***

1−0.10**
0.16***

CDP2013 AQ
SIZE
TFSALEPREPORTEXPMean

TFSALEP1
0.34***REPORTEXP

1***, **, *, Significance levels at 1, 5 and 10%, respectively, for two-tailed tests.
Tetrachoric correlation statistics for pairs of binary variables and Pearson correlation statistics for pairs with a continuous variable.

75
3

!"#
!"#
!"#
!"#
!"#
!"!

!
!"#$%&'()*+,*-+.+)/%01234)%56%37%83*,(914+,:;83,2'-.+(<+,:%=3>2*,+1(?

otherwise.
variables are statistically significant at more than 5%
The error term ε is assumed to follow binomial distri- level. In model 2, the difference between sectors is notbution.
statistically significant. In model 3, all variables are
statistically significant at more than 10% level. AIC ofmodel 1, 2, and 3 are namely 215.57, 225.06, and 211.6.
5 Empirical Results
A model with lower AIC is better model. Therefore,
model 3 is the most straight forward model. As a reFigure 3 shows the response time to CDP and sustainsult, H2 , H3 , H4 , H6 are supported, while H5 is not.
ability report. Vertical axis represents the proportionof firms which never participate in CDP/sustainability!
report publishing, while horizontal axis represents the!"#!"#$%&'%(&)&$*+,-./0$!!
!!
response time (year). The base year for sustainabil!
!
ity report publishing is set at 1997 because of Kyoto!
!
Protocol and GRI establishment assuming that firms!
!
have come to recognize sustainability report as a way
! ! !
!
!
to disclose non-financial information. the base year for
! ! !
!
!
! ! !! ! !
! ! !! ! !
!
CDP participation is set at the year when a firm receive
!
!
!
!
!
the questionnaire, assuming that firms do not recognize
!
!
!
!!
!!
!
!
!
!
!
!
CDP till they receive the questionnaire.
As for sustainability report publishing, the proportion of non-published firms is gradually decreasing overtime. It seems that firms tend to publish the report
after the recognition, and have gradually been capable of publishing and that sustainability report havebeen common way to disclose in Japan. On the other! ! ! ! ! ! ! ! ! ! !"!"!" !" !"!"
hand, as for CDP participation, the proportion of non!"#$%&'$()*!
participating firms reach to 50% just after two years
Figure 3: Response timefrom the recognition, and stay at the same level. Firms
are bi-polarized: participating quickly and never answering. It seems that CDP participation is not recognized as a good way to disclose despite its known
capabilities.
6 Conclusion
Number of firms that have complied with both CDP
and Sustainability report is 234. 86.8% firms published The likelihood of disclosure is higher for firms thatsustainability reports before participating in CDP. The (1)participated in CDP in a previous year, (2)areexperience of publishing sustainability report seems to larger, (3)are more global, and (4)have published susbe a higher priority for Japanese firms, this is true even tainability report. Moreover, Japanese firms tend towith the fact that the base years were set differently. disclose voluntarily (publishing sustainability report)
before on-demand disclosure (CDP participation).
Therefore, H1 seems to be correct.
Non-financial information disclosure has been
Table 2 provides results of logistic models for determinants of answering the questionnaire. In model 1, all adopted by firms as a way to maintain good reputa-

Independent variables
Constant
CDP2013 AQ
SIZE
TFSALEPREPORTEXP
INDUSTRIES
Consumer Discretionary
Consumer Staples
EnergyFinancials
Health Care
Industrials
Information Technology
Materials
Telecommunication Services
Number of observations
Chi-squared
degrees of freedom
p-valueLog-likelihood value
Pseudo–R–squared
AIC

Table 2: Binary logit regressions.
Model 1Model 2
Coeff.
Std. err.
Coeff.
Std. err.
−8.06***
2.06−11.79***3.00
5.47***
0.42
5.51***
0.44
0.86**
0.33
1.33**
0.43
1.81**
0.70
1.34
0.82

0.85
1.57
0.52
0.25
1.40
0.82
1.47
2.37
−0.34
497
479.233
0.00−103.780.826
215.57

Model 3
Coeff.
Std. err.
−8.91***
2.17
5.16***
0.42
0.92**
0.34
1.45*
0.72
1.10*
0.44

1.28
1.39
2.09
1.21
1.40
1.27
1.35
1.32
2.39
497
487.7412
0.00−99.530.835
225.06497
485.634
0.00−100.58
0.833
211.6

The dependent variable is one if the firm responds to the CDP2014 questionnaire.
***, **, *, Significance levels at 1, 5 and 10%, respectively, for two-tailed tests.

tion. On the other hand, investors request compara- study. Also, to consider the external factors, using dyble data to make investing decisions. Environmental, namic models could be applied.
social and governance (ESG) and social responsible investment (SRI) are the factors that motivate them toshare the interest in the disclosure. However these do References
not seem to be understood deeply by both investors [1] John Elkington. 1997. Cannibals with Forks: The Triple
and firms in Japan.
Bottom Line of Twenty-First Century Business. Capstone,
Oxford.
Kolk et al.(2008) suggested that CDP progressedmuch less with regard to the cognitive and value di- [2] Wooldridge, M., J.. 2010. Econometrics analysis of crossmensions. Collecting comparable data is also imporsection and panel data 2e 565-576, The MIT Press: Cambridge, Massachusetts.
tant for policy makers and research institutions. Since
Japanese environmental policies have tried to encour- [3] Stanny, E.. (2013). Voluntary disclosure of emissions by US
age firms’ voluntary action and seem to end up withfirms, Business Strategy and the Environment 22, 145-158.
high adoption of sustainability report publishing and[4] Luo, L., Lan, Y., & Tang, Q.. (2012). Corporate incentives
bi-polarization in CDP participation, it is important
to disclose carbon information: Evidence from the CDP
to make high public recognition of SRI.
Global 500 Report, Journal of International Financial Management & Accounting, 23:2.
Data used in this research is limited to top 500 firms
in Japan and does not reflect the others. Also, this re- [5] Stanny, E., & Ely, K.. (2008). Corporate environmental disclosures about the effects of climate change, Corporate Social
search only focused on the characteristics of firms that
Responsibility and Environmental Management 15, 338-348.
disclosed although in reality firms’ activities are influenced by external factors such as depressions, disasters [6] Matsumura, E., Prakash, R., & Vera-Munoz, S.. (2014).
Firm-value effects of carbon emission and carbon disclosure.
and so on.
American Accounting Association 89, 695-724.
The variables used in this research can predict which
type of firms are more likely to respond in a next year, [7] Kolk, A., Levy, D., & Pinkse, J.. (2008). Corporate responses
in an emerging climate regime: the institutionalization andbut can not increase firms’ response to on-demand discommensuration of carbon disclosure, European Accounting
closure because those only represents firms characterReview 17, 719-745.
istics that can not change easily. In order to motivate
firms to disclose their environmental information, in- [8] CDP. (2014). CDP Japan 500 Climate change report 2014．
vestigating the true determinants behind the variables
used in this research could be considered for further
76
4

77

78

79

80

Research on Maximum Wind Speed Radius of Typhoon
Passing through Japanese Southern Ocean Basin
Student Number: 13M18215

Name: Wenjie WUSupervisor: Hiroshi TAKAGI᪥ᮏ༡᪉ᾏᇦࢆ㏻㐣ࡍࡿྎ㢼ࡢ᭱኱㢼㏿༙ᚄ࡟ࡘ࠸࡚ࡢᇶ♏ⓗ◊✲࿋ ᩥ₩㧗₻࡟ࡼࡿேⓗ⿕ᐖࢆῶ㍍ࡍࡿࡓࡵ࡟ࡣ㸪ᩘ್ࢩ࣑࣮ࣗࣞࢩࣙࣥࡢண ⢭ᗘࢆୖࡆࡿࡇ࡜ࡀ㔜せ࡛࠶ࡿ㸬ࡋ࠿ࡋ㸪ࡑࡢ⢭ᗘ࡟኱ࡁࡃᙳ㡪ࡍࡿࣃ࣓࣮ࣛࢱࡢ୍ࡘ࡛࠶ࡿྎ㢼ࡢ᭱኱㢼㏿༙ᚄࡢタᐃࡣ≉࡟㞴ࡋ࠸㸬௒ࡲ୍࡛⯡ⓗ࡟౑ࢃࢀ࡚ࡁࡓຍ⸨ࡢ⤒㦂ᘧࡣ⡆౽࡛᭷⏝࡛࠶ࡿࡀ㸪᥎⟬ㄗᕪࡀ኱ࡁࡃ㸪㧗₻ࢆ㐣ᑠ࠶ࡿ࠸ࡣ㐣኱ホ౯ࡍࡿྍ⬟ᛶࡀ࠶ࡿ㸬ᮏ◊✲࡛ࡣ㸪㧗₻⅏ᐖࡀከⓎࡍࡿ୰ᅜࡢᮾἢᓊᇦ࡟᥋㏆ࡍࡿྎ㢼࡟╔┠ࡋ㸪ࡑࡢᆅᇦ࡟ୖ㝣ࡍࡿྎ㢼ࡢண ⢭ᗘࡢྥୖࢆ┠ᣦࡍ㸬ࡇࡢࡓࡵ㸪᪥ᮏ༡᪉ᾏᇦ࡟࠾࠸࡚ධᡭྍ⬟࡞Ẽ㇟᝟ሗࢆ⏝࠸࡚㸪ᚑ᮶ࡢᡭἲࡼࡾࡶ᥎ᐃ⢭ᗘࡢ㧗࠸᭱኱㢼㏿༙ᚄࡢࣔࢹࣝࢆ᪂ࡓ࡟ᥦ᱌ࡋࡓ㸬

1. Introduction

the usage of objective analysis data was considered.

Extensive storm surge disasters caused by extremely
strong typhoon have occurred frequently in recent
years and it is considered that typhoon is likely to
become stronger in the future due to climate change.
In order to save human’s life, it is required to provide
the evacuation warning based on an accurate forecast
on storm surges. For this reason, enhancement ofsimulation accuracy will become increasingly
important. However, the maximum wind radius(hereafter referred to as Rmax), which is one of thetyphoon parameters and has large effect on thesimulation accuracy of storm surge, is especiallydifficult to be chosen.
 The information of Rmax is usually not provided
from meteorological agencies such as JMA (Japan
Meteorological Agency) when they issue typhoon
reports or forecasts. Hence, those who carry out storm
surge simulation need to estimate the radius bythemselves. The empirical formula for estimating
Rmax proposed by Kato (2005) has been commonlyused among coastal engineers. The Rmax can be
estimated according to the pressure on the center of
the typhoon. Although this model is simple and useful,the estimation error is not negligible and thus storm
surge heights could be underestimated. Therefore, it isnecessary to develop a more reliable estimation
method of Rmax, which is expected to improve theaccuracy of storm surge simulation.
This research focuses on the eastern coastal region ofChina, particularly Zhejiang Province, where storm
surge disasters frequently occurred in the past. A
number of meteorological data, which were observed
at several observatories in the southern ocean basin ofJapan in the past 25 years, have been collected andanalyzed in order to improve the Rmax model.

2.1 Summary of the objective analysis data
Two types of analysis data were discussed in thisresearch and the summaries are as follows.
(i) ERA-Interim data
ERA is the acronym for ECMWF (European Centre
for Medium-Range Weather Forecasts) Re-Analysis
and this data can be downloaded from the server of
ECMWF.
Table.1 Detail of ERA-Interim data
Data format
NetCDF or GRIB
Period1979 ~ about 2 months prior to present
Time00,06,12,18 UTC
Area
GrobalGrid
0.125°× 0.125°(ii)GPV (MSM) data
GPV is the acronym for Grid Point Value and this datais calculated under MSM (Meso Scale Model) which
is a numerical forecast model of JMA. The data usedon this research is hourly, composed with objective
analysis data as initial value and the forecast data
filled at the intervals from the original GPV data by
Research Institute for Sustainable Humanosphere andthe format is changed to NetCDF.
Table.2 Detail of GPV (MSM) data
Data format
NetCDF
Period2002 ~ one day prior to present
Time of~ Feb. 28, 2006: 00,06,12,18 UTC
initial value Mar. 1, 2006 ~ :00,03,06,09,12,15,18,21 UTC
Area
22.4°N ~ 47.6°N, 120°E ~ 150°E
Grid~ Feb. 28, 2006 :longitude 0.125°× latitude 0.1°
Mar. 1, 2006 ~ :longitude 0.0625°× latitude 0.05°

2. Validation of the usage of objective
analysis data
To improve the estimation accuracy of Rmax, themodel should be developed with reliable data which
describe the characteristics of the typhoon correctly.
Given that the number of observatories is limited, first

2.2
data

81

Applicability of the objective analysis

ERA data obtained at 6-hours interval appears to be
too coarse to estimate the typhoon pressure depression
accurately. Therefore, ERA data is considered not
appropriate to be used for the purpose of this research.

Five typhoons shown in Table.3 are highlighted as therepresentative examples of the strong typhoon passing
through Japanese Southern Basin and landing on theeastern coastal region around Zhejiang Province. The
paths of them are show on Fig1. An assessment wasmade to identify whether these 2 types of analysis data
is applicable to the research considered.

Observed pressure (hPa)1010

Table.3 Detail of the typhoon examples
Typhoon
No.
0413
0515
0608
0712
1211

Typhoon period
Aug. 6㹼Aug. 15, 2004
Sept. 5㹼Sept. 13, 2005
Aug. 5㹼Aug. 11, 2006
Sept. 15㹼Sept. 20, 2007
Aug. 1㹼Aug. 11, 2012

Centralpressure
950 hPa945 hPa925 hPa925 hPa965 hPa

990
980

GPV

970

ERA

960
950
940
940 950 960 970 980 990 10001010

Pressure of analysis data (hPa)

Fig.2 Comparison between the observation data andthe analysis data

2.2.1 Method 1: Pressure comparison at different
places in broad area
The data of sea level pressures (SLP) at five locations
were used for the applicability test for ERA and GPV.
Because the observation data including SLP isavailable at two Japanese observatories, Naha andIshigakijima, the comparison was also made between
the objective and the observation data.

2.2.2 Method 2: Pressure comparison at thecenter of the typhoon
Although GPV data is in good agreement withobservation data, it has been also pointed out that thecentral pressure of the pressure field expressed by
GPV data is higher than the real central pressure of thetyphoon. (Kawaguchi et al., 2007). Thus, the second
validation method is to compare the pressures at thecenter of the typhoons between GPV data and the
Typhoon Best Track data.
The comparison shows that GPV data only agrees
with Best Track data in the case of T1211, but
overestimate the pressures in the other cases. The
maximum errors are 12 hPa, 37 hPa, 57 hPa and 33
hPa in the cases of T0413, T0515, T0608 and T0712
respectively. Fig.3 is the comparison result of thecentral pressure of typhoon 0608 that shows how GPV
overestimates the pressures during the period when
the center of the typhoon is inside the area of GPV
data. It is considered that the GPV data overestimates
the typhoon pressure as the grid size is not smallenough. Therefore, GPV data is also not appropriate to
be used for the purpose of this research.

ShanghaiTaizhou
Naha

Fuding1000

Ishigakijima

1000

Central Pressure (hPa)

Fig.1 The paths of the 5 selected typhoons and the
5 locations where comparison is madeThe comparison yielded the findings as follows:(1) GPV data is in good agreement with observation
data, while ERA data is only reliable in estimating thetyphoons when the pressure depression is not
significantly large. The error of ERA data appears toreach 30hPa and even larger. Fig.2 shows thecomparison result of the pressure at Naha andIshigakijima when the observed pressure was lowest
during the typhoon passage.
(2) At the places where the observation data is notavailable, the SLP in ERA also shows 20~30 hPa
higher than GPV data when the SLP in GPV data
depresses under 980 hPa.

990
980
970

Best
Track

960
950

GPV

940
930
920
2006/8/7

2006/8/8

2006/8/9

2006/8/10

Fig.3 Comparison of the central pressure (T0608)

3. Development of a new estimation
method of maximum wind radius
823.1 Selection of the typhoon
Since the analysis data cannot be used to make acomparison to estimate the accuracy of the simulation,
only possible data source for the present research
could be the observation data at neighboring
observatories. There are 10 JMA observatories whose
observation data are available inside the research area
(20°N~30°N, 120°E~132°E). The criteria for thetyphoon selection are as follows:ձ Typhoon occurred after 1990 when theobservation data began to be recorded at every
hour (every 3 hours or 6 hours before 1990).
ղ Typhoon passed through the area of 20°N~30°N,
120°E~132°E in conformity to the target area.
ճ Extremely strong typhoon whose lowest centralpressure is under 930 hPa, by which huge storm
surge disaster might occur.
մ The distance between one or more of theobservatories and the center of the typhoon is lessthan 100km.

9416

925→93047 (Ishigakijima)

9609

930→92526 (Iriomotejima)

9918

930

39 (Kumejima)

0314

930→910→930

12 (Miyakojima)

0418

925→9300608

930→925→930

 63 (Miyakojima)

0613

930→920→930

18 (Ishigakijima)

0704

930

0712

930→925→930

7 (Iriomotejima)

0715

925→930

13 (Yonakunijima)

0815

905→925→930

88 (Yonakunijima)

1011

930

52 (Yonakunijima)

1215

920→910→930

4 (Nago)1216

920→930100 (Naha)

1217

920→93032 (Naha)

18 (Nago)

23 (Naha)

3.2 Estimation of maximum wind radiusby simulation
Myers model (1954) was used to calculate thepressure profiles of the 17 typhoons. The estimatedpressures were compared with the observed value tofind the value of Rmax for each typhoon at each time
period. The procedure is as follows:
I. Linearly interpolate the Best Track data into the
1-hour interval data.
II. Search the observatories whose distance to thecenter of the typhoon is less than 300km.
III. Estimate Rmax according to Kato’s model (2005)
and perform the simulation. Compare theestimate pressure to the observed pressure at theplaces found at step ϩ.
IV. Repeat the comparison with different settings ofRmax at every 5 km. If the estimate pressure ishigher than the observed pressure, raise the valueof maximum wind radius, otherwise lower it.
V. The Rmax calculated by the best estimatedpressure that is the closest to the observed data is
considered an optimum radius.

Fig.4 JMA observatories’ location and the paths of theselected typhoons

Although 621 Typhoons have occurred from 1990 tothe present, only 17 typhoons are satisfied with all
criteria and decided as the target typhoons. Typhoons,
which satisfy the criteria mentioned, are listed ontable4 with their central pressures, and the distance tothe closest observatories. The name of theobservatories from east to west is Minamidaitoujima,Naze, Okinoerabu, Nago, Naha, Kumejima,
Miyakojima,
Ishigakijima,
Iriomotejima
andYonakunijima. The number in bold indicates centralpressures when closest.

3.3  New method for the estimation ofRmax
A new model, which could become an alternative
method exceeding the previous models, is proposed.

3.3.1 Correlation with central pressure
Both the empirical formulas of Kato (2005) and
Kawai (2005) estimate Rmax according to thetyphoon central pressure. To verify the centralpressure as determining variable, the correlation
between central pressure and the Rmax estimated by
the methods mentioned was investigated.
The average of the Rmax shows: 1) an increasing
tendency with the rise of the central pressure of thetyphoon, which is the same as the plots by two

Table4 Detail of the research objects
TyphoonNo.

Central Pressure
(hPa)

9019

925→890→910→930

9313

930→925→930

Distance (km) to thecloset observatories
51 (Naze)
19 (Kumejima)83

inside the range with gradient of 0.172㹼0.360 in
Fig.6, it is considered necessary to use these limit
values also when do a storm surge simulation.

empirical models; 2) a good agreement with Kawai’s
model, while lower values than Kato’s model; and 3)
an increasing exponentially with the rise of the centralpressure which is the same as Kawai’s model, while
Kato’s model shows a linear trend.
Fig.5 shows each Rmax of the 17 typhoons wheneach typhoon is the closest approach to theobservatory. It is apparent that Rmax estimated in thisresearch shows a lower value than Kato’s model andhave a wide range differ from typhoon to typhooneven the central pressures are the same. Furthermore,
the determination coefficient R2 is as low as 0.0602,
which demonstrates a low correlation between theplots and the regression curve.

100
90
80
70
60
50
40
30
20
10
0

Maximum windspeed radius(km)

Maximum windspeed
radius (km)

90
80
70

y = 0.67x - 572.7
R² = 0.0602
RMSE = 15.8

50
30
20

905 910 915 920 925 930 935
Central Pressure(hPa)

Fig.5 The estimated Rmax of the 17 typhoons

3.3.2 Storm force wind radius
The authors focused on another typhoon parameter,storm force wind radius, as an indicator to estimate
Rmax. Storm force wind area is the area where thewind speed exceeds 25m/s. Storm force wind area isanalyzed as a circle, but for the reason of spatial bias,
the radius is expressed by the combination of wideand narrow radius. During a typhoon approaches,
JMA forecasts and publicizes this radius along withthe other typhoon information. Since the storm force
wind radius expresses the size of typhoon, the storm
force wind radius is considered to be correlated withRmax.
Fig.6 shows the correlation between storm force
wind and Rmax. For sake of simplicity, storm force
wind radius is calculated by averaging wide andnarrow radius. For Rmax, the values when each
typhoon is the closest approach to the observatory areplotted. The determination coefficient R2 is as high as
0.579, which demonstrates a high correlation.

5. References[1] Kato, F. (2005): Study on risk assessment of storm
surge flood, Technical Note of National Institute forLand and Infrastructure Management, No.275, p10.
(In Japanese)[2] Kawai, H., Honda, K., Tomita, T., Kakinuma, T.
(2005): Characteristic of Typhoons in 2004 andForecasting and Hindcasting of Their Storm Surges,
Technical Note of the Port and Airport ResearchInstitute, No.1103㸪pp. 3-12. (In Japanese)[3] Kawai, H., Kawaguchi, K., Ohkama, T., Tomoda,N., Hagimoto, Y., Nakano, T. (2007): Simulation of
Storm Surge in Seto Inland Sea with Wind Field
Estimated by Empirical Typhoon Model andMesoscale Model, Proceedings of Coastal
Engineering, JSCE, Vol.54, pp. 286-290. (InJapanese)[4] Takagi, H., Kashihara, H., Esteban, M. Shibayama,
T. (2011): Assessment of future stability of
breakwaters under climate change, Coastal
Engineering Journal, World Scientific Ltd., Vol.53,
No.1, pp.21-39.

The new formula is delivered as follows,ሺ௥భ ା௥మ ሻ
ଶ





100
200
300
400
Storm force wind radius㸦km㸧

The present paper concluded: 1) the applicability of
JMA and ECMWF objective analysis data wasinvestigated, and it was revealed that the resolution ofthese data does not satisfy the quality for the present
typhoon research, 2) correlation between Rmax andthe typhoon central shows a low correlation with highvariability. This implies that the previous models
using the central pressures such as Kato’s model
should be revisited, 3) correlation between Rmax andthe storm force wind radius shows a high correlation
will less variability, and 4) a more precise estimation
method of Rmax using the storm force wind radius is
proposed.

0㹰଴ ൌ ܽ ‫כ‬Overallrange

4. Conclusion

Kato'smodel

10

Regression
line

Fig.6 Correlation between Rmax and storm force windradius

Regre
ssion
line

40

17
typhoons

017
typhoons

60

y = 0.229x
R² = 0.5814
RMES = 10.52(1)where, r0 is the estimate of Rmax, r1 is the wide radiusof storm force wind, and r2 is the narrow radius. a is
the gradient and equal to the regression coefficient,0.229. Since the overall range of the samples are84Separating global warming and urbanization effects to rainfall during summer
Student Number: 13M18072

Name: Natsumi KAWANO;\YQ5jEYQ5Supervisor: Manabu KANDA>@lm6Jn

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&m< 4N&BjEYQ5GZJn'9"jE<m H5&0PCT
V3 %jEYQ5Jn[:V38jE*-+/('2] ;\YQ5Jn"$!D 
m<? hd#& 1

In future climate case, the ensemble average10 of downscaled 4
global circulation model (GCM) outputs (CSIRO, MIROC, MRI,GFDL) were used as basic datasets. Due to ensemble averaging,
distinct fluctuations from each GCM was neglected. Japan
Meteorological Agency (JMA) 3-hr Mesoscale Model products
were used as initial and boundary condition.

Introduction

Climate change due to global warming is a major concern
worldwide1. Among the triggering factors of global warming are
anthropogenic heat and greenhouse gas emissions. These factors arelargely linked to urbanization. Urban Heat Island (UHI)2, a
phenomenon when urban areas have higher air temperature than its
surrounding rural areas, is an obvious proof of urbanization’s impact
to warming. The effects of UHI (e.g. localized heavy rainfall, heatstress) not only the surrounding weather but is also directly tied tohuman lifestyle and health. For example, frequent localized heavy
rainfall, due to UHI, may lead to flooding within the city3. Flooding
can lead to economic problems, infrastructure damages, and diseases.
Due to the stagnation of heat in urban areas, casualties due to heatstress may rise and quality of sleep will worsen4. In the future, urban
population is expected to rise which may lead to a more intense UHI.
The UHI intensity would be enhanced during night time by 20% in
future climate5. Pseudo Global Warming (PGW) analysis suggest
extreme hot days in metropolitan area would double that of now6.
Furthermore, the effect of global warming is larger than urbanization
effect under the future land use scenario and PGW method7. So far,modelling future climate assume constant and homogeneous urban
parameters thus lacking the analysis on the actual contribution ofurban areas to global climate change. Also, only future temperaturefields and UHI intensity were analyzed. In urban areas, localized
heavy rainfall cases have been detected but its modelling coupledwith realistic urban parameters were mostly for hindcasting studies.
Past localized heavy rainfall in Tokyo were simulated using realistic
urban8,9. Long term rainfall trends using weather models withrealistic urban parameters are still lacking.
This study aims to conduct one month simulations using PGW
method with the emphasis on actual urban effects using realistic
parameterizations and distribution. In this study, two one case studies
were conducted for Nagoya and Kanto region. First, land use andglobal warming effects were mentioned by using future land use
scenario and PGW method. Second, the separate influences ofclimate change and urbanization to rainfall was determined.

2

2.2 Nagoya simulation
WRF simulations were conducted for August 2011 (current case)and 2090s (future case). One-way nesting with two domains wasused. First domain covered whole of Japan and second domain
covered Aichi prefecture. The domain resolutions were 25-km,
5-km and 1-km.
Two future land use distribution were considered for Nagoya (Fig1).
One is denoted centralized case, the other is the decentralized case.
Centralized case has one large urban which is centered Nagoya city.
However, decentralized case has four urbanization areas that are
centered Nagoya, Yokkaichi, Toyohashi and Gifu. Total urban area
is conserved between these two urban scenarios.

Gifu
Nagoya

Yokkaichi

NagoyaToyohashiFig 1 Future land use scenario (left: centralized, right: decentralized)

2.2 Tokyo simulation
For Kanto region, simulations were conducted for August 2010
(current case) and 2090s (future case) without changing the urban
parameters for the future. It is to be noted that the 2090s simulation
background is not the same with Nagoya. The 2090s simulation’sbase year is 2010. First domain covered whole of Japan and second
domain covered Kanto region. The resolutions were 5km and 1km
for first and second domain, respectively. And urban parameter wasconsidered in this simulation.

Methodology

2.1 Model description
In this research, Weather Research and Forecasting (WRF) Model
Version 3.3.1 was used.

85

URB and VEG. These settings could consider the urban effect infuture and current climate, respectively. URB has the urbanized area
(red area) in central Kanto region. On the other hands, VEG case isreplaced urban area to glass land (light green area) to exclude theurban effect.

KUMAGAYATSUKUBAKUKI
ABIKO
HACHIOJI

TOKYOFUCHUO
KY

Y CHIBABAOLand use category

T
YOKOHAMASAGAMI BAYFig 2: Simulation area for Kanto

2.3

PGW method

The PGW method11 is used to predict the future climate of a region
using dynamical downscaling. Instead of immediate downscaling of
future forecasts from the GCM, this method is forecasting futureclimate relative to an existing past climate condition. First, a PGWdataset is needed which is calculated from the difference of averagemonthly climatologies in 2090s and 2000s. The GCM used to
calculate climatologies in 2090s came from the Intergovernmental
Panel on Climate Change (IPCC) AR3 A1B scenario.
Second, the PGW dataset is added to the past lateral boundary.
Figure 4 shows the temperature distribution of the lateral boundary
in the future and current cases. The future lateral boundary, sum of
current case and PGW dataset, shows a temperature higher thancurrent case by 2 - 3 C. The future lateral boundary was used todownscale future climate.
GlobalPGW
warmingdatasets
effects

PGW
Futurecase
datasets

Monthly
average in
2090s

Current reanalysis
datasetsFig 5 Land use category in URB (left) and VEG (right)

3

Results and Discussion

3.1 Nagoya simulation
Table 1 showed the results of temperature, wind speed andprecipitation in all cases. Future temperatures were found to increaseby 2.3 C In addition, precipitation was increased in the future forboth cases. However, wind speed was not change in future andcurrent climate.
Next, a specific heavy rainfall event was selected for further analysis.
Fig 6 showed the total amount of distribution in future and current
climate. Heavy rainfall occurred from August 20th to 21st. The
rainfall distribution was similar in centralized and decentralizedcases. In the future, heavy rainfall area will occur in central Nagoya
city while current heavy rainfall will be relocated to Toyohashi city.
These results showed that future land use scenario did not affect therainfall so much. Detailed urban effect should be considered.

Monthly
average in2000s

GlobalPGW
warmingdatasets
effects

Fig 3. Pseudo global warming method

Table 1 Statics value of temperature, wind speed and precipitationin 2090s and 2011
Temperature []Centralized case

Decentralized case

2090s

2011

2090s

2011

Temperature [C]27.2 (+2.3)

24.9

27.2 (+2.3)

24.9

Wind speed [m/s]

3.4 (+0.1)

3.3

3.4 (+0.1)

3.3

Precipitation

1369.7 (+470.1)

899.6

1692.0 (+807.3)

884.7

[mm]
Gifu

Fig 4. Temperature at 2m distribution (Left: current, Right: future)

2.3 New urban parameterization

Yokkaichi

In this simulation, latest urban parameterizations were coupled withsingle-layer urban canopy model to consider the urban effects12. The
highlight parameters of this research are as follows;(1)
Anthropogenic Heat Emission (AHE) and Anthropogenic
Moisture Emission (AME) distribution(2)
Roughness length (z0) and displacement height (d)distribution(3)
Sky view factor (VF) distribution(4)
Actual urban fraction distribution

Toyohashi

(a)

(b)

(c)

(d)

Fig 6 Rainfall distribution((a)Centralized_2090s, (b)Centralized_2011,(c)Decentralized_2090s, (d)Decentralized_2011)

Two types of simulations were done, one is URB and the other is
VEG. URB included the urban parameters in urban area and VEG
excludes the urban effect. Fig 5 showed land use information of86

Precipitation [mm]

Nagoya3.2 Kanto simulation

global warming.
On the other hands, precipitation distribution was different between
future and current cases. In current simulation, more precipitation
occurred above the urban area. However, future precipitation did notincrease as much.
From fig 10, current simulations reproduced a localized heavy
rainfall. URB_2010 simulated a heavy rainfall stationary above theurban area while VEG_2010 rainfall spot moved further west. The
reason behind the URB_2010 rainfall pattern is due to theconsideration of urban parameters in the simulation. High buildings
delayed the sea breeze.

Precipitation [mm]

Precipitation [mm]

Temperature []Temperature []Figure 6 shows the temperature and precipitation distribution in
URB_2010 and Observation. Observation data was from AMEDASin JMA. Nine AMEDAS points were picked up to compare with thecurrent urbanized simulation results (2010_URB) namely, Tokyo,
Fuchu, Yokohama, Hachioji, Chiba, Abiko, Kuki, Kumagaya andTsukuba. URB_2010 and Observation were similar to each other.

Fig 6 temperature and precipitation distribution(Left: URB_2010, Right: Observation)

3.2.1 Global warming effect between 2090s and 2010

Precipitation [mm]

Fig 7 Temperature and precipitation field(left: URB(2090s-2010), right: VEG(2090s-2010))

In this section, global warming effect is detected by subtracting
simulated parameters of future climate and current climate. Fig 7
showed the temperature and precipitation intensity distribution in
URB (2090s-2010) and VEG (2090s-2010).
In temperature distribution, future climate temperature is higher thancurrent climate by 2 C However in URB case, higher temperaturearea shifted northwards. The reason behind this is the differences insimulated pressure gradient between sea and land. In 2090s_URB
includes the global warming and urbanization effect, temperature
increased on land generating stronger winds than 2010_URB. Wind
advects warmer air from urban areas further inland.
As for rainfall, it will decrease in the future above urban areas suchas Tokyo. To analyze the rainfall, August 19th rainfall event waschosen.
Fig 8 showed the URB (2090s-2010) precipitation, convergence anddivergence fields in 17:00 and 18:00. Future precipitation
distribution was more dispersed than current precipitation because of
the convergence front from Kashima nada and Tokyo bay. On 17:00,
current simulation has strong convergence and divergence spots
above the boundary of Tokyo and Kanagawa prefecture. Thesespots were created by two convergence fronts from Tokyo bay andKashima nada. However, future scenario simulated a northern
Kashima nada convergence front resulting to an expanded
convergence region.

URB_2090s

URB_2010
Div [s-1]

URB_2090s

3.2.2Urbanization effect between URB and VEG
In this section, urbanization effect was considered by subtract
simulated parameters of URB and VEG. Fig 9 showed thetemperature and precipitation distribution in 2090s (URB-VEG) and2010 (URB-VEG). Temperature fields showed that higher
temperature area existed above urbanized areas because of theurbanization. However, future higher temperature area was wider
than current area because of the heat island effect as a feedback to

URB_2010 17:00

18:00 Conv [s-1]

Fig 8 Rainfall, convergence and Divergence fields

87Temperature []Precipitation [mm]

Figure 9 Temperature and precipitation field(left: 2090s(URB-VEG), right: 2010(URB-VEG))

were estimated using PGW coupled with the latest urban
parameterization.
First Nagoya simulation, global warming and future land use
scenario were considered. Future temperature and rainfall increased
in a centralized urban future and decentralized urban future.
Second Kanto simulation, global warming and urbanization effects
were considered using PGW and urban parameterization. In theseresults, current case simulation and observation data agrees in bothtemperature and precipitation fields. Global warming affects thetemperature and precipitation distribution because of its influence tothe convergence front of Kashima nada and Tokyo bay.
Urbanization increases and widens temperature distribution aboveurban areas. Current urban conditions will increase precipitation
above the urban area due to the roughness generated by buildings.
However, the effect of urbanization to rainfall in the future becomesless apparent.

Reference[1][2]

URB_2010

[3][4]

Precipitation [mm]

[5]

VEG_2010

[6]

[7]

[8]

URB_2090s

[9]
[10]
[11]
[12]

VEG_2090s 17:00

18:00

Fig 10 Precipitation distribution on 17:00 and 18:00
In the future, rainfall distribution was similar for both URB and
VEG. This suggests that in the future, rainfall will not be sensitive tourban areas. It is to be noted that so far comparisons in this study,
urban conditions were not subjected to change between now and in
the future. From 100-yr observation data, 3.2 K rise in temperature
was observed in Tokyo, which is far larger than the global
temperature increase. Real temperature and precipitation increase
due to urbanization can be specified further in the future if
modifications in urban conditions were also reflected in the future
simulations.

3

Conclusion

In this study, global warming and urbanization effect to weather

88

Sato, Tomonori, Fujio Kimura, and Akio Kitoh. Journal of
Hydrology 333.1 (2007): 144-154.
McCarthy, Mark P., Martin J. Best, and Richard A. Betts.
Geophysical Research Letters 37.9 (2010).
Yonetani, Tsuneharu. Journal of Applied Meteorology 21.10(1982): 1466-1471.
Okano. Y., T. Ihara, Y. Genchi. Journal of Heat Island
Institute International Vol.3 (2008): 22-33.
Hara. M., H. Kusaka, F. Kimura, Y. Wakazuki, 2010,
Nagare, 29.5 (2010): 353-361
Kusaka Hiroyuki, Masayuki Hara, and Yuya Takane.
Journal of the Meteorological Society of Japan 90.0 (2012):
47-63.
Adachi, Sachiho A., et al. (vol 51, pg 1441, 2012). Journal of
applied meteorology and climatology 51.11 (2012):
2074-2075.
Shimoju R., M. Nakayoshi, and M. Kanda, (2010). Journalof Hydraulic Engineering, JSCE, 54., pp349-354
Nakano, M. Nakayoshi, and M. Kanda, (2013). Journal of
Hydraulic Engineering, JSCE
Kawase, Hiroaki, et al. Journal of Geophysical Research:
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Hydrology 333.1 (2007): 144-154.
Varquez, Alvin C.G, Makoto Nakayoshi, and Takuya
Makabe. Journal of Japan Society of Civil Engineers, (2014)

Development and Sustainable management of Construction Authorization Database for the World
Heritage Site: Case of Department of World Heritage Luang Prabang, in Lao P.D.R.
Student Number: 13M18103

Name: Natsuko Sakanashi

Supervisor: Shinobu Yamaguchi and Jun-ichi Takadaୡ⏺㑇⏘ᆅᇦࡢࡓࡵࡢᘓ⠏チྍࢹ࣮ࢱ࣮࣋ࢫࡢᣢ⥆ⓗ㐠⏝࡜㛤Ⓨ: ࣛ࢜ࢫேẸẸ୺ඹ࿴ᅜ
ࣝ࢔ࣥࣃࣂ࣮ࣥୡ⏺㑇⏘ᒁࡢࢣ࣮ࢫᆏ᲍ ⳯ὠᏊ
ࣝ࢔ࣥࣉࣛࣂ࣮ࣥୡ⏺㑇⏘ᒁࡢᘓ⠏チྍᴗົࢆᨭ᥼ࡍࡿࡓࡵࠊᘓ⠏チྍࢹ࣮ࢱ࣮࣋ࢫࡀ㛤Ⓨࡉࢀࡓࠋࡇࡢ◊✲࡛ࡣࠊᘓ⠏チྍࢹ࣮ࢱ࣮࣋ࢫ㐠⏝࡟ᑐࡋ࡚⏕ࡌ࡚࠸ࡿၥ㢟Ⅼࢆᣢ⥆ⓗ㐠⏝ࡢほⅬ࠿ࡽ᥈ࡿ࡜క࡟ࠊᨵၿ࣭ホ౯ࢆ⾜࠺ࡇ࡜ࢆ┠ⓗ࡜ࡍࡿࠋၥ㢟ศᯒ࡜㐺⏝ྍ⬟ᢏ⾡ࡢẚ㍑ࡢ
⤖ᯝࠊࢹ࣮ࢱ࣮࣋ࢫࡢᨵၿࡀ⾜ࢃࢀࡓࠋ࣮ࣘࢨࣅࣜࢸ࢕࣮࡜⟶⌮ࡢほⅬ࠿ࡽホ౯ࢆ⾜࠸ࠊࢹ࣮ࢱ࣮࣋ࢫࡢ⟶⌮࣭㐠⏝ࡀ᪥ᖖᴗົ࡜ࡋ࡚ྍ⬟࡛࠶ࡿࡇ࡜ࡀ☜ㄆࡉࢀࡓࠋ

1

Heritage Preservation and Development Master Plan
(PSMV). The authorization process directory impacts on
the heritage townscape. In authorization section of DPL,
large volume of documents and applications were storedin paper-based media, and they have difficulty in the
management of these information. Enoki (2008, p.17)
summarized the aim of construction authorization
database as follows: [2]1) Memorizing the processes to give construction
permission based on authorization work
2) Preservation of authorization data as digital for
long-term preservation and reduction of physicalstorage space
3) Providing searching and analysis processes forsaving time to access information
In the field of cultural heritage preservation, database iscommonly used for data archiving. World heritage site is
managed and preserved under the heritage management
plan and it is expected to follow its plan to keep theheritage inscription. Information related to authorizing
renovation of architectural building is highly controlled
and thus, only experts are allowed to be involved inmanaging such information. Therefore, the developmentof the database for cultural heritage is considered vital tomanage accurate information and to be shared amongprofessional concerned. This requires close cooperation
between ICT engineers and cultural preservation experts.
In DPL, the local ICT team members and construction
authorization professionals have played major roles inconstructing the database.
However, Okumura (2012) clarified the construction
authorization database was not updated since 2009 [3].
Based on the analysis, two solutions were implemented.
First, PHP was introduced as alternative of web
application of Zope. The support of connecting function
between Zope and PostgreSQL ended. Second,"Evernote" was introduced as knowledge management
tool in order to access the difficulty of knowledgetransfer. In spite of these efforts, through local discussion,interviews in the DPL in August 2013, it was found thatthe databases still had problems for its sustainable use.

Introduction

Thanks to its outstanding universal values, town ofLuang Prabang was inscribed as the first UNESCOWorld Heritage Site in Lao P.D.R in 1995. The
inscription of world heritage site drew not onlyinternational attention and rapid tourism boom, but also
unplanned expansion of development. Since 2003,
Tokyo Institute of Technology (Tokyo Tech) and
Department of World Heritage Luang Prabang (DPL) in
Lao P.D.R., have been collaborating for strategic
management of the world heritage site by introducing
applicable Information and Communication Technology(ICT).
As one of such activities, database management
systems were developed with reflecting local conditions.
The implemented databases have five characteristics: 1)
free and open source software (FOSS); 2) client server
architecture; 3) Web-based interface; 4) choice of easyto-learn software; and 5) multi language support. One ofthe databases, the construction authorization database
was created in 2007 to support the authorization processof construction permission [1].
However, it was found that the database encountered
several problems in its sustainable use in 2013. This
study intends to explore sustainable solutions for theconstruction authorization database.

2
Research background andresearch objectives
2.1
Implementation of construction
authorization database
In order to support the authorization process ofconstruction permission, the construction authorization
database was developed in 2007. The authorization
process is to give permission for the constructionbuildings in heritage conservation areas based on

89input, so users faced difficulty. Second, lack ofknowledge and skills transfer was evident due to highmobility of human resources within the local team.
Although the training for ICT use including databasedevelopment was provided with total of 53.2 weeks from2005 to 2007 [1], the team members were facing
difficulty of transferring the knowledge and skill to newteam members. Loss of training materials due to thefailure of the hard disk drive in 2007 added moredifficulties.
These issues clarified the specific needs to improvethe database. First, simplification of data input processwith user-friendly interface was required. This databaseis to be uploaded by authorization section staff members,
who have less proficiency in English and ICT skills.
Simple interface and Lao explanation especially forsolving errors and explanations for data input were foundvital. Second, training for database management wasnecessary. The skills of ICT team required were not up to
its expectation to manage the database. The training was
one of the important factors to empower management of
database by local ICT team.

Implementation and management of the database iseasy to fail without careful planning even in developed
country. This study site is located in the least developed
country. The issues, which this database was faced, had
to be analyzed not only from the database management
perspectives but also other perspectives. This research
could be seen as the ICT and FOSS for development
research. The characteristic difficulties of ICT (such as
limited cost, human resources, and infrastructure) andthose of FOSS for development project (such as
requirement of high ICT skills, and poor user support)
also had to been taken into consideration [4] [5].

2.2

Research objectives and significanceThis study intends to explore sustainable solutions forconstruction authorization database. The following threeobjectives were set: 1) analysis and identification ofproblems of the current database based on literature
reviews and local discussions; 2) development ofapplicable database reflecting the local context; and 3)
evaluation of usability and management of the database.
The significance of this research can be explained by
the following two points. First, efficient use of thedatabase is able to enhance the preservation of the townof Luang Prabang. Second, this research is expected tocontribute as an example of management of the databasein the world heritage site in least developed country. The
database is one of the major ICT tools employed incultural heritage site. However, the management of thedatabase is difficult even in developed countries.
Previous research covers useful ICTs implementation,
but few researches are available discussing databasemanagement for the world heritage site in least
developed countries. This action-based researchhighlights important issues to be considered whendeveloping and managing applicable database for world
heritage preservation with reflecting local condition in
the least developed country.

3

3.2

Identification of solutions

Based on the analysis, the possible solutions for easyto-use user interface were investigated. This process took
three steps: 1) comparative analysis on potential solution;2) developing prototype interface; and 3) conducting
usability test with the prototype.
As a first step, the potential technologies providinguser-friendly interface were identified. The possible
technologies include introduction of new Microsoft Excel,
Libre Office Base which is open source software for
database management, and improvement of current Webbased user interface. For the comparative analysis, five
perspectives were carefully considered reflecting local
conditions for its sustainable use, namely, popularity
among users, level of skills of local team, reusability of
the current resources, level of ease of development, andcost of software. As a result, the modification of thecurrent web-based user interface was identified as thebest solution because of fully utilization of the current
level of skills of local team and efficient use of thecurrent resources.
Following the above result, the prototype web-baseduser interface was developed. The prototype was tested toassess its validity and usability. This prototype hasbasically same functions with the original one in terms ofdata input, search, and additional editing. However, thisprototype had become simpler reflecting opinions oflocal staff members. Specifically, data inputting process
was simplified with prioritizing collecting attributes from41 to 10 with Lao language explanation.
A total of 23 cases of usability tests were carried out
with the authorization section members. The tests
clarified satisfying results, enabling to integrate theconstruction authorization database into their daily work.
Users’ evaluation proved that this database has become
easier to input, search, and edit information. This
prototype was appreciated since DPL staff members withless ICT experiences and English skills could also use thedatabase to efficiently manage a large volume of data. At

Methodology

The research processes are divided into the following
five steps: 1) analysis of issues and needs; 2)
identification of solutions; 3) database improvement; 4)evaluation; and 5) identification of issues for further
improvement. Each process was corresponded to theaction research methods which is a research being
motivated to solve specific problem in the real world.

3.1
Analysis of the current issues andneeds
The analysis of the current issues and needs of thedatabase was carried out through literature reviews andlocal discussions between August 2013 and March 2014.
The discussion and interview were conducted with thelocal ICT team members and authorization section
members.
A series of analysis identified the following two
problems. First, the database interface is complex. It
requires too many types of information and time for data90the same time, additional issues were found including
needs for local language modification and better layout of
web-based user interface.

3.3

Database improvement

Based on feedback collected through the testing of the
prototype, the construction authorization database was
improved by ICT team members after training. This
process included 1) providing training for databasemanagement at the local site; and 2) database
improvement.
The training for database management was organizedin September and October 2014 for ICT team members.
It was organized two hours per day for nine days in
English and local Lao language. The contents included
following items: 1) Linux OS usage and setting up ofserver; 2) database management system; 3) structure of
Web-based interface which was written by HTML and
PHP; and 5) Evernote software. In addition, training ofVirtual Machine was also provided. It was considered as
an option to make backups of server easier. Training
participants installed Linux OS with VMware player on
their computer running Windows OS. VMware player ispopular commercial Virtual Machine software, but freely
distributed. In each training session, handouts and sample
source codes were distributed to the participants. Eachfunction of program was understood by executing source
codes by participants themselves. And also for the user ofthe database, staff member of authorization section, theintroduction of basic technology used for the construction
authorization database was provided.
After the training, database improvement was carriedout for three days by local ICT team members. Based on
the development of prototype interface and its usability
test, the improvement processes were designed including
the following three points. First, the original input form
required to input too many attribute. It was necessary toprioritize important data. The number of attributes fordata input was decreased from 41 to 10. Second, Lao
language explanation were added and shown at the same
page with English one. The necessary attributes andexplanation sentences were translated into Lao language.
Third, the user interface has pull down lists for easy data
input. To further improve of the usability, the values inpull down lists were arranged in alphabet order.

4

Figure 1: Improved user interface

Figure 2: Original user interface
interface. Right after the usability test, interviews wereorganized to analyze users’ point of view.
Through the usability test and interviews, thefollowing three major advantages were found. First,
needed time for data input was shortened. Previously, thetime for data input took 30 minutes or more. However,with the improved user interface, an average was four
minutes and three seconds. Second, all participantsconfirmed the searching function was easy to use. The
people without high ICT skills can search and find theinformation easily. Third, it was mentioned Lao language
explanation helped data input. Therefore, the staff
members of authorization section decided to integrate thedatabase into their daily work.
On the other hands, the issues were also identified suchas existence of redundant values in pull down list, andinappropriate order of attributes in input form. Further
review of the user interface was necessary. In addition, theuser manual needed to be further improved to be able toidentify necessary information at a glance.

Evaluation

Improved database was evaluated through theusability tests and case trials. The usability test was
carried out with authorization section staff members. The
case trials tests were performed to evaluate whether
database improvement is able to be conducted by ICTteam members.

4.1

4.2

Case trials

Case trials and interviews were conducted to evaluate
whether the local ICT team members were equipped withskills to improve the construction authorization databasereflecting participants’ feedback. Three ICT teammembers were assigned two specific tasks of improving
construction authorization database. First task was tochange the order of one attribute in input form. Anothertask was to add the value in pull down list. These tasks
were expected to receive continuous request by the users.

Usability test

The usability test was conducted in October, 2014 toassess the user interface improved by local ICT teammembers. The participants were asked to perform thetasks namely, data input and searching with new user

91

reintroducing the benefit of the knowledge management
tool. Third, improvement of manual for both manager anduser was found necessary. The training materials fordatabase management have to be reorganized for daily use.
The user manual has to add more detailed information.

To attain these tasks, the participants had to understandstructure of Web-based interface, basic skills of PHP and
HTML, Apache server application, database management
system, and database tables.
Through the case trial test and interviews, thefollowing three advantages were found. First, most of theteam members were able to complete the required tasks.
With this result, it is certain that the local ICT team would
be able to improve the user interface as request arrives.
Second, all participants mentioned that the training helped
the participants to understand how to manage database.
The staff member having no knowledge of databasemanagement also completed the task and was positive formanagement of the database after the training. Third, they
all agreed that ICT team could improve and take backupsof the construction authorization database. The
participants were also eager to acquiring more skills andknowledge of the database.
At the same time the issues were clarified. The ICTteam members were concerned about how to cope with
unexpected errors. For the further sustainable managementof the database, more experiences and training sessions are
required. ICT team members did not utilize referencematerials as expected. To promote better knowledgetransfer, it is considered that enhanced knowledge
management tool is necessary.

5

6Conclusion

In this study, sustainable solutions for construction
authorization database were explored. The database wascreated in 2007 to support the authorization process ofconstruction permission. However, the databaseencountered the problem in its sustainable use.
Through the analysis of issues and needs of thedatabase, following needs were identified: 1)
simplification of data input process with user-friendly
interface; and 2) organization of training for the
management of the data. After the training for ICT team,
improvement of the current user interface took place
based on comparative analysis of the solutions and theprototype testing.
Through the evaluations, it was clarified that the
improved user interface satisfied the needs of users of theauthorization section. From the management point ofview, ICT team members acquired basic skills for
managing this database through training.
Construction authorization database is now to be
integrated into their daily work to promote long-term
utilization at DPL. Improved database has become
applicable for local use. At the same time additional
training opportunities are vital for the local professionals
further to enhance their development.

Discussion

Through the evaluations, following three advantages ofimproved user interface were identified.
First, improved user interface satisfied theauthorization staff members because of its usability. The
required time for data input was dramatically shortened.
Easy search function was expected to enhance their daily
work. In addition, the staff member having lessproficiency of English and ICT skills could also utilize thedatabase. Easy-to-use user interface enhances the use of
the database and support the construction authorization
process effectively. Second, the construction authorization
database was highly accepted among users and managers.
Authorization staff members decided to integrate thedatabase into their daily work. ICT team members agreed
with management of the database as their daily work. The
DPL staff members participated in entire improvement
processes, such as identification of issues and needs, andusability test. It was considered that the participation of
the processes enhanced the acceptance and ownership ofthe database among users. Third, through the case trials, it
was proved that ICT team members can improve the userinterface and create backups by themselves as daily work.
At the same time, it has following three issues. First,
ICT team members are concerned about how to deal with
unexpected errors. They are not confident enough to face
unfamiliar errors. For further sustainable management of
the database, the training should be required. Second, thecontribution of the training materials for solving lack ofknowledge and skills transfer is limited. There is atendency that the staff members do not refer to thematerials. It is necessary to enhance the use of thematerials and knowledge management tool, such as
developing easy documentation, translation of documents,

References[1] Yamaguchi S. “Application of information andcommunication technology for promoting sustainable
development of the world heritage site”, Final research reportfor Grant-in-Aid for Scientific Research (A) in Grant-in-Aid
for Scientific Research (KAKENHI), Fiscal year from 2005
to 2007, 2008.
[2] Enoki S. “Application of information and communication
technology in cultural world heritage site; case of luang
prabang, lao PDR”, Master's thesis, Tokyo Institute ofTechnology, 2008.
[3] Okumura T. “Application of knowledge management system
in development project: A case of world heritage site in luang
prabang, lao P.D.R.”, Master's thesis, Tokyo Institute ofTechnology, 2012.
[4] Almamy Touray, Airi Salminen, Anja Mursu. “ICT Barriers
and Critical Success Factors in Developing Countries”, The
Electronic Journal of Information Systems in Developing
Countries, 2013, vol. 56, ISSN: 1681-4835.
[5] Katim S. Touray. “Constrains against the adoption and use of
FOSS in developing countries”, Linux.com, 2004, [Online],
Available: http://archive09.linux.com/feature/35055,(Accessed on January 7, 2015).

92

MODELING ADVECTION VELOCITY OF SURFACE TEMPERATURE FOOTPRINTS
Student Number: 13M18037

⾲㠃

Name: Eiji IWATSUKA

Supervisor: Manabu KANDA

ᗘࣇࢵࢺࣉࣜࣥࢺࡢ⛣ὶ㏿ᗘࡢࣔࢹࣜࣥࢢᒾሯ ⱥ἞

TIV ࡟ࡼࡾ᥎ᐃࡉࢀࡿ⛣ὶ㏿ᗘ࡜㸪㢼㏿ࡢ㛵ಀ࡟ࡘ࠸࡚ࡢ⌮ゎࡣᮍࡔ୙༑ศ࡛࠶ࡿ㸬ࡑࡇ࡛ᮏ◊✲࡛ࡣ㸪ேᕤⰪࢢࣛ࢘ࣥࢻ࡟࡚㸪TIV ほ ࢆᐇ᪋ࡋࡓ㸬ࡑࡢᚋ㸪⛣ὶ㏿ᗘ࡜㢼㏿ࡢἼᙧࢆẚ㍑ࡋࡓ㸬ࡲࡓ㸪⛣ὶ㏿ᗘ࡜㢼㏿ࡀ⥺ᙧ㛵ಀ࡟࠶ࡿ࡜௬ᐃࡋ㸪ࡑࡢẚ౛ᐃᩘࡀẼ㇟ࣃ࣓࣮ࣛࢱ࡜ዴఱ࡟స⏝ࡋ࡚࠸ࡿ࠿ࢆ᳨ウࡋࡓ㸬ࡉࡽ࡟㸪⛣ὶ㏿ᗘ࡜ከ㧗ᗘ࡛ ᐃࡉࢀࡓ㢼㏿್ࢆẚ㍑ࡋ㸪⛣ὶ㏿ᗘࡀ࡝ࡢ㧗ᗘࡢ㢼㏿࡜୍⮴ࡍࡿࡢ࠿ࢆ㸪Ẽ㇟ሙࢆ⪃៖ࡋ࡞ࡀࡽ᳨ウࡋࡓ㸬

1 Introduction
Since the 1990’s, rapid development of engineering
technology such as computational resource and camera
technique brought fluid visualization to the stage where
quantitative evaluation can be executed. This impact
soon brought extensive contribution to the meteorology
field. For example, secondary vortex inside an urban
canopy was investigated by Takimoto et al. [1] using
PIV (Particle Image Velocimetry). Also, Yagi et al. [2]succeeded measuring turbulent structure 76.2 meters
above sea level operating the Doppler lidar. However,outside observation measurement method for horizontal
wind velocity distribution is currently still limited. PIV
captures flow field of few meter scale, and Doppler
lidar’s measuring range is few hundred to thousand
meters. In other words, method to observe turbulent
scale of ten to one hundred meters is still problematic.
Within this circumstance, TIV (Thermal Image
Velocimetry) technique which is used in this paper canestimate two dimensional wind velocity using thethermal image, and can be the solution [3]. TIV wasdeveloped based on researches proposing estimation ofwind field relating with surface temperature [4]. It
observes excel at the lacking range mentioned above
and has successfully contributed to wind estimationnear building walls and dust devils [5]. Yet, there has
been lack to quantitative discussion on relation between
wind speed and advection velocity estimated from TIV.
In this study, two-dimensional horizontal advection
speed vector observed from thermal camera and windspeed measured by sonic anemometer will be compared.
Along with this, new expertise will be given to theheight where estimated TIV advection speed andmeasured wind speed matches (zTIV).

(a)

(b)

(c)

(d)

Fig. 1: TIV estimation flow. (a) Visible image of surface.
(b) Brightness temperature image of surface. (c)Brightness temperature deviation image of surface. (d)Two-dimensional TIV velocity vector.
the effect of surface color patterns & surface reliefs,
brightness temperature deviation is extracted (Fig. 1(c)).
Applying the image correlation analysis to the extracted
brightness temperature deviation pattern, advection
velocity will be derived just like the PIV (Fig. 1(d)).
Assuming advection velocity and wind near surface arein linear relationship, multiplying the proportionality
constant will end up obtaining the two-dimensional TIV
velocity vector. This will be the whole flow of TIV
estimation.

3 Experimental Set-Up
2 Wind Velocity Estimation Theory Using TIV

The experiment was conducted at an artificial football
field in Ti-Tech from September 2nd to 27th, 2013
during clear weather. Surface temperature data at
100Hz were captured by a FLIR SC5000 Thermal
camera, which was mounted 49m above ground level(a.g.l.) at the building next to the field. Five sonicanemometers (Kaijo DA600 TR90AH) measured theturbulent velocity components (u,v,w) and the airtemperature at 50 Hz at 0.15m, 0.30m, 0.60m, 1.2m,
1.6m a.g.l. inside the camera field-of-view. Total 19
datasets were obtained. Analysis was done by each

Consider a case where the surface is heated and thetemperature difference between air and surface isextreme. In this condition, active turbulent organizedstructure will develop near the surface. As it moves
around with the wind flow, heat transfer between
surface and air occurs, ending up with a non-uniform
surface temperature distribution. This phenomenon isrecorded using a thermal camera and capturing thesurface brightness temperature image (Fig. 1(b)).
Afterwards, by using the high-pass filter and removing

93

Fig. 3: Frequency analysis of mainstream wind velocity
component. Spectrum obtained from mainstream windspeed measured by sonic anemometer is shown in red.
Spectrum obtained mainstream direction component of
TIV velocity is shown in blue.

Fig. 2: Observation scenery. (a) Aerial photograph ofobservation site. Trapezoid indicates the shooting areaof thermal camera. Sonic anemometer was set inside thecircle and Thermal camera was set inside the red square.
(b) Sonic anemometer. (c) Thermal camera. (d) Overview
sketch of the observation.

4.2 Waveform Comparison
Velocity and Wind Speed

betweenTIV

Fig. 4 shows the comparison in one observation caseat 0.20Hz sampling frequency. Focusing on the low
frequency fluctuation, TIV velocity and wind speed
roughly matched. The correlation coefficient between
the two velocities were 0.61. On the other hand, focusing
on small velocity fluctuation, TIV velocity included
spike-like fluctuation which could not be identified inwind waveform. This intermittent spike-like noise wellmatches with the spectrum peak affirmed inintermediate frequency domain in Fig. 3.

observation data as units of 10 minutes. The
observation scenery is exhibited in Fig.2.

4 Result
4.1 Spectrum Analysis
Fig. 3 shows spectrum of mainstream wind speed atheight 1.6m, and mainstream direction component of
TIV velocity. This result was obtained by first,
measuring surface brightness temperature at 100Hz.
After that, advection velocity of brightness temperaturedeviation pattern was estimated at 3.3Hz frequency.
Next, moving average was taken at 5.0Hz and advection
velocity was estimated. A broken and dashed line isnoted at 3.3Hz and 5Hz, respectively. The solid line
indicates spectrum model of a neutral surface boundary
layer which was obtained from Kaimal et al. [6]. A
precipitous energy drop can be seen in the frequency
range higher than 3.3Hz. In addition, the spectrum isremoved in the range higher than 5.0Hz.
Spectrum observed from sonic anemometer in thisobservation case well matched with Kaimal spectrum
model. On the other hand, it didn’t match with spectrum
obtained from TIV. Mainly three characteristics wererecognized from spectrum obtained from TIV velocity. 1)
It clearly matched with the spectrum obtained fromsonic anemometer in the low frequency domain. 2)
Spectrum obtained from TIV velocity is underestimatedin high frequency domain. 3) Spectrum obtained fromTIV velocity is overestimated in intermediate frequency
domain (0.10~1.0 area)

(a)

(b)
Fig. 4: Waveform graph of velocity. Red line indicatesTIV velocity. Blue line indicates wind speed observed
from sonic anemometer. (a) Compared at u direction. (b)Compared at v direction.

944.3 Linear Relationship Established between
TIV Velocity and near Surface Wind Flow

1.5

Gradient

The fact that TIV velocity and wind speed holds a
proportional relationship was known since this TIV
method was developed. Theoretically, wind speed will bederived by multiplying the TIV velocity toproportionality constant. However, it is still not yet
understood what kind of parameter the proportionality
constant (Gradient) relies on. For the current stage, thisis obtained by direct comparison of TIV velocity andwind speed.
This section will discuss the weather parameter effecton proportionality constant. Fig. 5 shows TIV velocity
and wind speed each plotted at u direction and v
direction within 10 minutes (intercept is set to 0). The
colored and uncolored circle each indicates 0.017Hz and
0.20Hz sampling frequency. Proportionality constant(Gradient) was 0.72 in this 0.017Hz observation case.
When the gradient value is smaller than 1 as shown inthis graph, TIV velocity is relatively higher than thewind speed. When the gradient value is larger than 1,
the two relation will become opposite.
The relation between proportionality constant(Gradient) and stability for all 19 observation cases in
0.017Hz sampling frequency is shown in Fig. 6. The
graph shows that, as stability moves from unstable tonear neutral, gradient also shifts from lower to higher
value. Result of Fig. 6 can be explained from physicalinterpretation as below.
TIV velocity tracks the footprints of turbulent
organized structures moving near the surface. Usually,
when the stability of flow field becomes closer to neutral,buoyancy decreases, causing turbulent organizedstructures to break into pieces. Then, TIV will only
track small turbulent organized structures which will
relatively be slower compared to the wind speed (Fig. 7).

1.0

0.5

0.0

0.0

-0.1

-0.2

-0.3

-0.4

z/L
Fig. 6: Relation between proportionality constant(Gradient) and stability at 0.017Hz sampling frequency.

Fig. 7: Conceptual diagram of turbulent organizedstructure (Gradient<1 case). Red arrow indicates TIV
velocity. Blue arrow indicates wind speed at height z.

4.4 TIV Velocity Height
Relation between TIV velocity and wind speed
includes altitude dependency, which makes it difficult toevaluate with the meteorological parameters. Thus, inthis section, we will obtain the height (zTIV) where 10
minutes averaged TIV velocity and wind velocity
matches. In the surface area, outside the buffer region,
three-dimensional turbulent organized structures exist(Sott E. et al. [7]). Considering surface brightness
temperature pattern as the footprints of turbulent
organized structure which has characteristic of rigidbody motion, TIV velocity can be translated as center ofgravity speed of the turbulent organized structure. So
zTIV, as defined above, can be interpreted as turbulent
organized structure’s center of gravity height.
Generally, based on Monin-Obukhov similarity theory,
vertical wind speed profile holds logarithmic law. In thiscase, universal function (4.4.1) is enabled to use as thevertical wind profile model.
ൌߖெ ൌ ݈݊

Fig. 5: Scatter plot of TIV velocity and wind speed.
Colored and uncolored circle each indicates 0.017Hz and
0.20Hz sampling frequency.

95

‫כݑ‬
ߖ  ሺͶǤͶǤͳሻ݇ ெሺ‫ݔ‬଴ଶ ൅ ͳሻሺ‫ݔ‬଴ ൅ ͳሻଶ‫ݖ‬൅ ݈݊ ଶ൅ ʹሺ‫ି݊ܽݐ‬ଵ ‫ ݔ‬െ ‫ି݊ܽݐ‬ଵ ‫ݔ‬଴ ሻሺ‫ ݔ‬൅ ͳሻሺ‫ ݔ‬൅ ͳሻଶ‫ݖ‬଴method 1, and model wind profile from method 2 isshown in Fig. 9(a), (b) each for the highest and lowest
stability case. Regardless of the zTIV estimation method,
the Fig. 8 graph shows that, as stability moves fromneutral to unstable, zTIV value also shifts from lower tohigher value. This result can also be explained fromphysical interpretation.
Mentioned above, zTIV value indicates turbulent
organized structure’s center of gravity height. As theatmospheric stability becomes more unstable, buoyancy
increases, which will cause the turbulent organizedstructure to stretch in longitudinal direction or the
turbulent organized structure itself enlarges, ending up
with high center of gravity height (Fig. 7).

ଵ‫ ݖ‬ଵȀସ‫ݖ‬଴ ସ‫ ݔ‬ൌ ቀͳ െ ͳ͸ ቁ ǡ‫ݔ‬଴ ൌ ቀͳ െ ͳ͸ ቁ ‫ܮ‬‫ܮ‬Mainly three type of methods were tested to estimate
zTIV. 1) Fitting in the vertical wind profile model
equation (4.4.1) based on Monin-Obukhov similarity
theory considering roughness length z0 as constantvalue for all 19 observation cases. After this is done, zTIV
is estimated from inter section point of TIV velocity andmodel wind profile. 2) Fitting in the vertical wind profile
model equation (4.4.1) based on Monin-Obukhov
similarity theory considering roughness length z0 as
variable value for each observation case. After this isdone, zTIV is estimated from inter section point of TIV
velocity and model wind profile. 3) Consider a case when
TIV velocity interpose between two observed wind speed
points. Calculate the linear equation which passes
through two observation wind speed points. After this isdone, zTIV is estimated from inter section point of TIV
velocity and the linear equation.
Relation of zTIV estimated from the three theory above
and stability is shown in Fig. 8. The graph of observed
wind profile, TIV velocity, model wind profile frommethod 1

method 2

5 Conclusion
In this research, horizontal surface wind speed
distribution in an outdoor artificial turf football field
were estimated from a TIV observation experiment thattook place during sunny daytime in Sep. 2013. Two
dimensional horizontal advection speed vector
estimated from thermal camera and wind speed
measured by 5 sonic anemometers were compared forall 19 observation cases, each as units of 10 minutes.
Proportional relation was then confirmed in scatter plotof advection velocity estimated by TIV and wind speed
observed by sonic anemometer. The proportionality
constant (Gradient) had strong relation with theatmospheric stability. Also zTIV, the height where TIV
velocity & wind speed matches were estimated using
three different methods. Relation between zTIV andstability was elucidated.

method 3

3.0

zTIV [m]

2.5
2.0

1.5
1.0

Reference

0.5[1] Takimoto K., Sato A., Onomura S., Kanda M.:’PIV
Measurement of urban canopy turbulence –Intercomparison study between outdoor and indoor
urban scale model’, Water engineering paper㸪Vol53㸪
2009[2] Yagi A., Takimoto K., Fujiwara T., Inagaki A.,
Fujiyoshi Y., Kanda M. :‘Estimation of circumferential
velocity from observed radial velocity -- Velocity Image
Velocimetry (VIV)’, Water engineering paper 㸪 Vol68,
2012[3] Inagaki, Atsushi, et al.: ‘Thermal image velocimetry.’
Boundary-layer meteorology, 149.1 (2013), pp1-18.
[4] Christen A. and Voogt J. A.: ‘Linking atmosphericturbulence and surface temperature fluctuations in astreet canyon’, The 7th International Conference on
Urban Climate, A3-6, 2009[5] Inagaki A., Kanda M.: ‘Application of Thermal
Image Velocimetry for Measurement of near Surface
Atmosphere Flows’, Nagare 32, pp307-312, 2013[6] Kaimal, J. C., et al.: ‘Spectral characteristics ofsurface̺layer turbulence’, Quarterly Journal of theRoyal Meteorological Society, 98.417, pp563-589, 1972
[7] Hommema, Scott E., and Ronald J. Adrian.: ‘Packet
structure of surface eddies in the atmospheric boundary
layer.’ Boundary-layer meteorology 106.1 (2003): pp147170.

0.0

0.0

-0.1

-0.2

-0.3

-0.4

z/L
Fig. 8: Relation between zTIV and stability. zTIV obtained
from method 1 is shown in red circle. zTIV obtained frommethod 2 is shown in blue rhombus. zTIV obtained frommethod 3 is shown in cross mark.

Fig. 9: Observed wind profile, TIV velocity, model windprofile from method 1, and model wind profile frommethod 2. (a) Highest stability case from Fig. 8. (b)Lowest stability case from Fig. 8.

96

Estimation of the contained CO2 emissions from the supply chain of Chinese manufacturing industry
Student Number: 13M18273

04+

Name: Li Tianyue

Supervisor: Shinya HANAOKA

! CO2 &5'1
) ,$The excessive CO2 emissions from manufacturing make China become the largest CO2 emission country. The objectives of thisstudy are to estimate direct and indirect CO 2 emissions from the supply chain of Chinese manufacturing industry sectors by regions,and to propose the effective solution to CO2 emissions reduction. Moreover, 5 future emissions scenarios for power generation sector
and smelting sector are created. The high-emission upstream sectors in supply chain are proved as the key for CO2 reduction.
Improvement of industrial volume of power generation resources are found as the most effective solutions.
1.
Introduction
1.1.
Background
Chinese economy develops rapidly since 1980s. During
2000-2014, the average GDP growth rate of China achieves
14.3%. Since 2010, China becomes the second largest economy
in the world[1]. As the side effect of economic boom, China’s
energy consumption increases rapidly, especially coal. China has
been the largest CO2 emissions country since 2010. At 2013,
China expelled 10.3Gton CO2, accounting for 26.4% of global
CO2 emissions[2]. China’s CO2 emissions are not only a domestic
problem but also a serious global issue. China announced toreduce CO2 emissions from the upcoming peak no later than2030, on the COP 20 in 2014.
Industrialization is the key contributor to China’s economic
growth. China is famous as “World Factory”. Manufacturingaccounts for 32% GDP since 2000[1]. Unfortunately, 31% ofnational CO2 emissions is the side product of the developed
manufacturing[4]. It is impossible to reduce CO2 emissions bydecelerating the manufacturing development. In order to makeCO2 reduction compatible with economic growth, themechanism of CO2 emissions from manufacturing is necessary
to be investigated.
1.2.
Composition of CO2 emissions
As illustrated in Fig.1, CO2 emissions can be estimated by 2
ways, direct CO2 emissions and indirect CO2 emissions.
Direct CO2 emissions of one company, company A for instance,
refer to the CO2 emissions from fuel combustion and industrial
process on the production site of company A. But on the
production site, company A will demand electricity power orheat, and purchased material or intermediate products from theother companies. In this case, the direct CO2 emissions fromproducing the demanded production by company A are theindirect CO2 emissions of company A. In a word, indirect CO2
emissions can reveal what kind of demand-product relationships
are there among the companies, and how they impact CO2
emissions.
!"##$%&'()*+=&*$+%)2&>&2 

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

8+7-#$%&'()#&*$+%#

6+7&%))"2'-$
<'7)('$&%/'2#)"%+*&##/-;))

   

9/-/#:&5)"%+51*$),-.
,-.#/$&)01&2)*+(31#4+-)-) "%+*&##/-;
'-5'-5)/-51#$%/'2)"%+*&##     

Fig.1 Supply chain of manufacturing industry
In the context of supply chain among different sectors, one
sector demands material from upstream sectors, provides
intermediate products to downstream sectors, and sells final
products to consumers. Direct CO2 emissions of this sector
consist of the CO2 emissions from producing both intermediate
and final products. The contained CO2 emissions of this sector
are defined as the sum of direct CO2 emissions from producing
final products and indirect CO2 emissions from all its upstream
sectors.

97

1.3.
Literature Review
He (2013)[4] estimates China’s national direct CO2 emissions
during 2000-2010, and direct CO2 emissions of different
provinces during 2006-2010. However, He only estimates direct
CO2 emissions by sectors in 2010. Guo et al. (2010)[5] estimatesthe contained CO2 emissions of 13 manufacturing sectors in
China and America in 2005. The authors don’t provide the data
in other years, based on a more elaborate sector inventory.
Weber et al. (2008)[6] gives contained CO2 emissions data of 10
sectors in 1992, 1997, 2002 and 2005. The authors don’t
compare the contained emissions of different sectors, and don’t
discuss the relationship between sectors and CO2 emissions. Lin
et al. (2010)[7] estimate the contained CO2 emissions of 28
sectors in 1997, 2002 and 2007. Also, the authors estimate thecontained emission of 30 provinces in 2002 and 2007. However,
the authors don’t consider the future emissions variation trend.
Furthermore, all these previous studies don’t use the newest
Input-Output table (I-O table) in 2010 and estimate the future
CO2 emissions by sectors.
1.4.
Study Objectives
This study aims to (1) estimate the contained CO2 emissions bysectors in recent years for 30 provinces; (2) analyze theestimation results of (1), then find the emissions variation trend;(3) investigate the effect of governmental policy, technology
innovation, and variation of production volume. Then, estimate
the future CO2 emissions to propose an effective approach to
reduce CO2 emissions.
So far, there are few previous studies concerning the contained
CO2 emissions. Especially, no one has ever analyzed it bysectors and by provinces. Moreover, this study originallyestimates the future emissions of key sectors, by hypothesizing 5
scenarios for electricity generation sector and smelting sector.
Based on the estimation results, a more detailed and realizable
solution can be proposed for CO2 emissions reduction.
2.
Methodology
2.1. Database
There are mainly 3 available databases:
China Energy Statistical Yearbook (CESY) 2000-2010: Energy
consumption statistics.
Intergovernmental Paned on Climate Change (IPCC) guideline:
Lower heating value and carbon emissions factor.
National I-O tables 2000, 2002, 2005, 2007 and 2010:
intermediate and final products of economic sectors in thenationwide scale
Regional I-O tables 2002, 2007: intermediate and final productsof economic sectors in 30 provinces.
However, the categories of sector inventories in CESY and I-O
tables are different. This study combines these two inventories tomake a 16-sector inventory.
2.2.
Direct Emissions Estimation
The direct CO2 emissions can be estimated by the equation
below[8].
Di=Σqikjkcko44/12………………………Eq1
Di: direct emissions of i-th sector, (ton-CO2)

Table 3 Inventory of other industries

k: coal, cokes, crude oil, kerosene, diesel, gasoline, natural gas,fuel oil[8]qik: consumption i of k-th fuel, (t)jk: lower heating value of k-th fuel, (1000FJ/t) [8]ck: carbon emissions factor of k-th fuel, (t-C/1000GJ) [8]o: oxidation ratio (1.0 de-facto) [8]2.3.
Contained Emissions Estimation
Contained CO2 emissions of one specific sector can be
estimated by using I-O tables and contained CO2 emissions
intensity.[9]I-O table is constructed on the simple idea that goods andservices produced by economic sectors should be registered in atable by origin and by destination. Goods produced by economic
sectors serve as inputs in other sectors in order to produce their
final products also called outputs.

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3.1.
Direct CO2 emissions
As shown in Fig.2 the direct emissions keep increasing since
2000. From 2005, the growth rate becomes much faster. The
direct emissions in 2010 have doubled that in 2000. During thesame period, China’s GDP keeps a high growth rate. In 2007,
GDP growth rate is even 14%. From 2008, GDP growth rate
drop to around 9%. This proves the rapid development of
economy, especially that of manufacturing, is the main reason ofincreasing direct CO2 emissions. Fig.2 shows that sector 6, 7, 8
and 9 cause much more CO2 emissions than other sectors,
because these sectors consume much more coal than others.

Table 1 Example of Input-Output table          
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Fig.2 Direct CO2 emissions by sectors



3.2.
Contained CO2 emissions
3.2.1.
Contained CO2 emissions intensity
Fig.3 shows that contained CO2 emissions intensity keeps
decreasing since 2000. Technology innovation is supposed to be
the main reason. The contained CO2 emissions intensity of
production and supply of electric power and heat power (sectorvi) is much higher than other sectors. This sector consumes more
fuel than other sectors, especially coal in China.
Sector 6, 7, 8 and 9 have very large contained CO2 emissions
intensity. Recall that these sectors dominate the national direct
CO2 emissions as shown in Fig.2. Since contained CO2
emissions are composed of direct and indirect emission, theircontained CO2 emissions intensities are inevitably large.
Sector 10, 11, 12 and 13 also have large contained CO2
emission intensity, as shown in Fig.3. However, they don’t
significant amount of direct CO2 emissions. It implies that theindirect CO2 emissions intensities of these sectors must be large.
The reason is that their upstream sector, smelting sector, is ahigh-emission sector.
There are two reasons why there are some sectors having larger
emissions intensity. The upstream sectors in the supply chain
suffer from large direct CO2 emissions intensity. On the other
hand, the downstream sector suffers from large indirect CO2

Table 2 Inventory of manufacturing industry sectors 

!""!#



3.
Estimation Result
This study estimates the direct CO2 emissions for 16
manufacturing sectors in 2000, 2002, 2005, 2007 and 2010.
Table 2 shows the 16-sector inventory, and Table 3 shows theinventory of the other industries.

  

!"""#



X=AX+F OR X=(I-A)-1 ………………………………….Eq2
d=[d1,d2,……,dn], where di=Di/xi….……………………Eq3
di : CO2 emissions per output of sector i
Di : total CO2 emissions of sector i
E=d X=d (I-A)-1F…………………..…………………Eq4
E: total emissions of sector i!!
!! ! ! ! ! ! !
!
!
!!!! !! !!! !
!! ! ! ! !! ! !!!! !! ! ! ! ! ! !!
…Eq5!
!
! !!!!! !!!! !
!!
!! ! ! ! ! ! !
ei: contained CO2 emissions intensity (t-CO2/10k yen)
! !!" !! ! !! ………..…………………………………….……Eq6
Ej: contained CO2 emissions of sector j

Food products, beverages and tobacco
Manufacture of Textile
Manufacture of Textile, Wearing Apparel and Accessories, Leather, Fur,
Feather and Related Products and Footwear
Wood and products of wood and cork
Pulp, paper, paper products, printing and publishing
Processing of Petroleum, Coking and Processing of Nuclear Fuel
Chemicals
Manufacture of Non-metallic Mineral Products
Smelting and Pressing of Metals
Manufacture of Metal Products
Manufacture of General and Special Purpose Machinery
Manufacture of Automobiles,Railway, Ship, Aerospace and Other
Transport Equipments
Manufacture of Electrical Machinery and Apparatus
Manufacture of Computers, Communication and Other Electronic
Equipment
Manufacture of Measuring Instruments and Machinery
Other Manufacture

98emissions intensity. In a short word, low-emission sector not
only consumes small amount of fuel, but also demands small
amount of raw material or intermediate products from thosehigh-emissions upstream sectors.

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Fig.3 Contained CO2 emissions intensity by sectors in different year

3.2.2.
Contained CO2 emissions in the national scale
Fig.4 shows that the contained CO2 emissions of 16 sectors
keep increasing during 2000-2010, even if the contained CO2
emission intensities decrease. That’s because the production
volume increases much faster.
Sector 7 and 9 generate extremely large amount of contained
CO2 emissions. That’s because these two sectors in China haveexcessive production volume. Therefore, they expel enormous
amount of direct emissions, as shown in Fig.2. Moreover, theupstream sectors of sector 7 and 9 have large contained CO2
emissions intensity, which will contribute a large amount of
indirect emissions.
The contained emission of sector 6 and 8 are not as large as
those of sector 7 and 9. However, Fig.3 shows that these two
sectors also have large contained CO2 emissions intensity. The
reasonable exploration is that the production volumes are small.
Thereby, a small amount of direct CO2 emissions are expelled
Moreover, their indirect emission benefit from low-emission
upstream sectors.
Sector 11, 12, 13 and 14 are usually at the downstream of
supply chain. They demand a large amount of materials fromhigh-emissions upstream sectors, such as sector 9., direct andindirect emissions of these 4 sectors are both large due to thelarge production volumes. Thereby, their contained emission
must be large.
It became clear that the contained emissions are determined bya lot of factors, such as the production volume, type of fuel,location in the supply chain, contained CO2 emissions intensityfrom the upstream sector.

  

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Fig.5 (a) Total contained CO2 emissions from manufacturing in
30 provinces at 2002 and 2007(No data in Xizang)&'()*+(,-"&.%"
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Fig.5 (b) Contained CO2 emissions of 16 sectors in 30 provincesat 2002 and 2007descending order by GDP in 2007

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As shown in Fig.5 (b) almost all 30 provinces double thecontained CO2 emissions from 2002 to 2007. The contained CO2
emissions from sector 7 and sector 9 contribute a large emissions
share in all 30 provinces. Particularly, sector 14 in Guangdong
province has a significant amount of contained emissions. And
sector 8 in Fujian province expels a large amount of contained
CO2 emissions, compared with those provinces on the same
GDP level. In short, the composition of contained emissions also
depends on the economic structure.

          4.
Future CO2 emissions estimation
This study aims to estimate the peak value of China’s CO2
emissions at 2030, as Chinese government announced in the
COP 20 in 2014. This study also aims to estimate the CO2
emissions at 2050 when CO2 emissions go down from the peakvalue. In order to realize the targets, this study hypothesizes the
5 scenarios below.
4.1.
Scenario creation
Scenario 1: assumes that China’s manufacturing will maintainthe current growth rate and structure. Meanwhile, it assumes that
all the other factors, which affect the contained emissions, don’tchange. Based on the direct emissions statistics during 2010s,
direct emissions can be approximated as a time-variant function.
The direct emissions in 2030 and 2050 can thereby be estimated.

Fig.4 Contained CO2 emissions of 16 manufacturing sectors in
different years

3.2.3. Contained emissions in the 30 provinces
According to Fig.5 (a) the provinces in the east coast of China
have larger amount of contained CO2 emissions. That’s because
they have more developed manufacturing and larger production
volume. In a word, the amount of contained emissions depends
on the economic volume.
99

This study also estimates the I-O tables and contained CO2
emissions intensity in 2030 and 2050 by RAS method [10], byusing the I-O tables in previous 5 years. The estimated directemissions and I-O tables will be used in the next 4 scenarios.
Scenario 2: This study then picks the power generation sector as
subject, since it contributes indirect emissions in almost all othersectors. This study notices that although China relies heavily on
coal, the power efficiency of coal lower than those of developed
countries. Thereby, this study assumes that the power efficiency
of coal will be improved to top level in the word. Then, thisstudy uses the improved power efficiency of coal to estimate thefuture contained emissions. During the estimation, this study
doesn’t change the current composition of power generation.
Scenario 3: this study notices that the Chinese government isencouraging the development of green power. The current
composition of power generation will change inevitably. This
study estimates the refined composition of power generation in
the future, then estimates the contained CO2 emissions in 2030
and 2050. Note that the current power efficiency is still used inthis scenario, which is different from scenario 2.
Scenario 4: this study then chooses smelting sector as subject.
Smelting sector is well known as the bone of industry, which
provides a significant amount of intermediate products andindirect emissions to other manufacturing sectors. Especially, theproduction volume of China’s smelting sector is excessively
large and the carbon emissions factor is not low enough.
Aforementioned, smelting sector must be the key of CO2
reduction. From the statistics in recent years, this study estimates
the future production volume of smelting sector and carbon
emissions factor, then estimates the future emissions.
Scenario 5: combines the assumptions in scenario 2, 3 and 4.
This study estimates the future contained CO2 emissions of
smelting sector with a higher power efficiency of coal, lessquantity of coal consumption and lower carbon emissions factor.
4.2.
Future CO2 emissions Estimation Result
The estimation results of all 5 scenarios in 2030 and in 2050
are shown and compared in Fig.6.
                
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 
 





5.
Conclusions
This study estimates the direct and contained CO2 emissions forevery key sector of China’s manufacturing in the context of
supply chain. It also estimates the future CO 2 emissions by
hypothesizing 5 scenarios.
(1) In the supply chain, those high-emissions upstream sectors,
such as smelting sector and chemical sectors, bring moresignificant impacts on downstream sectors. These sectors
should be lock as the key CO2 emissions reduction targets.
(2) The high-emissions upstream sectors take large share of
total contained emissions in 30 provinces. The economic
structure determines the composition of CO 2 emissions.
Both the manufacturing volume and structure affect theregional contained CO2 emissions.
(3) Improving the efficiency of fuel combustion and refining
the composition of power generation can reduce contained
emissions effectively. They can be the feasible solution to
reduce CO2 emissions in the future. As production volume
is controlled in the reasonable range, the contained CO2
emissions will drop from the peak value after 2030.
!
References[1]The World Bank: Data-GDP growth
http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG[2]European Commission: CO2 time series 1990-2013 perregion/country
http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts1990-2013[3] International Energy Agency: CO2 emissions from fuelcombustion highlights,IEA,2014[4]He Y,(2013) Chinese energy carbon dioxide emissions control
objectives and geographical distribution statistics,D[5]Guo j., Zou L.L., Wei Y.M.(2010) Energy Policy,38,1389-1397[6]Weber C.L,Peters G.P, Guan D., Hubacek K.(2008) Energy
Policy.36(9).3572-3577[7]Lin N., Liang R.L., Shimazaki Y., Soma T.(2010) 4,327-337[8]2006 IPCC Guidelines for National Geenhouse Gas Inventories[9] Nansai K., Kondo Y., Kagawa S., Suh S., Nakajima K., Inaba R.,
Tohno S.(2012) Environmental Science & Technology, 46(16),9146-9154.
[10] #"(2009)    %
('1*2, -6.37/ 21   

Clearly, the contained CO2 emissions of every sector in 2050 arelarger than those in 2030. Recall that this study assumes China’s
economy will maintain a high growth rate in scenario 1. In thatcase, China’s GDP in 2050 will be much larger than that in 2030.
Even if other factors are improved, it is too difficult to reduce
total emission. Since scenario 1 assumes other factors are not
improved, it is reasonable that scenario 1 has the largest amountof CO2 emissions in all 5 scenarios.
With respect to scenario 1, the contained emissions are
significantly reduced in scenario 2 and scenario 3. This reveals
that improving of power efficiency of coal and varying power
generation composition are very effective to reduce thecontained CO2 emissions.
In scenario 4, sector 9-14 expel less CO2 emissions with respect
to scenario 1, which means smaller production volume andbetter carbon emissions factor can reduce the contained
emissions of smelting sector and its downstream sector. The
effectiveness is limited however, if the emissions status of power
generation sector doesn’t improve.
In terms of CO2 emissions reduction, scenario 5 is the best.
That’s because it makes full use of the advantages of scenario 2,
3 and 4. It is worth noting that many sectors only generateslightly more emissions in 2050 than in 2030. This implies that
China can almost realize its goal by merely improving thecontained CO2 emissions status of power generation sector andsmelting sector.

  !

Fig.6 Contained CO2 emissions based on scenario

100

CORROSION RESISTANCE OF STAINLESS STEEL IN CONCRETE AFTER LONG TERMEXPOSURE IN MARINE ENVIRONMENT
Student Number: 13M18149 Name: Tomohiro Nagata Supervisor: Nobuaki Otsuki㛗ᮇ㛫ᾏὒ⎔ቃ࡛᭚㟢ࡉࢀࡓࢥࣥࢡ࣮ࣜࢺ୰ࢫࢸࣥࣞࢫ㗰ᮦࡢ⭉㣗᢬ᢠᛶỌ⏣ ᬛ኱
ᾏὒ⎔ቃ࡛ࡢ㕲➽ࢥࣥࢡ࣮ࣜࢺᵓ㐀≀ࡢ౑⏝ࢆ⪃࠼ࡓ㝿㸪⃭ࡋ࠸ሷᐖ࡟ᑐࡋ࡚ࢫࢸࣥࣞࢫ㗰ᮦࡢ౑⏝ࡀ᭷ຠ࡜⪃࠼ࡽࢀࡿࡀ㸪ࢫࢸࣥࣞࢫ㗰ᮦࢆ౑⏝ࡋࡓࢥࣥࢡ࣮ࣜࢺᵓ㐀≀ࡢ㛗ᮇ⪏ஂᛶࡣᚲࡎࡋࡶ᫂ࡽ࠿࡛࡞࠸㸬ࡇࡢࡓࡵ㸪ᮏ◊✲࡛ࡣᾏὒ⎔ቃ࡛⣙  ᖺ᭚㟢ࡉࢀࡓࢥࣥࢡ࣮ࣜࢺ࡜ࣉࣞࢫࢺࣞࢫࢺࢥࣥࢡ࣮ࣜࢺ౪ヨయ୰ࢫࢸࣥࣞࢫ㗰ᮦࡢ⭉㣗᢬ᢠᛶ࡜᭷ຠࣉࣞࢫࢺࣞࢫຊࢆホ౯ࡋ㸪ࢫࢸࣥࣞࢫ㗰ᮦࢆ⏝࠸ࡓࢥࣥࢡ࣮ࣜࢺᵓ㐀≀ࡢ㛗ᮇ⪏ஂᛶࢆホ౯ࡋࡓ㸬⤖ᯝ㸪ࢫࢸࣥࣞࢫ㗰ᮦࢆ౑⏝ࡋࡓሙྜ㸪ࢥࣥࢡ࣮ࣜࢺᵓ㐀≀ࡢ₯అᮇ࡜㐍ᒎᮇࡢྜィᮇ㛫ࡣ  ᖺ௨ୖ࡜࡞ࡾ㛗ᮇ⪏ஂᛶࡀྥୖࡍࡿࡇ࡜㸪686 ࢫࢸࣥࣞࢫ㗰ᮦࡢ⭉㣗᢬ᢠᛶࡀ࢚࣏࢟ࢩ⿕そ㸪1L㸪ᬑ㏻㗰ᮦࡼࡾ㧗ࡃ࡞ࡿࡇ࡜㸪᭚㟢  ᖺᚋࡢ 686 ࢫࢸࣥࣞࢫ 3& 㗰ᮦࢆ౑⏝ࡋࡓሙྜࡢ᭷ຠࣉࣞࢫࢺࣞࢫຊࡣᬑ㏻ 3& 㗰ᮦࢆ౑ࡗࡓሙྜ࡜ྠ➼࠿኱ࡁࡃ࡞ࡿࡇ࡜ࡀࢃ࠿ࡗࡓ㸬

2. Specimens

1. Introduction

2.1 Used material

For the use of concrete structure in marine environment,
severe steel corrosion due to chloride attack is serious. To
reduce the risk of deterioration of concrete structure due tosteel corrosion for long term, use of stainless steel can beeffective. However the long term durability of concrete
structure using stainless steel is not clear. Thus in this study
corrosion resistance of stainless steel in concrete after longterm exposure in marine environment is investigated anddiscussed to clarify long term durability of concrete structureusing stainless steel.
To accomplish this objective, corrosion resistance of
SUS430 stainless steel in concrete after 28 years’ exposure inmarine environment is investigated. Also, those of epoxy
coated steel, Ni steel, and normal steel in the same specimen
is investigated.
And effective prestress force and corrosion resistance of
SUS304 stainless PC steel in prestressed concrete after 31
years’ exposure in marine environment is investigated.
There may be few research about the corrosion resistanceof stainless steel in concrete after such long term exposure inmarine environment.
According to these objectives, in this study 3 examination
items are set as shown below.
(1) Lifetime prediction of concrete structure using SUS430
stainless steel(2) Investigation of corrosion resistance of SUS430 stainlesssteel through the comparison with those of other steels inconcrete after 28years’ exposure in marine environment(3) Investigation of effective prestress force and corrosion
resistance of SUS304 stainless PC steel in prestressed concrete
after 31 years’ exposure in marine environment.

The following shows the materials used in this study.
As mixing water, tap water and seawater are used. As cement,
Ordinary Portland Cement (OPC) and High Early StrengthPortland Cement (HES) and Blast Furnace Slug powder(BFS) are used. As steel bars, φ9mm SUS430 stainless steel
bar, φ10mm epoxy coated steel deformed bar, φ9mm 3.5% Ni
replaced steel bar and φ9mm normal steel bar, φ7mm SUS304
stainless PC steel bar, and φ7mm normal PC steel bar are used.

2.2 Overview of specimens
In this study, two types of specimens are used.
(1)Concrete specimen
First one is concrete cylinder specimen (φ150×300mm) madein 1986. Fig.1 shows the overview of this specimen. In thisspecimen, 3 steel bars are embedded with cover depth 7, 15,30mm. And the bottom of this specimen is coated with epoxy
resin. Mix proportion of specimens are shown as Table.1. And
initial crack is induced to specimens embedded with SUS430
stainless steel, epoxy coated steel and Ni steel.
(2)Prestressed concrete specimen
Second one is prestressed concrete specimen (150×100×920mm) made in 1983. Fig.2 shows the over view of thisspecimen. This specimen was made with flexural crack in thecenter by loading tool. The anchors of PC steel in the edges of
specimens are covered with mortar. Steel type of anchors isthe same steel type of PC steel. Mix proportion of specimens
are shown as Table.2. And initial prestress force of PC steel is
65kgf/mm2.
These specimens have been exposed in seawater tank whichemulates tidal zone.

101Table.1 Mix proportion of cylinder concrete specimen
Unit content[kg/m3]
No Steel type

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Water
W/C
type

W

OPC BFS

S

G

0.47
0.48
0.46
0.46
0.46
0.46
0.45
0.46
0.49
0.48
0.48
0.47
0.48
0.46
0.49
0.48
0.46

154
153
148
147
147
146
145
146
158
152
152
151
153
147
158
152
146

270
320
208
144
96
144
96
144
320
144
96
144
320
144
320
144
144

849
795
783
773
761
768
759
753
774
750
741
732
795
773
774
752
768

1062
1078
1094
1102
1110
1110
1118
1126
1094
1102
1118
1142
1078
1102
1094
1118
1110

Tap
water
Normal steel

Sea
water

SUS430 stainless steel,
Epoxy coated steel,Ni steel

Tap
water
Sea
water

112
176
224
176
224
176
176
224
176
176
176
176

[L/m3]
AE waterreducing
agent
3.2
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.7

Slump Airflow content[cm] [%]

Water
reducing
agent
0.4
0.6
0.7
0.8
0.8
0.7
0.8
0.6
0.7
0.8
0.9
0.8
0.6
0.8
0.7
0.8
0.7

7.5
7.8
7.6
8.2
8.6
8.0
7.4
8.0
8.8
8.1
8.5
7.4
7.8
8.1
7.9
8.3
7.6

Air
content

Initial crack
width [mm]4.80.1, 0.2, 0.3

4.3
4.0
3.9
3.9
4.2
4.0
3.7
4.4
4.1
4.6
4.2
4.0
3.8
4.2
4.4
4.0
4.2

Table.2 Mix proportion of prestressed concrete specimen
Water
W/C
Type
Normal PC steel
Tap
0.45
SUS304 stainless PC steel water
Steel Type

Unit content[kg/m3]
W
HES S

G

[L/m3] Slump
flow
AE

160

1029

1.6

356

802

5.9

Conducting wire

3.Experimental results and discussion

Epoxy
resin

15mm

150mm

3.1 Lifetime prediction of concrete structure using
SUS430 stainless steel

φ9mmSteel
14mm

In this section, lifetime of concrete structure using
SUS430 stainless steel with cover depth 50mm ispredicted. In this study, lifetime of concrete structureis defined as shown in Fig.3. To predict these lifetime,
chloride ion diffusivity, corrosion threshold chloride
ion contents for initiation period, and corrosion rate
for propagation period are investigated.
(1)Chloride ion diffusivity and corrosion thresholdchloride ion content
To clarify chloride ion diffusivity and corrosion thresholdchloride ion content, chloride ion content with cover depth 7,
15, 30mm in concrete specimens No13 with SUS430 stainlesssteel and No15 with SUS430 stainless steel are investigated.
Evaluation method is conform to JIS A 1154. Table.3 shows
the result. In tables “OPC tap water” means No13 with
SUS430 stainless steel, “OPC seawater” means No15 with
SUS430 stainless steel, and by using these data, chloride ion
diffusivity is evaluated. Evaluation method is conform to
JSCE-G 572. And the equation is used Fick’s diffusion

7mm

300mm

10mm

Fig.1 Overview of concrete specimen

Fig.2 Overview of prestressed concrete specimen

equation as shown in Eq.1
C(x,t)=C0(1-erf(x/2ҀDc*t)+Ci
Eq.1(C: Amount of chloride ion content (kg/m3), x: depth from theexposed surface (mm), t: exposure time (year), C0: Amountof chloride ion content at concrete surface Ci: Initial density ofchloride ion content in concrete (kg/m3)Dc: Chloride ion
diffusivity coefficient (cm2/year),erf: error function)
102

Frequency
of corrosion
Numberof corroded
steel bars[%]area[%]

Corrosion

Cracking
occursCorrosion
start
Initiation period

Propagation period

Fig.3 Lifetime of concrete structure

100
90
80
70
60
50
40
30
20
10
0

Epoxy coated
NormalNickelSUS430 Stainless0

Table.3 Chloride ion content in each cover depth
Chloride ion
Chloride ion
Chloride ioncontent in 7mm
content incontent incovering
15mm covering 30mm covering
depth[kg/m3] depth [kg/m3] depth [kg/m3]
OPC tap water
18.4
13.8
OPC seawater
17.6
11.8
-

FrequencyNumberof maximumof corroded depth[%]steel bars[%]
corrosion

Fig.4 Frequency distribution of corrosion area

Table.4 Chloride ion diffusivity
Chloride ion
Chloride ioncontent on the diffusivity

1

2

3

4

Maximum corrosion depth[mm]Fig.5 Frequency distribution of maximum corrosion
depth of steel in concrete

Table.5 Rate of crack, average corrosion area, andaverage maximum corrosion depth

Table.5 Lifetime prediction
Initiation Propagation
period[year] period[year]
OPC tap water
25~60
33
OPC seawater
35~85

40
35
30
25
20
15
10
5
0

0

surface[kg/m3 ] [cm2 /year]
OPC tap water
23.5
1.9
OPC seawater
21.3
0.9

Specimen

10 20 30 40 50 60 70 80 90 100
Corrosion area[%]

Lifetime[year]
58~93
SUS430stainless steel
Epoxy coatedsteel
Ni steel
Normal steel

Table.4 shows the chloride ion diffusivity of each specimen.
And in this study corrosion threshold chloride ion content isdefined as 13.8~18.4 kg/m3 in OPC tap water, and11.8~17.6kg./m3 in OPC seawater because SUS430 stainlesssteel in investigated specimens have corroded.
By using these data, duration that chloride ion content in
concrete at 50mm from the surface reaches corrosion
threshold chloride ion content is calculated as initiation periodof concrete structure using SUS430 stainless steel.
(2) Corrosion amount which makes crack and corrosion
rate
To clarify corrosion amount at the beginning of crack, Tottori
diagram as shown in Eq.2 is used, and corrosion amountwhich makes crack is defined as 55.5mg/cm2. 1)
Wcr= 10(c/φ)
Eq.2(Wcr: amount of corrosion product at the beginning of steel
corrosion(=55.5mg/cm2) c:cover depth(=50mm), φ:diameterof steel bar(=9mm))
To clarify corrosion rate, corrosion amount of SUS430
stainless steel which is most corroded in OPC specimens is

Rate of crack
due to steel
corrosion[%]Averagecorrosionarea[%]

Averagemaximum
corrosion
depth[mm]

0

4

0

23

12

-

17
47

6
26

0
0

predicted from corrosion depth. And corrosion amountdivided by exposure span is defined as corrosion rate. In thisstudy, corrosion rate is defined as 1.7 year/mg.
By dividing corrosion amount which makes crack bycorrosion rate, propagation period of concrete structure using
SUS430 stainless steel is obtained as 33 years.
(3)Lifetime prediction
Table.5 shows lifetime obtained in this study. In this study
lifetime is defined as sum of initiation and propagation period.
From this table initiation period of concrete using SUS430
stainless steel is 25~60 years, and 35~85 years. Propagation
period is about 33 years, and lifetime is over 50 years.
Generally it is considered that lifetime of concrete using
normal steel is about 40 years. Thus use of stainless steel can
improve the long term durability of concrete structure.

103

3.2 Corrosion resistance of SUS430 stainless steel
in concrete

Table.6 Corrosion area and maximum corrosion depth
Steel typeIn this section corrosion resistance of SUS430 stainless steel
in concrete specimen is investigated. To clarify corrosion
resistance, corrosion area, corrosion depth, crack of concretedue to steel corrosion are evaluated with image analysis, depthof focus method, visual investigation(1)Frequency distribution of corrosion area
Fig.4 shows the frequency distribution of corrosion area ofsteel in concrete. This figure shows corrosion area of SUS430
stainless steel concentrates 0%. On the other hand, corrosion
area of other steels spread from low level to high level(2)Frequency distribution map of maximum corrosion
depth
Fig.5 shows the frequency distribution of maximum corrosion
depth of steel in concrete. This table shows that maximum
corrosion depth of SUS430 stainless steel concentrates below
2mm. On the other hand, corrosion area of other steels spreadfrom low level to high level.
(3)Crack of concrete due to steel corrosion and
Table.5 shows the rate of crack, average corrosion area andaverage maximum corrosion depth of each steel. This tableshows there is no crack in specimens with SUS430 stainlesssteel and average corrosion area and maximum corrosion
depth is smaller than those of other steels. These results showthat corrosion resistance of SUS430 stainless steel in concrete
is higher than those of other steels in concrete specimen.

Initial crack
width[mm]Corrosionarea[%]

0.1
0.2
0.3
0.1
0.2
0.3

5.9
0.7
2.3
27.9
38.2
19.7

SUS304stainless
PC steel
Normal PC
steel

Maximum
corrosion
depth[mm]
0.2
0.3
1.4
0.9
0.6
0.7

in prstressed concrete is smaller than that of normal PC steel
except the case of initial crack width 0.3mm.
These results show that residual ratio of effective prestressforce of specimens using SUS304 stainless PC steel is higher
than that of normal PC steel, and average corrosion resistanceof SUS304 stainless PC steel is higher than that of normal PC
steel except one specimen.

4.Conclusions
In this study the following conclusions are obtained(1) Corrosion starting period of reinforced concrete using
SUS430 stainless steel with cover depth 50mm is predicted as25~60, 35~85 years, and crack starting period due to steel
corrosion is predicted as over 50 years. This period is much
longer than general lifetime of that using normal steel.
(2) In the case of concrete after 28years’ exposure in marine
environment, no crack due to steel corrosion exists in concretewith SUS430 stainless steel, on the other hand crack due tosteel corrosion exists in specimen using epoxy coated steel, Ni
steel, normal steel. And average corrosion area and maximum
corrosion depth of SUS430 stainless steel is smaller than thoseof other steels. These results show corrosion resistance of
SUS430 stainless steel is higher than those of other steels inconcrete after long term exposure in marine environment.
(3) In prestressed concrete after 31years’ exposure in marine
environment, effective prestress force of specimens using
SUS304 stainless PC steel is higher than that using normal PC
steel. And average corrosion area of SUS304 stainless PC
steel is smaller than that of normal PC steel. But one of the
SUS304 stainless PC steel corrodes hard. This phenomena
should be investigated in the future.

3.3 Effective prestress force and corrosion
resistance of SUS304 stainless PC steel inprestressed concrete
In this section effective prestress and corrosion resistance of
SUS304 stainless PC steel are investigated. To clarify effective
prestress and corrosion resistance, effective prestress,
corrosion area, corrosion depth are evaluated with loading test
and steel cutting method proposed by Niitani2), image analysis,
depth of focus method.
(1)Effective prestress force
By loading test and steel cutting method, effective prestress of
SUS304 stainless PC steel in prstressed concrete is evaluated.
According to these test, Average residual ratio of effective
prestress force of SUS304 stainless PC steel in prstressed
concrete specimens is evaluated as 70%. On the other hand,residual ratio of effective prestress force of normal PC steel is54%. This result shows residual ratio of effective prestressforce of SUS304 stainless PC steel is higher than that ofnormal PC steel.
(2)Corrosion resistance
Table.6 shows the corrosion area and maximum corrosion
depth of each specimen. This table shows that corrosion areaand maximum corrosion depth of SUS304 stainless PC steel

References[1] S. Tottori (2002). Lifetime estimation of concrete structure
based on exposure test about steel corrosion in concrete,
Doctor Thesis in Kyoto University,2002[2] Niitani Kyoji, Watase Hiroshi, Sakata Kenji, Ayano
Katsunori: Study on Effective Stress Estimation Method
for Concrete Members 㸪 Concrete Reseach andTechnology, Vol. 20, No.2, pp.27-37㸪2009.5
104Development of Numerical Model Evaluating Ground Scourbehind Coastal Dykes due to Tsunamis
Student Number: 13M18132
Name: Shouhei NAGASE
Supervisor: Hiroshi TAKAGIẼᾮ஧┦ὠἼゎᯒ-ᆅ┙Ὑ᥀㐃ᡂࣔࢹࣝࡢᵓ⠏࡜⌧ᆅᾏᓊሐ㜵࡬ࡢ㐺⏝Ọ℩ ⩧ᖹ ὠἼ࡟ࡼࡿሐ㜵ࡢ◚ቯࡸࡑࡢ⫼ᚋ࡛ࡢὙ᥀ࡢ࣓࢝ࢽࢬ࣒ࡣࡇࢀࡲ࡛༑ศ࡟ゎ᫂ࡉࢀ࡚࠾ࡽࡎ㸪ࡲࡓ⌧ᆅࢫࢣ࣮ࣝ࡟㐺⏝ྍ⬟࡞ゎᯒࣔࢹࣝࡣ☜❧ࡉࢀ࡚࠸࡞࠸㸬ᮏ◊✲࡛ࡣ᪤Ꮡࡢὶయࣔࢹࣝ࡟ᆅ┙Ὑ᥀ࣔࢹࣝࢆ᪂ࡓ࡟⤌ࡳ㎸ࡳ㸪Ỉ⌮ᶍᆺᐇ㦂ཬࡧ⌧ᆅࡢ⿕ᐖ≧ἣ࡜ẚ㍑ࡋ࡚ࡑࡢጇᙜᛶࢆ᳨ウࡋࡓ㸬ࡑࡢ⤖ᯝ㸪࠸ࡃࡘ࠿ࡢㄢ㢟ࡣṧࡗ࡚࠸ࡿࡶࡢࡢ㸪ᾋ㐟◁㔞⟬ᐃࡢಀᩘࢆㄪᩚࡍࡿࡇ࡜࡛᭱኱Ὑ᥀῝ࢆ⢭ᗘⰋࡃ෌⌧࡛ࡁࡿࡇ࡜ࢆ♧ࡋࡓ㸬A) Slab (ʹǤʹͷ ൈ ͳͲ ൈ ͲǤʹͷ ) rotatedand backwall (ͳ ൈ ͳͲ ൈ ͲǤʹͷ ) fell
downB) Slab moved without rotation and backwall fell
downC) Slab moved without failure of the backwallD) Slab and backwall remained intact

1. Introduction
The 2011 Great East Japan Earthquake and Tsunami
caused the devastation of sea dykes, execrated by
overflow and scouring behind the structure. Throughthis experience, the Committee for Technical
Investigation on Countermeasures for Earthquakes
and Tsunamis of Cabinet Office emphasized thatcoastal protection facilities need to be further resilient
so that a moderate structural performance which
contributes to a reduction in tsunami impacts could besustained even after the dyke has been partially failure.
In order to put this into practice, it is necessary tounderstand failure processes of sea dykes due to acomplex mechanism of tsunami overflow andscouring.
In many studies, the failure processes of dykes havebeen speculated by an investigation through the field
investigation. Although small scale hydraulic modelexperiments to study scouring around dykes have been
conducted, a large scale experiment equivalent toactual size has not been made by any research groupyet. Regarding the numerical model reproducing
scouring, most of the models proposed by otherresearchers were verified by comparing the resultswith the phenomenon observed in a small scale modelexperiment, without further comparison with a large
scale model or an extensive scouring around an actualdyke.
The present research investigated failure processes ofsea dykes due to a complex mechanism of tsunami
overflow and scouring by the field investigation at
Ishinomaki City. A numerical model that evaluatesscouring behind dykes was developed by using a
gas-liquid two-phase numerical analysis, and it wascompared with the scouring observed through the field
investigation.

Scouring depth and width around the dykes at thewest side of the coast appeared to be shallower thanthat at the east side. This implies that the incoming
tsunami varied in height and direction at different
locations even in the same coast.

Fig.1 Extensive scouring behind the dykes at
Minamihama, Ishinomaki

2. Field Investigation
The authors conducted a field investigation in
Ishinomaki City and measured the scouring depth andelevations around the dyke using a high-precision
GPS and leveling staff. The scouring depth reached up
to 1.15m, and the hole width was measured at 11.5m
at the east side behind the dykes. 4 different failure
patterns of dykes appeared:

Fig.2 Elevation around dykes

Bed material was composed of sands with diameter
ranging between 0.5 and 1mm and gravels between 2
and 4mm.

105

“with” and “without”, indicating the influence ofreflected tsunami.

3. Flow characteristics around dykes
In order to understand flow characteristics arounddykes when tsunami overflowed, a gas-liquid
two-phase numerical analysis and tsunami
propagation simulation were performed.

Mt.HiyoriyamaTsunami propagation simulation

3.1

Delft3D Flow (Ver.3.42) based on a non-linear
shallow water equation was applied to the simulationof tsunami propagation from the deep sea to shallow
water.

Dykes

Tsunami direction

Table.1 Calculation Setting
Item

OutlineCalculation(a)540m(b)180m

area

Fig.5 Subject area(c)60m(d)20m

Time step(a)~(c)0.02min(d)0.005min

Tide level
condition
Computation
time

T.P.+0.0m
2011/3/11 14:46:00~ 16:30:00
Input data wascreated by linearly

Boundary

interpolating theconditionobserved tsunami
elevations at two
locations㻌Fig.6 Flow condition behind the dyke comparison between
2 topographical cases, with and without the
Mt.Hiyoriyama

Angle

Water level
Velocity

Water level (m)Fig.3 Computational domains㻌
point A: East side behind the dykes
point B: 120m seaward of the dykes

3.2
Time
(i) Comparison calculation value
with observed water mark at point A

Assessment of the dyke failure

An assessment was made to identify how completely
the dyke and the ground were damaged and scoured.
To do so, ANSYS FLUENT (Ver.15.0) was applied toinvestigate the tsunami overflow passing the sea dykes
for two cases: A: without scouring, B: with scouringof which shape is the same with the cross-section
measured by the authors (Fig.2).

Time(ii) Tsunami flow at point A and B

Fig.4 Result of tsunami propagation simulation

3.1.1 Tsunami overflowing the dyke
Fig.4 (i) indicates that the calculation shows a good
agreement with the observed tsunami height nearby
the dyke. Fig.4 (ii) shows that the tsunami approachedalmost normal to the dyke (the positive angle isdefined as clockwise from 0 degree that aims at thenorth).

Table.2 Calculation Settings for vertical 2D simulation

Computational gridComputation time
Time step

3.1.2 Influence of tsunami reflection from ahillside
Mt. Hiyoriyama is located at about 800m landward of
the dyke, likely caused tsunami reflection from its
hillside. Fig.6 shows the comparison of tsunami
overflow between 2 cases: Case 1 is the actual
topography and Case 2 is a hypothetical topography
deleting the hill. In Fig.6, both water level andvelocity change differently after 15:44:30 between

Boundary condition
Basic equation
Turbulence modelFree surface analysis
model

106

20cm
Start:2011/3/11/15:39:00
End:2011/3/11/15:44:30
0.005sec
Water surface elevation at
the offshore boundary(See Fig.4(ii))
Navier-Stokes equation,
Continuity equation
Standard k-ȜVolume of Fluid Methodଵ ଵ ଵɂ ൌ ʹǤͳ݃ିସ ‫ݍ‬ଶ ݄ସ ‫ ڮ ڮ ڮ ڮ ڮ ڮ‬ሺǤ ͳሻ ൅ Ͳͷ

Table.4 Dehghani et al.

Froud number
݄ଵ Ȁ݄
ߝȀ‫ܪ‬
ͲǤͲͷͺ ൏ ‫ ݎܨ‬൏ ͲǤͲ͹͸ͲǤʹͷͲǤ͹ͲǤͲͷͺ ൏ ‫ ݎܨ‬൏ ͲǤͲ͹͸ͲǤͶͳͲǤͲ͹ͺ ൏ ‫ ݎܨ‬൏ ͲǤͲͻ͵ͲǤ͵ͷͲǤ͹ͲǤͲ͹ͺ ൏ ‫ ݎܨ‬൏ ͲǤͲͻ͵ͲǤͶͷͲǤͺͲǤͲͻ͸ ൏ ‫ ݎܨ‬൏ ͲǤͳ͵͹ͲǤͷͲǤ͹ͲǤͲͻ͸ ൏ ‫ ݎܨ‬൏ ͲǤͳ͵͹ͲǤͺͳ‫ ݎܨ‬ൌ ܷȀሺ݄݃ሻଵȀଶ, ܷ: velocity, ݄: dyke height, ݄ଵ : overflow depth,‫ ܪ‬ൌ ݄ ൅ ݄ଵ , ݃: gravity acceleration, ‫ ݍ‬ൌ ܷ݄ଵ

Time

Fig.7 The slab rotation or lift up condition

5. Development of numerical model

Fig.7 shows that the concrete slab will not be turned
over by the tsunami overflow if without the scouring
behind. Given these results, it can be assumed that thedyke was damaged by the tsunami as follows:

evaluating ground scour
In the present research, a numerical model evaluating
ground scouring was developed, and the model
accuracy was compared with the model experimentconducted by Hatogai et al. (2012). Finally, the model
was applied to reproduce the actual scouring behindthe dyke in Ishinomaki.

1) scouring behind the dyke occurred due to theoverflow,2) as a result of extensive scouring, the backwall
turned over,3) erosion beneath the dyke occurred, and4) the slab was rotated by the uplift force

5.1

Fig.8 shows the development of turbulent eddies
behind the dyke. The second eddy, which appeared
when the scouring progressed to some extent, isconsidered to accelerate instability of the structures.

߲‫ݍ‬஻
ͳ
߲‫ݖ‬
ൌ
൅ ‫ݍ‬௦ െ ‫ݓܥ‬௦ ൰ ‫ ڮ ڮ ڮ‬ሺǤ ʹሻ൬߲‫ ͳ ݐ‬െ ߣ ߲‫ݔ‬‫ݍ‬஻ : bed load, ‫ݍ‬௦ : suspended load, ‫ ܥ‬: suspended
sediment concentration, ‫ݓ‬௦ :fall velocity
A conceptual diagram of this model is shown in Fig.9.
ܰ and ܰ ൅ ͳ indicates the number of neighboring
cells. Using the definition in Fig.9, Eq.2 can be
re-written as Eq.3. Amount of bed and suspended load,
qB and qs respectively, can be calculated by Eq.4
presented by Takahashi et al. (2011). In addition, thismodel includes modification of ground level gradientcaused by slope failure, which reshapes the ground toan equilibrium profile.

Table.3 Case Settings
Case
Case A
Case B

Outline of numerical model

The scouring is calculated by Eq.2, using a flexible
mesh for the uncovered ground behind the dyke.

OutlineNo scouring
Scouring had generated until reflected tsunami arrived

Case A

Case Bܰ

Second eddy
First eddy‫ܥ‬ே ‫ݓ‬௦ ே

Fig.8 The position of turbulent eddy moving݅െͳ

4. Scouring calculated by previous‫ݍ‬஻ ே

ܰ൅ͳ‫ݍ‬௦ ே

‫ܥ‬ேାଵ‫ݓ‬௦ ேାଵ݅‫ݍ‬஻ ேାଵ

‫ݍ‬௦ ேାଵ

Fig.9 Conceptual diagram of scouring model

models
Noguchi et al. (1997) (Eq.1) and Dehghani et al.
(2010) (Table.4) derived the relation of scouring
depthሺɂሻ and flow condition based by small scale
hydraulic model experiments. In order to estimate
scouring depth in Ishinomaki, overflow water leveland velocity calculated in the previous section wereused. However, it was founded that both methods
could overestimated scouring depth.

݅൅ͳ

‫ݍ‬஻
െ ‫ݍ‬஻ ே ‫ݍ‬௦ ே ൅ ‫ݍ‬௦ ேାଵ
ͳ
߲‫ݖ‬௜
ൌ
൅
ቆ ேାଵ
Ɂ‫ݔ‬
ʹ
߲‫ ͳ ݐ‬െ ߣ‫ܥ‬ே ‫ݓ‬௦ ே ൅ ‫ܥ‬ேାଵ ‫ݓ‬௦ ேାଵെቇ ‫ ڮ‬ሺǤ ͵ሻʹ‫ݍ‬஻ ൌ ʹǤ͸߬‫ כ‬ଵǤହ ඥሺ‫ ݏ‬െ ͳሻ݃݀ ଷ‫ݍ‬௦ ൌ ͳǤ͸ ൈ ͳͲିହ ߬‫ כ‬ଶ ඥሺ‫ ݏ‬െ ͳሻ݃݀107

‫ ڮ ڮ ڮ ڮ‬ሺǤ Ͷሻ߬‫ כ‬: Shields number, ‫ݏ‬: specific gravity of bed material,݀: diameter of one, Ɂ‫ݔ‬: distance between neighboring
cells

5.2

Model verification
model experimentwith

calculation resource for the simulation.

hydraulicIt was identified that scouring depth could be greatly
influenced by the coefficients used in the model. In
particular, a coefficientሺܽሻ, which determine amountof suspended load, appears to be one of the dominantfactors. Hence, the coefficient was examined in therange of between ʹǤͲ ൈ ͳͲିସ and ͷǤͲ ൈ ͳͲିସ tofind a number that best describes the scouringobserved in the model test. Consequently thecoefficient of ܽ ൌ ʹǤͲ ൈ ͳͲିସ was selected. Fig.10
shows the numerical results with this coefficient alongwith the model result. Fig.10 also shows that themodification considering slope failure improves theestimation of the ground profile particularly in termsof the location at which the maximum depth appeared.

Fig.11 Coefficient comparison results

Case Ϩ, Case ϩ, Case Ϫ and Case ϫare adoptedʹ ൈ ͳͲିସ , ͵ ൈ ͳͲିସ , Ͷ ൈ ͳͲିସ , ͷ ൈ ͳͲିସ as thecoefficientሺܽሻ respectively.

6. Conclusion
In this research, the following results were obtained.
1) Mechanisms of sea dykes destruction
The failure processes of the sea dykes in Ishinomaki
were revealed. The investigation by the field survey
and simulation suggests that the turbulent eddy, whichappeared when the scouring progressed to some extent,
could accelerate instability of the structures.
2) Numerical model evaluating ground scour
The numerical model evaluating ground scour behind
coastal dykes due to tsunamis was developed. The
proposed model could estimate the scouring depth at areasonable accuracy, though there still remains a
number of a drawback.

Fig.10 Comparison results

5.3

Application of the model to coastal
dykes in Ishinomaki

7. References[1] 㔝ཱྀ㈼஧㸪బ⸨ᵐྖ㸪⏣୰ⱱಙ (1997)㸸ὠἼ㐳ୖ࡟ࡼࡿㆤᓊ㉺Ἴ࠾ࡼࡧ๓㠃Ὑ᥀࡟኱つᶍ ᶍ ᆺ ᐇ 㦂 㸪 ᾏ ᓊ ᕤ Ꮫ ㄽ ᩥ 㞟 㸪 Vol.44 㸪
pp.296-300㸬
[2] Dehghani, A.A., Bashiri, H., and Meshkati,
S.M.E. (2010): Local scouring due to flow jet atdownstream of rectangular sharp-crested weirs,
WATER AND GEOSCIENCE 2010, 5TH
IASME WSEAS, pp.127-131.
[3] Takahashi, T., Kurokawa, T., Fujita, M. andShimada, H. (2011)㸸Hydraulic Experiment on
Sediment Transport due to Tsunamis with
Various Sand Grain Size, J.JSCE, Ser.B2,
Coastalengineering,
Vol.67,
No.2,
pp.231-235.(in Japanese)[4] Hatogai, S., Suwa, Y. and Kato, F. (2012)㸸
Hydraulic model experiments on scour landwardof the coastal dike induced by tsunami overflow,
J.JSCE, Ser.B2, Coastal engineering, Vol.68,
No.2, pp.406-410.(in Japanese)

Bed materials in Ishinomaki have various diameters,
which could result in increasing bearing capacity ofthe soil. Therefore, slope failure will not
instantaneously take place in an actual ground unlike
the case of the hydraulic model test. In considerationof this, the analysis for the case of Ishinomaki did notconsider the modification of slope failure. It was
assumed that bed materials have uniform diameter(0.6mm) in this analysis. The results with the different
values of the coefficient a is shown in Fig.11. This
result indicates that the proposed model could
estimate the scouring depth at a reasonable accuracy,
though there still remains a number of a drawback.
One of the drawbacks is that the scouring immediately
behind the backwall tends to be underestimated. This
could occur due to inflexibility of the computational
grids between the solid grids (structure) and theflexible grids (soil).The usage of more detailed meshor a gridless analysis such as particle method
simulation could contribute to solve this problem,
though these methods will pose enormous time and108

Phenol Removal by Activated Carbon
Using Physical Activation Derived From Exhausted Coffee Grounds
Student Number: 13M18020㻌 Name: Junki IKUTAME㻌 Supervisor: Hirofumi HINODE䝁䞊䝠䞊ᢳฟṧ´⏤᮶άᛶⅣ䛾〇㐀䛸䝣䜵䝜䞊䝹㝖ཤ⬟⏕⏣┠ ⣧ᕼ
䝁䞊䝠䞊ᢳฟṧ´䛿㣗ရᗫᲠ≀䛷䛒䜚䚸䝁䞊䝠䞊ᢳฟṧ´䜢Ⅳ㓟䜺䝇㈿ά䛥䛫䛯άᛶⅣ䛿䚸䝭䜽䝻Ꮝ䛜Ⓨ㐩䛧䛯ᵓ㐀䛸䛺䜚䚸䛭䛾ẚ⾲㠃✚䛿 1,303 m2/g 䛷䛒䛳䛯䚹䝁䞊䝠䞊ᢳฟṧ´⏤᮶άᛶⅣ䛿䚸ᾮ┦䛛䜙䛾䝣䜵䝜䞊䝹㝖ཤ䛻㠀ᖖ䛻ຠᯝⓗ䛷䛒䜚䚸Langmuir ᆺ྾╔➼ ᘧ䛛䜙⟬ฟ䛥䜜䜛᭱኱྾╔㔞䛿䚸௚䛾ᩥ⊩್䜢ୖᅇ䜛 556 mgphenol/gcarbon 䜢♧䛧䛯䚹䛥䜙䛻䚸100 mgphenol/L 䝣䜵䝜䞊䝹⁐ᾮ䛛䜙䛾㝖ཤᐇ㦂䛻䛚䛡䜛ᖹ⾮⃰ᗘ䛿 3.12 mgphenol/L 䛷䛒䜚䚸Ỉ㉁ở⃮㜵Ṇἲ䛻ᐃ䜑䜙䜜䛯᤼Ỉᇶ‽್ 5 mgphenol/L 䜢‶䛯䛧䛯䚹

1 Introduction
Coffee is a very popular beverage in many parts of theworld and the second largest commodity in the world after
petroleum. Moreover, green coffee trade transaction has
been on an upward trend since 1995. Approximately
6,700 kilo tonnes of consumable coffee were exported in
2011[1]. The extraction process of coffee often generates
significant amounts of exhausted coffee grounds (ECG)
which represent approximately 50% of the total input
mass of coffee feedstock[2]. Nevertheless, utilization of
ECG as a fuel source and as animal feed is restricted due
to its high moisture content and chemical composition[3].
Hence, the effective utilization of ECG needs to be
developed.
Approximately 95% of the consumption amount ofphenol (C6H5OH) have been used for chemical synthesis
in Japan[4]. Discharged wastewater containing phenol
from these chemical industries significantly affects thepollution of public water and sewage[5]. Since phenol
concentration in aqueous solution has to be controlled to 5
mg/L or less according to Water Pollution Prevention Law
in Japan, it is necessary to remove phenol by some water
treatment before releasing the wastewater to environment.
Various techniques have been used for phenol removal.
Especially, adsorption process is the simplest, the quickest,the most efficient and the most economical alternative forremoving phenol[6]. Among the adsorbents, activatedcarbon has highly developed porosity, internal andexternal surface area and it has high removal efficiency
towards aromatic compounds such as phenol[4].
In spite of this, commercial activated carbon is still
considered as expensive material in many countries due tothe use of non-renewable and relatively expensive raw

material (i.e. coal and wood). Therefore, they areenvironmentally unfeasible for most pollution control
applications [4].
It has been shown by many researchers that
lignocellulosic materials, such as coconut shell canbecome effective raw materials for the preparation ofactivated carbon[8]. However, compared to the total
amount of coconut and green coffee that Japan had
imported in 2011, green coffee was imported
approximately 3,000 times as much as coconut[1]. Due tothis reason, more ECG wastes are generated in Japan andtherefore, it is more available as a raw material to produce
activated carbon.
For these reasons, their use as a raw material for theactivated carbon has emerged as a potential alternative forcost reduction and solution for the waste disposal as well.
The objectives of this study are to convert ECG into
useful activated carbon and to investigate its performancefor phenol removal in water matrix.

2 Experimental
2. 1

Material

The ECG were donated by the beverage compan
y in Shizuoka Prefecture, Japan and ECG with a particle size in the range of 1.0 – 2.0 mm were used as a raw material for preparation of activated carbon.
The organic elements (i.e., carbon, hydrogen, nitrogen,oxygen and sulfur) analysis of the ECG sample was conducted using elemental analyzer (Vario EL, Elementar).

109

2. 2

Preparation of Activated Carbon

commercial granular activated carbon (CAC)
manufactured by Wako Pure Chemical Ind., Ltd. ,which
was crushed, sieved and classified into 0.6 – 1.0 mm
before the above mentioned characterization, was also
used for comparison.

Activated carbon was prepared with different
preparation temperature following this procedure: ECG
with a particle size in the range of 1.0 – 2.0 mm were
loaded in a horizontal mullite tube placed in a tubular
furnace (EPKR-12K, ISUZU). Before pyrolysis, thereactor was purged with pure nitrogen gas for 15 min at a
rate of 150 mL/min. Pyrolysis was carried out to obtain
the biochar at 800ºC with a heating rate of 10ºC/min
under pure nitrogen flow of 150 mL/min. Upon reaching
the desired temperature, it was kept for 1 h before
switching the gas from N2 to CO2 for activation foranother 1 h. After switching the gas, physical activation
started to obtain the activated carbon at 800ºC under CO2
flow of 150 or 300 mL/min. After physical activation,
they were cooled down under pure nitrogen flow, washedwith hot deionized water only or 1 N HCl in order toremove inorganic matter and dried at 110ºC overnight.
After HCl washing, they were washed extensively with
deionized water until the pH of the washing solution
reached 6.5 – 7. The prepared activated carbon werecrushed, sieved and classified into 0.6 – 1.0 mm. The
preparation conditions of AC800-1, AC800-2 andAC800-3 is displayed in Table 1.

2. 4

Special grade reagent phenol (Wako Pure ChemicalInd., Ltd.) was used to prepare the phenol aqueous
solutions. All phenol removal tests were carried out in
amber polypropylene containers of 250 mL by adding 0.1
g of the activated carbon in a 100 mL phenol aqueous
solution with various initial concentrations (50 – 2500
mgphenol/L) and placed on a reciprocal shaker at 70 rpm
until achieving equilibrium at room temperature without
pH adjustment. An aliquot of the aqueous solution was
sampled at various time intervals and the phenol
concentration was determined using an ultraviolet-visible
spectrophotometer (UV-Vis, UV-1800, Shimadzu) at λ =270 nm. Solutions with concentration higher than 250
mg/L were determined by dilution. CAC which was
crushed, sieved and classified into 0.6 – 1.0 mm before
the above mentioned tests was also used for comparison.
The adsorbed amount of phenol (qe [mgphenol/gcarbon])was calculated by the following Eq.㻌 (1):ሺ‫ܥ‬௜ െ ‫ܥ‬௘ ሻܸሺͳሻ
‫ݍ‬௘ ൌ ݉where, Ci is the initial phenol concentration [mgphenol/L];
Ce is the equilibrium phenol concentration [mgphenol/L]; V
is the volume of the aqueous solution after phenol
removal test [L]; m is the mass of the activated carbon[gcarbon]Table 1 Preparation conditions for
AC800-1, AC800-2 and AC800-3

2. 3

Phenol Removal Test

Characterization

The surface morphologies of the activated carbon
were observed by scanning electron microscope (SEM,
VE-8800, KEYENCE).
Nitrogen adsorption-desorption isotherm at 77 K wasdetermined in Autosorb-1 MP (Quantachrome
Instruments). Using the Brunauer-Emmett-Teller (BET)
equation, data from the isotherm was used to determine
the BET specific surface area (SBET [m2/g]). The total porevolume (Vtotal [cm3/g]) were estimated on the basis of theliquid volume of nitrogen adsorbed at the highest relative
pressure (P/P0 ~ 0.99). The micropore volume (Vmicro[cm3/g]) were obtained by the αs-plot method (P/P0 =0.40) and mesopore volume (Vmeso [cm3/g]) was calculated
from the difference between Vtotal and Vmicro. A

In this study, equilibrium adsorption data were fitted toLangmuir isotherm model. The assumption of Langmuir
isotherm model is as follows[9][10]:1. All the adsorption sites are equivalent and adsorption
occurs through the same mechanism.
2. Adsorbed molecules do not interact.
3. The enthalpy of adsorption is independent of how much
have been adsorbed amount.
4. The surface has a specific number of sites where thesolute molecules can be adsorbed.
5. The adsorption involves the attachment of only one
layer of molecules to the surface (i.e. monolayer).

110

3. 3

The linear form of Langmuir model can be expressed by
Eq. (2):
‫ܥ‬௘
‫ܥ‬௘
ͳ
ൌ
൅
ሺʹሻ‫ݍ‬௘ܺ௠ ܺ௠ ‫ܭ‬௘where, Xm is the maximum adsorption capacity[mgphenol/gcarbon]; Ke is adsorption equilibrium constant[L/mgphenol]

Result of Phenol Removal Test

3 Results and Discussion
3. 1

Characterization of ECG

The elemental analysis in Table 2 revealed the highcontents of carbon and low contents of ash in the ECG.

Figure 2 Effect of contact time on removal of phenol

Table 2 Elemental composition of ECG [g/100 g-dry]Figure 2 illustrates the plot of residual concentration ofphenol in solution versus the contact time with theadsorbents at the initial concentration of 100 mgphenol/L.
Although CAC showed higher and faster removal
performance than AC800-3, AC800-3 made the phenol
concentration at equilibrium (3.12 mgphenol/L) satisfy theenvironmental permissible limit which is 5 mg/L or less.

3. 2

Characterization of Activated Carbon

The microstructure of AC800-3 and CAC are shownin Figure 1. The presence of heterogeneous pore structures
was dominant and randomly distributed on AC800-3.
Although the morphological characteristic of AC800-3 issimilar to AC800-1 and AC800-2, that of CAC weresignificantly different compared to AC800-3 because ofdifferent raw material and activation method.

(a)

(b)

Figure 3 Langmuir isotherms of phenol removal
Figure 1 SEM micrographs of (a) AC800-3 (b) CAC
The adsorption isotherms of phenol onto AC800-3
and CAC are described in Figure 3. As initialconcentration increased, the difference of concentration atequilibrium between AC800-3 and CAC was increased.
Table 5 shows the parameters of Langmuir isotherm
model on AC800-3 and CAC. As shown in Table 5, thevalues of maximum adsorption capacity (Xm) of AC800-3
and CAC are 556 mgphenol/gcarbon and 909 mgphenol/gcarbon,
respectively.

Table 4 Porosity properties of the activated carbon

Table 4 represents the porosity properties of AC800-1,
AC800-2, AC800-3 and CAC. AC800-3 showed muchhigher values of SBET, Vtotal and Vmicro than AC800-2 andAC800-3. However, CAC showed the higher values
among the activated carbon. Table 4 also revealed thatthey mainly consisted of a microporous structure.

The maximum adsorption capacity values, Xm,
reported in literature for phenol removal on different
adsorbents are represented in Table 6.

111

Table 5 Paramters of Langmuir isotherm model

The best material for phenol removal reported inliterature was activated carbon derived from macadamia
nut shell. However, the maximum adsorption capacity
value of AC800-3 is much higher, which can beassociated to its higher surface area and highly developed
micropore structure.
Table 6 Summary of maximum adsorption capacity valuesof phenol on different adsorbents reported in literature

[11]
[12]
[13]
[3]
[14]

4 Conclusions
The ECG has great potential to be used as a raw
material in the preparation of activated carbon by CO2
activation. AC800-3 made the phenol concentration atequilibrium (3.12 mgphenol/g) satisfy the environmental
permissible limit which is 5 mg/L or less when initialconcentration is 100 mgphenol/L. The maximum capacity ofAC800-3 was found to be 556 mgphenol/gcarbon from
Langmuir model equation. The results above clearly
indicate that AC800-3 acted as a highly efficient adsorbent
for the removal of phenol from water matrix.

References[1] FAOSTAT (URL: http://faostat3.fao.org/)[2] Adriana S. Franca, Leandro S. Oliveira, Mauro E.
Ferreira. “Kinetics and equilibrium studies ofmethylene blue adsorption by spent coffee grounds”.
Desalination, 249(1), 2009, pp. 267 - 272[3] Wen-Tien Tsai, Sii-Chew Liu, Ching-Hsiang Hsieh“Preparation and fuel properties of biochars from thepyrolysis of exhausted coffee residue”. J. Anal. Appl.
Pyrol., 93(2012), pp. 63 - 67[4] ໬Ꮫ≀㉁䛾ึᮇ䝸䝇䜽ホ౯᭩ Ver 1.0 No.32䠊
NEDO (New Energy and Industrial Technology
Development Organization)䠊2005 ᖺ 11 ᭶Ⓨ⾜䠊[5] PRTR㻌 (Pollutant Release and Transfer) information
plaza Japan. Ministry of the Environment(URL: http://www.env.go.jp/chemi/prtr/risk0.html)[6] Liana Alvares Rodrigues, Maria Lucia Caetano Pintoda Silva, Manoel Orlando Alvarez-Mendes,
Aparecido dos Reis Coutinho, Gilmar Patrocinio
Thim. “Phenol removal from aqueous solution by
activated carbon produced from avocado kernel
seeds”. Chem. Eng. J., 174(2011), pp. 49 - 57
[7] B.H. Hameed, F.B.M. Daud. “Adsorption studies ofbasic dye on activated carbon derived fromagricultural waste: Hevea brasiliensis seed coat”.
Chem. Eng. J., 139(1), 2008, pp. 48 - 55
[8] Mohamed L. Sekirifa, Mahfoud Hadj-Mahammed,
Stephanie Pallier, Lotfi Baameur, Dominique Richard,
Ammar
H.
Al-Dujaili.
“Preparation
andcharacterization of an activated carbon from a datestones variety by physical activation with carbon
dioxide”. J. Anal. Appl. Pyrol., 99(2013), pp. 155 –160[9] Amrita Vishwa Vidyapeetham Virtual Lab(URL: http://amrita.vlab.co.in/)[10] P. W. ATKINS䠊䇾28. ᅛయ⾲㠃䛾㐣⛬䇿䠊䜰䝖䜻䞁䝇≀⌮໬Ꮫ䠄ୗ䠅䠊ᮾி໬Ꮫྠே䠈2006䠈pp. 932 - 935
䠄➨ 6 ∧ ➨ 7 ๅ䠅[11] Liana Alvares Rodrigues, Loriane Aparecida deSousa Ribeiro, Gilmar Patrocínio Thim, Rafael
Reinaldo Ferreira, Manoel Orlando Alvarez-Mendez,
Aparecido dos Reis Coutinho. “Activated carbon
derived from macadamia nut shells: an effective
adsorbent for phenol removal”. J. Porous Mater.,
20(2013), pp.619 - 627[12] H.B. Senturk, D. Ozdes, A. Gundogdu, C. Duran, M.
Soylak. “Removal of phenol from aqueous solutions
by adsorption onto organomodified Tirebolu
bentonite: equilibrium, kinetic and thermodynamic
study”. J. Hazard. Mater., 172(2009), pp. 353 - 362[13] B.H. Hameed, A.A. Rahman. “Removal of phenol
from aqueous solutions by adsorption onto activatedcarbon prepared from biomass material”. J. Hazard.
Mater., 160(2008), pp. 576 - 581[14] K.P. Singh, A. Malik, S. Sinha, P. Ojha.
“Liquid-phase adsorption of phenols using activatedcarbons derived from agricultural waste material”. J.
Hazard. Mater., 150(2008), pp. 626 - 641_____________________________________________*

E-mail: ikutame.j.aa@m.titech.ac.jp,*Phone: 03-5734-3245

112

Synthesis of Zeolites from Lake Sludge Using Microwave Heating Method
and Their Applications as Adsorbent
Student number: 13M18209 Name: Wenjing WANG Supervisor: Hirofumi HINODE࣐࢖ࢡࣟἼຍ⇕ἲ࡟ࡼࡿ†἟ởἾ⏤᮶ࢮ࢜ࣛ࢖ࢺࡢྜᡂཬࡧ྾╔ᮦ࡬ࡢᛂ⏝⋤ ᩥ㟼㟘ࣨᾆࡣᐩᰤ㣴໬ࡀ㐍ࡳሁ✚ởἾࡢὼ ࡀ⾜ࢃࢀ࡚࠸ࡿࡀࠊᚑ᮶ࡢᇙࡵ❧࡚ฎ⌮ἲࡣᇙ❧ᆅࡢῶᑡ࡜
㌾ᙅᆅ┙ࡢᙧᡂ࡞࡝ࡢၥ㢟ࢆࡶࡓࡽࡋ࡚࠸ࡿࠋᮏ◊✲࡛ࡣࠊᗫᲠ≀࡜࡞ࡗࡓ†἟ởἾ࠿ࡽ྾╔ᶵ⬟ࡸ࢖࢜ࣥ஺᥮ᛶ⬟ࢆࡶࡘࢮ࢜ࣛ࢖ࢺࢆྜᡂࡋࠊሁ✚ởἾࡢฎ⌮ၥ㢟ࢆゎỴࡋࠊ᭦࡟†἟ởἾࢆᗫᲠ≀ᮍ฼⏝㈨※࡜ࡋ࡚฼⏝ࡋỈ㉁ࢆί໬࡛ࡁࡿ෌⏕㈨※ࢆྜᡂࡍࡿࠋࢮ࢜ࣛ࢖ࢺྜᡂ࡟࣐࢖ࢡࣟἼࢆ⏝࠸࡚ࠊྜᡂ
᫬㛫ࠊࢮ࢜ࣛ࢖ࢺࡢ≉ᛶཬࡧ㔜㔠ᒓ࢖࢜ࣥࡢ㝖ཤຠᯝ࡟ᑐࡍࡿᙳ㡪࡟ࡘ࠸᳨࡚ウࡋࡓࠋ
1. Introduction

2. Experimental

Lakes are important sources of fresh water,
however contaminants and nutrient salts areeasily accumulated because of the closed water
systems and human activities around the lakes.
One of the most effective methods used torehabilitate lakes is dredging the sludge. For
resource protection, the method of recycling
should be considered. According to previous
study, lake sludge which contains silica andalumina can be used as raw material tosynthesize zeolites by alkali hydrothermal
synthesis method[1]. Zeolites are considered as
good cation exchange materials and having good
adsorption ability. They are used for heavy metal
ions removal in recent years [1].
The heating method of zeolite synthesis can bedone by convection oven or microwave oven.
Microwave heating belongs to dielectric heating,an internal heating method. The object to beheated is the heating element during heating.
When it is put in the electric field of the microwave,
molecules (permanent dipole) are subject to theradio forces. Molecules in electrical neutral state
are displaced and polarized. They will vibrate
violently according to the frequency of microwave.
( In the case of 2450 MHz, 2.45 billion times/sec. )The advantages of using microwave heating arethat the output is easy to control, only 10 min isrequired to ramp to the target temperature, andafter heating, cooling down is done automatically.
Lead ion is a typical heavy metal ion. High
toxicity of lead can damage neurological,
biological and cognitive functions, especially thatof children. Lead poisoning can cause a highdegree of intelligence and attention decline, andalso affect the growth, vision and athletic ability.
High content of lead ion in body can cause
anemia and brain and viscera damage, but also
lead to coma, convulsions and even death.
There are 23 million people die of leadpoisoning worldwide each year. In China, there
are several villages in Henan, Yunnan, Shaanxi,and Hunan provinces suffering heavy lead
pollution [2].
In this research, lake sludge was used as theraw material for zeolite synthesis using
microwave heating, and the resulting zeolites
were applied for lead ion removal, and theperformance was evaluated.

In this study, the sludge from Kasumigaura Lakein Ibaraki prefecture was used. The sludge wasdried, crushed and sieved (–150 μm), and thendried again at about 100Υ for 24 hours.
For zeolite synthesis, Box-Behnken design[3] an
optimization method using fewer experiment setscompared to the conventional one, was used to
optimize the synthesis condition. The responses
in Box-Behnken design are values of cationexchange capacity (CEC). The center run of thisdesign is the synthesis condition we assume tobe the optimal one.
In this research, the center run chosen was theone showed the highest CEC value in theprevious result, which is NaOH concentration of
2M, temperature of 120Υ and Si/Al molar ratio of
1.9.
Synthesis of zeolites
Lake sludge (1.6 g) was treated in different
NaOH solution concentration (1, 2 and 3 M, each
8 ml in volume) and put in a Teflon reaction vessel.
The mixture is mixed by magnetic mixing for 24hours or ultrasonic mixing for 3 hours. Afterwards,
the teflon vessel was put into a microwave ovenand heated at 90Υ, 120Υor 150Υ for 5 hours.
Afterwards, the zeolites were washed with
deionized water and dried at 60Υ.
Characterization of zeolites
After synthesis, the main phase of zeolite isstudied by X-ray diffraction (XRD), morphology isobserved by Scanning Electron Microscope(SEM) and cation exchange capacity is measured
by Chapman method utilizing Inductively Coupled
PlasmaAtomicEmissionSpectroscopy(ICP-AES)
CEC measurement
Sodium acetate method is used to measure thecapacity of exchanging Na+ to NH4+. Sodium
acetate and ammonium acetate solution are used.
The concentration of each chemical is 1 mol/L.
First do the adsorption of Na+ for 4 times, in orderto make sure all the adsorption sites are filled by
Na+. Then 99.7% 2-propanol solution is used towash the sodium ion not adsorbed. After washing,ammonium acetate is used to exchange sodium
ion.
113

Lead ion removal experiment
Pb(NO3)2 was used to prepare solution for Pb2+
removal experiment. The concentration ofPb(NO3)2 solutions were adjust from 50 ppm to
600 ppm with volume of 50 ml. The amount ofzeolite was 0.05 g.
pH of each Pb(NO3)2 solution - zeolite mixture is
kept at 5.0s0.2.

The predicted CEC results are calculated fromthe Box-Behnken design. From this optimizing
model the optimal synthesis condition in the caseof magnetic mixing 24 hours was NaOH 1.81 M,
temperature 109Υ, and Si/Al molar ratio 1.3,
while the optimal synthesis condition in the caseof ultrasonic mixing 3 hours was NaOH 2.31 M,
temperature 119Υ, and Si/Al molar ratio 1.9.
CEC values were 191.5 meq/100 g and 215.2
meq/100 g, respectively.

3. Results and discussion
3.1 XRD analysis and CEC values

In this research, the zeolite showed the highest
CEC of 247.5 meq/100 g is zeolite P1(Na) which
is synthesized under the condition of NaOH 2 M,
120Υ, and Si/Al molar ratio of 1.9 after ultrasonicmixing for 3 hours.

According to the XRD analysis, we can know themain phases of zeolites synthesized at different
alkali concentrations, temperatures, and Si/Al
molar ratios. After optimizing the CEC value using
Box-Behnken design, the optimal synthesis
condition was obtained.

Comparing to the previous result, using
microwave heating method, the formation ofzeolite P1(Na) was improved. It showed higher
CEC than the one synthesized by conventional
heating method which showed CEC of 171
meq/100 g. And it is far higher than thecommercial zeolite with CEC of 160 㹼
190meq/100 g[5].

Table.1 Main phase and CEC values of products obtainedusing various alkali concentrations, temperatures,and Si/Al ratios
Unit of CEC is meq/100 g.
Magnetic mixing 24 hours
Main
X1

X2

X3

Main

CEC

phase

223.3

234.3

P1, Q

247.5

233.3

245.9

234.3

P1, Q

237.3

233.3

233.8

234.3

215.2

233.3

145.3

134.1

153.3

131.5

CEC
phase

0

0

0

P1, U,(2M)(120ഒ)

(1.9)

Q

0

0

0

P1, U,(2M)(120ഒ)

(1.9)

Q

0

0

0

P1, Q,(2M)(120ഒ)

(1.9)

U

1

1

0

(3M)

(150ഒ)

(1.9)

1

-1

0

(90ഒ)

(1.9)

-1

1

0

(150ഒ)

(1.9)

-1

-1

0

(1M)

(90ഒ)

(1.9)

48.7

P1, Q,

152.5

167.8

Q, P1

75.9

87.0

172.3

166.8

A, P1

68.9

95.6

FࠊP1,U

133.4

128.7

U, P1

155.0

117.7

P1, Q

202.1

206.8

P1, Q

94.2

131.5

228.7

234.2

Q, P1

144.2

117.5

170.0

190.8

205.5

190.0

121.1

169.6

149.0

100.5

113.5

129.0

F,

0

-1

1

(90ഒ)

(2.5)

0

-1

-1

P1, U,

(2M)

(90ഒ)

(1.3)

Q

1

0

1

U, P1,(3M)(120ഒ)

(2.5)

Q

1

0

-1

U, P1,(3M)(120ഒ)

(1.3)

Q

-1

0

1

(1M)

(120ഒ)

(2.5)

109.6

26.9

P1, A,

(2M)

135.1

A

1

-1

67.0

64.9

(2.5)

(1.3)

82.3

39.5

1

1

U

A,

(150ഒ)(150ഒ)

P1, Q,Fig (a) The SEM image of zeolite P1(Na)(The
particle size is about 4μm).
Fig (b) The SEM image of Unnamed zeolite(Theparticle size is too small to detect the particle sizeby SEM).
Fig (c) The SEM image of Faujasite Na(very small
particle size).
Fig (d) The SEM image of Analcime C(Theparticle size is about 17μm).

P1

0

0

The surface images of each type of zeolite wereobserved by Scanning Electron Microscope(SEM). In this research, 4 types of zeolites aresynthesized using microwave heating method.

U, Q,

(2M)

(2M)

3.2 Morphology of zeolites

CEC

P1, Q
U, Q,

(1M)

Predicted
CEC

U, A,
F,

(3M)

Ultrasonic mixing 3 hours

Predicted

P1, Q,
U
158.9

178.8

232.1

212.2

U

(a)

(b)

(c)

(d)

A, P1,
P1, Q

Q

-1

0

-1

P1, Q,(1M)(120ഒ)

(1.3)

U

U, Q,
234.3

213.5
P1

P1: Zeolite P1(Na) – Na6Al6Si10O32࣭12H2O
A: Analcime-C – Na(Si2Al)O6࣭H2O
U: Unnamed zeolite – Na6[AlSiO4]6࣭4H2O
F: Faujasite-Na – Na14Al12Si13O51࣭6H2O
Q: Quartz

Fig 2. SEM images of 4 types of zeolites

114

P1 (Na) synthesized by conventional heating
method at 55.3 mg/g[1]3.3 Lead ion removal performance
Determination of removal time was done using
300 ppm, 100 ml of Pb(NO3)2 and 0.1 g of zeolite
P1(Na) which showed the highest cationexchange capacity.

Figure 5 shows the Langmuir isotherm ofsynthesized zeolite P1(Na).

300

100
90
80
70
60
50
40
30
20
10
0

250
Qe (mg/g)

Removal percentage (%)

350

200
150
100
50
0
0

0

5

10
15
Time (h)

20

100

200
300
Ce (ppm)
Ce - Qe
experimental data25

Fig 3. The effect of removal time

Fig 5. The Langmuir isotherm curve

Figure 3 shows that 24 hours is needed toachieve equilibrium.

The experimental data fit the Langmuir curve
very much.

Langmuir adsorption isotherm

Dubinin-Astakhov adsorption isotherm

In order to calculate the maximum of removal
amount, Langmuir adsorption isotherm was used.
Langmuir equation is as follow:

Dubinin-Astakhov adsorption isotherm is usuallyused to evaluate the adsorption performancefrom aqueous solutions. By this adsorption
isotherm, the maximum adsorption amount andadsorption energy can be calculated.
The equations and parameters are as follows:

Qe = Qm ×

kCe࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭Eq(1)1 + kCeAnd linear equation of Langmuir isotherm is asfollow:

æ Cs ö÷ ࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭Eq(3)èCøé æ e ö2 ù
q = q0 × exp ê- ç÷ ú ࣭࣭࣭࣭࣭࣭࣭࣭Eq(4)êë è 2 E ø úû

e = R × T × ln ç

Ce/qe (g/L)

C e Ce1=+࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭࣭Eq(2)Qe Qm kQm
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

2

2

æ 1 ö é
æ C öù
ln q = ln q0 - ç
÷ × ê R × T × ln ç s ÷ú࣭࣭Eq(5)è C øû
è 2E ø ë

y = 0.0031x + 0.0079
R² = 0.9993

ɛ: The adsorption potential (J/mol)Cs: Initial concentration (mg/L)
C: The equilibrium concentration(mg/L)
0100
Ce - Ce/qe200

Ce (ppm)⥺ᙧ (Ce - Ce/qe)300

T: Temperature (K)R: Ideal gas constant (J/mol࣭K)

Fig 4. Linear plot of Langmuir isotherm

2 : A correction factor for the adsorption energy

Ce stands for the equilibrium concentration(ppm) of Pb2+ in removal experiment; qe stands
for the experimental equilibrium removal amount(mg/g); Qe is the theoretical Langmuir removal
amount (mg/g); and Qm stands for the theoretical
isotherm saturation capacity (mg/g).
The plot of experimental data (fig4.) shows that
this Pb2+ removal experiment fit the Langmuiradsorption isotherm very well with R2 = 0.9993.
The theoretical maximum removal amount is322.6 mg/g. which is much higher than the zeolite

q: The amount of a given adsorbate in theadsorbent under equilibrium conditions (mg/g)
q0: The maximum amount of the adsorbed species
per unit of sorbent (mg/g)
E: The adsorption energy (J/mol)

115

Al3+ for Si4+, the more Na+ ions are required tobond to adsorption sites. So zeolite P1(Na) is
improved in this research.

Eq(5) is the linear form of Dubinin-Astakhov
equation. Fig 6 shows the linear plot based on
experimental data.

ɛ2 - lnq0

In Lead removal experiment, 24 hours is enoughto achieve adsorption equilibrium. From theLangmuir linear plot, R2 = 0.9993 meaning that
this removal experiment fit Langmuir isotherm.
Computing by Langmuir isotherm, the maximumof removal amount shows 322.6 mg/g (77.8
meq/100g) which is lower than its CEC value of
247.5 meq/100 g. The ion exchange ratio
efficiency is about 31%. It is considered that theremoval mechanism belongs to ion-exchange.
Confirming by Dubinin-Astakhov adsorption
isotherm, the maximum adsorption amount is327.1 mg/g with adsorption energy of 12.5 kJ/mol.
This adsorption energy value is considered toshow this Pb2+ removal experiment isaccomplished by ion exchange capacity of zeolite
P1(Na).

6
5.8
y = -0.0032x + 5.7904
R² = 0.9873

5.6
5.4
5.2
5
4.8
0

100
linear plot200⥺ᙧ (linear plot)

300

Fig6. Linear plot of Dubinin-Astakhov adsorption isotherm

The plot of experimental data (fig6.) shows that
this
Pb2+
removalexperimentfit
theDubinin-Astakhov adsorption isotherm with R2 =0.9873. According to this linear plot thetheoretical maximum removal amount calculated
is 327.1 mg/g.
In Dubinin-Astakhov adsorption isotherm if E = 8
to 16 kJ/mol, the adsorption mechanism belongs
to ion exchange. In this research the adsorptionenergy calculated from linear plot is E = 12.5
kJ/mol meaning that the Pb2+ removal belongs to
ion exchange.

Reference:[1] Yan Shao, Synthesis of Zeolites from Lake
Sludge, International Development Engineering,Tokyo Institute of Technology[2] Human rights watch, “My Children Have Been
Poisoned” May, 2010

4. Conclusions[3] Myers, R.H., Montgomery, D.C. &Anderson-Cook, C.M. (2009) "Response Surface
Methodology," Third Edition. Chapter 7.

Recycle of lake sludge is successfully done inthis study.

[4] Chapman, H.D., ”Cation-exchange Capacity,”
Method of Soil Analysis(1965)

By adjusting CNaOH, heating temperature, and
Si/Al molar ratio, 4 types of zeolites can besynthesized. Their cation-exchange capacity is asfollow:
ZeoliteP1(Na) (247.5 meq/100 g) 㸼Unnamed
zeolite (135.1 meq/100 g)㸼Faujasite Na (82.3
meq/100 g)㸼Analcime C (39.5 meq/100 g)
Synthesized zeolite P1 (Na) shows higher CEC
value than commercial zeolite which is 160 㹼
190 meq/100 g[5].

[5] TSK ᮾ᪂໬ᡂᰴᘧ఍♫ ࢮ࢜ࣛ࢖ࢺᡂศཧ⪃ศᯒ್

Zeolite P1(Na) with highest CEC synthesized
after ultrasonic mixing is much higher than theone of 171 meq/100g in previous study.
The internal heating by ionic polarization(Na-OH) and orientation polarization (H2O) andstirring during heating time are considered to
improve uniform heat transmission which results
in a better zeolite formation. Another considerable
reason is that ultrasonic mixing with frequency of
38 kHz is considered to result in a good alkali
treatment. During the treatment the mixture
vibrates violently according to the frequency ofultrasonic. Sludge is mineralized well by NaOH
during ultrasonic mixing. Si-O-Al structure is wellformed from Si and Al, so zeolite P1(Na) structure
is formed completely. The more substitution of116

Impact on domestic air cargo demand by changing aviation circumstances
Student Number: 13M18089  Name: Ryuuhei KUDOU

Supervisor: Shinya HANAOKA⯟✵⎔ቃኚ໬ࡀᅜෆ⯟✵㈌≀㟂せ࡟୚࠼ࡿᙳ㡪


ᕤ⸨❳ᖹ

ᮏ◊✲ࡢ┠ⓗࡣࠊ㏆ᖺࡢ⯟✵⎔ቃኚ໬ࡀᅜෆ⯟✵㈌≀㟂せ࡟୚࠼ࡓᙳ㡪ࢆ᫂ࡽ࠿࡟ࡍࡿࡇ࡜࡛࠶ࡿࠋࡲࡎྍ⬟ᛶࡢ࠶ࡿ⎔ቃኚ໬せᅉࡢᙳ㡪ࢆ᳨ウࡋࠊࡑࡢෆࡢ *'3ࠊ⥲ᗙᖍᩘࠊ୍౽࠶ࡓࡾࡢᗙᖍᩘࢆኚᩘ࡜ࡋ࡚ࠊᅜෆ⥺඲యࠊᖿ⥺඲యࠊ࣮ࣟ࢝ࣝ⥺඲యࡢ௚ࠊ≉ᐃࡢ  ㊰⥺࡟ࡘ࠸࡚ࠊᅜෆ⯟✵㈌≀㔞࡜ࡢ㛵ಀࢆ࣋ࢡࢺࣝ⮬ᕫᅇᖐ㸦9$5㸧ࣔࢹࣝ࡟ࡼࡗ࡚ศᯒ
ࡋࡓࠋࡑࡢ⤖ᯝࠊᅜෆ⥺ࠊᖿ⥺ࠊ࣮ࣟ࢝ࣝ⥺඲య࡛ࡣࢢࣞࣥࢪ࣮ࣕᅉᯝᛶࡀ☜ㄆࡉࢀࡓࡀࠊ≉ᐃ㊰⥺ࡢሙྜࡣ㊰⥺ẖ࡟␗࡞ࡿ⤖ᯝࡀ♧ࡉࢀࡓࠋ

1.

Introduction

as case study routes.
III.  Competitiveness with other transport modes
Main items of air cargo are document and mechanicalparts. Thus air cargo volume is smaller than othertransport modes. Indeed air cargo volume is verysmaller than that in other transport modes even incase of between Tokyo and Kyushu region [4]. Hence itis difficult to check impacts by changing othertransport modes widely. Therefore we investigatedspecific change of other mode, discounts of highway.
40% discount started from ’08 February, 50%
discount started from ’08 October 30% discount inweekday started from ’09 March. And there is 30%
discount before this period. When we compare cargovolume in this period, there are impacts on air cargo
by 50% discount in midnight and weekday discount,but in this period, global financial crisis happened
and it is difficult to distinguish the impact fromdiscount of highway tolls and economic trend. Hence
we need to more analysis about whether there is
impact by this discounts or not but we are notconcerned here with this factor more.
IV.
International transit air cargo volume indomestic routeIn case of routes between Kyushu and Haneda, theshare of international cargo in domestic air transport
is 2%~5% from 2005 to 2013. Recently directly export
from and import to Haneda airport but I think the
impact of domestic air transport from internationalcargo is not so big.
V.
Expansion of air slot in Haneda airport
The frequency of flight gradually increasing in almost
all routes between main airport in Kyushu andHaneda airport [1]. Thus offering cargo capacity may
increase.
VI.
Miniaturization of aircraft
On the other hand, seats per a flight is gradually
decreasing. Miniaturization of aircraft may be affectcargo capacity too, so we would like to analyze thisfactor with offering cargo capacity.
VII. Operation of freighter
Figure 1 is change of air cargo volume of Haneda Saga route. We can see in this figure that there is not
so change when cargo dedicated flight start operation
but there is big change when freighter start operation.
It suggests that operation of cargo dedicated flight

Recently Japanese aviation circumstances changed
greatly. For instance, economic trend which due toGreat East Japan earthquake and high exchange rateof the yen and streamlining of Aviation Company
because of soaring fuel price. Indeed, main Japanese
airlines reported they changes their aircraft from wide
body one to smaller one, like Boeing 787 and Boeing 737.
These changes are considered to impact on domestic air
cargo in Japan in the same way.
Ministly of Land, Infrastracture and Transport(MLIT) [1] shows the difference in domestic cargo volume
between trunk flight routes and local flight routes.
Trunk flight routes means air routes between New
Chitose (Sapporo), Haneda, Narita, Itami, Kansai,
Fukuoka, and Naha airports. And local routes meansothers routes. And MLIT report current status of many
regional airports. In this way there are some papers andreports about each factor and each airport, but there arefew papers which use stateistical analysis of domestic
air cargo from wide viewpoint and take into accountrecently aviation circumstances widely.
In this paper, we discuss the impacts on domestic air
cargo by aviation circumstances, and choose somefactors of impacts, then analyze VAR model andGranger causality between these factors and domestic
air cargo demand by whole domestic routes, trunk
routes, local routes, and some specific flight routes.

2.

Selection of factors to give the impact on
domestic air cargo demand

2.1. Discussion on possible factors
I.
GDP
GDP reflects to economic trend. General trend of air
cargo is similar to GDP, that is, be increasing. Inoue[2] and Chang [3] have concluded that there are
relationships between GDP and international air
cargo. Thus we need to analyze the relationshipbetween GDP and domestic air cargo.
II. DistanceIn the ranking of domestic cargo volume by route,there are many routes which be linked with Haneda
airport and the more the distance from Haneda islonger, the more cargo volume is larger. Therefore we
discuss Kyushu region and main airports in Kyushu

117

Cabinet Office [5], and aviation data from MLIT [1]. Here,PS are series which divide S by frequency of flight thus
correlation between PS and S is possible. Indeed, itshould be noted that there is correlation only in Kushiro
route.

[1,000ton]
10.0

5.0

3.
0.0

Haneda to Saga

Methodology

3.1. Outline of VAR model
Vector Auto Regression (VAR) model is Auto Regression
model which be augmented to use multi-variables. Thismodel is used for economic forecasting and economic
analysis. VAR model is one of Time Series Analysis, thus
it is relatively easy to get required data by statistics.
VAR model which has 2 variables and lag 1 is below;

Saga to Haneda

Figure 1 Cargo volume of Haneda-Saga route‫ݕ‬௧ ൌ ܽ௬  ൅  ߣ௬ ‫ݕ‬௧ିଵ ൅ ߚ௬ ‫ݔ‬௧ିଵ ൅ ߝ௬௧does not have big impact on cargo volume but
freighter has impact on it. However flight routes
which be operated freighter are not so much, so wehave to consider each routes. Thus this is not myconcern in this paper.
VIII. Operation hours
Midnight operation is important to increase air cargo
for international hub airport but many airports
started midnight operation with operating cargo
dedicated flight. Thus it is difficult to distinguisheffects by midnight operation and by operation ofcargo dedicated flight.
IX.
Other factors
In addition to these changes, management strategyof Airline Company, like using regional airport asinternational cargo hub and freighter’s stopping at
airport affect airport’s cargo volume. However these
are issue of each airport thus this is not my concern
in this paper.

‫ݔ‬௧ ൌ ܽ௫ ൅  ߣ௫ ‫ݕ‬௧ିଵ ൅ ߚ௫ ‫ݔ‬௧ିଵ ൅ ߝ௫௧

(1)
(2)

Where,‫ݕ‬௧ , ‫ݔ‬௧ : Time series data which refer to the period( t =1, 2, 3,͐, T)ߝ௬௧ , ߝ௫௧ : disturbance terms
ܽ௬ ,ܽ௫ : constant terms
ߣ௬ , ߣ௫ , ߚ௬ ǡ ߚ௫ : coefficients

This equation means ‫ݕ‬௧ is explained by 1 term ago(lag = 1) values of itself and‫ݔ‬௧ .
VAR model cannot use when ‫ݕ‬௧ and ‫ݔ‬௧ have Unit
Root. When we use VAR model with constant term tothese ‫ݕ‬௧ and‫ݔ‬௧ , it caused Spurious Regression. If wewould like to use VAR model to these variables, take adifference of these variables. VAR model in difference(DVAR) is expressed as below;

2.2. Factors selection and flight routes
For above results, I would like to analyze to 3 essential
factors, GDP, offering cargo capacity, and average sizeof aircraft. The variables used are: GDP, offering seats
to reflect cargo capacity, average seats per a flight toreflect average size of aircraft, and domestic air cargodemand.
We analyze these factors about whole domestic
routes, trunk routes, and local routes. And I choose 4
flight routes which between Haneda airport andSapporo (CTS), Kushiro (KUH), Fukuoka (FUK), andNagasaki (NGS) airport. Sapporo route and Fukuoka
route are trunk routes and Kushiro route and Nagasakiroute is local routes. The reason why choose Sapporo
and Fukuoka routes is that these routes are the mostheavily routes about domestic air cargo. The reasons
why choose Kushiro and Nagasaki routes are that these
airports are in same region of Sapporo and Fukuoka,
Hokkaido and Kyushu and, these flight routes has been
decreasing the average seats per a flight. Thus weanalyze 7 cases.
From here, we express G as GDP, C as Cargo Volume,
S as Offering Seats, and PS as seats per a flight. Datais quarterly from 1994 to 2013. I obtain GDP data from߂‫ݕ‬௧ ൌ ܽ௬  ൅ ߣ௬ ߂‫ݕ‬௧ିଵ ൅ ߚ௬ ߂‫ݔ‬௧ିଵ  ൅ ߝ௬௧(3)

߂‫ݔ‬௧ ൌ ‫ݔ‬௧ െ ‫ݔ‬௧ିଵ(5)߂‫ݔ‬௧ ൌ ܽ௫ ൅ ߣ௫ ߂‫ݕ‬௧ିଵ ൅ ߚ௫ ߂‫ݔ‬௧ିଵ ൅ ߝ௫௧(4)

And when there is Cointegration between ‫ݕ‬௧ and‫ݔ‬௧ which have Unit Root, VAR model and DVAR cannot
use. In this case, we use Vector Error Correction (VEC)
model. VEC model is expressed as below;߂‫ݕ‬௧ ൌ ܽ௬ ൅ ߣ௬ ߂‫ݕ‬௧ିଵ ൅ ߚ௬ ߂‫ݔ‬௧ିଵ  ൅ ߝ௬௧(6)߂‫ݔ‬௧ ൌ ܽ௫ ൅ ߣ௫ ߂‫ݕ‬௧ିଵ ൅ ߚ௫ ߂‫ݔ‬௧ିଵ ൅ ߝ௫௧(7)

൅ ߟଵ ሺ‫ݕ‬௧ିଵ ൅ ߰‫ݔ‬௧ିଵ ሻ

Where,

൅ ߟଶ ሺ‫ݕ‬௧ିଵ ൅ ߰‫ݔ‬௧ିଵ ሻߟ ቀ‫ݕ‬௧ିଵ ൅ ߰‫ݔ‬௧ିଵ ቁ : error correction term

3.2. Unit root
When series ‫ݔ‬௧ is not stationary but ߂‫ݔ‬௧ is stationary,
it is called that ‫ݔ‬௧ has Unit Root. One of typical Unit
Roots process is random walk, and that is expressed as
below;

118

Where,ȣ: constant termܺ௧ ൌ ߤ ൅ ܺ௧ିଵ ൅ ߝ௧

causality. The reason why this causality is occur is thatcointegration is stationary, thus there is equilibrium inlong-run term. When we want to investigate long-run
Granger causality, we examine adjustment factor in
VAR model is significance or not by t-test. It should be
noted that Granger causality is different from causality
in general meaning.

(8)

In this equation, coefficient of ܺ௧ିଵ is 1, so this timeseries is called as unit root process. Many economic
indicators are known as they have unit root.
As an example, we regress ࡳ௧ (GDP) and R, which islogarithm of below random walk RW to next model;ࡾࢃ௧ ൌ ͳ ൅ ࡾࢃ௧ିଵ ൅ ߝ௧ ߝ௧ 㹼ܰሺͲǡͶሻ

(9)ࡳ௧ ൌ ܽ ൅ ܾࡾ௧(11)ࡾ௧ ൌ ሺࡾࢃ௧ ሻ

Where,
a: constant term
b: coefficientࡳ௧ has Unit root. Result equation is below;‫ܩ‬௧ ൌ ͳʹǤ͹ʹͺ ൅ ͲǤͳͲʹ ‫ܴ כ‬௧

4.

(10)

4.2. Lag number
We use 4 as lag number. It means these variables referto 1 year before. Other method to decide lag number isusage of information criterion.

(12)ሾͶʹͳǤʹͳ͵ͳሿ ‫  כ‬ሾͳʹǤͶʹ͸͸͸ሿ ‫כ‬*: 1% significance.
The numbers in the brackets indicate t-values ofcoefficients. R2 of this equation is 0.664403 and Adjusted
R2 is 0.660101. Of course there is no relationshipbetween GDP and ࡾࢃ௧ , but this result seems
meaningful. This is Spurious Regression.

4.3. Cointegration test
We did Johansen’s cointegration tests between variables
and C about each route. Because we are not certain
about intercept of VAR and intercept and time trend of
Cointegration, thus calculate all model and adopt themodel and number of cointegration which has thesmallest Akaike information criterion (AIC). Table 1 isresults of cointegration tests. 1st row is air route, 2nd row
is examined variable with C, 3rd row is number ofcointegration, and 4th row is type of VAR and
Cointegration. Table 2 shows classification of VAR
model type. 1st row is Number of type, 2nd row is whether
there is intercept in examined VAR, 3rd row is whether
there is intercept in cointegration, and 4th row iswhether there is time trend in cointegration incointegration.

3.3. Cointegration
When 2 variables‫ݕ‬௧ and ‫ݔ‬௧ have unit root, sometime
there are constants a and b which complete equation‫ݕ‬௧ +ܾ‫ݔ‬௧ is stationary. In this case, these variables issaid that they have cointegration. Error correction termin equation of VEC means this cointegration. Error
correction term expresses error from long-term
equilibrium of ‫ݕ‬௧ and ‫ݔ‬௧ , and adjustment factor
controls direction and speed of effect from cointegration.
3.4. Granger causality
There are the paper which use VAR model and Granger
test for air transport market by Inoue [2] and Chang [3].
When forecast from using past ‫ݕ‬௧ and ‫ݔ‬௧ is more
applicable than forecast from using only ‫ݕ‬௧ , it is said
that there is (short-run) Granger causality from ‫ݔ‬௧ to‫ݕ‬௧ . When we want to investigate there is Granger
causality or not, we examine below equation;
‫݌‬

‫݌‬

‫ ݐݕ‬ൌ ෍ ߣ݅ ‫ݐݕ‬െ݅ ൅ ෍ ߚ݅ ‫ݐݔ‬െ݅ ൅ ߝ‫ݐ‬Where,
p: lag number

ͳͳResult4.1. Unit root test
Almost all variables are non-stationary in levels but
stationary in 1st difference. Exception are C of local
routes, S of Kushiro, C of Fukuoka, and S of Nagasaki.
C of Fukuoka and S of Nagasaki are stationary in levels
and differences, and C of local routes and S of Kushiro
are non-stationary in levels and differences. However if
I do seasonal adjustment to these data, the results aresame of other variables, non-stationary in levels but
stationary in 1st differences. Thus we deal with thesevariables as they have unit root.

Table 1 Result of Cointegration test
Whole Japan

Trunk routes

Local routes

G

S

PS

G

S

PS

G

S

PS

Coint.
Type

1
1

0
1

1
5

1
4

1
4

1
5

1
1

1
1

1
4

㻌
㻌

G

PS

G S

CTS
S

KUH
PS

G S

PS

0 0 1 1 1 1
Type
4 1 4 4 4 5
G: GDP S: Offering Seats

Coint.

(13)

FKU
G S

NGS
PS

1 1 1 1 1 0
4 1 1 4 1 1
PS: Seats per a flight

Table 2 Type of VAR and Cointegration

If null-hypothesis ߚ௜ =0Ѧi Ѯ p is reject by F-test, we
think there is Granger causality from ‫ݔ‬௧ to ‫ݕ‬௧ .
Moreover, in case of there is cointegration between
variables, there is possibility of long-run Granger

119

Type

1

2

3

4

5

Intercept in VAR

No

No

Yes

No

Yes

Intercept in Coint

No

Yes

Yes

Yes

Yes

Trend in Coint

No

No

No

Yes

Yes

D) About Sapporo (CTS) route, there is no resemblancewith about whole trunk routes, and few Granger
causality are confirmed. This result suggests thatcargo demand in this route is independent of thesefactors, or simply my modeling is wrong.
E) About Kushiro (KUH) route, there are noresemblance with whole local routes in the same way.
Important point of this route is there are Granger
causalities from size of aircraft to cargo neverthelessthese cannot be find in whole local routes.
F) Only result of Fukuoka (FKU) route are similar toresult of whole trunk routes. This result suggeststhat Fukuoka route is typical route in trunk routes.
G) Nagasaki (NGS) route are not similar to result ofwhole local routes. As we can find similar result inFukuoka route, there are long-run Granger
causalities from GDP to each cargo demands. This
result suggests that Kyushu region is more affectedby economic trend.

Table 3 Result of Granger Causality Test
ΔG to
ΔC

ΔC to
ΔG

ΔS to
ΔC

ΔC to
ΔS

ΔPS
to ΔC

ΔC to
ΔPS

A) Whole Domestic routes
Long
Adj.
Short

*
-0.239**-0.029***-0.528-0.016*B) Whole Trunk routes
Long
Adj.
Short

-0.085**-0.081*-0.4370.013**-0.559-0.064C) Whole Local routes
Long
Adj.
Short

-0.01
**

0.003

*
-0.235**-0.127**-0.066*
0.222*D) Sapporo (CTS)
Long
Adj.
Short

-

Long
Adj.
Short

**
-0.172

Long
Adj.
Short

*
-0.272
*

-

-

0.01
**

**
-0.32

0.01
**

E) Kushiro (KUH)
**
0.013
**

0.108

**
0.241
**

**
-0.531
**

-0.02
*

*
-0.392-0.069F) Fukuoka (FUK)
0.013*-0.393-0.022
**

5.

Long
Adj.
Short

**
-0.091
**

**
0.011

*
-0.229**-0.063**-Conclusions

In this paper, at first we discussed 9 factors to give theimpact on domestic air cargo demand and chose 3
essential factors, GDP, offering cargo capacity, andaverage size of aircraft as factors which have big impact.
Second, we analyzed the relationships between these 3
factors and cargo demand in whole domestic routes,
whole trunk routes, whole local routes, and 4 specificroutes by using VAR model.
Throughout the VAR model analysis, the results
represents that these 3 factors affect cargo demand inwhole domestic cargo and miniaturization of aircraft
affect cargo demand in trunk routes rather than local
routes. Furthermore the results to examine each trunk
route or each local route are different from the results
to examine whole trunk routes or whole local routes.
Thus, when we need to consider about each route, weshould analyze each route to understand thecircumstance of the route clearly.

G) Nagasaki (NGS)-*(**) denotes statistical significance at 1% (5%) level.

4.4. Granger causality test
Table 3 shows results of Granger causality tests. These
cointegrations take 1 for coefficient of C. 1st row is
direction of Granger causality, 2nd row is results of longrun Granger causality, 3rd row is adjustment factor of
cointegration in VAR model, and 4th row is results ofshort-run Granger causality.
A) About whole domestic routes, we see that GDP has
short-run and Long-run Granger causality to cargodemand. Offering seats has short-run Granger
causality, and seats per a flight has long-run
Granger causality to cargo demand. This results
suggests that these factors affect cargo demand.
This result also suggests that cargo demand affectoffering seats and size of aircraft.
B) About trunk routes, important differences fromwhole domestic are there is long-run causality fromcargo to GDP. This result suggests that cargo affectGDP. And other important point is average size ofaircraft has long-run Granger causality to cargodemand as with whole domestic routes.
C) About local routes, the important point is there is no
Granger causality from average size of aircraft tocargo but there are Granger causalities from cargo
to offering cargo capacity and to size of aircraft. This
result may suggest that it is not true that decreasingof cargo demand in local routes are caused byminiaturization of aircraft but small cargo demandcause small aircraft and few cargo capacity.

References[1]. MLIT, “Annual Statistics of Air Transport”, 19942013.
[2]. Inoue, G. and Tansei, K. (2011), “Study on
Application of Time-series Analysis to Air
Transport Demand Estimation”, Technical Note of
National Institute for Land and InfrastructureManagement, No.652.
[3]. Chang, Y. and Chang, Y. (2009), “Air cargo
expansion and economic growth: Finding theempirical link”, Journal of Air Transport
Management Vol.15, pp 264-265.
[4]. MLIT, “Regional Mobility Survey of Cargo” 2000 2012.
[5]. Cabinet Office. (2014), “Real Gross Domestic
Product (seasonally adjusted series)”.

From here, we compare each route between Haneda
airport with whole trunk routes or whole local routes.

120

Sugar production from agar by recombinant Brevibacillus choshinensis
Student Number: 13M18066

Name: Sanomi KASHIMURA Supervisor: Kiyohiko NAKASAKI㑇ఏᏊ⤌᥮࠼ࣈࣞࣅࣂࢳࣝࢫ࡟ࡼࡿᐮኳ࠿ࡽࡢ⢾⏕⏘ᶔᮧ ᙬ஀⨾ࣂ࢖࣐࢜ࢫ࡜ࡋ࡚⣚⸴࡟╔┠ࡋࠊ⣚⸴୰ࡢᐮኳ࠿ࡽࣂ࢖࢜ࣜࣇ࢓࢖ࢼ࣮ࣜ࡟౑⏝ࡍࡿཎᩱ࡜࡞ࡿ࢞ࣛࢡ
ࢺ࣮ࢫࡢ⏕⏘ࢆヨࡳࡓࠋࡣࡌࡵ࡟ᐮኳࢆศゎࡍࡿ 2 ✀㢮ࡢ㓝⣲ࢆ⏕⏘ࡍࡿ⤌᥮࠼⳦ࡢస〇࡟ྲྀࡾ⤌ࡳࠊᚓࡽࢀࡓ⤌᥮࠼⳦ࡢ㑇ఏᏊ㓄ิࢆỴᐃࡍࡿࡇ࡜࡛┠ⓗࡢ⤌᥮࠼⳦ࡀᚓࡽࢀࡓࡇ࡜ࢆ☜ㄆࡋࡓࠋ⥆࠸࡚స〇ࡋࡓᐮኳศゎ㓝⣲⏕⏘⤌᥮࠼⳦࡟ࡼࡾᐮኳ࠿ࡽࡢ࢜ࣜࢦ⢾⏕ᡂࠊ࢜ࣜࢦ⢾ศゎ㓝⣲⏕⏘⤌᥮࠼⳦࡟ࡼ
ࡾ࢜ࣜࢦ⢾࠿ࡽࡢ༢⢾⏕ᡂࢆ☜ㄆࡍࡿࡇ࡜ࡀ࡛ࡁࡓࠋࡉࡽ࡟ 2 ✀㢮ࡢ⤌᥮࠼⳦ࢆΰྜᇵ㣴ࡍࡿࡇ࡜࡛ࠊ
ᐮኳ࠿ࡽ࢞ࣛࢡࢺ࣮ࢫࢆ⏕⏘࡛ࡁࡓࠋ

1. Introduction

In this research, construction of 2 kinds ofrecombinant Brevibacillus choshinensis whichcontains either β-agarase gene or α-agarase gene andthe hydrolysis of agar by the recombinant B.
choshinensis were attempted.

In recent years, biorefinery defined as production of
bulk chemicals and fuels such as lactic acid andbioethanol from biomass has attracted attentions. Thus,
many studies have been performed on the productionof them from biomass, such as lignocellulosic biomass[1], paper sludge [2], and other wastes such as starchand food wastes. Seaweed is proposed as one of themost promising biomass for biorefinery, since it is
known to contain no lignin that makes
polysaccharides in biomass resistant to hydrolysis.
In particular, red seaweed has high content offermentable sugar such as glucose and galactose, andthus, it is expected that the hydrolysis andfermentation of the red seaweed can produce various
bioproducts, such as ethanol, lactic acid, and succinicacid, with high yields.
Agar is a polysaccharide contained in red seaweed
and it is composed of galactose and anhydrogalactose.
Two types of enzyme, β-agarase and α-agarase are
required for enzymatic hydrolysis of agar because it
has 2 types of bond called as β-1, 4 bond and α-1, 3
bond in agar. Firstly agar is degraded by β-agarase
into oligosaccharide, and secondly neoagarobiose
which is one of oligosaccharides is degraded by
α-agarase into galactose and anhydrogalactose (Fig. 1).
For the production of galactose from agar, commercial
β-agarase is available, however it is expensive, andα-agarase is not commercialized. Therefore, the use ofsome microorganisms that produce β and α-agarase
enzyme are considered to be more feasible.

2. Materials & Methods
2.1
Constructionofrecombinant
choshinensis possessing β-agarase geneB.

A fragment encoding β-agarase gene was amplified
from chromosomal DNA of Cellvibrio sp. OA-2007
by PCR. This amplified fragment was ligated in
pNCMO2 vector for the construction of
pNCMO2-β-agarase plasmid. The plasmid of
pNCMO2-β-agarase was then introduced into B.
choshinensis.
After the construction of the recombinant B.
choshinensis that have β-agarase gene, sequence
analysis of the plasmid introduced to B. choshinensis
was carried out to ensure that β-agarase gene wasintroduced properly.
This recombinant B. choshinensis was cultivated onagar medium containing 50mg/L of neomycin at 30Υ
for 2 days to check the expression of β-agarase gene.
Then, the recombinant B. choshinensis wascultivated in liquid medium containing 50 g/L ofneomycin and 50 g/L of agar at 30Υ for 0, 1, 3, 5
days in order to produce oligosaccharide from agar.
The oligosaccharide produced in the culture of therecombinant B. choshinensis was analyzed by highperformance liquid chromatography (HPLC) and thinlayer chromatography (TLC). In HPLC analysis,
KS-802 was used as column under 50Υ and 0.8
ml/min of flow rate. In TLC analysis, the culture of
the recombinant B. choshinensis was filtered through
0.45μm membrane filter and spotted on silica plate,and developed using the solvent (butanol: ethanol :distilled water = 3 : 1 : 1). The spots were visualizedby spraying with 10% H2SO4 in ethanol containing
0.2 % naphthalendiol and heating [3].

Fig. 1 Degradation of agar by 2 kinds of agarase1212.2 Construction of  recombinant
choshinensis possessing α-agarase gene

three spots corresponding to neoagarobiose(designated as 2s), neoagarotetraose (4s) andneoagarohexose (6s) were observed for each medium
after 3 days (Fig. 5). These results suggested that therecombinant B. choshinensis with β-agarase geneproduced oligosaccharide from agar.

B.

A fragment encoding α-agarase gene was amplified
from chromosomal DNA of Cellvibrio sp. OA-2007
by PCR. The construction of the recombinant B.
choshinensis was carried out with the similar method
as described in 2.1, and the sequence analysis of theplasmid introduced to B. choshinensis was carried out.
The recombinant B. choshinensis was cultivated inliquid medium containing 50 g/L of neomycin and
10 % of oligosaccharide mixture including 16 g/L of
neoagarobiose in the mixture at 30Υ for 0, 1, 3, 5
days in order to produce galactose. The culture of therecombinant B. choshinensis was analyzed by HPLC
and TLC under the same condition as described in 2.1.

β-agarase

2.3 Mixed culture of the two kinds of therecombinant B. choshinensis
For the comparison of the enzymatic activity in the
different medium, the recombinant B. choshinensis
with β-agarase gene and the recombinant B.
choshinensis with α-agarase gene were cultivatedindividually in MT, LB and MT (without glucose)liquid medium containing 50 mg/L of neomycin at
30Υ for 0, 1, 2, 3, 5 days and the OD600 of the culture
was measured.

Fig. 2 Result of sequence analysis of β-agarase
gene introduced to B. choshinensis (Forward)

The recombinant B. choshinensis with β-agarase
gene and that with α-agarase gene were cultivated
together in MT liquid medium containing 50 mg/Lof neomycin and 50 g/L of agar at 30Υ for 0, 1, 3, 5
days in order to examine the production of galactose
from agar. The culture was analyzed by TLC under thesame condition as described in 2.1.
Fig. 3 Pits around the colonies of recombinant B.
choshinensis that have β-agarase gene

3. Results & Discussion
3.1 Oligosaccharide production by recombinant
B. choshinensis possessing β-agarase gene
The result of the sequencing of β-agarase gene
introduced to B. choshinensis is shown in Fig. 2. The
sequence was 100 % the same with expected sequence.
This result shows β-agarase gene was successfully
introduced into B. choshinensis.
After the cultivation of the recombinant B.
choshinensis that have β-agarase on agar medium, thepits around the colonies were observed (Fig. 3). The
formation of the pits indicates that the agar washydrolyzed by β-agarase expressed in B. choshinensis.
Then, HPLC chromatogram of the culture obtainedon 0 day and 5 day was shown in Fig. 4 and TLC
chromatogram of the culture obtained on 0, 1, 3 and 5
day was shown in Fig. 5. On the HPLC chromatogram,
three peaks corresponding to neoagarobiose,
neoagarotetraose and neoagarohexose were observed.
In addition, on the results of the TLC chromatogram,

A

4s
6s

2s

B

Fig. 4 HPLC chromatogram on the production ofoligosaccharide from agar
(A: 0 day sample, B: 5 day sample)

122choshinensis with α-agarase gene. In addition, in the
TLC analysis, the spot corresponding toneoagarobiose (2s) was observed on 0 day anddisappeared after 1 day, and then the spots ofanhydrogalactose were observed (Fig. 8). These
results suggested that the recombinant B. choshinensis
with α-agarase gene degraded neoagarobiose intogalactose and anhydrogalactose.

2s

4s
2s

6s
A

Galactose+ AHG

Fig. 5 TLC chromatogram on the production ofoligosaccharide from agar(ձ, ղ, ճ and մ correspond to the culture
obtained on 0, 1, 3, 5 day)B

3.2 Galactose production by recombinant B.
choshinensis possessing α-agarase geneFig. 7 HPLC chromatogram on the production ofgalactose from oligosaccharide(A: 0 day sample, B: 5 day sample)

The sequence of α-agarase gene is in Fig. 6. The
sequence was 100 % the same as that of being
expected as α-agarase gene. It shows that α-agarase
gene was successfully introduced into B. choshinensis.

AHG
2sα-agarase

Fig. 8 TLC chromatogram on the production ofgalactose from oligosaccharide(ձ, ղ, ճ and մ correspond to the culture
obtained on 0, 1, 3, 5 day 0, 1, 3, 5 day)Fig. 6 Result of sequence analysis of α-agarase gene
introduced to B. choshinensis (Forward)
Then, HPLC chromatogram of the culture obtainedon 0 day and 5 day was shown in Fig. 7 and TLC
chromatogram of the culture obtained on 0, 1, 3 and 5
day was shown in Fig. 8. On the chromatogram of
HPLC, the peak correspond to neoagarobiose wasobserved on 0day and disappeared on 5day, thenappeared the peak of galactose and anhydrogalactose.
It indicates that neoagarobiose (2 sugar) was degradedand galactose and anhydrogalactose (AHG) were
produced in the culture of the recombinant B.

3.3 Galactose production by the mixed cultureof two kinds of recombinant B. choshinensis
The courses of OD600 of the culture of therecombinant B. choshinensis with β-agarase gene wereshown in Fig. 9 and that with α-agarase gene wasshown in Fig. 10. It was found that the growth of therecombinant B. choshinensis was the fastest and final
cell density was highest in MT medium among MT,
LB and MT (without glucose) medium.
123

AHG
2s
galactose

Fig. 9 OD600 of the liquid medium of the recombinantβ-agarase B. choshinensis

Fig. 11 TLC chromatogram on the production ofgalactose from agar(ձ, ղ, ճ and մ correspond to the culture
obtained on 0, 1, 3, 5 day 0, 1, 3, 5 day)4. Conclusions
In this study, the recombinant B. choshinensis thathave β-agarase gene was successfully constructed. By
the cultivation of the recombinant B. choshinensis
containing agar in the medium, oligosaccharides wereobtained.
In addition, the recombinant B. choshinensis thathave α-agarase gene was also successfully constructed.
By the cultivation of the recombinant B. choshinensis
in the medium containing oligosaccharides, galactose
and anhydrogalactose were obtained.
Then, galactose was successfully obtained by themixed cultivation of the recombinant B. choshinensis
that have α-agarase gene and β-agarase gene
cultivated simultaneously in MT liquid medium.

Fig. 10 OD600 of the liquid medium of therecombinant α-agarase B. choshinensis
In the TLC analysis of the filtered culture of the tworecombinant B. choshinensis in the MT liquid medium
containing agar, the spots of oligosaccharides wereobserved after 3 day cultivation (Fig. 11). This resultshows that the recombinant B. choshinensis that haveβ-agarase produced oligosaccharides from agar. The
color intensity of the spot of neoagarobiose (2s) is nothigh compare to the other oligosaccharides such as
neoagarotetraose (4s) and neoagarohexose (6s),because neoagarobiose was degraded into galactose
and anhydrogalactose soon after it was produced. And
the spots of anhydrogalactose were observed above
neoagarobiose spots and became high intensity.
Anhydrogalactose and galactose are produced when
neoagarobiose are degraded into monosaccharides.
Thus, it was considered that galactose was
successfully obtained from agar using 2 kinds ofagarase produced by the recombinant B. choshinensis.

References[1] Garde A, Jonsson G, Schmidt AS, Ahring BK.
Lactic acid production from wheat straw
hemicelluloses hydrolysate by Lactobacillus
pentosus and Lactobacillus brevis. Bioresource
Technology., 81, 217-223 (2002)[2] Nakasaki K., Adachi T. Effects of intermittent
addition of cellulase for production of l-lactic
acid from wastewater sludge by simultaneous
saccharification and fermentation. Biotechnology
Bioengineering., 82, 263-270 (2003)[3] Yun E. J., Shin M. H., Yoon J., Production of
3,6-anhydro-l-galactose from agarose byagarolystic
enzymesof
Saccharophagus
degradans 2-40. Process Biochemistory., 46,
88-93 (2011)124

Prototype of an autonomous energy harvesting system using boost converter
Student number: 13M18014 Name: Ryushi ASAMI Supervisor : Kunio TAKAHASHI
࢚ࢿࣝࢠ࣮ࣁ࣮࣋ࢫࢸ࢕ࣥࢢ⏝⮬ᚊᆺ᪼ᅽࢥࣥࣂ࣮ࢱࡢヨసὸぢ ❧ᚿ
ᮏ◊✲࡛ࡣࠊ㟁※ࡢ᭱኱ฟຊⅬࢆ⮬ື᳨࡛ฟ࣭㏣ᚑࡋࠊ⮬ศ⮬㌟ࡢືຊࢆ⟶⌮ࡍࡿ⮬ᚊᆺࡢ࢚ࢿࣝࢠ࣮ࣁ࣮࣋ࢫࢸ࢕ࣥ
ࢢ⿦⨨ࡢไᚚ᪉ἲࢆᥦ᱌ࡋࠊヨసࡋࡓࠋ⿦⨨ࡀ㟁※ࡢฟຊࢆ☜ㄆࡋࠊฟຊ࡜⮬㌟ࡢᾘ㈝㟁ຊࢆẚ㍑ࡍࡿ࢔ࣝࢦࣜࢬ࣒ཬࡧ
ᐇ⌧ࡍࡿࡓࡵࡢᅇ㊰ࡢタィ࡟ࡘ࠸࡚㏙࡭ࡓࠋࡲࡓࠊヨసࡋࡓ⿦⨨࡛ᐇ㝿࡟ฟຊࡀኚືࡍࡿ⇕㟁⣲Ꮚ࡜ኴ㝧㟁ụ࠿ࡽ⵳㟁ࢆ⾜࠸ࠊ᭱኱ฟຊⅬ᳨ฟ࣭㏣ᚑᶵ⬟ࡢ⵳㟁ຠ⋡࡬ࡢᙳ㡪࡜ࠊࢫ࣮ࣜࣉᶵ⬟ࢆ฼⏝ࡋࡓࢫࢱࣥࣂ࢖≧ែࡢ᭷ຠᛶࢆ☜ㄆࡋࡓࠋ

1. Introduction
The energy harvesting means gathering unused tiny
and instable energy from the environments, such as alight on small space or exhaust heat [1]. It is significant
for application such as autonomous wireless sensor
networks [2].
A circuit has been sold that can charge 24[V] batteries
from photovoltaic cells with following changes ofMaximum Power Point (MPP) of power source andvoltage of storage (MPP Tracking, MPPT) in 2013. But
the circuit can’t always follow MPP, and can’t charge
from lower power source less than 10[W].
Tanaka proposed an optimization method to store tinyelectric energy using boost converter [3] in 2011. Basedon the study, we developed a PC-controlled MPPT circuit.
It scans MPP of DC power source and follows it. But
controlling by PC is unpractical because PC consumes
more energy than it harvests.
The purpose of this study is to make a prototype of theautonomous energy harvesting circuit by using microcontroller, which charge energy from at least 50[mW] to
1[W].

The circuit supplies the energy, which preliminarily
charged in the storage device, to the micro controller
through a voltage regulator. The circuit measures a MPPof the power source (1) and voltage of the storing
capacitor ܸେ (2) regularly. As soon as measurement isfinished, the controller calculates optimized duty ratio of
PWM inverter (3), which Tanaka calculated in his study[3], and emit it to control the circuit to charge the storage
devices, or go to sleep mode (4). This study, themeasurement of MPP is executed once by 5[s], themeasurement of ܸେ and calculation of duty ratio are
executed once by 0.5[s].

2. Suggestion of function of autonomous circuit
The block diagrams of the autonomous system is shownin Fig.1. The schematic of autonomous circuit is shownin Fig.2.
In order to control the boost converter circuit, thecontroller must get the information of MPP of the powersource and the voltage of the storage device (ܸେ ) because
they change momentarily. The energy consumption of
the controller must come from its energy storage to work
autonomously. Thus, the controller has to judge that thepower source has enough power to charge. It is betterthat the energy consumption of controller is as low as
possible, so we have chosen a micro controller as thecircuit controller.

Fig.1 Block diagram of autonomous system

Fig.2 Schematic of the prototyped circuit

Fig.3 MPP scanning circuit
Fig.3 is the MPP scanning circuit. The power source
charges a capacitor in the circuit when connected to thiscircuit. The controller measures ܸ୔ǤୗǤ and ܸଵ . ܸଵ means‫୔ܫ‬ǤୗǤ , current from the power source. When the circuit isconnected to the power source, the capacitor’s
voltage ܸଶ ൌ Ͳሾሿ , and the ܸ୔ǤୗǤ ൌ Ͳሾሿ too. But ܸଶ125becomes higher, the ܸ୔ǤୗǤ becomes higher too. The
controller continues measuring ܸ୔ǤୗǤ and ܸଵandcalculating ܲ୔ǤୗǤ ൌ ܸ୔ǤୗǤ ‫୔ܫ כ‬ǤୗǤ . The highest ܲ୔ǤୗǤ and itsܸ୔ǤୗǤ ǡ ‫୔ܫ‬ǤୗǤ are MPP ܲ୑୔ ǡ ܸ୑୔ ǡ ‫ ୔୑ܫ‬, and controller recordsܲ୑୔ ǡ ܸ୑୔ ǡ ‫ ୔୑ܫ‬as ܲ୲ୟ୰୥ୣ୲ ǡ ܸ୲ୟ୰୥ୣ୲ ǡ ‫୲ܫ‬ୟ୰୥ୣ୲ .
The controller also calculates amount of the chargeableenergy and compare it to energy consumption by usingܲ୲ୟ୰୥ୣ୲ ǡ ܸ୲ୟ୰୥ୣ୲ ǡ ‫୲ܫ‬ୟ୰୥ୣ୲ . If the chargeable energy is not large
enough compared to the energy consumption, thecontroller stops the circuit and switch itself into sleep
mode to save energy consumption, and regularly wakes
up and calculates the chargeable energy again.
3. Prototype and experiment
We made a prototype of the circuit mentioned at
section2 (Table.1, Fig.4). And we arranged Tanaka’s
formula of charging efficiency because of counting energy
loss of MPPT, energy consuming of controller, voltage
drop of switching FET in efficiency. The arranged
formula is shown in   ߟ୫ୣୟୱ୳୰ୣୢ ൌభమο ஼ሾ୊ሿ௏ౙమ ሾ୚ሿ௉౉ౌ ‫்כ‬౪౥౪౗ౢ

ாౙ౞౗౨ౝ౛ౚ ሾ୎ሿ௉౉ౌ ሾ୛ሿ‫்כ‬౪౥౪౗ౢ ሾୱሿ   ߟ୲୦ୣ୭୰୷ ൌ ߟୡ୦ୟ୰୥ୣ െாౢ౥౩౩௉౉ౌ ‫்כ‬౪౥౪౗ౢ

‫ܧ‬୪୭ୱୱ ൌ ‫ܧ‬ୡ୭୬୲୰୭୪୪ୣ୰ ൅ ‫ܧ‬ୢ୧ୱୡ୭୬୬ୣୡ୲ୣୢ ൅ ‫ܧ‬ୢ୰୭୮ ൅ ‫ܧ‬୪ୣୟ୩‫ܧ‬ୡ୭୬୲୰୭୪୪ୣ୰ ൌ ‫ܫ‬ୡ୭୬୲୰୭୪୪ୣ୰ ሾሿ ‫ܸ כ‬ୡ ‫୲ܶ כ‬୭୲ୟ୪‫ܧ‬ୢ୧ୱୡ୭୬୬ୣୡ୲ ൌ ܲ୑୔ ‫ܶ כ‬ୢ୧ୱୡ୭୬୬ୣୡୣୢ
‫ܧ‬ୢ୰୭୮ ൌ ܸୢ୰୭୮ ‫୲ܶ כ ୔୑ܫ כ‬୭୲ୟ୪‫ܧ‬୪ୣୟ୩ ൌ௏ౙమோౚ౟౬ ሾஐሿ

ߟୡ୦ୟ୰୥ୣ ൌ‫୲ܶ כ‬୭୲ୟ୪ோ౉ౌ ௏ి
ோ௏ిǡీ

‫כ‬൭

෩

෩

మ
௚෤ిǡీ
ቀଵି௘ షሺభష೏ሻ೅ ቁቀଵି௘ ష೏೅ቁ݀ሻ݃෤େǡୈ ൫݃෤େǡୈ െ ʹ൯൱ ்෨ ൫ଵି௘ ష೅෩ ൯

ൌ

ܽୱୟ୤ୣ



ܴୢ୧୴ ൌ ͺǤͺሾȳሿܴଵ ൌ ͳǤͲሾȳሿ‫ܥ‬୅ୈେ ൌ ͲǤͳሾ ሿ

SII
S-812C50AY-B-G(5V)

Fig.4 Figure of the prototyped circuit
Using the arranged formula, we measured the charging
efficiency of the prototyped circuit in this condition,using a photovoltaic cell as the power source.
ܸ୑୔ ൌ ͳǤͺͶሾሿǡ ‫ ୔୑ܫ‬ൌ ͲǤͳͲͲሾሿǡ ܲ୑୔ ൌ ͲǤͳͺͶሾሿǡ݂୔୛୑ ൌ ͳʹǤͷሾ ሿǡ ܸୢ୰୭୮ ൌ ͲǤͲͶͲሾሿܶୢ୧ୱୡ୭୬୬ୣୡୣୢ ൌଵ଼଴ሾ୫ୱሿܶǡ ܶ୲୭୲ୟ୪ ൌ ͸ͻͶሾሿହ଴଴଴ሾ୫ୱሿ ୲୭୲ୟ୪ܸେ ሺ ሻ ൌ ͸Ǥͳ͵ሾሿ,ܸେ ሺ ሻ ൌ ͸Ǥ͸͸ሾሿ

From this, we calculated ߟ୲୦ୣ୭୰୷ and ߟ୫ୣୟୱ୳୰ୣୢ .ܽୱୟ୤ୣ isset as 1.

െ ሺͳ െ

‫ܧ‬ୡ୭୬୲୰୭୪୪ୣ୰ contains switching loss (ߟ୤୭ [4]).
‫ܫ‬ୡ୭୬୲୰୭୪୪ୣ୰ ൌ current consumption of the controllerܶୢ୧ୱୡ୭୬୬ୣୡୣୢ ൌ times without charging because
scanning MPPܸୢ୰୭୮ ൌ voltages drop by MPP measure FETܴୢ୧୴ ൌ voltage dividing resistorܽୱୟ୤ୣ ൌ  Resister(voltage dividing)Resister(current measure)
Capacitor(ADC)
Regulator (CMOS-LDO)ߟ୲୦ୣ୭୰୷ ൌ ͻͲǤͳΨ െ ሺͷǤͺΨ ൅ ͵Ǥ͸Ψ ൅ ʹǤʹΨ ൅ ͲΨሻ ή ͳ ൌ ͹ͺǤͷΨߟ୫ୣୟୱ୳୰ୣୢ ൌ ͹ͷǤͶΨ

ofTable.1 Parts listܸ୑୔ ൌ ʹሾሿPower Source‫ ୔୑ܫ‬ൌ ͷͲͲሾሿ
(Photovoltaic cell)
Power Sourceܴ୑୔ ൌ ͳͲሾȐሿ
(Thermoelectric devices *4)
Capacitor(storage)
ELNA 2R5D107T *4ܸ୑୅ଡ଼ ൌ ͳͲǤͲሾሿ‫ ܥ‬ൌ ʹͺǤͲ͸ሾ ሿܴେ ൌ ͳʹͲሾȳሿ
Controller
Microchip PIC16F1938‫ܫ‬ୡ୭୬୲୰୭୪୪ୣ୰ ̷Ͷሾ ሿǡͷሾሿൌ ͳǤ͸͸ሾሿ
MOSFET(PWM)
TOSHIBA TPC6006-H
ܴୈୗ ൌ ͹ͺሾȳሿܸ୲୦ ൌ ʹǤ͵ሾሿ
MOSFET(MPP measure)
ROHM RTF015N03
ܴୈୗ ൌ ʹͲͲሾȳሿܸ୲୦ ൌ ͳǤͷሾሿ‫ ܮ‬ൌ ͶǤ͹Ͷሾ ሿ
Inductorܴ୐ ൌ ͲǤʹሾȳሿܸ୲୦ ൌ ͲǤͷሾሿDiodeܴୈ ൌ ͳͲͲሾȳሿWe got almost same efficiencies between the theoreticalvalue and the measured value. From this, we set ܽୱୟ୤ୣ ൌʹǤͲ and the theoretical efficiency can be used for judging
the controller go to sleep mode or not.
We compared the efficiency of the prototyped
autonomous circuit and the circuit without MPP
scanning. Without MPP scanning, should be input fixedܸ୲ୟ୰୥ୣ୲ ǡ ‫୲ܫ‬ୟ୰୥ୣ୲ preliminarily.
We used thermoelectric devices as a power sourceenergy by utilizing a temperature difference between
both sides of the devices (Fig.5), one side is heated byboiling hot water, 360[g] and the other side is cooled bywater 100[g] and ice cubes 100[g]. Temperature of theroom and water was 18 [degrees]. In this condition, we
measured the charging.

Fig5. Schematic of thermoelectric devices experiment
We experimented 4 patterns, which means withautonomous MPP scanning and 3 patterns of without126

scanning which fixed ܸ୲ୟ୰୥ୣ୲ ǡ ‫୲ܫ‬ୟ୰୥ୣ୲ toܸ୲ୟ୰୥ୣ୲ ൌ ͳǤ͹ሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤͳ͹ሾሿǡܸ୲ୟ୰୥ୣ୲ ൌ ͳǤʹሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤͳʹሾሿǡܸ୲ୟ୰୥ୣ୲ ൌ ͲǤ͹Ͳሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤͲ͹ͲሾሿǤThose experimental charging are stopped when thecircuit go to sleep mode, or ܸେ turn into decrease.
Fig.6 is the result of chargingܸେ . It indicates with theautonomous MPP scanning charged energy the fastest
and the most. Adding, it stop charging and go to sleep
before ܸେ turn into decrease. Scanning and judging
sleep or not works well. Other patterns couldn’t judge
sleep or not because they couldn’t get parameter ofactualܲ୑୔ .
From these results, the autonomous circuit contributes
charging efficiency from thermoelectric devices well.

We experimented 4 patterns, which means withautonomous MPP scanning and 3 patterns of without
scanning which fixed ܸ୲ୟ୰୥ୣ୲ ǡ ‫୲ܫ‬ୟ୰୥ୣ୲ toܸ୲ୟ୰୥ୣ୲ ൌ ͳǤͻͲሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤʹͺሾሿǡܸ୲ୟ୰୥ୣ୲ ൌ ͳǤͺͺሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤͳ͵ሾሿǡܸ୲ୟ୰୥ୣ୲ ൌ ͳǤͺͲሾሿ‫୲ܫ‬ୟ୰୥ୣ୲ ൌ ͲǤͲͶͲሾሿǤ

Autonomous
6.4
Fixed Vtarget=1.2[V]

VC[V]

6.3
Fig.8 Schematic of a photovoltaic cell experimentFig.9 is the result of charging ܸେ . It indicates with theautonomous MPP scanning don’t charge more energy
always from photovoltaic cell than without scanning.

6.2
Fixed Vtarget=0.7[V]6.1

Fixed Vtarget=1.7[V]

6

7
500
Time[s]

1000

VC[V]

0

Fig.6 ܸେ charged by thermoelectric devices
Fig.7 is the result of ܸ୔ǤୗǤ when charging. Autonomous
MPP scanning follows MPP time by time.

6.5

2:Autonomous:Fixed Vtarget=1.9[V],Itarget=0.28[A]:Fixed Vtarget=1.88[V],Itarget=0.13[A]:Fixed Vtarget=1.8[V],Itarget=0.040[A]Fixed Vtarget=1.7[V]VP.S.[V]60

Fixed Vtarget=1.2[V]1Fixed Vtarget=0.7[V]Autonomous
Sleep mode
0

200

400

600
Time[s]800

1000

Fig.7 Output voltages ܸ୔ǤୗǤ of thermoelectric devices
We compared the same, using a photovoltaic cell as
power source (Fig.8). We light the photovoltaic cell byusing halogen lamp with variable stabilized power
supply. To change output of power source, we changed
voltage of halogen lamp 12[V], 9[V], and 6[V] by
5[minutes].

200

400
600
Time[s]800

Fig.9 ܸେ charged by photovoltaic cell
One of the reason of the without scanning circuit’s
efficiency is not less than the autonomous, is shown in
Fig.10. ܸ୔ǤୗǤ of without scanning has similar behavior toܸ୔ǤୗǤ of the autonomous scanning, and it means they
tracking MPP without scanning. The autonomous shouldstop charging and switch to scan circuit, and the loss
decrease the autonomous’ efficiency.
Another reason is shown in Fig.11. It shows change ofPower-Voltage curves of photovoltaic cell. ܸ୑୔ of thephotovoltaic cell changes only 100[mV] in contrast ܸ୑୔of the thermoelectric devices changes more than 1[V].
Amplitude of ܸ୔ǤୗǤ is larger than 100[mV], so changes ofܸ୑୔ does not influenced to efficiency. Photovoltaic cell
can approximate as a diode and current source, and socurrent mainly changes when its generating powerchanged.

127

From both reasons, the autonomous circuit doesn’t
contribute charging efficiency from photovoltaic cells
well. But to judge charge or sleep, autonomous circuit
needs to get MPP parameters in some way.

2

Autonomous

Fixed Vtarget=1.90[V]Itarget=0.28[A]Fixed Vtarget=1.88[V]Itarget=0.13[A]Fixed Vtarget=1.80[V]Itarget=0.040[A]

1.9
1.8
1.7
1.6

source don’t changes its ܸ୑୔ , like photovoltaic cells,
more than amplitude of switching.
From this study, we can try various kinds of direct
current power source for energy harvesting easily.
References[1] Energy Harvesting Consortium
http://www.keieiken.co.jp/ehc/ (2015)[2] ChenGregory. Millimeter-scale nearly perpetualsensor system with stacked battery and solar cells.
ISSCC p.288-289, (2010)[3] Tokyo devices http://tokyodevices.jp/items/113 (2013)[4] Masanori Tanaka ”Optimization of control
parameters of a boost converter for energy harvesting”
Journal of Physics: Conference Series 379 (2012)

VP.S.[V]

1.5
2

1.9
1.8
1.7
1.6
1.5

0 200 400 600 800
0 200 400 600 800
Time[s]Fig.10 Output voltages ܸ୔ǤୗǤ of photovoltaic cell

Voltage of halogen lamp = 12.0[V]

0.6

PP.S.[W]0.4

9.0[V]

0.2

0
6.0[V]
VMP[V]
01

VP.S.[V]2

Fig.11 Relation between P-V curves of photovoltaic celland Voltage of halogen lamp
4. Conclusion
This study focuses on prototyping autonomous energy
harvesting system. It is suggested that an algorithm ofautonomous system, including MPP scanning and sleep
mode. The prototyped circuit proved that MPP scanningand sleep mode of the circuit is effective.
From this study, following knowledge are obtained.
1. MPP scanning is effective when power sourcechanges its ܸ୑୔ , like thermoelectric devices, morethan amplitude of switching.
2. MPP scanning is not effective as 1. when power

128

ACCELERATION OF THE COMPOSTING BY INOCULATING STRAIN RB1
AND CONTROLLING COMPOSTING TEMPERATURE
Student Number: 13M18155 Name: Hidehira HIRAI Supervisor: Kiyohiko NAKASAKI㉂Უ RB1 ᩣߩធ⒳ߣ᷷ᐲ೙ᓮߦࠃࠆ㜞ㅦࠦࡦࡐࠬ࠻ൻ
ᐔ੗ ⑲ᐔ
㘩ຠᱷᷲߩࠦࡦࡐࠬ࠻ൻߦ߅޿ߡ‫᦭ޔ‬ᯏ㉄ߩ⫾Ⓧߪᓸ↢‛ߦࠃࠆ᦭ᯏ‛ಽ⸃ࠍ㒖ኂߔࠆ‫⎇ᧄޕ‬ⓥߢߪ‫᦭ޔ‬ᯏ㉄ಽ⸃⢻ࠍᜬߟ㉂Უ RB1 ᩣߩធ⒳ߣ‫ޔ‬RB1 ᩣߩᵴᕈࠍ⛽ᜬߔࠆߚ߼ߦ᷷ᐲࠍ 40°C ߦ⛽ᜬߔࠆᠲ૞ࠍⴕ޿‫࠻ࠬࡐࡦࠦޔ‬ൻㆊ⒟ߢ↢ᚑߐࠇࠆ㈶㉄ߩ⫾Ⓧࠍᛥ೙ߒ‫࠻ࠬࡐࡦࠦޔ‬ൻࠍ㜞ㅦൻߒߚ‫ߐޕ‬ࠄߦ‫ ࠍ⾰ࠎ߲ࠎߢޔ‬30%‫ޔ‬50%฽߻ේᢱߩ႐วߦ‫ޔ‬᷷ᐲࠍߘࠇߙࠇ 2 ᣣ㑆‫ޔ‬3 ᣣ㑆⛽ᜬߔࠆߣ㈶㉄ߩ⫾Ⓧ߇ᛥ೙ߐࠇ‫ޔ‬᷷ᐲߩ⛽ᜬᦼ㑆ߪේᢱਛߩߢࠎ߲ࠎ⾰ߩ㊂ߦଐሽߒߡ޿ࠆߎߣࠍ᣿ࠄ߆ߦߒߚ‫ޕ‬actual food waste contains more starch. Therefore, it isnecessary to investigate the effect of inoculating strain
RB1 in compost raw material containing more starchthan that of previous study.
In this study, it was aimed to achieve theacceleration of the composting period by inoculatingstrain RB1 in order to promote the degradation oforganic acids in compost raw material containing
starch.

1.㩷 Introduction
Management of the ever-increasing waste materials
produced in our society has become an essential areaof study in recent years. Among waste materials,
organic fractions such as food waste originating fromhouseholds, restaurants, and food processing factories
can be reclaimed as compost for agricultural use [1].
Composting has also attracted attention as a key
technology for recycling organic waste such as foodwaste. Therefore, much research is currently focusedon food waste treatment [2].
One of the significant problems in the food waste
composting is that the pH of the food waste decreases
because of the decay prior to the composting andanother problem is that the pH of the compostingmaterial decrease because of the production of theorganic acids during the composting. Low pH inhibits
microorganism activity and causes a delay of organic
matter degradation. Therefore, for the acceleration of
the composting, it is necessary to remove organic
acids that inhibit organic matter degradation.
Previously, Pichia kudriavzevii RB1 was isolated
from compost produced from garbage and rice bran
mixture in our laboratory and was used as an inoculumthat has high degradation activity against organic acids.
This study elucidated that the inoculation of P.
kudriavzevii RB1 accelerated the degradation oforganic acids initially contained in the compost raw
material [3].
The compost raw material in previous study
relatively contained a small amount of starch, whereas

2. Material & Methods
2. 1. Compost material
Material of each composting experiment, Run A,
Run B, Run C-1, Run C-2, Run D-1 and Run D-2,
were shown in Table 1. Run A, Run C-1, Run C-2,
Run D-1 and Run D-2 were with inoculation of yeast
strain RB1 at the cell density of 105CFU/g-ds and Run
B was without inoculation of RB1.
The rabbit food and rice were mixed at a ratio of 7:3
in Run A, Run B, Run C-1 and Run C-2 and at a ratioof 5:5 in Run D-1 and Run D-2 to representativemodel of food waste. Four kinds of organic acids,
acetic acid, propionic acid, butyric acid, and lactic
acid [4], characteristic to the food waste were added to
the raw compost mixture to achieve adjusted
concentrations of 2.90, 3.02, 2.43, and 12.45 g/kg-ds
for acetic acid, propionic acid, butyric acid, and lactic
acid, respectively. The mixture of rabbit food and rice,sawdust and seeding material were mixed at a ratio of
10:9:1. Distilled water was added to adjust the
moisture content to 60%.
129

2. 2. Composting operation
Schematic diagram of the composting system isshown in Fig. 1. At the start of the experiment,
approximately 3kg of the raw compost mixture wasput into the reactor, and the temperature of thecomposting material was set at room temperature, with
air introduced from the bottom of the reactor at aminimum flow rate of 45 L/h to maintain aerobic
conditions. When the temperature in Run C-1, Run
C-2, Run D-1 and Run D-2 reached 40°C by
self-heating due to organic matter decomposition, thetemperature was controlled at 40°C by increasing the
air flow rate to 200 L/h. The period of controlling
temperature in Run C-1 and Run C-2 was 1 day and 2days respectively, and that of Run D-1 and D-2 was 2days and 3 days respectively. After controlling
temperature, the air flow rate was returned to the firstvalue which was 45 L/h. The temperature in all runs
reached a final set temperature of 60°C, thetemperature was then controlled at 60°C by regulating
the air flow rate. In the later stages of composting, if
the temperature decreased to below 60°C despite theair feed rate being set at the minimum, the temperature
was maintained at 60°C with the heater surrounding
the reactor.
The concentration of CO2 in the exhaust gas fromthe reactor was measured continuously, and the CO2
evolution rate was calculated based on theconcentration of CO2 and the air flow rate.
During the composting process, the compost
material was turned once daily to ensure uniform
organic matter decomposition. At the time of turning,approximately 15 g of compost was withdrawn fromthe reactor every day. Distilled water was added toprevent the compost from drying out duringcomposting. The period of composting was 10 days.

Table 1. Compost material and operation of each run
Run name

Period of temp.
control (d)Ratio of rice in compost
raw material (%)

Inoculation ofstrain RB1

Run A

30

䃢

0

Run B

30

-

0

Run C-1

30

䃢

1

Run C-230

䃢

2Run D-1

50

䃢

2

Run D-250

䃢

3

2. 3. Physicochemical analysis
The compost samples collected every day were
measured for pH, moisture content, concentration oforganic acids and cell density of mesophilic bacteria,
thermophilic bacteria and fungi by dilution plate
method. The concentration of organic acids wasdetermined by using a high pressure liquid
chromatography (HPLC) system.
The concentrations of the organic acids and the celldensity of the microorganism were measured 3 times
per sample, and an average of the 3 measurements wasused. For each of the organic acids, error bar of theaveraged value was calculated as a 95% confidence
interval.
2. 4. Real-time PCR
The cell density of strain RB1 in the compost
samples, Run A (RT-PCR), Run B (RT-PCR), Run C-1(RT-PCR) and Run C-2 (RT-PCR), was measured byreal-time PCR method using the DNA extract from thesamples as a template. The primers specific to strain
RB1, RB1rtF, 5’-GACTCGCCTGAAAGGGA-3’, andRB1rtR, 5’-CTGATTTGAGGTCGAGC-3’, were
designed and used. The real-time PCR was performed
using Smart Cycle® II system (Cehpeid, California,
USA) and SYBR® premix EX TaqTM (TaKaRa
Biochemicals, Shiga, Japan)

3. Results & Discussion
3. 1. Composting of model food waste containing
starch of 30%
The courses of the temperature, cell density of fungiby dilution plate method and real-time PCR method
and concentration of organic acids for Run A and Run
B are shown in Fig. 2.
The temperature in Run A increased to around 55°C
during day 2 and day 3, and then decreased to around
30°C at day 4. After that, the temperature rapidly
started to increase again at day 5 and achieved to 60°C,
whereas in Run B, the temperature increased rapidly at
day 5 and achieved to 60°C at day 7. These results
Fig. 1. Schematic diagram of the composting system.
130

indicated that inoculation of the compost with yeast
strain RB1 could advance the composting process 3days, in this case where RB1 effectively increased thetemperature in the early stage of composting.
The cell density of fungi in Run A and Run A(RT-PCR) increased to around 108 to 109 CFU/g-ds at
day 1, whereas in Run B (RT-PCR), remained around
103 CFU/g-ds when the cell density of fungi in Run B
increased at day 5. These results indicated that themost of the fungi in Run A was yeast strain RB1 andthat in Run B was another fungus. The cell density offungi in Run A and Run B corresponded to the courseof temperature.
Four types of organic acids in Run A withinoculation of RB1 were degraded at day 1 faster thanin Run B without inoculation of RB1. The degradationof organic acids in Run A at day 1 corresponded to theincrease of the cell density of RB1, which indicatedthat RB1 promoted organic acids degradation in theearly stage of composting.

These results also revealed that significant amount ofacetic acid in Run A and Run B was accumulated atday 3 and day 7 respectively along with the increase of
the temperature. The decrease of the temperature in
Run A during day 2 and day 3 was caused by theaccumulation of acetic acid which strain RB1 couldn’t
degrade at day 3 because of decreasing the activity of
RB1 along with the increase of temperature. RB1
started to degrade acetic acid at day 5 after thetemperature decreased by day 4, and then thetemperature reached to 60°C. After that, acetic acidwas accumulated again, which inhibited the organic
matter degradation by the end of composting.
Therefore, it was necessary to keep the activity of RB1
in order to degrade acetic acid produced duringcomposting by strain RB1.

3. 2. Composting of model food waste containing
starch of 30% with controlling composting
temperature
Temperature control during the composting was
performed as a method to keep the activity of RB1.
The courses of the temperature, cell density of fungiand concentration of organic acids for Run C-1 andRun C-2 are shown in Fig. 3. The temperature in Run
C-1 and Run C-2 was kept at 40°C for 1 day and 2days respectively, after that, immediately increased to
60°C. The cell density of RB1 in Run C-1 and Run
C-2 was kept at 109 CFU/g-ds at day 3 in Run C-1 andat day 3 and day 4 in Run C-2 when the temperature
was controlled at 40°C,㩷 and then decreased to
non-detectable levels at less than 103 CFU/g-ds after
the temperature reached to 60°C at day 4 and day 5
respectively. For the concentration of organic acids in
Run C-1 and Run C-2, acetic acid was accumulated inboth Run C-1 and Run C-2 at day 3 and day 4 after
four types of organic acids were degraded at day 1.
The concentration of acetic acid in Run C-1 and Run
C-2 at day 3 was around 8 g/kg-ds that was less thanhalf of around 24 g/kg-ds in Run A, which indicatedthat keeping the RB1 activity by controlling
temperature decreased the amount of acetic acidaccumulation. After controlling temperature, theconcentration of acetic acid in Run C-1 increased toaround 16 g/kg-ds at day 6, whereas in Run C-2,
decreased to zero. This result elucidated thattemperature controlling period of 2 days was more
effective for decreasing the amount of acetic acidaccumulation than that of 1 day.㩷

Log. cell density of fungus Temperature (䇯C)
(CFU/g-ds)

80
70

Run A
Run B

60
50
40

Concentration of organic acids (g/kg-ds)

30

20
10
9
8
7
6
5
4
3
28
24
20
16
12
8
4
0
28
24
20
16
12
8
4
0

Run A
Run B
Run A (RT-PCR)
Run B (RT-PCR)

Run A

Acetic acid
Lactic acid
Propionic acid
Butyric acid

Run B

Acetic acid
Lactic acid
Propionic acid
Butyric acid

0

2

4

6
Time (d)810Fig. 2. The courses of temperature, cell density offungus and concentration of organic acids for Run A
and Run B.

131

80
Concentration of organic acids Temperature (䇯C)
(g/kg-ds)

Log. cell density of fungus
Temperature (䇯C)
(CFU/g-ds)

80
Run C-1
Run C-2

70
60
50
40

Concentration of organic acids (g/kg-ds)

30

20
10
9
8
7
6
5
4
3
28
24
20
16
12
8
4
0
28
24
20
16
12
8
4
0

Run C-1
Run C-2
Run C-1 (RT-PCR)
Run C-2 (RT-PCR)
Acetic acid
Lactic acidpropinic acid
Butyric acid

Run C-1

70

Run D-1
Run D-2

60
50
40
30
20
28
24
20
16
12
8
4
0

Acetic acid (Run D-1)
Lactic acid (Run D-1)
Propionic acid (Run D-1)
Butyric acid (Run D-1)
Acetic acid (Run D-2)
Lactic acid (Run D-2)
Propionic acid (Run D-2)
Butyric acid (Run D-2)

0

2

4

6
Time (d)810Fig. 4. The courses of temperature and concentrationof organic acids for Run D-1 and Run D-2

4. Conclusions
 In this study, it was achieved the decrease of theamount of acetic acid accumulation with inoculationof yeast strain RB1 by controlling composting
temperature, thereby accelerated the composting
period. Temperature controlling period needs to be
determined according to the amount of starch incompost raw material.

Run C-2
Acetic acid
Lactic acid
Propionic acid
Butyric acid

0

2

4
6
Time (d)810Fig. 3. The courses of temperature, cell density offungus and concentration of organic acids for Run C-1
and Run C-2

References[1] Al-jabi, L. F., Halalsheh, M. M., Badarneh, D. M.,
2008. Conservation of ammonia during food waste
composting. Environ. Technol. 29, 1067-1073.
[2]Abbasiliasi, S., Tan, J. S., Ibrahim, T. A. T.,
Ramanan, R. N., Vakhshiteh, F., Mustafa, S., Ling, T.
C., Rahim, R. A., Ariff, A. B, 2012. Isolation of
Pediococcus acidilactici Kp10 with ability to secrete
bacteriocin-like inhibitory substance from milk
products for applications in food industry. BMC
Microbiol. 12: 260.
[3] Nakasaki, K., Araya, S., Mimoto, H., 2013.
Inoculation of Pichia kudriavzevii RB1 degrades theorganic acids present in raw compost material andaccelerates
composting.
Bioresor.
Technol.
144,521-528.
[4] Sundberg, C., Franke-Whittle, I., Kauppi, S., Yu,
D., Romantschuk, M., Insam, H., Jönsson, H., 2011.
Characterisation of source-separated household waste
intended for composting. Bioresour. Technol. 102,2859-2867.

3. 3. Composting of model food waste containing
starch of 50% with controlling composting
temperature
The courses of the temperature and concentration oforganic acids for Run D-1 and Run D-2 are shown in
Fig. 4. After controlling temperature, the temperature
in Run D-1 increased and then decreased during day 5
and day 6 because of the accumulation of acetic acid,
whereas in Run D-2, increased to 60°C directly after
stopping controlling temperature at 40°C. This result
indicated that temperature controlling period of 2 daysin the composting contained starch of 30% could
prevent the accumulation of acetic acid after
controlling temperature, whereas in the composting
contained starch of 50%, couldn’t prevent it. Therefore,
it was revealed that the temperature controlling period
needs to be determined according to the amount ofstarch contained in the compost raw material.

132

MECHANISMS OF WASTEWATER TREATMENT PROCESS IN THE
ANAEROBIC-AEROBIC REACTORS SYSTEM
Student Number: 13M18178 Name: Ryutaro BESSHO Supervisor: Kiyohiko NAKASAKI᎘Ẽ࣭ዲẼᵴ࡟ࡼࡿᗫỈฎ⌮㐣⛬ࡢί໬ᶵᵓูᡤ 㝯ኴ㑻㏵ୖᅜ࡛ࡣࠊపࢥࢫࢺ࡞ᗫỈฎ⌮ἲ࡜ࡋ࡚ࠊ᎘Ẽᵴ࡜ዲẼᵴࢆ┤ิ࡟⧅ࡆࡓ⡆᫆࡞ί໬ᵴᆺ⿦⨨ࡀከ⏝ࡉࢀ࡚࠸ࡿࡀࠊᚑ᮶࠿ࡽࡑࡢ⿦⨨ࡣ⤒㦂࡟ᇶ࡙࠸࡚タィࡉࢀ࡚࠾ࡾࠊί໬ᶵᵓࡣ᫂ࡽ࠿࡟ࡉࢀ࡚࠸࡞࠸ࠋ
ࡑࡢࡓࡵࠊί໬ᶵᵓࢆ᫂ࡽ࠿࡟ࡋࡓୖ࡛ࡢ᭱㐺⿦⨨ࡢタィࡀồࡵࡽࢀ࡚ࡁࡓࠋᮏ◊✲࡛ࡣᐇ㦂ᐊつᶍࡢࣜ࢔ࢡࢱ࣮ࢆ⏝࠸࡚ேᕤᗫỈࡢί໬㐣⛬࡟࠾ࡅࡿ᭷ᶵ≀⃰ᗘኚ໬ࢆゎᯒࡍࡿࡇ࡜࡛ᚤ⏕≀ࡢ᭷ᶵ≀ศゎάᛶࢆᐃ㔞ࡋࠊࡲࡓᚤ⏕≀ྀࢆゎᯒࡍࡿࡇ࡜࡛✀⳦ࡢຠᯝࡀⷧ࠸ࡇ࡜ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ
supply whereas anaerobic treatment does not require
air supply. In addition, the increase of the cell mass inthe anaerobic treatment is smaller than the case of
aerobic treatment. Therefore anaerobic treatment costlower, however, the quality of effluent is inferior tothat of aerobic treatment [1].
In developing countries, the anaerobic-aerobic
treatment process, where multi anaerobic reactors andan aerobic reactor are used, is applied (Fig. 1). In
anaerobic-aerobic treatment, influent are treatedflowing through the anaerobic reactor at first and the
aerobic reactor at second. Anaerobic-aerobic treatment
has a higher degradation rate than anaerobic treatment
and takes a lower cost than aerobic treatment.
However the anaerobic-aerobic reactors aredesigned based on not the mechanisms of thewastewater treatment but the experiences. In this study,
the organic compounds consumption rates wasquantified and the microbial community was analyzed
to examine the effect to add seed at the start of ananaerobic-aerobic treatment and to reveal themechanisms of the organic compound removal in ananaerobic-aerobic treatment.

1. Introduction
Recently in Philippines, water pollution inrivers, lakes and marshes caused by the pollutant suchas industrial wastewater, agricultural wastewater,
heavy metals and other materials, have become more
serious with economic growth and population increase,thus solutions to the water pollution problem havebeen desired. In Philippines, the water quality
standard and the effluent standard are defined by
DENR administrative order No. 34 and No. 35,
respectively. Industrial effluent must meet thestandard of COD (Chemical Oxygen demand) that are
defined against the respective water area.
In order to decrease the COD of the effluent,
microbial treatment is widely applied due to its highdegradation efficiency and low cost. Microbial
treatment can be classified into aerobic treatment andanaerobic treatment by whether air is provided to themicroorganisms or not. Aerobic treatment, which is
widely applied in Japan and many other developed
countries, has high degradation rate, but it is costly
due to the necessity of the electrical power for air

2. Materials and methods
2.1. Treatment of synthetic wastewater in theanaerobic-aerobic reactors at laboratory
scale
Three kinds of the synthetic wastewater, theorganic carbon source of which were 1.39 g/L ofsucrose [2], 17 g/L of peptone [3] and 20 g/L of starch[4] were used. The installation for anaerobic-aerobic
treatment, which were consisted of 4 anaerobic
reactors (R1, R2, R3 and R4) and an aerobic reactor(R5), were prepared at laboratory scale imitating the

Fig. 1. The schematic diagram of the anaerobic-aerobic
reactors.

133

2.2. Calculation of the COD removal rates
by the microorganisms
The curve fitting on the courses of COD werecarried out in order to quantify the organic compounds
consumption rates by the microorganisms in eachreactor during the anaerobic-aerobic treatment. The
organic compounds consumption rates by the
microorganisms were calculated by the curve fit andthe following equation (1); dCi/dt, Ci and Ci-1 were
given by the curve fit.

݀‫ܥ‬௜݀‫ܥ‬௜ ‫ܨ‬൬ ൰ൌെ൅ ሺ‫ ܥ‬െ ‫ܥ‬௜ ሻሺͳሻ݀‫ ݐ‬ୡ୭୬ୱǤ݀‫ ܸ ݐ‬௜ିଵwhere Ci is COD of the ith reactor, F is flow rate ofthe medium and V is the volume of the reactor

2.3. Microbial community analysis of thesynthetic wastewater in each reactors
The DNA of the wastewater sample was
extracted using DNA extraction kit, ISOIL for Beads
Beating (Nippon Gene Co., Ltd., Toyama, Japan).
After purification of the DNA, TaKaRa Ex Taq Hot
Start Version (Takara Bio Inc., Shiga, Japan) was used
for PCR amplification by using TaKaRa PCR Thermal
Cycler Dice (TP600; Takara Bio Inc., Shiga, Japan)
with the following primer sets deriving from the 16S
rRNA gene: 357FGC, 5’-CGC CCG CCG CGC GCG
GCG GGC GGG GCG GGG GCA CGG GGG GCC
TAC GGG AGG CAG CAG-3’, and 518R, 5’-ATT
ACC GCG GCT GCT GG-3’ for bacteria. DGGE
analysis was carried out using D-code DGGE
Complete System (BioRad Laboratories, CA, USA).
The PCR product was mixed with an equal volume of
2× gel loading dye (10 mM Tris–HCl at pH 8.0, 20
mM EDTA at pH 8.0, 0.05% [w/v] bromophenol blue,and 70% glycerol) and loaded onto a 10% (w/v)
polyacrylamide gel in a 1× TAE buffer (40 mM Tris–
acetate at pH 7.4; 20 mM acetate, 1 mM Na2EDTA)
with a denaturing gradient ranging from 30% to 60%.
Using the DGGE image as a reference, the bands ofinterest were excised from the gel on a Dual Intensity
Ultraviolet Transilluminator (Model TDS-20; UVP,CA, USA). Sequencing reactions for the purified DNA
bands were carried out with a BigDye Terminator v3.1
Cycle Sequencing Kit (Applied Biosystems, CA,USA), while sequencing was performed using an ABIPRISM 310 Genetic Analyzer (Applied Biosystems,
CA, USA). The results of the sequencing analyses
were compared to the sequences of the 16S rRNA
genes available in the common databases (DDBJ, andGenBank) [3].

Fig. 2. The schematic diagram of the anaerobic-aerobic
reactors at laboratory scale.

reactors used in a cassava processing plant in
Philippines. The schematic diagram of the reactors isshown in Fig. 2. The volume of the synthetic
wastewater was 635 mL for each reactor. Each reactor
was filled with the synthetic wastewater and 2 mg/L ofseed, NS series TW (Nanosys International, INC.,
Manila, Philippines) at the start of the treatment. In
the treatment, the synthetic wastewater was supplied
from the influent tank to anaerobic R1 at a constantflow rate of 2.2, 4.4 and 2.2 mL/min in the case of thetreatment of the 3 kinds of synthetic wastewatercontaining sucrose, peptone and starch as organiccarbon source, respectively by peristaltic pump andthen the effluent from R1 flowed into R2. The
synthetic wastewater flowed through R1, R2, R3, R4
and R5 and finally flowed out as effluent. All thereactors were incubated at 35°C during the treatment.
The periods of the treatment of the 3 kinds of thesynthetic wastewater containing sucrose, peptone andstarch were 30, 12 and 12 days, respectively. The
synthetic wastewater containing sucrose, peptone andstarch were treated at a HRT of 24, 12 and 24 hours,
respectively.
Approximately 10 mL of the sample was takenfrom each reactor every day for the following
measurements. The pH and ORP of the synthetic
wastewater in each reactor were measured every day
using pH and ORP meter. The COD of the synthetic
wastewater in each reactor was measured every 2 days
using Reagent Set for Water Analyzer No.44 COD and
DIGITALPACKTEST
MULTI(Kyoritsu
Chemical-Check Lab., Co., Tokyo, Japan)

134

3. Results and discussion
3.1. Courses of COD and COD removal rateby the microorganisms in each reators
The courses of COD in the anaerobic-aerobic
treatment in the case of sucrose, peptone and starch
are shown in Fig. 3. In the case of sucrose, almost no
differences were observed among other reactors,
which suggested that only R1 had a large COD
removal rate and almost no degradation occurred in
the other reactors. Thus a HRT of 4.8 hours of R1, wasenough for treatment of 500 mg/L of wastewater. In
the case of peptone, the differences among thereactors were observed because the loading of
supplied wastewater was high than the organic
compound consumption; the organic loading rate in
the case of sucrose and peptone were 1.91 g/h/L and
0.104 g/h/L respectively. Therefore it was suggested
that the organic compounds not degraded in R1 weredegraded in R2 and likewise the degradation oforganic compounds was occurred in each reactor.
The COD removal rates corresponding to thedegradation activity of organic compounds, during theanaerobic-aerobic treatment of the synthetic
wastewater containing sucrose and peptone, are shownin Fig. 4. The COD removal rates in the case of
sucrose was calculated to be 78.8 mg/L/h on day 12 in
R1 by equation (1) and the COD consumption rate bymicroorganisms whereas the other reactors had almost
0 of COD removal rates. Thus it was revealed that
almost only the microorganisms in R1 had thedegradation activity when 500 mg/L of wastewater
was treated in anaerobic-aerobic reactors at a total
HRT of 24 hours. In the case of the peptone, the COD
removal rate of R1 was calculated to be 721 mg/L/hon day 12 whereas the COD removal rates in R2, R3,
R4 and R5 were 54.7, 249, 42.9 and 126 mg/L/h

Fig. 4. Courses of COD removal rates by microorganisms
during the anaerobic-aerobic treatment of the synthetic
wastewater containing sucrose and peptone.

respectively. Unlike the case of sucrose, the COD
removal rates of R3 and R5 on day 12 also had acertain value not negligible as R1, which showed thatthe microorganisms in the reators in the downstream
and the last aerobic reactor degraded acertain amountof organic compounds when the wastewater, the COD
of which was high as 4600 mg/L were treated. It wasalso observed that the COD removal rates of R2, R3
and R4 had a peak around day 5-7, when the COD
removal rate of R1 was increasing. It was consideredthat the organic compounds degradation activity in R1
was relatively low around day 5-7 so that the organic
compound supplied into R1 was not degradedsufficiently. Therefore it was suggested that theactivity of organic compounds degradation increasedin R2, R3 and R4 because the organic compounds notdegraded in R1 flowed into the following reactors.
The COD removal rate of R5 increased around day 5
and leveled off, which showed that the organic
compounds that could not be degraded underanaerobic condition were degraded under aerobic
condition.

3.2. Microbial community in the synthetic
wastewater
The DGGE images of the seed and day 30 in thecase of sucrose, day 8 in the case of peptone and day 6in the case of starch are shown in Fig. 5. It was
confirmed that no changes observed in the band
patterns after day 30, 8 and 6 in the case of sucrose,
peptone and starch, respectively. Quite different
microbial communities could be observed among the
3 kinds of the synthetic wastewater and they were also
different from the seed. The analysis based on 16SrRNA gene revealed the closely related sequences
corresponding to the characteristic bands in the DGGE
image. The phylogenetic tree derived from the 16S

Fig. 3. Courses of COD during the anaerobic-aerobic
treatment of the synthetic wastewater containing sucrose andpeptone.

135different among the 3 kinds of the wastewater. Most of
the dominant species were closest to Clostridium sp.,which are found in soil, dust, intestines and feces, andwas barely contained in the seed. The species, suitable
for the condition of the treatment and slightly existed
at the start of the anaerobic-aerobic treatment, became
dominant and contributed to the degradation oforganic compounds. In contrast, the bands of thedominant species in the seed did not appear during theanaerobic-aerobic treatment, therefore it was
considered that the most of the added species at thestart of the treatment did not contribute to the removalof organic compounds.
Fig. 5. The DGGE fingerprints of the seed and R1, R2,
R3, R4 and R5 on day 30 in the case of sucrose, on day 8
in the case of peptone, on day 6 in the case of starch.

4. Conclusions
The COD removal rates, the activity of organic
compounds degradation by microorganisms, were
quantified, which showed that almost only the
microorganisms in the first reactor degraded theorganic compounds under the condition of low
loading whereas the microorganisms in the second andthe following reactors also degraded under thecondition of high loading.
The analysis of microbial community revealed
that the most of the added species at the start of thetreatment did not contribute to the organic compounds
romoval in the anaerobic-aerobic treatment whereas
the species barely contaied in the seed, which are
known to be found insoil, dust, intestines and feces,contributed greatly. Hence it was suggested that theeffect to add the seed at the start ofanaerobic-aerobic treatment was insignificant.

rRNA gene sequences of the characteristic bands
observed in the DGGE image and the related species
was build based on neighbor-joining method (Fig.
6).
It was revealed that the dominant species wereReferences[1] N. Kataoka, ͆᎘Ẽᛶ⏕≀ฎ⌮ᢏ⾡ࡢ≉ᚩ࡜Ⓨᒎ ࡢ ὶ ࢀ Characteristics and Development
ChronologyofAnaerobic
Biotreatment
Technology,͇࢚ࣂࣛ᫬ሗ No.229, pp. 27̽38(2010).
[2] A. P. Rollon. (1999) Anaerobic digestion of fishprocessing wastewater with special emphasis onhydrolysis of suspended solids: Taylor & Francis,
Inc.
[3] S. H. Kwon and K. Nakasaki, “Relationshipbetween changes in microbial community and thedeterioration of methane fermentation which
treats synthetic peptone wastewater,” J. Ind. Eng.
Chem., Vol. 21, No. 25, pp. 443–450 (2015).
[4] S. Roh, Y. N. Chun, J. Nah, H. Shin, and S. Kim,
“Wastewater Treatment by Anaerobic Digestion
Coupled with Membrane Processing,” J. Ind. Eng.
Chem., Vol. 12, No. 3, pp. 489–493 (2006).
Fig. 6. Phylogenetic tree derived from the 16S rRNA genesequences of the characteristic bands observed in the DGGE
image and the related species.

136