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

TRIDE-2016-02

May 9, 2016

Abstracts of Bachelor Theses
Presented in Feburary 2015

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

Preface
Bachelor theses of Department of International Development Engineering, Tokyo Instituteof Technology were presented successfully on August 4, 2014 and February 27, 2015,
respectively. This technical report consists of the abstracts of those theses.

TECHNICAL REPORT OF INTERNATIONAL DEVELOPMENTENGINEERING
TRIDE-2016-02
TABLE OF CONTENTS(COMPLETING IN SEPTEMBER 2014)
SOLVENT EXTRACTION OF HETEROCOMPOUNDS CONTAINED IN PETROLEUMHEAVY FRACTION....................................................................................................... HIRONOBU HASEGAWA 1(COMPLETING IN MARCH 2015)
THE IMPACT OF URBANIZATION ON WATER UTILITIES PERFORMANCE IN A
DEVELOPING COUNTRY: A CASE STUDY OF THE PHILIPPINES....................................................................................................... SHINTARO OYA 3
NUMERICAL STUDY ON DUST DEVIL-LIKE VORTICES OVER BUILDING ARRAY....................................................................................................... TAKAFUMI SUEISHI 5
IMPROVEMENT OF FISHER LINEAR DISCRIMINANT BASED ON NORMAL
DISTRIBUTION....................................................................................................... CUNG VAN DUNG 7
インドネシアの国内航空市場における直行便と乗継便の経路選択分析....................................................................................................... 姜 佳文 9
RELATION BETWEEN WIND VELOCITY, ROTATION SPEED AND TORQUE OF
DRAG TYPE WIND TURBINE FOR SMALL WIND POWER GENERATION....................................................................................................... YUSUKE TOKUDA 11
NUMERICAL ANALYSIS OF THE 1917 STORM SURGE IN TOKYO BAY BASED ON A
REPRODUCED TOPOGRAPHY....................................................................................................... MASAHIRO KAMBARA 13
ESTIMATION OF CO2 EMISSIONS IN HOUSEHOLD EXPENDITURES FOR URBAN
AND RURAL AREA OF CHINA....................................................................................................... GUAN JIANXIN 15
REGULARIZATION OF SUPPORT VECTOR MACHINE WITH VARIABLE KERNELFUNCTIONS....................................................................................................... DAN WANG 17
インドネシア産天然ゼオライトを用いたマンガン除去......................................................................................... ハリス レザ セプティアーノ 19

INFLUENCE OF INITIAL CHLORIDE CONTENT AND WATER CEMENT RATIO ON
CORROSION OF STEEL BAR IN MORTAR EXPOSED TO MARINE ENVIRONMENTFOR 32 YEARS....................................................................................................... AYAKO MIZUMA 21
ドップラーライダーで観測された水平風速パターンの自動分類....................................................................................................... 李 安琪 23
フルデュプレックスで構成される基地局間無線バックホールリンクの自己干渉に関する基礎的検討....................................................................................................... リュウ コウ 25
プロペンを還元剤としたMO 添加ZRO2-CEO2 複合触媒によるNO 選択還元....................................................................................................... 許 正航 27
FIELD SURVEY AND NUMERICAL ANALYSIS OF STORM SURGE INUNDATION IN
TACLOBAN CITY CAUSED BY THE 2013 TYPHOON HAIYAN....................................................................................................... SIYANG LI 29
BATCH EQUILIBRIUM EXTRACTION OF MODEL PETROLEUM HEAVY FRACTION....................................................................................................... TOMONORI MASUDA 31
THE EFFECT OF NON-POLICY FACTORS ON THE DIFFUSION OF EV....................................................................................................... JUNYA ISHIDA 33
DISSIPAITION ENERGY IN THE PROCESS OF ADHESION AND DETACHMENTBETWEEN ELASTIC BEAM AND RIGID BODY....................................................................................................... TAKERU KUSAKARI 35
方向適応変換による画像符号化のための係数予測に関する研究....................................................................................................... 朱 正奇 37
DIFFUSION OF ELECTRIC VEHICLES AND CHARGING INFRASTRUCTURES: THE
CURRENT CONDITION IN JAPAN....................................................................................................... MASAAKI ICHIMURA 39
FIELD SURVEY AND NUMERICAL ANALYSIS FOR FLOODS IN HO CHI MINH CITY....................................................................................................... SHINTARO MAEDA 41
STUDY OF MULTILINGUAL SEMI-MACHINE TRANSLATION BASED ON
COLLECTIVE INTELLIGENCE....................................................................................................... WUNING LI 43
THE EFFECT OF ARC CURRENT AND DISTANCE BETWEEN ELECTRODES ON
REIGNITION EXECUTABLE TIME IN DC ARC DISCHARGE....................................................................................................... YUKI ORIMO 45
FACILITATION OF METHANE PRODUCTION FROM WASTEWATER CONTAINING
SOYBEAN OIL BY MICROBIAL PRETREATMENT

....................................................................................................... KAZUMASA TANAKA 47
ARCH ACTION IN EMBANKMENTS SUBJECTED TO SUBSIDENCE OF FOUNDATION....................................................................................................... YOSHITO MIYATA 49

Solvent Extraction of Heterocompounds Contained in Petroleum Heavy Fraction
Student Number: 10-18700

Name: Hironobu HASEGAWA Supervisor: Ryuichi EGASHIRA, Hiroaki HABAKI
feed oils are shown in Table 3. Three kinds of feed oil, LCO,CGO and AR contain considerable amount of nitrogen andsulfur. And nitrogen species in LCO are almost non-basic.

1. Introduction
Some heavy fractions produced in the petroleum refinerycontain heterocompounds, originating from crude oils.
Nitrogen heterocyclic compounds, especially basic nitrogen
heterocyclic compounds, were reported to cause degradation offuel oil quality and catalyst poisoning, and sulfur compounds infuel oils should result in serious air pollution. Then the removalof these heterocompounds from the fractions was expected tomake the catalytic reaction stably operated or improved, keep
the quality of fuel oil, and maintain the air environment[1].
Solvent extraction has been utilized as a commercialized
method to separate aromatic compounds from petro-derived
fractions. Separation of nitrogen heterocyclic compounds bysolvent extraction has been studied, in which coal tar
absorption oil or catalytic cracked oil was used as feedoil[2][3][4]. However the separation of heterocompounds fromthe heavy fractions has been insufficiently investigated.
In this study, the solvent extraction was applied to theseparation of heterocompounds from heavy fractions generated
in the petroleum refinery process, such as light cycle oil (LCO,
b.p. 425-631K), coker gas oil (CGO, b.p. 431-660K) andatmospheric residue (AR, b.p. above 633K). Then, the effects
of solvent species and operating conditions on the separation ofheterocompounds were examined.

uV(x1,000)
5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

-0.5
75.0

100.0

125.0

150.0

175.0

200.0

min

Fig.1 Gas chromatogram of AR

3.2 Definitions
The yield and distribution ratio of component i, Yi and miafter k-th extraction are defined as follows:(1)(2)
where yi,k and xi,k are the mass fractions of component i in eachphases at k-th extraction.

2. Experimental
2.1 Analysis
The liquid phases, such as feed oils, were analyzed by gas
chromatograph(Shimadzu
GC-2010),thechemicalluminescence method (JIS-K 2609), ultraviolet fluorescencemethod (JIS-K 2541-6), potentiometric titration method todetermine the compositions of the phases, especially, in termsof total nitrogen, total basic nitrogen, and total sulfur contents.

Table1 Experimental conditions of single batch extraction
SystemFeed
LCO, CGO, AR, AR1, AR2
Solvent
Methanol, Sulfolane, Furfural
0.4 Acetic acid, m-Xylene, Heptane
Conditions
Mass of feed, R0
[kg]
0.02
1(Methanol, Sulfolane,
Solvent/feed ratio,[-]
Furfural, 0.4 Acetic acid)E0/R010(m-Xylene or Heptane)
Temperature
[K]
303
Time
[hr]
48

2.2 Batch Extraction
The experimental conditions of single batch extraction aresummarized in Table 1. LCO, CGO and AR, obtained from theMiddle East, were used as the feed oils from actual fractions in
the petroleum refinery. Six kinds of solvents were selected.
Sulfolane and furfural are utilized in the commercial extraction
processes. Methanol, aqueous solution of acetic acid, xylene
and heptane were chosen from the solvents in the researchsteps. The extract phase obtained from contact of AR andxylene or heptane was also utilized as feed oils, AR1 or AR2,
respectively. The specified amounts of feed, R0, and solvent,E0, were put in conical flask and shaken at 303K for 48 hours
to be equilibrated. After reaching equilibrium conditions, theraffinate and extract phases, R1 and E1, were separated into
each other with separatory funnel. The experimental conditions
by multiple batch extraction with fresh solvent are summarizedin Table 2. LCO, CGO and AR2 were used as feed oils andmethanol was selected as solvent. After phase separation ofeach extraction, fresh solvent was contacted with the obtainedraffinate and repeated for 3 times.

Table2 Experimental conditions of multiple batch extraction
SystemFeed
LCO, CGO, AR2
Solvent
MethanolConditions
Initial mass of
[kg]
0.02
feed, R0
Mass of solvent, E0[kg]
0.02(Feed : LCO, CGO)
Solvent/feed ratio,[-]
1(Feed : AR2)
E0/R0
Temperature
[K]
303
Time
[hr]
48
Repetition, n[-]
3

3. Results and Discussion
3.1 Composition of feed oils

Table 3 Nitrogen and sulfur contents in feed oils
Feed oil
Basic
Nitrogen
Nitrogen
[ppm]
[ppm]
LCO
380
17
CGO
730
333
AR
2400
726

The gas chromatogram of AR is shown in Fig.1. The AR
contained a large number of components and a portion of themmight not be detected by GC due to the high boiling points. The
periodically appeared sharp peaks on the chromatogram might
represent alkane hydrocarbons with the different carbon
numbers and the other components, such as, aromatics andheterocompounds, were detected as a lot of tiny peaks between
the sharp peaks. The LCO and CGO were also the same.
Accordingly, it was difficult to quantitatively determine thecompositions of the feed oils. Nitrogen and sulfur contents in

Sulfur
[ppm]
1600
25000
38000

3.3 Batch Extraction
In the cases of LCO and CGO feeds, the feed and solvent1

distribution ratios of some parts of nitrogen compounds in AR
might be low and difficult to extract into solvent phase.

formed heterogeneous liquid-liquid two phases. The mixture of
AR and xylene formed the homogeneous single phase and thatof AR1 and furfural was also homogeneous. It was virtually
impossible to sufficiently contact AR with any solvent due toquite low fluidity of AR and the phase separation was
impossible as well. The mixture of AR and heptane formed theliquid-solid heterogeneous phases. Heptane acted on as
deasphalting agent and asphaltene was obtained as a solid.
The effects of the combination of the feed and solvent on
the yields, Yi, and distribution ratio, mi, of nitrogen and sulfur
are shown in Figs. 2 and 3, respectively. In most cases, thenitrogen and sulfur heterocompounds were extracted in theused solvents. The Yis and mis of nitrogen were larger with any
solvent than those of sulfur. The Yis and mis (i = N, S) with
furfural solvent were higher than those with the other solvents.
The aqueous solution of acetic acid was ineffective to extract
both compounds. When AR was contacted with heptane, thesolid phase, asphaltene, was obtained and its concentration of
nitrogen was 7200ppm, much higher than that in AR,
2400ppm. The yield of nitrogen in the asphaltene, 1 − YN,1, wasapproximately 0.3 and the separation of nitrogen also attainedby the deasphalting operation with heptane. The mis of nitrogenwere larger with AR2 feed containing heptane than those with
AR1 containing m-xylene. The nitrogen heterocompounds weremore extracted in the less polar feed than those in the morepolar feed[5].

1

YN,k [-]

0.5

0

Yi [-]

0.5i

NitrogenSulfur1
2
3
Repetition number k [-]

101

Feed
LCO
CGO
AR
AR1
AR2
LCO
CGO

mN,k [-]

100

0

10-1

10-2

1
3
2
Repetition number k [-]Fig.5 Effects of the kind of feed oil and repetition numbers on
distribution ratio of nitrogen, mN,k

4. Conclusion

SUL MeOH FUR AA HEP

The petroleum heavy fractions contained a large numberof components and substantial amount of nitrogen and sulfur.
Nitrogen and sulfur heterocompounds in the heavy fractions
could be removed by solvent extraction. Some parts of nitrogen
heterocompounds in atmospheric residue were difficult to
extract.

Fig.2 Effects of feed oil and solvent on yield, Yi,1, in single
batch extraction
101

Nomenclature100
mi,1 [-]

R
E
xi
yi
Yi
mi
LCO
AR
SUL
MeOH

10-1

10-2

Feed
LCO
CGO
AR2

Fig.4 Effects of the kind of feed oil and repetition numbers onyield of nitrogen, YN,k

1
Key
□
◇
○
△
▽
■
◆

Key
□
◇
▽

SUL MeOH FURAA-Mass of raffinate phase-Mass of solvent phase-Mass fraction of i in feed phase-Mass fraction of i in solvent phase-Extraction yield of i
-Distribution ratio of i
-Light cycle oil
CGO-Atmospheric residue
FUR-Sulfolane
AA-Methanol
HEP-Coker gas oil-Furfural-Acetic acid
-Heptane

SubscriptiN
k0

Fig.3 Effects of feed oil and solvent on distribution ratio, mi,1,
in single batch extraction
The effects of feed on the YN and mN in the multiple batch
extraction are shown in Figs. 4 and 5, respectively. In any stageof extraction, the nitrogen compounds were extracted. In thecases of LCO and CGO, YNs and mNs were kept relatively highuntil the third stage. On the other hand, in the case of AR2, YN
and mN in the second and third stages were much lower thanthose in the first. The properties of nitrogen compounds
contained in AR distributed wider than those in LCO and CGO,
since the boiling range of AR was wider, so that the-component i-nitrogen species-at equilibrium state of k-th extraction experiment-at initial state

Reference[1]石油学会;石油精製プロセス,第五章,接触分解 125-138[2]Kodera, Y., et al., Fuel, 70, (6) 765 (1991)[3]Qi, J., et al., Fuel, 77, (4) 255 (1998)[4]Nagai, M., 東京工業大学 1999年卒業論文[5]Shimada, Y., et al., J. Chem. Eng. Jpn, 46, (6) 376 (2013)

2

The impact of urbanization on water utilities performance in a developing country:
A case study of the Philippines
Student Number: 09_04820
1.

Name: Shintaro OYA

Introduction

number of indicators, which are represented by

Performance of a water utility can be seen in a
wide

range

Supervisor: Naoya ABEofaspects.

Internationalamount of nonrevenue water, operational costs,

Benchmarking Network for Water and Sanitationwater production, etc. on their reports. In thisUtilities

way, principal Component Analysis (PCA) was(IBNET)benchmarking

setstandardThe

Key Performance Indicators (KPIs) such as the

the
for

the

first

globalwater

andconducted in order to convert KPIs into thewastewater sector and provides access to thesmaller number of variables.

enormous information of water utilities. This

Urban population, urban population growth

information source is relevant for those water

rate and urban population rate were selected asutilities in developing countries as they are facing

proxies

rapid increase of water demand due to the rapid

demographic data of municipalities and cities

growth of population.

where the water utilities are placed was obtainedtodescribe

urbanization.

The

Safe water supply has significant impact on

from the Census of the Philippines. Impact ofpublic health. It is getting more important tourbanization on the performance of water utilities

understand how performance of water utilities inin each year was examined with the following

developing countries is affected by urbanization.

multiple regression model:2.

𝑃𝐶𝑛 = 𝛽0 + 𝛽1 𝑈𝑃 + 𝛽2 𝑈𝑃𝐺𝑅 + 𝛽3 𝑈𝑃𝑅 + 𝜀

Objectives
The objectives of this study are: (1) towherewater utilities in the Philippines varies and; (2) tocomponent (PC); UP is population living in urban

assess the impact of urbanization on performanceareas; UPGR is growth rate of population livingof water utilities.

in urban areas; UPR is ratio of population living

𝑃𝐶𝑛

is

score

of

the

nth principal

understand briefly how the performance amongin urban areas against total population; and ε3.

Literature Review

is an error term.

Previous research has discussed the effect ofurbanization on urban water. Not technicaldifficulties

but

poor

management

5.

generally

Results
KPIs

were

summarized

into

four

PCs

causes inferior performance. It is required tocontaining approximately 70% of the variancefocus on the actual conditions and consider how(see Table1). The PCs in 2004 were interpreted as

the performance is affected by urbanization.

factors respectively describing supply volume,(Hashimoto (2010))

operational efficiency, financial vulnerability andmanagement efficiency. In terms of 2009, PCs

4.

show the extent of service efficiency, nonrevenue

Data and Methodology
IBNET provides the information of 52 utilities

water

in the Philippines both in 2004 and in 2009 with aissue,

management

operational efficiency.

3

efficiency

andTable 1. Results of principal component analysis

Table2. Results of multiple regression analysis

Stepwisemethodandvolume and financial vulnerability, the PCs in

explanatory variables were selected above (see2009 described efficiency of service, management

Table2). The results in 2004 show that an

and operation; (2) although population increase inincrease in urban population had impact onurban areas had positive impact on performance

supply volume expanding (PC1) and operational

size in 2004, urbanization had negative impact on

efficiency

worsening(PC2).

efficiencies in both years and nonrevenue water

efficiency

(PC4)

also

was

was

introduced,

Management
influenced

by

issue

urbanization. In terms of 2009, populationhas

becomeworsebypopulation

concentration on urban areas in 2009.

concentration on urban areas affected nonrevenue
water issue getting worse (PC2), and a rise inReferences

urban population growth rate had negative[1] Berg, et al. 2011. The IBNET Water Supply

and Sanitation Performance Blue Book

impact on the operational efficiency (PC4).

[2] Department of economic and social affairs,
6.

United Nations. 2014. 2014 revision of theConclusion

World Urbanization Prospect

From these results, it is concluded that: (1)while integrated components in 2004 showed[3] Hashimoto, K. 2010. Situations in developing

relatively fundamental factors such as supply

countries. Mizkan water forum archives 2010

4

NUMERICAL STUDY ON DUST DEVIL-LIKE VORTICES OVER
BUILDING ARRAY
Student Number: 11_12787

Name: Takafumi SUEISHISupervisor: Manabu KANDA

1. Introduction
In daytime of sunny day, vortices called Dust devils
occur at arid region like a desert. They are small-scalevortices made by ground heating and visible by dust andother particles. The lifetime is only a few minutes.
The dust devil has been studied based by fieldobservations and numerical simulations. In past
numerical studies, dust devil-like vortices (DDVs) weresuccessfully simulated using Large-eddy simulation
model. (1) They focused on DDVs over a flat
homogeneous field. Recently, DDVs were found also inbuild-up area. (2) In this study, DDVs were simulated
over regular cubic arrays or urban morphology to
examine the effects of ground obstacles on the DDVs.

Figure 1. Horizontal cross section of vertical velocity w
for Case Topo at z=63m, t=5045s.

2. Simulation Setup
The study was carried out using PALM (PArallelized
LES Model) which was developed in Leibniz University
Hannover.
Three simulations were conducted by changing thesurface geometry. One has flat surface named “CaseFlat”. Another one has cubical array named “Case Topo”.
The other has realistic urban geometry in Shinjuku
named “Case City”.
The simulation conditions of Case Flat and Case
Topo was as follow. The simulation domain was 768m by
768m by 1572m. Grid spacing was 2m except for verticalgrid spacing above 800m. No mean background wind
was imposed. A constant surface sensible heat flux of
0.24Kms-1 were applied. The initial potential
temperature was constant at 300K up to 700m andincreased at 0.02Km-1 above that. Cyclic lateralboundary conditions, no slip conditions at the lower andfree boundary conditions at the upper boundary wereused.
In Case Topo, a single cube size was 32m by 32m by
32m, and street width was also 32m. A constant surface
sensible heat flux of 0.24Kms-1 were applied fromground and roof top.
In Case City, the simulation domain was 1000m by
1000m by 1919m. Grid spacing was 2m horizontally and3m vertically except for vertical grid spacing above
1080m. Heat flux was calculated by LOCALS-UCSS
which is developed by Ashie et al (2011). Radiation at
Shinjuku on 5 August 2014 at 12:00 was considered inthis model and the result was used as PALM heat flux
input.

Figure 2. Perturbation pressure contour and horizontal
velocity near DDV.
Vortex centers were determined by using
perturbation pressure and vorticity as follow. (1)
(1) p
4.0Pa
(2) | | 1.0(3) | | should be local maximum.
Where p is the perturbation pressure.
is verticalvorticity, and it is defined as follow.

 v ut x y(1)

Where x and y represent horizontal axis of Cartesian
coordinates, and u and v are the corresponding
horizontal velocity components.

3. Results
Figure 1 shows that horizontal cross section of verticalvelocity w for Case Topo at z = 63m, t = 5045s.

5

Figure 1 demonstrates that there were someconvergence lines. These convergence lines formed apart of spoke-like pattern. Most of DDVs were on
convergence lines. These observations agree well with
Case Flat or previous study. (1) However DDVs occurred
not only from convergence lines but also from edge ofbuildings.
Figure 2 shows that flow structure near strongest
DDV in Case Topo. Contour shows perturbationpressure, and black vectors show horizontal velocity.
Black rectangles show the position of buildings.
The diameter of DDV is about 20m. Near the DDV
center, perturbation pressure was decreased. Absolutevalue of vorticity was increased, and updraft also
occurred.
There were 5.43 DDVs in Case Flat and 18.60 DDVs
in Case Topo on average.
Figure 3 shows that the relationship between
distance from DDV center and tangential velocity fortime averaged vortices for strongest DDVs in Case Flat
and Case Topo. These data were time averaged over t =4800s ~ 4900s for Case Flat, t = 5006s ~ 5053s for Case
Topo.
Figure 3 demonstrates that tangential velocity
becomes the pick at about 4m from DDV center. In
Rankin’s model, this relationship is represented asfollow.

 rK
 r 2 (o  r  r0 )v 0
 K (r  r )0
 rFigure 3. Relationship between distance from DDV
center and tangential velocity.

Figure 4. Building height contour and DDV positions inCase City.

(2)

4. Conclusion
From 3 simulations, we can get summary as follow.
(1) DDVs developed on convergence lines.
(2) Buildings helped DDV to grow up by edge effect andwinds caused by buildings.
(3) Comparing Case Flat and Case Topo, Case Topo’s
number of DDVs were much than that of Case Flat. But,
the strength of Case Topo was weaker than that of Case
Flat.
(4) Flow structure of Case Flat’s DDV and that of Case
Topo had similarity. These can be explained by using
Rankin model.

Where v is tangential velocity, r is distance from DDV
is radius of forced vortex, K is constantcenter,respectively. The form of tangential velocity was similar
to Rankin’s model, so the radius of forced vortex became
about 4m. Tangential velocity of Case Topo’s DDV wasless than that of Case Flat.
In Case City, there were less heat flux than that ofCase Flat and Case Topo.
Figure 4 shows that positions of DDVs in Case City.
These DDVs were found in 300 time steps. Contour
shows the building height and black points show thepositions of DDVs.
From Figure 4, DDVs occurred near high buildings.
This is because there were winds caused by buildings.
These winds helped making DDVs. DDV positions alsoneeded to be on convergence lines, the same as Case
Flat and Case Topo. But DDVs need some spaces todevelop bigger. So there were not DDVs high dense area
at north-east part and south-west part in Case City
domain.

5. Reference[1] S.Raasch and T.France (2011), Structure andFormation of Dust Devil-Like Vortices in the
Atmospheric Boundary Layer: A High-Resolution
Numerical Study[2] Fujiyoshi Y, Yamashita K, Fujiwara C (2009),
Detection of Organized Airflow in the Atmospheric
Boundary Layer and the Free Atmosphere Using a 3DScanning Coherent Doppler Lidar[3] Ashie Y, Kono T (2011), Urban-Scale CFD Analysis
in Support of a Climate-Sensitive Design for the Tokyo
Bay Area

6

Improvement of Fisher linear discriminant based on
normal distribution
Student ID number: 10-15305

1

Full name： CUNG Van Dung

Introduction

Supervisor： Yukihiko YAMASHITA

The sum of variances of feature values is given by

Fisher linear discriminant analysis is not optimal for
hv, ΣW vi.
(8)class separation even assuming a normal distribution.
Fisher linear discriminant is defined as maximizing
To solve this problem, Yokota et al. proposed an itthe
following ratio with respect to v .
erative method based on Chernoff or Bhattachyya distance for binany class problem. Its equation is given
hv, ΣB vi
by a generalized eigenvalue problem with a correction
.
(9)
JF =
hv,
ΣW vi
term. In the case of multi-class problem, it is necessary
to extend this theory. Unfortunately, since the evaluThis problem can be solved by the following generation function becomes complicated, it is not possible
alized eigenvalue problem .
to obtain a simple formula. Therefore, in this study,
i propose a method to obtain approximated solution
ΣB v = λΣW v.
(10)based on a correction term. I showed its advantage by
experiment.

3
2

Fisher linear discriminant

=

Bhat-

Error rate of recognition of distributions is given by

Let µc be the average of pattern in class C (c =1, 2, . . . , C), and let Σc be the variance-covariance matrix . Eξ ∈Ωc f (ξ) indicates the expected value of f (ξ).
We haveµc

Correction term bytacharyya distance
1 X
CCe = 1−

k=1

Z
pk fk (x)dx, (11){x|pk fk (x)=max pi fi (x)Eξ ∈Ωc ξ,Eξ ∈Ωc (ξ − µc )(ξ − µc )T .

(1) where p is the probability of category Ω and f (x) is
k
k
k
the
probability
densityfunctionofpatternincategory
Σc =
(2)
Ωk .
The feature value is given by the inner product of For two probability density functions f1 (x) and f2 (x),the feature extraction vector v and a pattern. The Chernoff distance is given byZ
mean of feature value in class c mc is given byα1−αdC ≡ − log(f1 (x)) (f2 (x))
dx.
(12)
D
mc = Eξ ∈Ωc hv, ξi = hv, µc i.
(3)RIn particular, when α = 1/2, it is called the Bhattacharyya distance. Assume that f1 (x), and f2 (x) areCC
the probability density functions of the normal disXXΣB =(µk − µl )(µk − µl )T .
(4) tributions of which means and variances are m1 and
k=1 l=k+1
S1 , and m2 and S2 , respectively. Chernoff coefficientJC ≡ e−dC is given by
The inter-class variance of feature value is given by

The inter-class covariance matrix ΣB is given by

C
C
X
X

s
(mk − ml )2 = hv, ΣB vi.

(5) J
C

k=1 l=k+1

|S 1 |1−α |S 2 |α1
exp − hm1 − m2 ,|(1 − α)S 1 + αS 2 |2−1α(1 − α) ((1 − α)S 1 + αS 2 )The variance of feature value in class c is given byEξ∈Ωc (hv, ξi − mc )2 = hv, Σc vi.

=E(m1 − m2 ) .
(13)

(6)

I obtain K feature extracting vectors v 1 , v 2 , . . . , v K .
The sum of the variance-covariance matrices of patThe feature extraction matrix V is given byterns ΣW is given by
ΣW =

C
X

V = (v 1 v 2 . . . v K ).
Σc .

(7)

c=1

71

(14)

0.12

0.115
bct-FLDA
FLDA

0.11

bct-FLDA
FLDA
0.11
0.105

0.09

Error rate

Error rate

0.1

0.08
0.07

0.1
0.095

0.06
0.09

0.05
0.04

0.085
0

2

4

6

8

10

0

1

2

m2y

2
(a) Error rate (σ2y
= 8)

3

4

5
Sg2y

6

7

8

9

10

(b) Error rate (µ2y = 0)

Figure 1: Classification of 3-dimensionalnormal distributions by best division




1 0 0
3
For recognition, the distance of distributions should20 µ2 =  µ2y  , Σ2 =  0 σ2ybe large. The sum of the Bhattacharyya coefficients is0
0
01
given by
 


s p
0
1 0 0

C
C
X X
2 |S k ||S l |1µ3 =  3  , Σ3 =  0 1 0 JB =exp − hmk − ml ,1
0 0 8
|S k + S l |4
k=1 l=k+1
2(S k + S l )−1 (mk − ml )(15) Figure 1 (a) shows error rates when σ2yis 8 and µ2y ischanged from 0 to 10. Figure 1 (b) shows error rates
Therefore, by minimizing JB with respect to V , it is when µ is 0 and σ 2 is changed from 0.5 to 10. From2y2ypossible to determine the feature extraction matrix V . Figure 1 (a), we can see around µ = 1.7 both er2y
However, minimization of eq (15) is difficult. Therefore ror rates are maximum and they are almost the same.
I solve this problem approximately based on the itera- In other region bct-FLDA outperforms FLDA clearly.
tive method for binary class and one feature extraction From Figure 1 (b), we can see bct-FLDA outperforms
vector. I call this method the Bhattacharyya correction FLDA clearly for σ 2 >= 2. As increasing σ 2 , the2y2yterm Fisher linear discriminant analysis (bct-FLDA). difference becames larger. In addition, the iteration
(n)
(n)
(n)
Let v 1 , v 2 , . . . , v N be feature extraction vectors at in calculation of bct-FLDA was converged in several
n-th iteration. The feature extraction matrix V (n) is times.
expressed by(n) (n)(n)

V (n) = (v 1 v 2 . . . v K ).

5

(16)

Conclusions

The generalized eigenvalue problem to be solved isgiven by
!
C
X
Σc
ΣB −
v (n+1) = λΣW v (n+1) .
(n) , Σ v (n) i
hv
c
c=1
(17)
I denote eigenvectors in descending order of the eigen(n+1)
(n+1)
(n+1)
values as v 1, v2, . . . , vN
. They are feature
extraction vectors obtained at the n-th computation.
The initial vectors of this iterative calculation were set
to feature extraction vectors of FLDA. The process isrepeated until convergence.

I proposed a new feature extraction method based on
FLDA and Bhattacharyya distance. It provided a correction term to the generalized eigenvalue problem. I
showed its advantage by recognition of three threedimensional normal distributions. For future works,
it is necessary to analyze the obtained approximatedsolution and apply the proposed method to real problems.

4[2] Yokota Tatsuya, Wakahara Toru, Sakano Hitoshi, Yamashita Yukihiko ”Correction Term of Fisher Discriminant
Analysis Based on Normal Distribution”, IEICE Technicalreport. PRMU, vol.112, no.495, pp.31-36, 2013.

References[1] R.A.Fisher,”The use of multiple measurements in taxonomic
problems,”Annals of Eugenics, vol.7, no.2, pp.179-188, 1936.

Experiment

In this experiment, three three-dimensional normal distributions are recognized. I extracted two feature extration vectors.
 


0
1 0 0
µ1 =  0  , Σ1 =  0 1 0 
0
0 0 1[3] M. Loog and R.P.W.Duin,”Linear dimensionality reduction
via a heteroscedastic extension of Ida: The chernoff criterion,”IEEE Trans. Patter Anal. Mach. Intel., vol.26, no.6,
pp.732-739, 2004.

82インドネシアの国内航空市場における直行便と乗継便の経路選択分析
学籍番号：11_08455
1

氏名：姜 佳文

はじめに

指導教員：花岡伸也

空会社の選択理由では、GA 旅客が時間帯と安全性を重視

インドネシアは東西に非常に長く、約 13,000 の島を抱し、JT 旅客が低運賃を重視する傾向がある。

える国であるゆえに、航空輸送は重要な役割を果たしてい次に、航空会社別の旅客特性を詳しく分析した。両社のる。しかし、ほとんどの大規模空港の容量が不足している。

利用者の中で、JT の 20 歳以下の旅客の割合は GA の 2 倍

特に、ジャカルタのスカルノハッタ空港は処理能力の 2 倍の 16％であり、若い年齢層が JT を好む傾向が見える。世以上の旅客数が利用し、非常に混雑している。

帯月収では、3 milIDR (約 2.8 万円)以下の利用者の割合はスカルノハッタ空港の混雑を解消するため、インドネシ

GA が 16%で、JT が 31%である。GA の 3 milIDR~10 milIDR

ア東部にあるマカッサル空港を、本格的なハブ空港として(約 9.3 万円) の利用者の割合は約７割で、JT は約 5 割で活用するハブ・アンド・スポーク・システムが考えられてある。世帯月収が 20 milIDR (約 18.6 万円)を超える利用者いる。しかし、新しいシステムでは、地方空港への中型機の割合は GA が 3%で、JT が 5%である。旅行目的は顕著による直行便を減らし、大型機でマカッサル空港に行き、な差はなく、両社ともビジネス旅客が 3 割以上占めていそこから小型機で地方空港に行く乗継便を増やす必要がる。航空券の購入手段として、旅行代理店で購入する利用ある。そこで、インドネシア国内の航空利用者について以者の割合は GA が 7 割以上で、JT が約 6 割である。イン下の 2 点を明らかにする必要がある。
（１）旅客が直行便

ターネットで購入する利用者の割合は GA が 18%で、JTと乗継便を選択する理由、（２）フルサービスキャリア

が 26%である。マイレージプログラムの加入者の割合は(FSC)とローコストキャリア(LCC)利用者が経路選択で考GA が 31%で、JT が 16%である。日本と違って、インドネ

慮する要因に違いがあるかどうか。

シアの航空利用者の多くはマイレージプログラムに加入

したがって、本研究の目的は、FSC のガルーダ・インド

していないことがわかった。昨年の旅行経験数が 1 回以下

ネシア航空（以下 GA）と LCC のライオンエア（以下 JT）の利用者の割合は GA が 24%で、JT が 32%である。2~3 回に対し、利用者属性分析を行い、また選好調査を用いて航の利用者の割合は GA が 46%に対して、JT が 35%である。

空経路選択モデル（直行便/乗継便）を構築し、経路を選択

6 回以上の利用者の割合は GA が 10%で、JT が 17%であする際に重視する選択要因を明らかにする。

る。
また、各路線においての利用者属性の分析も行った。た

2

現地調査

とえば、Jayapura 行きの長距離路線で、直行便利用者が出

インドネシア国内の航空利用者が航空会社と経路を選発時刻と到着時刻を重視し、乗継便利用者は安い運賃を重択する際の優先事項を明らかにするため、2014 年 7 月に視する傾向がある。Ambon 行きの短距離路線では、ある

ジャカルタとマカッサルの空港で 12 日間のアンケート調

乗継便の到着時間が朝の 7 時半であるので、ビジネス客の査を行った。有効サンプル数 3079 の内、直行便サンプル

割合は全体平均を上回っている傾向がある。そのほかの利数が 1,998 で、乗継便サンプル数が 1,081 である。

用者の多くは満席で直行便を購入できないため、仕方なく調査対象路線はジャカルタ発東インドネシア行きの路

乗継便を選択していたことがわかった。

線の中で、GA と JT が直行便と乗継便（マカッサル経由）を 提供す る６路 線であ る。目 的地は Kendari、Palu,、

4

Gorontalo、 Manado、 Ambon と Jayapura である。

選好調査
業務目的と非業務目的の旅客が利用する航空経路を選

調査票では個人属性（性別、年齢、住所、職業、同居者択する際に重視する選択要因を明らかにするため、2015

数、世帯月収）、便名と目的地、昨年の旅行経験数、旅行

年 1 月下旬に航空利用経験があるジャカルタ市民を対象

目的、経路決定者、支払運賃、航空券購入手段、マイレーにインターネット調査を行い、有効サンプル数 362 を収集

ジプログラム、航空会社の選択理由、経路の選択理由及びした。

別の経路を選択しなかった理由の、全部で 11 問である。

現地調査の各路線の利用者属性の分析結果を踏まえて、選好調査の対象路線は Jayapura、Palu、Ambon、Manado で、

3

利用者属性分析

それぞれに対して調査票 1 式を設定した。選好調査では

現地調査データ全体で見ると、旅客が経路を選択するとGA と JT の直行便・乗継便の合計 4 つの選択肢を提供し、き、適切な出発と到着の時間帯を重視する傾向がある。航回答者にそれぞれ業務目的と非業務目的での選好順位を9答えてもらった（業務目的旅行では雇用者が運賃を支払うっている。修正済尤度比は推定したモデルが選択結果に対設定とした）。選択肢を構成する属性は運賃、頻度、総時する適合性を表す基準であり、0.2 以上の場合は信頼でき間（乗継時間含む）である。それぞれのレベルは現地調査

るモデルと言える。

データの分析を踏まえて、3 つずつに設定した。直交法を用い、シナリオ数を 729 から 36 に減らし、ランダムに 4

表 2 非業務旅行航空経路選択モデル推定結果

つのブロックに配分した。よって、一式の調査票につき 9

分析対象つシナリオに対して選択順位をつける必要がある。

説明変数

全体
推定値

t値

推定値

t値

-0.465-3.320**より現実に近い航空経路選択モデルが構築されるよう、

運賃 (milIDR)-0.475-3.364

選好調査の回答者の性別、年齢と収入の各層の割合はイン

頻度（便数/週）-0.039-0.651--

ドネシアの人口統計データおよび現地調査の集計結果に

総時間 (h)-0.390-4.090**-0.385-4.056**

基づき設定した。選好調査の回答者の中、自営業が 4 割占

定数 1（GA 直行）-1.208-5.507**-1.207-5.502**めている。また、昨年の旅行経験数が 3 回以下の回答者が定数 2（JT 直行）

0.402

2.126*

0.427

2.299*

85％である。

定数 3 (GA 乗継）

0.901

11.412**0.891

11.565**5

航空経路選択モデル
本研究では順位付けデータを独立な選択データに分解**

修正済尤度比

0.248

0.248

時間価値

7763 yen/h7725 yen/h

サンプル数

し、交通分野で多用されているロジットモデルを用いて尤

＊（ ：5%有意水準

362＊＊：1%有意水準）度関数を計算した。本研究ではランクロジットモデルを用いた。表 1 と表２は業務旅行、非業務旅行の航空経路選択

非業務旅行航空経路の選択では、頻度の符号がマイナス

モデル推定結果である。

になってしまうため、頻度を含む、含まないの 2 通りの推定をした。2 通りの推定の中、運賃と総時間の符号がマイ

表 1 業務旅行航空経路選択モデル推定結果
分析対象
説明変数

全体

ナスで、t 値は 1%有意になっているので、重要な選択要因

自営業のみである。修正済尤度比は両方とも 0.2 以上になっており、

推定値

t値

推定値

t値

-

-

-0.255-0.929非常に高い。選好調査のデザインで、直行便の運賃はウェ

頻度（便数/週）

0.080

1.404

-

-

ブ運賃に従っているが、乗継便の基準運賃をウェブ運賃の総時間 (h)-0.574-5.757**-0.328-1.603

8 割に相当する運賃に設定したことが、原因の一つと考え

定数 1（GA 直行）

0.680

3.405**

0.729

1.515

られる。

定数 2（JT 直行）

1.248

6.455**

1.533

3.676**

定数 3 (GA 乗継）

1.215

14.761**

0.896

5.079**

運賃(milIDR)推定結果が信頼できることを示した。ただし、時間価値が6

結論

修正済尤度比

0.238

0.190

本研究ではインドネシア国内の航空利用者を対象に、

サンプル数

362

80

現地調査データで直行便と乗継便の利用者属性の分析を：1%有意水準）

行い、選好調査データで業務目的と非業務目的旅行の航空＊（ ：5%有意水準＊＊

経路選択モデルを構築し、旅客が航空経路を選択する際に業務旅行航空経路の選択では、分析対象が有効サンプル

考慮する選択要因を明らかにした。修正済尤度比が高く、全体と自営業サンプルのみの 2 通りの推定になる。選好調

サンプル比率が実態と一致しているため、モデルの説明力

査データの基礎集計では、収入に関係なく、GA 直行便をがあると判断できる。ただし、推定モデルでは頻度の重要

1 位に選択した回答者の割合が最も多い。したがって、運性が低いため、今後選好調査データと現地調査データを統賃の推定値の符号がプラスになってしまうため、全体推定

合する推定を行い、頻度の重要性を再検討する必要がある。

では運賃以外の説明変数で推定を行った。また、自営業の場合は業務旅行であっても自分で運賃を支払うことにな

参考文献

るので、自営業のみのサンプルで推定するときは、運賃を

交通工学研究会編：やさしい非集計分析, 丸善出版株式会

説明変数として設定した。全体の推定では、総時間は符号社, 2001がプラスで、t 値も 1%有意なことから、最も重要な選択要因である。自営業のみの推定では、運賃と総時間の符号がマイナスで、増加すると効用が下るという妥当な結果とな

10

Relation between Wind Velocity, Rotation Speed and Torque of Drag TypeWind Turbine for Small Wind Power Generation
Student Number: 09_16176
1.

Name:Yusuke Tokuda

Introduction

Supervisor: Kunio Takahashia torque meter (UTMⅡ; torque range up to 0.05Nm) for somecertain resistance, wind velocities, and blade number, as
shown in table 1. Each measurement is repeated twice withtimes interval of ten seconds.

In recent years, electric power generation using renewableenergy including wind power generation has attracted
considerable attention throughout the world due to exhaustionof fuel, and environmental issue. Besides of large scale plants,
small scale wind power generation operating at low wind at
the living environment has also attracted attention. Since thisscale of generation is instability, it is therefore necessary to
track for maximum power point. As shown in Fig. 1, theefficiency of the wind power generation is determined by theefficiency of the wind turbine, the generator and the storage
circuit. As related researches, the efficiency the storage circuit,i.e. maximum power point tracking, has already been studied[1], and that of the generator is currently under consideration.
In this study, a wind power generation at the living
environment, i.e. wind velocity of 3-5m/s. Within this wind
range, drag type wind turbine can gain higher power
coefficient than lift type wind turbine [2], and doesn’t require
starting torque. Whereas, lifting power of the wind turbine
drastically decreases [3]. Therefore, drag type wind turbine ismore efficient than lift type wind turbine.
The purpose of this study is to investigate the relation
between the rotation speed, torque of drag type wind turbine
and the wind velocity for small wind power generation at low
wind (3-5m/s) experimentally.

Table 1. Experimental parameters
blade number
2,3,4,8,12,16,20
wind velocity [m/s]U5, 4.7, 4.5, 4.2, 4, 3.7, 3.5, 3.2
10, 7, 5, 4, 3, 2.5, 1.5, 1, 0.9, 0.8, 0.7,
0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.1,
0.05, 0.03, 0.01, 0.0003, 0.0001

Resistance [kΩ]R

2.2 Experimental Results
The measurement results of the torque and the rotation
speed of the turbine are obtained as Fig.4. Fig. 4(a) shows therelation between the torque and the rotation speed for several
wind speed. Fig. 4(b) shows the relation between the torqueand the rotation speed for several load resistances. Fig. 4(c)shows the relation between the rotation speed and the windspeed for several load resistances.

0.002

0.00140.00120.001
Torque T[Nm]Torque T[Nm]0.00150.00080.0006
U=5[m/s]
U=4.7[m/s]U=4.5[m/s]U=4.2[m/s]U=4[m/s]
U=3.7[m/s]

0.0004

Fig.1 wind power generation system model

0.00020

2. ExperimentRotation Speed  [rpm]

0

500

200

(b)

400
600
800
Rotation Speed  [rpm]1000

R =10000[R =5000[R =1000[

1200

1000

0.001

0.0005

100
200
300
400
Rotation Speed  [rpm](a)2.1 Experimental tools
To investigate effects of projected area of the wind turbine’s
blades on its torque, several turbines of different number of
blades (the blades have the same shape and size) arefabricated (Fig.2).

R=25[
R=500[R=600[R=3000[ limit limit800

 limit

600

400

Fig.2 wind turbine design

3

3.5

4
U0

(c)

4.5

5

Ulimit = 4.72[m/s]Wind VelocityU[m/s]Fig.4 Experimental results

3. Discussions
3.1 Relation between torque and rotation speed
The relation between the torque and the rotation speed has alinear tendency, as shown in Fig. 4(a), whereas, that shown in
Fig. 4(b) doesn’t. Therefore, from Fig. 4(a), the torque and therotation speed are assumed having a relation as eq. (1),(1)
T  k (U ) (U , R)  C (U )Fig.3 experimental apparatus
The experiment is conducted in a wind-tunnel, which cancontrol wind velocity in the range of 2-5m/s, as shown in Fig.3.
The torque and the rotation speed of the wind turbine areadjusted by a variable load resistor connected to the power
generator. The torque and the rotation speed are measured bywhere, the slope
and the intercept
as functions of the wind velocity.

11

are consideredEquation (6) can predict the torque by 7 parameterswhich are estimated from experimental results.

0.003

C(U)

Climit = 0.00297

3.3 Comparison of Estimated and Measured value
The output of the wind turbine obtained from experimental
values is compared to eq. (6). As shown in Fig. 7, they are
qualitatively matched, especially at the vicinity of the
maximum point of the power.

0.002

1
0.001
3.5

4.0

4.5

U=4.7
U=4.5
U=4.0
U=3.7

5.0

Wind VelocityU[m/s]Ulimitc = 4.69[m/s]

Fig.5


kkU  Ck (U  U limit_k )k (U )  klimit (U limit_k  U )Power P[W]The slopeandthe intercept of eq. (1) are obtainedby applying least-squares method, as shown in Fig. 5. Bothandhave linear relation before a certain point,and constant after the certain point. We call this certain point
as Ulimit_k and Ulimit_c. Therefore, their relation can be described
as

0.5

(2)

 c C(U U l i m) i t _ c
 c kU(3)
CU( )   l Ci m(Uil ti m i Ut )_ c
where

0

10000

Fig.7 resistance and wind mill output

are estimated from experimental results.

3.4 Control criteria
When the wind turbine output is at maximum point,
resistance Rmax is

3.2 Relation between rotation speed and wind velocity
From the measurement result shown in Fig. 4(c), the relation
between the rotation speed and wind velocity is estimated aseq. (4),

0 (U  U 0 ) (U , R)  kU  C (U o  U  U limit _  )limit (U limit_  U )5000
Resistance R[Rmax = (-kCU + CCkU
- w )n2kCw (kkU + Ck ) kcw
1(7)

Therefore, eq. (7) suggests a possibility of control criteria for
maximum power point tracking by measuring the resistanceand the rotation speed, as shown in Fig. 8.

(4)k = 2.47.16
200

k

C

500

100

Fig.8 control system
0
0

5000

10000

0

0

5000

4.Conclusion

10000

Resistance [Resistance [・Fig.6 k C・By applying least-squares method,
is obtained as aconstant value, as shown Fig.6. As the same manner, byapplying least-squares method,
can be estimated as afunction of resistance asC  kC R n・(5)

5.References
[1]

where
is estimated from experimental result.
Finally, the rotation speed, wind velocity and resistance are obtained as
the following relation.

0 (U  U 0 ) (U , R)  kU  kC R n (U o  U  U limit ) (U limit limit  U )A specific of drag type wind turbine is derived from 8
experimental values.
The windmill output is obtained by wind velocity andresistance.
we suggest possibility that maximum power point
tracking form resistance and rotate speed that is ameasured value.

[2]

[3]

(6)

12

Y.Honda,Master
Thesis,Tokyo
InstitutionofTechnology,“Control Method of Boost Switching
Regulator for Generators with Voltage Variation 2014”
H. Tokuyama and I Ushiyama “Experimental
Determination of Optimum Design Configuration forMicro Wind Turbines at Low Wind Speeds” T. Japan
Wind Energy Association, Vol24, No.4 (2000)
Izumi
Ushiyama,“Fushakogakunyumon”,
Morikitashuppan

Numerical analysis of the 1917 Storm Surge in Tokyo Bay
based on a reproduced topography
Student Number: 10_07323

Name: Masahiro KAMBARA

fill the blank areas, which are inevitably present between past and present coastlines, with smoothed interoperated elevations.
The current topography is shown in Fig. 1, and the
topography before the interpolation and final reproduced topography are shown in Figs. 2 and 3, respectively.

1. Introduction
Storm surge caused by typhoons have occurred inmany parts of the world since ancient times. In recent
years, Hurricane Sandy that hit New York in 2012 andTyphoon Yolanda that hit Philippines in 2013 havebeen known as remarkable typhoon, which caused devastating storm surge.
October 1, 1917, storm surge occurred in Tokyo Bay
due to the passage of a powerful typhoon crossing Tokyo in the early morning of that day. Casualties and destroyed houses by this storm surge reached up to 1,301
people and 43,083, respectively, and it became theworst disaster caused by storm surge in Tokyo Bay atleast in the last few centuries.
In the first place, Tokyo Bay is vulnerable to storm
surge. Sea water intruding into the bay brought by typhoon tends to be amplified because of the shape of thebay and relatively shallow water depth. Sea level increase due to typhoon wind is known to be proportional
to the length of the bay and inverse proportional to thedepth of the sea [1]. The frequency of major typhoon
which struck Tokyo area is not necessarily often, occurring once every 5 years or so. However, if a strong typhoon approaches the bay with a critical travelling direction, significant storm surge could happen due tothese topographical features. To investigate storm surge
risk in the Tokyo Bay Area, the present research conducts the following four things, focusing on the eventof the 1917 typhoon:
1.
2.
3.
4.

Supervisor: Hiroshi TAKAGI

Figure 1: Current topography of Tokyo Bay

Figure 2: Before the interpolation

To reproduce the coastline of Tokyo Bay a centuryago,
To simulate the typhoon and storm surge, encompassing Tokyo Bay,
To compare inundation between the simulationand the past documents which describes the situation in the Tokyo Bay, andTo assess the vulnerability of the current Tokyo
Bay to future storm surges.

Figure 3: Reproduced topography

2. Reproduction of old coastline

3. Numerical analysis

Because of land changes in the past century due todredging and landfill, there are many differences in the
topography between the current and the past Tokyo Bay.
To the authors' best knowledge, there have been no digital elevation map of the old Tokyo Bay that describes
detailed topography and bathymetry in a century ago.
Therefore, first we reproduced the past topography using both old paper-based map and recent digital elevation map, incorporating coastline and river in the past.
A method called Kriging interpolation was applied to

The model for storm surge simulation used is composed of a typhoon model and the fluid model using
Delft-3D Flow [2]. We conducted a series of numerical
trials to find meteorological conditions that best describes the storm surge due to the 1917 Typhoon bychanging the central pressure and the typhoon path.
These trials were required because of the limitation ofavailable meteorological information at that time.

13

present Tokyo relies on the hard structures. The inundation in 1917 is shown in Fig.6. And the result of current Tokyo Bay is shown in Fig.7.Inundation occurs ina wide range of the floodplain of Arakawa and Sumida
Rivers if there are no dykes. This is due to land subsidence by several meters in the past several decades.
However, the dykes were designed about 4m or evenhigher (T.P.) in Tokyo Bay [5]. In contrast, the highest
tidal level of this analysis is about 3m. Therefore, inundation would not occur even if a typhoon with the same
intensity with the 1917 Typhoon comes to the present
Tokyo Bay as long as such hard structures sustain theperformance during the passage of typhoons.

3.1 Analysis by changing the central pressure
Unlike the recent meteorological data which contains a number of information on a typhoon, available
data in those days are limited. The typhoon central pressure, which is one of the fundamental parameters tosimulate typhoon, is not available. Therefore, the central pressure has been changed until we found that thesimulation could evaluate the storm surge described in
the literatures [3] [4] at reasonable accuracy, while thetyphoon path is set to be constant. The simulation results and water elevation measured at Kamihirai areshown in Fig.4. We found that the simulation provides
the best estimate when the pressure is 830hPa, thoughsuch low pressure appears to be unrealistic.

4m

Water
elevation(T.P.)(m)

3
2.5
2
1.5

0m
Figure 6: Inundation due to the 1917 Typhoon, assuming the old Tokyo Bay

930hPa830hPamesured value

1
0.5
0
2:00

3:00

4:00

5:00

6:00

4m

7:00

Time(Octorber 1, 1917)

Figure 4: Result (change central pressure)0m

3.2 Analysis by changing the path

Figure 7: Inundation due to the 1917 Typhoon,
assuming Tokyo Bay without the dykes

Water elevation(T.P.)(m)Second, we changed the path of typhoon, while thecentral pressure is kept to be constant as 930hPa. The
result at Kamihirai is shown in Fig. 5. It shows that Path
3 is the closest value to the actual measured water level.
However, the profiles do not necessarily resemble eachother.

4. Conclusions
In this paper, we created a digital elevation in a century ago based on old maps and simulated the storm
surge in Tokyo Bay due to the 1917 Typhoon. It was
confirmed that people may not suffer from severe storm
surge if the same size of typhoon hit the present day ofTokyo Bay. However, it is feared that severe storm
surge would occur if the dykes protecting the waterfront lose their functionalities due to ground subsidence
in the past several decades.

3
2.5
2
1.5
Path1
Path2
Path3
mesured value

1
0.5
0
2:00

3:00

4:00

5:00

6:00

References[1] 合田良實，佐藤昭二:新訂版 海岸・港湾， 彰
国社，p.p.148，1981.
[2] Takagi H. et al.: Track Analysis, Simulation and
Field Survey of the 2013 Typhoon Haiyan Storm Surge,
J. Flood Risk Management, 2014.
[3] 土木学会: 東京湾ノ津浪，土木学会誌，第四巻，第二号，1918.
[4] 土木学会: 災後ニ於ケル東京市河海応急施設，
土木学会誌，第四巻，第二号，1918.
[5]東京港湾局: 東京湾海岸保全施設整備計画，
2012.

7:00

Time(Octorber 1, 1917)

Figure 5: Result (change the path)

3.3 Analysis for the current Tokyo Bay
Finally, we performed storm surge simulation, assuming the case that the 1917 Typhoon hit the present dayof Tokyo Bay. Although the present coastline has beenprotected by massive dykes, the present research neglected these structures in order to realize how much

14

Estimation of CO2 Emissions in Household Expenditures for Urban and Rural Area of China
Student Number: 11_07680

1.

Name: Guan Jianxin

2.

Introduction

Supervisor: Shinya HANAOKA

Estimation of Present Emissions

2.1.
Methodology
2.1.1.
Direct Emissions
In this study, the methodology for calculating direct emissions
is based on the methodology used in IPCC AR5. Thismethodology calculated all the sector`s direct CO2 emissions
from different energy sources like gas, gasoline and so on.
Detailed equation is Eq.1.
Dn = ∑k qnk ∙jk ∙ck ∙o∙44/12……………..Eq.1
Dn: direct emissions of province n (t-CO2/year)
k: 8 kinds of fuel (coal, coke, crude oil, gasoline, kerosene, diesel,
fuel oil, nature gas.)
qnk: consumption of province n fuel k (t/year)
jk: lower heating value of fuel k (TJ/Gg)
ck: carbon emissions factor of fuel k (kg-C/TJ)
2.1.2.
Indirect Emissions
The calculation of indirect emissions are used Input-Output
table (I-O table) (Table1). This table shows the demand of one
sector for other sectors. Firstly indirect emissions intensity ek iscalculated by using Eq.2-Eq.4. Eq.2 shows the input coefficientaij, and Eq.3 means matrix of I-O table. In this study, target ishousehold expenditure. In order to calculate indirect emission
intensity, only intermediate use part is used. In competition typeof I-O table, which is used in China, import and export is already
added and subtracted in intermediate use part and only shown infinal demand row. That means the result is not affected by importand export in considering household indirect emission.

The IPCC concluded that the rapid rise of GHG emissions
concentration is the result of human activities in its 5th
assessment report (AR5) [1]. China started the reform andopening-up policy since 1978. In these 30 years, Chinese
government changed its planned economy to market economy,
which resulted in rapid increase of economy. On the other hand,with the increase in economy rapidly increasing, CO2 emissionsof China also increased accordingly. On the basis of Emissions
Database for Global Atmospheric (EDGAR)’s statistic, in 2005
China`s CO2 emissions was 5,800,000 thousand ton, which
accounted for 20% of global emission, and surpassed UnitedStates, which was the country with CO2 emissions [2]. Therefore,
increasing CO2 emissions is a serious problem of China. One of
the reasons why these years CO2 emissions of China increased
rapidly is that high speed of urbanization and rapidly increased
household expenditure (demand). After the 22nd APEC
Economic Leaders Meeting, Chinese government announced
that China would strive to bring its spiralingCO2 emissions to apeak by 2030.
CO2 emission can be categorized 2 types, direct emissions andindirect emissions. In this study, direct emissions mean theemissions from different CO2 emitting regions which are derived
from industry sector, transportation sector, commercial sector,
and residential sector (Figure 1). And indirect emissions aredefined as the emissions from the regions where thecommodities are consumed according to expenditure of moneyon the items for daily household life. And indirect emission
divided into 2 parts. One is called indirect using, which means
emissions cause by household`s daily life commodity
expenditure (final demand, downstream of supply chian), theother is direct burning, which means that the emissions whenhousehold sector burns home energy (Figure 2). Considerationof the indirect CO2 emissions is important because it is necessary
to change viewpoint of emission responsibilities regarding
household expenditure in China.
The objective of this study is 1) to estimate direct and indirect
CO2 emissions from urban and rural areas of different provincesin 2002 and 2007 in China, and 2) to estimate future indirect CO2
emissions from urban and rural areas based on change in
population, urbanization and household expenditure.

Table 1 Input-Output Table
OutputIntermediate Use

Final

Total

Sector 1 Sector 2 … Sector j … Sector n total Demand Output
Sector 1 x 11
x 12 … x 1j … x 1n
f1
x1
Sector 2 x 21
x 22 … x 2j … x 2n
f2
x2
…
…
…
…
…
…
…
…
…
Intermediate Sector i x i1
… x inx i2 …
x ij
fi
xi
Input
…
…
…
…
…
…
…
…
…
Sector n x n1
x n2 … x nj … x nn
fn
xn
total
Added Value
…
…
v1
v2
vj
vn
Total Input
…
…
x1
x2
xj
xn
Input

aij = xij ⁄xj ……………….……......Eq.2
x=(I-A)-1 f……….……………….Eq.3
ek =dt (I-A)-1 ……………………...Eq.4
After calculating the indirect emissions intensity ek, it isnecessary to match the sector k in household expenditure withsector ij in I-O table. It is able to calculate indirect emissions byusing Eq.5.
IDn,u =Pn,u (∑k ick ×Ek,u +dbu )u=0,1………...Eq.5

Figure 1 Direct Emission

IDn: Indirect emissions of province n (t-CO2)
Pn: population of province n (person)
ick: indirect emissions intensity of item k (t-CO2/CNY)
Ek: household expenditure of item k per capita (CNY/Capita)
dbt :direct burning per capita (t-CO2/capita)
u: urban area is 1, rural area is 0
2.2.
Results
The direct and indirect emissions of China was calculated forthe year 2002 and 2007. The emission results for 2002 and 2007
are shown in Figure 3, direct emissions increased to double in2007, especially in Shandong, Hebei, Liaoning province, each ofprovince has a lot of industries or power plant.

Figure 2 Indirect Emission

15

On the other hand, indirect emissions was also increased(Figure 4). Although urban area is developed more than ruralarea, the increase speed of urban area is faster than rural area.
Urban area`s emissions in 2007 is about double of 2002.
Whereas the emission from rural area is increased just a bit.
Guangdong province is in the top of the list followed by Jiangsu
and Shandong province. And because of big gap of populationand household expenditure (especially population), the gap
between top and last is about 500 times. The emission from
Guangdong is 530 megaton, whereas Xizang has only 5 megaton,
which means the gap between provinces in indirect emissions is
huge.
10000

Unit：Gton

2007
Beijing
Guizhou
Hubei
Liaoning
ShanxiXizangFujianHainan
Hunan
Neimenggu
Shaanxi
Yunnan

Gansu
Hebei
Jilin
Ningxia
Shanghai
Zhejiang

Guangdong
Henan
Jiangsu
Qinghai
SichuanChongqing

Figure 3 Direct Emissions in the year 2002 and 2007

Urban

1000

2000

3000

7E+09

4000

6E+09

Unit：Gton
Unit:t-CO2

2002

Urban

2007

0

Rural

Rural
Beijing
Guizhou
Hubei
Liaoning
ShanxiFujianHainan
Hunan
Neimenggu
Shaanxi

Gansu
Hebei
Jilin
Ningxia
Shanghai

4E+09
2E+09
1E+09

Guangdong
Henan
Jiangsu
Qinghai
Sichuan0

Figure 4 Indirect Emissions in the year 2002 and 2007

3.

2030 Scenario2
2030 Scenario4
2050 Scenario2
2050 Scenario4

3E+09

Beijing

Anhui
Guangxi
Heilongjiang
Jiangxi
Shandong

5E+092030 Scenario1
2030 Scenario3
2050 Scenario1
2050 Scenario3

Guangdong

Anhui
Guangxi
Heilongjiang
Jiangxi
Shandong
TianjinZhejiang

2002

Jiangsu

8000

Shanghai

6000

Heilongjiang

4000

Hebei

2000

Tianjin0

XT+t: forecast data after t years
N: length of moving average
2. Scenario 2: consider about development policy of Chinese
government at provinces in Northeast and West regions, and“donut” phenomenon of Beijing and Shanghai. In other words,
urbanization rate will increase in provinces which affected bypolicy, and population will decrease in Beijing and Shanghai.
3. Scenario 3: household expenditure will change by logarithm
distribution (Eq.9).
y=12123ln(x)-106597………………Eq.9
4. Scenario 4: mixed scenario form scenario 2 and scenario 3.
3.2.
Results
Figure 5 shows that emission result, indirect emission fromalmost all provinces will increase in scenario 1, 2 and 3. Indirectemission from Beijing and Shanghai will decrease because of
donut phenomenon and household expenditure reduction in
scenario 4. Provinces such as Hebei, Zhejiang will have a big
difference in each scenario. The reason is that when donut
phenomenon occur in Beijing or Shanghai, people will relocate
to provinces around those cities, which leads to the increase ofindirect emission. Moreover, emissions by scenario 2 is higher
than scenario 3 which means that change of household
expenditure is more effective to indirect emission reduction thanurbanization.

(partial provinces)

Figure 5 Urban Indirect Emissions in 2030 and 2050

Future Forecast
4.

3.1.
Scenario Creation
1. Scenario 1: assumes that population and household
consumption will maintain the current growth (BAU) as follows:i. Population Forecast
In this study the country level forecast uses data from World
Population Prospects of United Nations (UN) [3]. To calculate
provinces level population, natural growth model is used [4], thismodel will forecast population by using a changing growth rate
and fix the growth rate by using UN`s data per 5 years and thenforecasts for next 5 years (Eq.6).
5

Pn,t+5 =P' n,t ×(1+Rn,t ) ………………Eq.6
Pn,t: calculated population of province n on the year tP’n,,t: fixed population of province n on the year t
Rn,t: growth rate in 5 years of province nii. Urbanization Forecast
Urbanization data is used for a logistic model of IIASA(International Institute for Applied System Analysis) [5]. Assume
that the urbanization will follow s-curve (Eq.7).
Ut = 1⁄1+C×e-r×(t-1979) ………………Eq.7
The parameter C and r are based on existing data by IIASA.
iii. Household Expenditure Forecast
Change of household expenditure will follow the Engel’s
coefficient, when income increases, the percentage of foodexpenditure will decrease. By checking data from other countries,
Engel’s coefficient is effective. So this study assume thathousehold expenditure follows Engel’s coefficient. Double
Moving Average Model (DMAM) [6] is used in this study (Eq.8).
xT+t =(2MT -MT ' )+ [1

N-1

Conclusion

The results of emissions in the year 2002 and 2007 show that
CO2 emissions increased rapidly due to the rapidly developmentof china in 2002 and 2007. And this study calculated indirectemission by 4 scenarios. According to the result, donut
phenomenon will affect huge city like Beijing and Shanghai inthe future. Furthermore the change in household expenditure ismore influence on indirect emission reduction than urbanization.
However, the indirect emission increase speed of whole China
will get slower after 2030, therefore, CO2 emissions will notdecrease because of the increasing household expenditure andurbanization whether by scenario 1, 2, 3 or 4. In other words, it
is difficult to realize the announcement of China.

References[1]Wang, X.: Visualization of Direct and Indirect CO2 Emissionof each Municipality in Japan,Bachelor theses of IDE, 2014.
[2] EDGAR: CO2 time series 1990-2013 per region/country,
http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts19902013.
[3]UN: World Population Prospect Total Population-Both Sexes,
http://esa.un.org/wpp/Excel-Data/population.htm.
[4]Deng, Y. et al.: Spatial pattern and its evolution of Chinese
provincial population and empirical study, Acta Geographica
Sinica, Vol.69, No.10, pp.1473-1486, 2014.
[5]Liu, S. et al.: Scenario Analysis on Urbanization and RuralUrban Migration in China, International Institute for Applied
Systems Analysis (IIASA), Austria, 2003.
[6]Liu, W.: The forecast about the trend of consumption
demands in unborn 15 year, Productivity Research, No.12, 2006.

(MT -MT ' )] t……….Eq.8

MT: single moving averageMT’: double moving average

16Regularization of Support Vector Machine with Variable Kernel
FunctionsStudent Number:11-04144

1

Name:Dan WANG

Introduction

Then eq.(1) can be written as

Kernel method is a data analysis methodology which
has been rapidly developed in the field of computer science in recent years. It was named after kernel functions, which works in a derived feature space where
the real coordinates of patterns never have to be calculated. Kernel methods have demonstrated very highperformance with the best known example of Support Vector Machine (SVM). The central idea of kernel method is mapping an original input vector intoa high dimensional feature space to change the nonlinear classification functions to linear classification
functions. However, to realize such excellent performance, the proper selection of feature mappings is very
important. For the purpose, the asymmetric kernel
method (AKM) was researched. Although all sample
patterns have the same parameter in the standard kernel method, in AKM, each sample pattern has its individual parameter.
In this paper, I propose variable kernel functions(VKF) to improve recognition performance, which canchange the kernel parameter according to the position.
I explain the framework of VKF, VKF-SVM and show
five weighted regularization terms in VKF-SVM. I also
show experimental results by using a toy problem andUCI datasets.

2

Framework for VKF

2.1

Mercer’s Theorem

Supervisor:Yukihiko YAMASHITA

d(x) = ⟨w, Φ(x)⟩ + θ,

w=

N
∑

αn Φ(xn ).

2.2

Asymmetric Kernel Method

AKM shows better experimental results by combining
several feature mappings. In AKM, I prepare several
feature mappings Φj (x)(j = 1, 2, ..., J) of which innerproduct can be calculated analytically and their kernel
functions are denoted byki,j = ⟨Φi (x), Φj (z)⟩.

2.3

N
∑

In the proposing VKF, I prepare nonlinear feature
mappings Φn for each learning pattern xn and only
one feature mapping Φ0 for an unknown pattern x.
The model of w in eq.(3) can be denoted byw=

N∑

αn Φn (xn ).

(5)

n=1

Then, the discriminant function is given byd(x) = ⟨w, Φ0 (x)⟩ + θ=N∑

αn kn,0 (xn , x) + θ.

(6)

n=1

In VKF, we can change the kernel parameter according to the disatances to the adjacent sampling points.

yn d(xn ) − 1 + ξn ≥ 0,
αn k(xn , x) + θ.

(4)

Framework for VKF

Let {(xn , yn )}N
n=1 be a set of samples, where each xn
VKF with SVM
is a sample pattern and yn is its label (±1). The in- 3
ner product between x and z is denoted by ⟨x, z⟩. In
SVM can be summarized as minimizing
this paper, I consider a linear discriminant model for aNbinary problem.
∑
2
∥w∥
+
C
ξn ,
In case of the standard kernel method, a Mercer kern=1
nel function k(x, z) is fixed and the discriminant function is given by
subject to (for any n =1,2,...N)d(x) =(3)n=1

ξn ≥ 0,

(7)

(8)

(1)

where C is a constant.
I prepare a new classifier named VKF-SVM whereVFK
is applied to SVM. For VKF, the regularization
Here, αn are coeﬃcients and θ is a threshold. Fromtermin(7) is denoted by
Mercer’s theorem, we can consider there exists a feature mapping Φ(x) that can satisfy
N ∑N∑∥w∥2 =
km,n (xm , xn )αm αn .
(9)k(x, z) = ⟨Φ(x), Φ(z)⟩.
(2)
m=1 n=1
n=1

17
1

0.6

Learned boundary
Original boundary
Training data

0.5

Table 1: Error rates for UCI datasets

0.4
0.3
0.2
0.1
0
0

0.2

0.4

0.6

0.8

1

(a) SVM
0.6

Learned boundary
Original boundary
Training data

0.5
0.4
0.3
0.2
0.1
0
0

0.2

0.4

0.6

0.8

1

(b) VKF-SVM with SSKFR

DN
Ba
Br
Di
Fl
Ge
He
Im
Ri
Sp
Th
Ti
Tw
Wa

SVM
10.52±0.50
25.38±4.52
23.15±1.70
32.31±1.84
23.46±2.08
15.39±3.28
2.97±0.57
1.50±0.10
11.20±0.74
4.13±2.20
22.29±1.05
2.40 ± 0.14
9.96±0.48

PrR
10.52±0.4925.23±4.70
23.17±1.8632.31±1.84
23.61±2.25
15.39±3.24
3.56±0.52
1.53±0.10
11.61±1.08
3.99±2.24
22.27±1.07
2.40±0.14
9.79±0.47

TSR
10.53±0.47
27.17±4.66
23.11±1.8032.31±1.83
23.73±2.14
16.01±3.35
3.73±0.72
1.56±0.13
11.85±0.97
4.12±2.19
22.30±1.04
2.14±0.13
9.95±0.42

DN
Ba
Br
Di
Fl
Ge
He
Im
Ri
Sp
Th
Ti
Tw
Wa

PKR
10.59±0.51
25.42±4.69
23.24±1.72
32.33±1.81
23.39±2.28
15.27±3.19
3.08±0.58
1.58±0.13
11.51±0.72
4.20±2.37
22.31±1.05
2.41±0.14
9.99±0.47

SKFR
10.65±0.49
25.19±4.61
23.33±1.7733.14±1.76
24.29±2.26
15.33±3.33
6.75±1.31
2.52±0.26
10.89±0.73
4.56±2.38
22.45±1.18
2.42±0.12
9.97±0.49

SSKFR
10.51±0.47
25.32±4.35
23.23±1.76
32.32±1.82
23.42±2.30
15.35±3.18
3.20±0.68
1.60±0.13
11.71±0.69
4.16±2.35
22.31±1.05
2.39±0.14
9.91±0.44

Figure 1: Results of toy problem
Then, I apply VKF-SVM to a toy problem and 13
types
of UCI datasets. In the toy problem, the error
I considered five weighted regularization terms to
rates
for
SVM, and VKF-SVM with PrR, TSR, PKR,
balance the regularization among αn .
SKFR,
and
SSKFR are 11.59%, and 11.16%, 11.49%,(1) Projected regularization(PrR): Let S0 be the
9.93%,
11.60%,
and 9.90%, respectively. From the resubspace spanned by Φ0 (xn ). Let P0 be the orthogsults
that
VKF-SVM
with SSKFR is the best with paonal projection operator onto S0 . The regularization2
rameters:
σ=0.0178,and C = 1.78. Fig. 1 (a) shows
term is defined by ∥P0 w∥ .
results
with
SVM,
and
Fig.
1 (b) shows results of VKF(2) Training sample regularization(TSR): Define the
SVM
with
SSKFR
with
the
best parameter. We canN∑regularization term as| ⟨w, Φ0 (xn )⟩ |2 .
see clearly that Fig. 1 (b) is more smooth.
n=0
I also use 13 types of UCI datasets for experiments.
(3) Peak Regularization(PKR): The regularization
Table
1 shows error rates for UCI datasets. From Table
term is given by
1,
we
can
see VFK-SVM with TSR in Tw outperformed
NN∑ ∑km,0 (xm , xm )kn,0 (xn , xn )km,n (xm , xn )αm αn . SVM clearly.
m=1 n=1(4) Sum of kernel function regularization(SKFR):
αn in the regularization term is replaced byN∑
kn,0 (xn , xm )αn .

5

In this paper, I proposed to apply VKF to SVM. The
method was verified by a toy problem. However, error
rates of UCI datasets were not improved very much.
In this experiment, the selection of 12 types of UCI
datasets may not be suitable. For future work, I haveto improve the method to decide σi and search datasetsthat are suitable to the proposed method.

m=1(5) Square root of sum of kernel function regularization(SSKFR):
αn in the regularization term is replaced√N∑bykn,0 (xn , xm )αn .
m=14

Conclusion

Experiment Result

In the experiment, I decide σj by the average of the References
distances from xj to its nearby 10 points. I use thesmallest σj as σ0 . I also use the Gaussian kernel func- [1] Naoya Inoue and Yukihiko Yamashita, ”Simultaneous learning of localized multiple kernels and classition (GKF) for a feature mapping. Then, the featurefier with weighted regularization” Structural, Synmapping with σj from a point to a function is definedtactic and Statistical Pattern Recognition, pp.354by
362, Springer Berlin Heidelberg, 2012.
) M2([2] G.Wahba, ”Support vector machines, reproducing2exp(−∥z − x∥2 /σj2 ). (10)(Φj (x))(z) =kernel hilbert spaces and the randomized gacy”
πσj2
Technical Report 984rr, Department of Statistics,
University of Wisconsin, 1998.
From eq.(4), the kernel function for VKF is given by(
ki,j (x, z) =2σi σjσi2 + σj2)M(

∥x − z∥2
exp − 2σi + σj2).

[3] B.W. Silverman ”Spline smoothing: the equivalentvariable kernel method” The Annals of Statistics,(11)
Vol.12, No. 3, 898-916, 1984.
182

インドネシア産天然ゼオライトを用いたマンガン除去
学籍番号：12B16311 氏名：ハリス レザ セプティアーノ
指導教官：日野出洋文及びウィナルト クルニアワン

1.

ほぼ同様で、1.0 N の塩化ナトリウム水溶液の代わ

緒言りに 1.0 N の塩酸を使用し、この処理で得られたゼ天然ゼオライトはその吸着及びイオン交換能にオライトを以下 HCl-NZL と呼ぶ。

より、水質浄化、特に重金属の除去剤として期待されている [1]。火山灰等から自然形成されたため不塩酸及び塩化ナトリウムの複合改質処理

純物が含まれており、人工的に合成されたゼオライ「NaCl+HCl 処理」
は「HCl 処理」を行った後、「NaCl

トより除去能は低いが、合成ゼオライトよりも安価処理」を行う処理であり、得られたゼオライトは以な水質浄化剤として開発が望まれる。また、既存の

下 Na-HCl-NZL と呼ぶ。
以上のすべてのゼオライトを X 線回折（XRD）、研究により、様々な改質・修飾処理による重金属除窒素吸着による BET 比表面積の測定、Chapman 法

去能の向上が検討されている。

による陽イオン交換容量実験 [2]を用いてキャラク

重金属の一つにマンガンが挙げられる。鉱物からの溶出や、工場からの排水、採掘活動がマンガンのタリゼーションを行った。

主な排出源である。マンガンを含む水は異臭味があ2.2. マンガン除去能評価

り、水管の閉塞、衣服への汚れなどの問題を引き起マンガンの水溶液は酢酸マンガンを用いて所定こす。さらに、マンガンを高濃度に含む水は、皮膚の濃度に調整し、地下水とほぼ同様な状況の pH 6.7の病気をはじめとして人体に影響をもたらす。

±0.2 とした。除去の実験はバッチ式で行った。

従って、本研究ではマンガンの除去能に着目し、マンガンの水溶液 20 mL に 0.75 g のゼオライト

天然ゼオライトのキャラクタリゼーション及び改質を添加し、12 時間以 30℃で上振動機を用いて振と処理を行うのが目的とする。

うした。液体をフィルターで収集し、ICP-AES でマ

2.

実験

ンガンの濃度を測定した。ゼオライトを用いたマンガンの除去量と平衡濃度の関係を Sips 式に当ては

2.1. キャラクタリゼーション・改質処理
本研究で用いられる天然ゼオライトはインドネめた。

シアの Lampung 産天然ゼオライトである。実験で

1

qe 

利用する前、120℃で乾燥させ、粒径 150~425μm

qmax (kS Ce ) n
1(1)に調整した。得られたゼオライトは以下 NZL と呼1  (kS Ce ) n

ぶ。次に、NZL を「NaCl 処理」「HCl 処理」及びqmax 及び qe は最大除去量及び除去量(mg/g)であり、Ce は平衡濃度(mg/L)を示す。また、ks 及び n は「NaCl+HCl 処理」を用いて改質を行った。

Sips の定数及び、均一性に関する定数である。

塩化ナトリウムの改質処理「NaCl 処理」での手順は次となる。NZL と/1.0 N 塩化ナトリウム水溶3.

液を固液比 100g/L になるように混合し、12 時間以

3.1. X 線回折

結果・考察

上振とうした。次に固液分離を行い、イオン交換水

X 線回折パターンにより、NZL は主にクリノプ

で 残 留 し た 塩 化ナ ト リ ウム を 洗 浄 し た 。濾 滓 を

チロライトに構造されており、不純物として曹長石

120℃で乾燥させ、粉砕し、篩で粒径を 150~425μ及び葉ろう石が含まれている。
「NaCl 処理」では結m に調整した。得られたゼオライトを以下 Na-NZL晶構造の変化があまり見られないが、「HCl 処理」と呼ぶ。

を行ったゼオライトのクリノプチロライトのピーク

塩酸の改質処理「HCl 処理」は「NaCl 処理」と強度の低下が見られた。これは、「HCl 処理」によ

19

3.4. マンガン除去測定

る脱アルミニウム化による構造変化からだと考えられる。

図 3 マンガン除去の Sips 等温線

Sips 式で分析されたマンガン除去実験の結果を

図 1 NZL 及びその改質の XRD パターン

図 3 に示した。
最も高い除去量を示すのが 9.40 mg/g(▲:クリノプチロライト、◆: 葉ろう石、×: 曹長石)

最大除去量を持つ Na-NZL であり、Na-HCl-NZL、

3.2. 陽イオン交換容量（CEC）
Chapman 法で測定した陽イオン交換容量の結

HCl-NZL、NZL の順に除去量は低下し、これは CEC

果を図 2 に示した。Na-NZL が最も高い CEC(234.9の順位と一致した。しかし、CEC の値とはほど遠く、

meq/100g)を有することがわかる。Na-HCl-NZL で

Na-NZL のマンガン除去から換算する 値は 34.2

は Na-NZL より低い結果を示したが、これは脱アル

meq/100g である。これは、イオン半径及びゼオラ

ミニウム化が起こり、交換容量が下がったからだと

イトの構造によるものであり、Na+が Mn2+より交換考えられる。

されやすいことがわかった。また、マンガンの除去
量は比表面積との相関が見られなかったため、除去の反応は吸着ではなく、イオン交換が支配的であると考えられる。

4.

結言
本研究で行った改質処理によってマンガン除去能を、最大 6.62 mg/g から 9.40 mg/g まで上昇させることができた。また、除去の過程ではイオン交換現象が支配的であることもわかった。

5. 参考文献1.

図 2 CEC 及び比表面積

USE

OF

(CLINOPTILOLITE)

3.3. 比表面積

NATURALIN

ZEOLITEAGRICULTURE.

窒素吸着の結果により BET 法で計算した比表面

Polat, Ersin, et al., et al. 12, Poland : Journal of

積を図 2 に示した。最も高い比表面積（74.51 m2/g）

Fruit and Ornamental Plant Research, 2004,を持つのが Na-HCl-NZL である。これは表面が塩酸

pp. 183-189.

を用いて洗浄された後、塩酸だけで交換できなかっ2. Cation-Exchange Capacity. Chapman, H.D.

た陽イオン（K+、Mg2+、Ca2+など）が、比較的小さ

USA : Agronomy Monograph, Methods of Soil

い Na+で交換されたからだと考えられる。よって、

Analysis, 1965, pp. 902-904.

大きな陽イオンによって塞がれた細孔が開き、比表面積が上昇したと考えられる。

20

Influence of initial chloride content and water cement ratio on corrosion ofsteel bar in mortar exposed to marine environment for 32 years
ID: 11_23733 	
  Name: Ayako MIZUMASupervisor: Prof. Nobuaki OTSUKI, Assistant Prof. Takahiro NISHIDA
1. Introduction
Chloride attack is one of the serious deterioration ofreinforced concrete under marine environment, and it isconsidered that the usage of low water cement
ratio(W/C) and initial chloride content is important for
preventing chloride attack. However the influence ofinitial chloride content on deterioration of chloride
attack after long-term exposure under marine
environment is not clear.
In this study, the influence of initial chloride content
and W/C on corrosion after 32 years exposure was
evaluated. Especially issues below were investigated.
1)
To clarify the influence of initial chloride content
and W/C on corrosion of steel bar within 1 year
exposure to marine environment.
2)
To clarify the influence of initial chloride content
and W/C on corrosion of steel bar after 32 years
exposure to marine environment using mortar
specimens.
3)
To evaluate the relations between influential
factors, such as chloride content and oxygen
permeability, and corrosion of steel bar after 32
years exposure to marine environment.
2. Experimental Work
1) Mortar specimen
Cement type is ordinary Portland cement, (density:
3.17g/cm3, specific surface area: 3180cm2/g) and sandcement ratio(s/c) is 2.0, there are 6levels in W/C;
40,45,50,55,60,65% and 6types in mixing water; tap
water, seawater, seawater diluted 0.5 and 0.25 fold, anddoubled and fourfold increase of chloride ion against
seawater. Specimens mark W in table1 shows
specimens mixing with tap water, 1S shows specimens
mixing seawater and 0.25S, 0.5S, 2S, 4S is shows theirconcentration and dilution rate.
Specimens were made by mortar and their size is
4x4x16. Steel bar in mortar was SR235 round steelbar,(Φ:9mm, length:100mm) and cover depth is 15mm.
2) Exposure environment
The specimens had been exposed for 32 years to tidal
environment of seawater in Kurihama bay at Yokosuka
city.
3) Measurement

In this study, anodic/cathodic polarization curves,
corrosion weight loss of steel bar and chloride content
were measured on specimens.
Anodic/cathodic polarization curves were measured
using potentiodynamic method. Electric current wasapplied with 1.0mV/sec of the sweep speed. Chloride
content in mortar and corrosion weight loss were
measured using JCI(SC-4) and JCI(SC-1) respectively.

3. Results and discussion
3.1Within 1year exposure
Figure1 shows the result of evaluation from theresults of anodic polarization. These numbers show thegrade of passive film and “0” indicates that it is easy tocorrode. In figure1, specimens become grade0 fasterwith increasing W/C. In the same W/C, specimens
become grade0 faster with increasing initial chloride
content. According to those results, it was confirmedthat W/C and initial chloride content hasten thedestruct of passive film, but with low W/C, influenceof initial chloride content becomes small.
Table1, criterionof assessment
bybyanodepolarization
curvesTable3. Criterionof assessment
anode polarization
curvesGrade
Criterion
2
0*Corrosion current density exceed 100μA/cm even once. (complete destruction)
1*Corrosion current density is in 10μA/cm2 to 100μA/cm2.
2
2*Corrosion current density exceed 10μA/cm even once and it is not in grade1 and 3.
2
2
3*Corrosion current density is in 1μA/cm to 10μA/cm .
2
4*Corrosion current density exceed 1μA/cm even once and it is not in grade3 and 5.
2
5*Corrosion current density never exceed 1μA/cm .
*Corrosion current density in this table is in (En+0.2V to En+0.6V)    
 

curing day                



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Figure 1, passivation grade

3.2 After 32years exposure
Figure2 and figure3 shows the effect of W/C andinitial chloride content on corrosion content. According
to these graph, it was confirmed that corrosion content
is increased with an increase in W/C. On the other21Corrosion weight loss (%)hand, influence of initial chloride content is smaller
than that of W/C.
20
18
16
14
12
10
8
6
4
2
0

3.4 Multiple regression analysis
Multiple regression analysis is one of the methodsof multivariate statistics, showing the objective
variable using explanatory variables. Table2 and table3
show “T-statistic” which shows the influence degreefrom result of multiple regression analysis. Generally,
it is said that where “T-statistic” exceeding 2.0 shows
the major effect. According to table2, influence of W/Con the corrosion is confirmed. From Table3, it was
confirmed that correlation between chloride content on
steel bar and corrosion content, and correlation
between oxygen permeability and corrosion rate.

20
18
16
14
12
10
8
6
4
2
0
0

1

2

3

4

40

45

50

55

40

45

50

60

Figure2, effect of W/C

65

55

60

65

2S

4S

W/C

initial chloride content (xS)
0S

0.25S

0.5S

1S

Figure3, effect of initial chloride content

Corrosionweight loss (%)	 3.3 Relations between influential factors and corrosion
1) Corrosion and chloride content
Figure4 shows the effect of total chloride content inmortar on corrosion after 32years exposure. According
to the graph, correlation was not confirmed between
chloride content in mortar and corrosion. Thus
estimation of corrosion content from chloride content
in mortar may be difficult.
On the other hand, figure5 shows the correlation
between corrosion and chloride content on steel bar.
Thus it is expected that chloride content on steel bar
effects on the corrosion for 32years.

Table2, T-statistic of materials to corrosion
explanatory
variable

Corrosionweight loss (%)	
 

10
5
0
20
40
60
Cl- around steel bar (µg/cm2)	
 Figure5, effect of chloride content on steel bar2) Corrosion rate and oxygen permeability
Figure6 shows the relation between corrosion rate andoxygen permeability. It was confirmed that oxygen
permeability influence on corrosion rate after 32years
0.2
exposure.
Corrosion rate
(mm/year)	
 1.33
-1.54

content

corrosion
rate

4.51

0.13

0.51

3.13

4. Conclusion
1) The Influence of water cement ratio on corrosion
after 32years exposure is larger than that of initialchloride content under sever chloride attack
environment.
2) Within 1year exposure, it was confirmed that W/Cand initial chloride content hasten beginning of
corrosion. Although, with low W/C, influence ofinitial chloride content on beginning of corrosion
become small.
3) Strong correlation between corrosion weight loss
and chloride content on steel bar was confirmed
after 32years exposure. On the other hand, thecorrosion rate of steel bar in mortar after 32 years
exposure is highly related to oxygen permeability.

15

5. References[1] Nobuaki Otsuki (1985) The study about influenceof chloride ion on corrosion of steel bars in concrete,
Port and Airport Research Institute, Vol. 24, No. 3,
pp.183-284.
[2] Fukute Tsutomu (1990) The study on the durabilityof marine concrete mixing seawater, Port andAirport Research Institute, Vol. 29, No. 3

0.15
0.1
0.05
0
3E-11

2.37
-0.30

oxygen permeability

20

2E-11

W/C

explained corrosion

Figure4, effect of total chloride content

1E-11

corrosionrate

Initial chloride content

objective
variable
explanatory
variable
chloride content
on steel bar

0
20
40
60
80
100
Total chloride content in mortar (kg/m3)	
 0

corrosion
content

Table3, T-statistic of properties to corrosion

12
10
8
6
4
2
0

0

objective
variable

4E-11

O2 permeability (mol/cm2・sec)	
 Figure6, effect of oxygen permeability on corrosion rate22

ドップラーライダーで観測された
水平風速パターンの自動分類
学籍番号：11B27079	
  氏名：李	  安琪	  指導教官：神田	  学，稲垣	  厚至

1. 背景と目的	  大気境界層の特性やメカニズムを理解する
ことは、都市の排熱や工業地域の大気汚染などの問題を解決する上で重要である。過去の研究によって、大気境界層内の水平風速場は大まかにストリーク構造とセル状構造の２種類に分けられることが指摘されている。 1)これまでの研究において、ドップラーライダーを用いて水平風速場を測定し、その可視化画像
から目視によって流れ場を分類することがな
されている。主流風速と大気安定度が水平風
速分布のパターンの出現頻度と密接な関係があることが分かっている 2)。	
  
	  しかし、目視分類法は主観的であり、かつ時間がかかるなどの欠点があり、長期間の観測データを客観的に解析することが難しい。
そこで本研究では、ドップラーライダーで観測された水平風速分布の可視化画像と VAD 法
3)で算出した主流風速に基づいて機械学習で判別分析をし、一年間の画像を 3 つのパターンに自動分類するアルゴリズムを開発した。それによる各パターンの出現頻度の日中変化と季節変化について調べた。	
  (a) Streak/No-streak(b) Fishnet

Figure	
   1	
   中 心 か ら 半 径 143pixel の 円 内 の 関
心 領 域 (上 )と そ の 中 に 存 在 す る 境 界 線 (下 )	  2. 観測、解析手法
観測概要 	  	  観測領域は東京都目黒区東京工業大学西 8号 館 屋 上 （ 地 上 高 42m） を 中 心 と し た 半 径
4km の円内であり、解析対象は、2012 年 3 月
1 日~2013 年 2 月 11 日の間でドップラーライ
ダーの水平スキャンによって実測されたデータである。2012 年 7 月 26 日~9 月 24 日の間、
空間分解能は 100m、その他の期間は 50m である。	
  データ前処理 	  	  Generic	
   Mapping	
   Tools	
   によって対象期間内
全てのデータを視線方向風速分布の二値画像と RGB 画像にし、関心領域(ROI)	
   を X、Y 軸に沿ってそれぞれ-­‐4.3km~4.3km に、画像サイズ
を 285	  x	  285	
  pixel に統一した。	
  	
  中心から半径 143pixel の円内の関心領域(ROI)に存在する視線風速は正(黒)と負(白)の境界線の長さを計算し、この境界線の長さはパターンによって変化すると推測した。	
  

23

	
  Velocity	
  Azimuth	
  Display	
  (VAD)	
  法を用いて、
水平主流風速(Atran)を含む５つの指標を最小二乗近似の回帰線によって推測した。	
  	  教師あり機械学習に用いるため、2012 年
10 月~2013 年 2 月の毎月 1 日~10 日の間のRGB 画像に対し、目視分類を行った。15946
枚の画像のうち、11033 枚が八木ら 4)の目視分類結果と同じことがわかった。	
  	
  さらに、データの品質管理上、一定の観測
領域半径	
   (空間分解能 50m,	
   100m のデータはそれぞれ 2025m,	
   2050m)内で Signal	
   to	
   Noise	
  Ratio(SNR)	
   が 6dB	
   以下の値が 1	
   %以上のデー
タは Error と分類し、解析対象外とした。	
  分類予測器 	  
	  線 形 判 別 と ２ 次 判 別 が あ り 、 MATLAB	  Statistic	
   Toolbox	
   を用いてそれぞれ目視分類を
行 っ た デ ー タ の う ち 'Fishnet',	
   'Mixed' と'Streak/No-­‐streak'に分類されたデータをもとに判別分析を行った。	
  (a)	
  Atran,	
  境 界 線 の 長さを変数とした線形
判 別 分 析 	  (b)	  Atran,境 界 線 の 長さを変数とした 2 次
判 別 分 析 	  (a)	  Spring	
  (Mar	  -­‐	
  May)	
  (c)	
  1/Atran,	
  境 界 線 の長さを変数とした線
形 判 別 分 析 	  (b)	
  Summer	  (Jun	  -­‐	
  Aug)	
  (d)	
  1/Atran,	
  境 界 線 の長さを変数とした 2
次 判 別 分 析 	  (c)	
  Autumn	
  (Sep	  -­‐	
  Nov)	
  Figure 2 各 変 数 の パ タ ー ン 散 布 図 と ２ 種 類の 判 別 分 析 の 境 界 線 (デ ー タ 数 3868)(d)	
  Winter	  (Dec	  -­‐	
  Feb)	
  3. 結果
	  境界線の長さ(Edge	
   Length)と主流風速(Atran)、
または 1/Atran を変数に基づいて、２つの判別タイプでそれぞれ分析を行った結果の誤判別率を以下の表に示す。	
  Table	  1	  The	
  re-­‐substitution	
  errors	
  of	
  2	
  types	  of	
  discriminant	
   analysis	
   and	
   2	
   types	
   of	
   variables	
  as	
  input	
  Re-­‐Discrimin
substitutio
Variables	
  (input)	
  -­‐ant	  type	  n	
  error	
  Atran,	
  Edge	  Length	
  Linear	  0.2055	  Atran,	
  Edge	  Length	  Quadratic	
  0.2306	  1/Atran,	  Edge	  Length	
  Linear	  0.1722	  1/Atran,	  Edge	  Length	
   Quadratic	
  0.1642	
  この結果より、誤判別率と判別境界線の形状
より、1/Atran	
   と Edge	
   Length	
   を変数としたほうがより正確的に自動分類できると考えられる。
そのうち線形判別分類器より２次判別分類器のほうは誤判別率が小さかった。	
  	
  ２次判別分類器をもとに、1 年四季全期間の分類を行った。各水平風速パターンの出現率の 	
   月 ご と の 変 動 を Figure	
   3 、 日 中 変 化 を
Figure	  4 に示す。	
  
100%	
  
80%	
  
60%	
  
40%	
  

Feb	
  

Jan	
  

Dec	
  

Nov	
  

Oct	
  

Sep	
  

Aug	
  

Jul	
  

Jun	
  

May	
  

Apr	
  

20%	
  

Mar	
  

	
  

	
  

	
  

	
  
Figure	  4 各 水 平 風 速 パ タ ー ン の 出 現 率 の 日 中
変 化 (Error を 除 く )	
  

	
  

0%	
  

	
  

	
  

	
  
Figure	  3 各 パ タ ー ン の 出 現 率 の 月 ご と の 変 動 	
  (データ数 93443)	
  24

4. 考察と結論
	  'Fishnet'と'Mixed'の出現率のピークは、デー
タ取得率が高い夏季には午前 10 時前に、デー
タ取得率がやや高い秋季には 11 時~12 時に見られた。一方、データ取得率が低いことや PPI
スキャンスケジュールの不統一性があるため、春季と冬季には'Fishnet'と'Mixed'の出現率のピークがそれほど見られなかった。	
  	  また、2 次判別分類器の 0.1642 の誤判別率
は相対的に低かった。しかし、目視分類は客観 的 な 分 析 で は な い た め 、 'Fishnet' と'Streak/No-­‐streak'の中間である'Mixed'	
   パターンの分類基準は不明瞭である。それに対して、
本研究成果により'Mixed'と'Fishnet'、または'Mixed'と'Streak/	
   No-­‐streak'の境界線を客観的に決めることができた。	
  参考文献
1) 藤吉康志,	
   山下和也,	
   藤原忠誠,	
   2005:	
   3 次元走査型コヒーレントドップラーライダーによる
大気境界層の流れの可視化,	
  天気,	
   52,	
   p.665-­‐666.	
  2) 有場次郎,	
  神田学,	
  2013:ドップラーライダーで観測された流れ場の分類と特性の把握,	
  東京工業大学国際開発工学科学位論文	
  3) Browing,	
   K.	   A.,	
   Wexler	
   R.,	
   1968:	
   The	  Determination	
   of	
   Kinematic	
   Properties	
   of	
   a	
  
Wind	
   Field	   Using	
   Doppler	
   Radar,	
   Journal	
   of	
  Applied	  meteorology	
  and	
  climate	
  7,	
  p.105-­‐108.	
  4) 八木綾子,	
   稲垣厚至,	
   神田学,	
   藤原忠誠,	
   藤吉康志,	
   2014:	
   相似測に基づいた大気境界層における水平乱流場の分類,	
   土木学会論文集 B1(水工学),	
  Vol.70,	
  No.4,	
  I_325-­‐I_330.	
  フルデュプレクスで構成される基地局間無線
バックホールリンクの自己干渉に関する基礎的検討
学籍番号: 11 27139

1

名前: リュウ

コウ

指導教員: 高田

潤一

研究の背景
アンテナ

近年，モバイルデータトトラヒックが急速に増加して

俯瞰図

GMT
GMR

いる．モバイルデータトラヒックは 2013 年には前年比81% 増加し，2018 年には 2013 年の 19 倍になると予測

GST
GSR

されている [1]．そのため，今後高速にデータを伝送する技術が必要となる．伝送速度を高めるためには，広帯

側面図ｄ域を利用することが必要である．現在，低周波数帯域に

マクロセル

D

散乱体

スモールセル

おいては，すでに多くの帯域が利用されているため実現が困難であるが，高周波数帯域ではまだ利用されていな

図 1: シミュレーション環境

い周波数帯域が存在するため，低周波数帯域を利用する

位置やアンテナ利得などのパラメタに影響されている．

ことに比べ，広帯域化の実現に将来性がある．しかし，よって，本研究では自己干渉電力と各パラメタの関係

高周波数帯域を利用する場合，電波の減衰が速いため，を定量的に分析し，自己干渉が許容できる強度以下とな

広い範囲をカバーすることができない．このため，マクるアンテナの条件を明確にする．

ロセル，及びスモールセルを組み合わせて利用する方法

2 干渉波受信電力の計算方法が提案されている [2]．
通常，基地局と基幹ネットワーク間には中継回線が干渉受信電力発生のメカニズムをパラメタとともに図存在し，これをバックホールという．バックホールは光1 に示す．自己干渉は，スモールセルから送信された電ファイバで繋がっているが，川や道路に分かれた場所に波が，アンテナ近傍にある建物等の散乱体により散乱さ回線の設置が困難であり，コスト削減するために新規のれるため，その干渉波はスモールセルの受信アンテナ

スモールセルと既存のマクロセルの間に無線でバックで受信されることで生じる．図 1 において，D はマクホールを設置する方法が検討されている．そして，高速

ロセルとスモールセル間の距離，d はスモールセルと散通信のため，マクロセルとスモールセル間のバックホー

乱体間の距離を表す．G はアンテナ利得を表しており，

ルはフルデュプレクス通信方式で構築する方法が提案さ

GMT ，GMR ，GST と GSR はそれぞれマクロセルとスれている．フルデュプレクス通信方式とは，同じ周波数

モールセルの送信と受信アンテナの利得を表す． θ はス

帯域を利用し同時に送受信を行う通信方式である [3]．

モールセルアンテナの仰角を表す．HM と HS はそれぞ

この通信方式では，通常のハーフデュプレクスに比べてれマクロセルとスモールセルの高度を表す．これらのパ周波数利用効率が 2 倍となる一方，送信信号が直接受信

ラメタを用いると各基地局の受信電力は以下のように表機に回りこむ自己干渉の問題があるため，まだ広く使わされる．

れていない．自己干渉の問題を解決できれば，更なる無PMR = PST + GST (0) + GMR (0) + PL (D)PSR = PMT + GMT (0) + GSR (0) + PL (D)

線通信の高速化が期待できる．自己干渉は，送受信アン

(1)
(2)

テナ間の相互結合と反射波による回り込みの 2 種類が存在する．送受信アンテナの距離 R とすると，相互結合は

ここで，PST はスモールの送信電力，PMT はマクロセ

1R2 に比例する．このとき，送受信アンテナを離すと，そルの送信電力，PL (D) は伝搬距離に依存する自由空間伝の影響が小さくなる [4]．しかし，アンテナの正面に散搬損失を表している．マクロセルとスモールセルの位置

乱体があると，反射波の影響により自己干渉が発生し，が決まると，変更可能なパラメタはアンテナ利得だけに受信機の低雑音増幅器が飽和し，信号処理による自己干なる．そのため，アンテナ利得を得ることができるよう

渉除去ができなくなる．また干渉量は，散乱体と基地局にバックホールの回線設計をする必要がある．そして，

25

回線設計においてアンテナ利得の配分を行えば，干渉50

d = 20m
d = 50m
d = 100m
d = 200m

電力も散乱体の距離 d によって式 (3) のように定まる．
40

G [dBi]

ただし，建物における散乱は，最悪値を想定し全反射と仮定する．このとき，回線設計を満足する範囲で自己干渉受信電力（PI ）を制御できるパラメタは GST (θ) と

30

20GSR (θ) のみなる．またシミュレーションで用いるアン
100テナは，円形一様分布開口面アンテナを想定するため，

1

3

4

5

6

7

8

図 2: PST =23dBm 時の自己干渉条件

用いて，PI は次の式で求められる．
50(3)

d = 20m
d = 50m
d = 100m
d = 200m

40

G [dBi]

本研究では，スモールセル側における反射波の受信電力について検討するが，パラメタを変化させるときに，
マクロセルとスモールセルを繋げるため，バックホール

30

20

に関する設計を行い，パラメタの変化範囲を計算する必要がある．そこで基地局間の距離，仰角や最大送信電力

10
0

1

2

3

4

5

6

7

などを式 (1) に代入し，最低限必要となるアンテナ利得

θ [deg]の計算を行い，自己干渉電力の影響がない条件を明らか

図 3: PST =15dBm 時の自己干渉条件

89の場合では，アンテナ利得の制限がない．このように，

にする．

3

9

θ [deg]

各方向の利得が計算できる．このとき，GST と GSR を

PI = PST + GST (θ) + GSR (θ) + PL (2d)2環境条件により使用すべきなアンテナ条件がわかる．

シミュレーション結果

4 結論

スモールセル側の方が自己干渉の影響が大きいため，スモールセルを主な対象とする．式 (3) により，干本研究おいて，フルデュプレクスでスモールセルの無渉電力に決まるのは PST ，GST ，GSR ，ｄ と θ のみで

線バックホールを構築するときに，自己干渉電力と各パある．シミュレーションの条件は，マクロセル側のアン

ラメタの関係を定量的に分析できた．そして, その結果

テナ利得バックホールが通信できると仮定し，スモールを利用し，アンテナ利得の選択により，自己干渉に影響

セル側の送受信アンテナ利得が同じと設定する．そのされない条件が明らかにした．

ため，もし PST と ｄ が決まると，自己干渉の受信電力

参考文献

がちょうど最低受信レベルになるような利得 G と仰角[1] Contact

θ に関する等高線の図が描ける．ここで，二つの例を出tion

す．図 3 と図 2 に示している線は各距離 ｄ の時に自己

Cisco,“Cisco

Forecast,

VNI

2013-2018,”

Service

Adop-

http://www.

cisco.com/c/en/us/solutions/

干渉電力が受信できる最低の条件を示している．これら

collateral/service-provider/の線より左側は自己干渉がある条件で，右側は自己干渉

vni-service-adoption-forecast/がない条件である．アンテナ利得が低い場合では，干渉

Cisco_VNI_SA_Forecast_WP.html

が発生しにくいが，マクロセル側の非常に高利得のア[2] Association of Radio Industries and Businesses

ンテナを使う必要があるため，実現するのは難しい．次(ARIB), “ARIB 2020 and Beyond Ad Hoc Groupに，干渉エリアの形はすごく特徴があるが，実はこれア

White Paper Mobile Communications Systems for

ンテナ放射パターンと似て，関連している．また，仰角2020 and beyond,” http://www.arib.or.jp/が小さいときも干渉がない部分についての理由はアンテ

english/20bah-wp-100.pdf

ナ放射パターンに非常に小さく，ヌルと呼ばれる部分で[3] A. Sabharwal, P. Schniter, “In-Band Full-Duplex

あるため，そこからほとんど電波を出さないため，反射

Wireless:

してきた干渉電力も小さいからだ．

Challenges and Opportunities,” IEEE,

pp.813-816, Department Rutgers University, July 2,

これらの図から，環境の条件が決まるとどのような利

2014

得のアンテナを使うべきかわかった．例えば，図 2 のよ

[4] Xiang-Yu Cao, Guo-Xian Dai, Shu-Chun Zhen, Jun

うに PST が 23 ｄ Bm の時．θ が 6 度の場合では，利得Gao, “The mutual coupling between horns in the near-が 25 ｄ Bi 以上のアンテナを使うべきである．そして，

field,” IEEE, pp.357-359, 18-20 Aug, 1998

図 3 のように PST が 15dBm のとき．同じく，θ が 6 度

26

プロペンを還元剤とした Mo 添加 ZrO2-CeO2 複合触媒による NO 選択還元
学籍番号：10_07837

１

氏名：許 正航指導教官：日野出 洋文

Winarto Kurniawan

が 0.71～1.00mm になるよう成型し、「焼成後」の触媒も用意

はじめにした。選択還元反応でプロペンの役割を確認するため、プロペ

ガソリンエンジンは、静粛性が高い特徴がある。比較的軽量

ンの有無による活性実験を行った。

で製造コストが安く、乗用車をはじめ小型商用車、自動二輪車などに広く利用される[1]。しかし、酸性雨や光化学スモッグの活性実験は固定床流通反応装置で行った。反応ガス組成は、原因物質の一つであるリーンバーンエンジン排気中の NOx の低

NO:1500 ppm，O2:10%，C3H6: 1500 ppm であり、He をバランス

減が大きな課題である。さまざまな対策方法の中に、炭化水素

ガスとして用いた。
調製した触媒 0.9-1.4g に、空間速度を 13000

による NOx の選択還元触媒（HC-SCR）が NOx と未燃炭化水素を

h−1（約 4 mL/s の総ガス流量に相当）に固定し、温度を 150-550ºC

同時に除去することができ移動発生源に由来する窒素酸化物

まで段階的に変更して活性実験を行った。NO 濃度は、窒素酸化を除去するために効果的かつ経済的な技術であることが証明

物分析計（島津製作所、NOA-7000）によって分析した。CO2 分されている。現在のパフォーマンスは、特に商用アプリケーシ

析には、ガスクロマトグラフ（GL サイエンス、GC-390）を使用

ョンでは、その低温領域での性能は十分ではない。HC-SCR 反応

し た 。 触 媒 の キ ャ ラ ク タ リ ー ぜ ー シ ョ ン の た め に 、 TG

では、異なる金属の組み合わせで生まれる“複合触媒機能”お-DTA(TG8120, Rigaku)、XRD(Multi Flex, Rigaku)、窒素吸脱

よび“二元促進効果”は固体触媒の働きを考える上で重要でか

着法（Autosorb-1MP, Quantachrome）を用いた。機械的混合法

つ非常に有用な現象である。

により調製した Mo 添加 ZrO2-CeO2 複合触媒の比表面積を表 1 に

そこで、本研究では、Mo 添加 CeO2-ZrO2 複合触媒の NO 選択還示した(以下、BC:焼成前、AC:焼成後)。焼成処理により、10%MoO3

元に対する活性を検討した。CeO2 は経済的な材料であり、酸素

添加触媒の比表面積は、78.1 m2/g から 42.9 m2/g に、20%MoO3

貯蔵物質として注目されている[2]。CeO2 それ自体は、高い NO

添加触媒の比表面積は、75.4 m2/g から 29.9 m2/g にそれぞれ低分解の活性を示さないため、ZrO2 を混合触媒の活性向上のため下した。調製した各複合触媒の XRD の測定結果を図 1 に示した。

に使用した [3]。また、既存の研究より、NO から NO2 への酸化反これより、調製した触媒は焼成処理により、新しい化合物

応を促進させる MoO3 は、炭化水素の還元剤としての働きをさら（Ce2Mo4O15）が生成したことが認められた。

に高めることもできると報告されている[4]。

表1

MoO3 添加触媒の比表面積

触媒
２

触媒調製および触媒のキャラクタリーゼーション

比表面積(m2/g)

10%MoO3+(ZrO2-CeO2)(BC)78.1

触媒は機械的混合法によって調製した。日本参照触媒部会の20%MoO3+(ZrO2-CeO2)(BC)75.4

JRC-ZRO-６ (以 下 ZrO2)と JRC-CEO-１ (以 下 CeO2)、 そ し て

10%MoO3+(ZrO2-CeO2)(AC)42.9(NH4 )6Mo7O24 ・4H2O の 3 種の触媒を前駆体として使用した。3

20%MoO3+(ZrO2-CeO2)(AC)29.9

種の前駆体を５５０℃で 4 時間焼成してから、ZrO2、CeO2 をモル比 1:1 になるように秤量し、次に、MoO3 を目的の混合比になるよう秤量する。 秤量した ZrO2、CeO2 と MoO3 をメノウ乳鉢で機械的に混合する。分散性を高めるために、少量のエタノールを加えた。試料を均一に粉砕し、常温で乾燥した後、円筒状のペレットにして、粒径が 0.71～1.00mm になるよう成型し、「焼成前」の触媒として使用した。焼成処理の触媒活性への影響を確認するため、常温で乾燥のステップまで調製した触媒をもう
一度５５０℃で 4 時間焼成し、円筒状のペレットにして、粒径

27

図1

MoO3 添加触媒の XRD 解析結果

図3

プロペン未流通下 MoO3 添加触媒の NO→N2 還元活性実験(BC)

３

触媒の活性実験結果と考察
図２に機械的混合法による「焼成前」の Mo 添加 ZrO2-CeO2 触媒の NO の活性実験結果を示した。ZrO2-CeO2 触媒に比べ、10%、
15%、20%の MoO3 を添加した ZrO2-CeO2 触媒は、活性の向上が確認できた。最大活性点は 10%添加触媒の 300℃で、約 46%の転化率を示した。MoO3 を添加した触媒は、温度変化に対しての活性の傾向はほぼ同じで、最大活性点を示す温度が ZrO2-CeO2 複合触媒より低くなった。

図4

MoO3 添加触媒の焼成後の NO→N2 還元活性実験(AC)４ 結論
機 械 的 混 合 法 に よ る プ ロ ペ ン を 還 元 剤 と し た Mo 添 加
ZrO2-CeO2 複合触媒の NO の選択還元反応は、比較的に高い活性を示した。10%、15%、20%の MoO3 を添加すると、 ZrO2-CeO2 複合触媒の活性を向上させることができることが確認できた。
10%MoO3 を添加した CeO2-ZrO2 が、最も活性の高い触媒であった。
図2

MoO3 添加触媒の焼成前の NO→N2 還元活性実験(BC)

その理由としては、ZrO2-CeO2 複合触媒と MoO3 の混合率が影響を与えるためであると考えられる。そして、Mo 添加 ZrO2-CeO2

図３には、プロペン未流通下の条件で機械的混合法による調触媒による NO 選択還元反応では、プロペンは不可欠なことも製した Mo 添加 ZrO2-CeO2 触媒を焼成する前の NO の活性実験結確認できた。また、「焼成後」の Mo 添加 ZrO2-CeO2 の触媒活性果を示した。これらは全部、NO 転化率が５％以下となった。こは、「焼成前」より低くなったのは、焼成処理による複合化合の結果より、Mo 添加 ZrO2-CeO2 触媒による NO 選択還元反応では、物の生成が原因だと考えられている。

プロペンは重要な役割を担っていることが確認できた。
図４には、機械的混合法による調製した「焼成後」の Mo 添

参考文献

加 ZrO2-CeO2 触媒の NO の活性実験結果を示した。NO から N2 への転化率は両方とも 10％以下であった。また、図 1 から、焼成[1] 図解雑学｢自動車のメカニズム｣,

古川修

監修, ナ

処理により、MoO3 添加触媒は、新しい化合物が生成したことをツメ社 (2009)

確認できたため、触媒活性の低下をもたらしたと考えられる。

[2] ｢化学大辞典｣, 大木 道則, 大沢 利昭 他編集, 東京
化学同人（1989）[3] ｢いまセリウムがおもしろい｣, (株)ティー・アイ・シィー,
初版 (2005)
[4] 金

28

東一, 学士論文, 東京工業大学（2012.3）

Field survey and numerical analysis of storm surge inundation in Tacloban City
caused by the 2013 Typhoon Haiyan
Student Number: 11_27091

Name: Siyang Li

Supervisor: Hiroshi Takagi

1. Introduction
Typhoon Haiyan (Yolanda, the local name) madetaken from Hotel Alejandro in order to compare it withlandfall in the Philippines on 8 November 2013, which

the simulated flow velocity. The velocity was estimate	 gave huge damages to the Leyte, Samar, and many otherto be about 0.6 m/s on 7:50AM November 8, 2013.

islands. According to the National Disaster Risk
Reduction and Management Council, it is reported that6245 people died, 28626 people injured, and 1039 people
were still missing by March 6, 2014. Haiyan is thestrongest storm recorded at landfall, and the strongest
typhoon ever recorded in terms of one-minute sustained
wind speed.
On November 8, Typhoon Haiyan made landfall in
Leyte, Samar and other islands causing catastrophic
destruction. According to the statistical result, a large
storm surge inundated most to the coastline of Leyte
Gulf, causing particularly large damage to the sea front

Figure 1: Field survey in Tacloban, Philippinesof Tacloban City, which is the biggest city in Leyte
Island, located at the north western end of Leyte Gulf.
This paper aims at revealing the characteristics of storm
surge caused by Haiyan, focusing on Tacloban City,
Leyte Island, where the largest number of casualties took
place. To do so, the numerical simulation was conducted
and verified by a comparison with the field survey by theauthors.
. 18 02. Overview of field survey
Because the elevation data measured by satellites is not
sufficiently accurate, it cannot be used for the purpose ofstorm surge simulation. The field survey was carried out
in Tacloban City from May 2 to May 4, 2014 (Fig.1),
and the following factors were measured: First,

3
3
3
3
3
3 (
3 )
3
3
3
3
3
3
3

3 32
63756
.3 7
4 9 3 383 79
8 7 23 8 57 23 83 38 9
((()
(
(

3 32
63756
4 9 63
5 29

(
(
(
))
((()

(
( (
)

)
( )

)
)

(
(

)

(

()
))

()
( (

(

()
)(

(

Figure 2: results of filed survey

elevations at about one hundred points were measured
using a handy GPS and laser rangefinder. Second,

3. Numerical analysis

inundation depth, arrival time of the storm surge, flow

The fluid simulating code, Delft3d-Flow, coupled withdirection and other relevant information were obtained

the typhoon model (Takagi et al., 2014) was employed

primarily by the interview with local residents. The

for the simulation. The elevation of Tacloban City was

results of field survey are shown in Fig 2.

created using the results of field survey (Fig.3). The

In addition, the flow velocity of the storm surge wasmodified map is expected to greatly contribute toalso estimated by a visual investigation of the video

improving the accuracy of the model.

29

8	
  
7	
  
6	
  

simulated	
  heights(m)

with	
  
buildings	
  without	
  buildings	
  

5	
  
4	
  
3	
  
3	
  

4	
  

5	
  

6	
  

7	
  

8	
  

measured	
  hights	
  (m)Figure 5: comparison of depth
Figure 3: Elevations used for the simulation in Tacloban
The Manning’s n value is one of the determining factorsof fluid mechanics. The simulation was repeated to findthe appropriate value for this factor by changing thenumber, and the result was obtained as shown in Fig.4.
Comparing the velocity between the simulation and thevideo inspection, the n value was estimated to be 0.04,
which is a similar value recommended by MLIT (2012)
for tsunami simulation.
Figure 6: inundation map during the surge peak
（08-Nov-2013 08:04:00）

2.5	
  computation	
  result	
  with	  buildings	
  

2	
  

4. Conclusions@low	
  velocity	
  thru	  video	
  investigation	
  

1.5	
  
velocity	  (m/s)

In this research, we conducted the field survey andcomputation	
  result	  without	
  buildings	
  performed

thenumerical

analysistoassessthecharacteristics of storm surge inundation in Tacloban.

1	
  The numerical simulation considering buildings can

0.5	
  
0	
  

estimate inundation and flow speed at a reasonableaccuracy when Manning’s n value is set to be 0.04. It
was demonstrated that the street shielded by dense0	
   0.04	
   0.08	
   0.12	
   0.16	
   0.2	
   0.24	
   0.28	
   0.32	  Manning's	
  n	
  valuebuildings could contribute to reducing flow velocity as
well as inundation depth due to storm surges.

Figure 4: comparison of velocity
The results of computation between “with buildings” andReferences“without buildings” are shown in Fig.5, indicatinga)

Takagi H. et al. (2014) Track Analysis, Simulation

significant overestimate for the case of “without”, while

and Field Survey of the 2013 Typhoon Haiyan

the simulation with buildings agrees well with theStorm Surge, J. of Flood Risk Managementb)measured heights. It is expected that the existence of

JTWC best track joint Typhoon warning center.
Retrieved 2014-11-30

dense buildings contributes to reducing flow velocity andc)lower the depth of storm surge inundation.

MLIT (2012) Guide to determining the potential
tsunami inundation (Version 2.00)

30

Batch Equilibrium Extraction of Model Petroleum Heavy fraction
Student Number: 11_22640

Name: Tomonori MASUDA Supervisor: Ryuichi EGASHIRA, Hiroaki HABAKI

1. Introduction

Table 2 Experimental conditions
SystemFeed
Model heavy fraction
Solvent
Methanol aqueous solution
Conditions
Mass of feed, 𝑅0
[kg]
0.02
Solvent/feed ratio, 𝐸0 /𝑅0[-]1
Mass fraction of Hetero 𝑦𝐻𝑒𝑡𝑒𝑟𝑜,0[-] 0.01~0.2
Mass fraction of methanol in solvent 𝑦𝑀𝑒𝑂𝐻,0 [-]0.8
Temperature
[K]
303
Time
[h]
48

Some petroleum-derived heavy fractions contain not only
homocyclic but also heterocyclic compounds at certain
concentration, and the latter should cause degradation of fueloil quality and be poisonous for catalysts used in the successive
processes, such as hydrocracking, desulfurization and so on.
Then the removal of these heterocyclic compounds from theheavy fractions has been studied for the catalytic reaction
stably operated or improved, keep the quality of fuel oil.
Solvent extraction has been utilized as a commercialized
method to separate aromatic compounds from petro-derived
fractions. Separation of nitrogen heterocyclic compounds(Hetero) by solvent extraction has been studied, in which coaltar absorption oil or catalytic cracked oil was used as feed oil[2-4]. However the separation of heterocyclic compounds fromthe heavy fractions has been insufficiently investigated.
In this study, liquid-liquid equilibrium extraction of model
heavy fractions with aqueous solutions of methanol wasmeasured. The effects of species of Hetero and other coexisting
components on the liquid-liquid equilibrium were studied.

2. Experimentals

2.1 Materials
The material systems used in this study are summarized inTable 1. The 3 types of nitrogen heterocyclic compounds(Hetero), such as quinolines (Qs), indoles (INs) and Carbazoles(CBZs),and 2 types of non-heterocyclic compounds (Nonhetero), such as aromatics and alkanes(AL), were selected andtotally 21 components were used. For each type of Hetero, thecarbon numbers in molecules were varied by changing theirfunctional group. To prepare the model feed solutions of binary
system, one each component was chosen from Hetero and Nonhetero components. The carbon number in the molecule, CN,
was estimated by regarding heteroatom as carbon in themolecule. Aqueous methanol solution, one of the popularsolvents used in the previous studies, was used as the solvent.
2.2 Experimental conditions
The experimental conditions are summarized in Table 2.
The mass fraction of the Hetero component in the model binary
feed solutions was set as 0.01. The specified amounts of feed,and solvent, were put in conical flask and shaken at 303K for
48 hours to be equilibrated. After reaching equilibrium
conditions, the raffinate and extract phases and were separated
into each other with separatory funnel. The liquid phases wereanalyzed by gas chromatograph (Shimazu GC-2010) withcapillary column (HR-1) to determine respective compositionsof both phases.
Table 1 Compounds in model systems
HeteroQuinolines(Qs)quinoline(Q, CN = 10), 2-methylquinoline(MeQ, CN = 11),2,6-dimethylquinoline(DMeQ, CN = 12), 4-phenylpyridine(PhPYR, CN = 12), benzo[h]quinolone(BeQ, CN = 14)
Indoles(INs)indole(IN, CN = 9), 2-methylindole(MeIN, CN = 10), 7ethylindole(EtIN, CN = 11), 2,3-dimethylindole (DMeIN,
CN = 11), 2-phenylindole(PhIN CN = 15)
Carbazoles(CBZs)
carbazole (CBZ, CN = 13), 9-methylcarbazole(MeCBZ,
CN = 14), 9-ethylcarbazole(EtCBZ, CN = 15)
Non-hetero
Aromatics
1-metylnaphthalene(1MeN, CN = 11),butylbenzene(BuBEZ, CN = 10)
Alkanes(AL)decane(C10, CN = 10), undecane(C11, CN = 11),dodecane (C12, CN = 12), tridecane(C13, CN = 13),
tetradecane(C14, CN = 14)

Fig.1 Mass fraction of Hetero in raffinate and extract phase

Fig.2 Effect of carbon number on distribution ratio, 𝑚𝑖

Fig.3 Effect of carbon number on selectivity 𝛽𝑖,𝑁𝑜𝑛−ℎ𝑒𝑡𝑒𝑟𝑜

3. Results and Discussion

3.1 Definitions
The distribution ratio of component 𝑖, 𝑚𝑖 and the31selectivity of component 𝑖 against Non-hetero, 𝛽𝑖,𝑁𝑜𝑛ℎ𝑒𝑡𝑒𝑟𝑜is defined as follow:
𝑚𝑖 = 𝑦𝑖,1 /𝑥𝑖,1(1)𝛽𝑖,𝑁𝑜𝑛ℎ𝑒𝑡𝑒𝑟𝑜 = 𝑚𝑖 /𝑚𝑁𝑜𝑛−ℎ𝑒𝑡𝑒𝑟𝑜(2)
Where 𝑦𝑖,1 and 𝑥𝑖,1 are the mass fractions of component 𝑖 ineach phases in extraction. Mass fraction excluding solvent in
extract phase , 𝑧 defined as follow:(3)
𝑧 = 1 − 𝑦𝑀𝑒𝑂𝐻,1 − 𝑦𝑊,1
Where k is all compounds used in model systems and solvent, n
is compounds used in solvent.
3.2 Equilibrium with model binary feed oil systems
In all cases, the liquid-liquid extraction could be stably
operated and stable emulsion was not formed. The compoundsof Q and C12, or MeQ and 1MeN could not be appropriately
analyzed by gas chromatogram since they could not be
separated by the used column in the gas chromatogram.
The plots of the mass fractions of Hetero components inthe extract phases against those in the raffinate phases areshown in Fig.1. The 𝑦𝑖,1 of Hetero components were larger
with C11 than in 1MeN-hetero systems. The 𝑦𝑖,1 of Qs and
INs were relatively high and those of CBZs were small witheach Non- hetero component.
The effect of CN on 𝑚𝑖 , is shown in Fig.2. In most case,𝑚𝑖 decreased as CN increased and 𝑚𝑖 of Hetero components
were larger than those of Non-hetero components. Among
Hetero components, 𝑚𝑖 of Qs and INs were almost
comparable with each other, and were larger than those of
CBZs. The 𝑚𝑖 of Hetero components were the largest with
C11, followed by those with BuBEZ and 1MeN. The 𝑚𝑖 of
Non-hetero components were independent of the coexisting
Hetero components to be constant for respective Non-heterocomponents. The 𝑚𝑖 was the smallest with C11, followed by
BuBEZ and 1MeN.
The effect of CN on 𝛽𝑖,𝑁𝑜𝑛−ℎ𝑒𝑡𝑒𝑟𝑜 is shown in Fig.3. The
𝛽𝑖,𝑁𝑜𝑛ℎ𝑒𝑡𝑒𝑟𝑜 decreased as CN increased. The 𝛽𝑖,𝑁𝑜𝑛ℎ𝑒𝑡𝑒𝑟𝑜 was
the largest with C11, followed by those with BuBEZ and 1MeN,
and 𝛽𝑖,𝑁𝑜𝑛ℎ𝑒𝑡𝑒𝑟𝑜 of IN with C11 attained more than 2800. Thisso high selectivity mi ght be caused by the watercontent in extract phase.
Figure 4 shows the effect of z on 𝑚𝑄 and 𝑚𝑖 of Nonhetero components when 𝑥𝑄,0 was varied from 0.01 to 0.2. Inlower range of z, 𝑚𝑄 decreased and 𝑚𝑖 of Non-heterocomponent increased as z increased. With less polar component,such as C11, more polar component, such as Q, preferably
dissolved into the solvent phase. The difference between 𝑚𝑖of Q and C11 was quite large and the ratio, 𝑚𝑄 /𝑚𝐶11 , wasmore than 1200. This characteristic feature became obscure in
the order of C11, BuBEZ, and 1MeN, that is to say, theascending order of the mass fraction of Non-hetero component
in the extract phase. In the higher range of z, although 𝑚𝑄 was
kept constant, 𝑚1𝑀𝑒𝑁 increased with z. When bothcomponents in the binary system are likely to dissolve intosolvent phase, the solubility of less polar component should beenhanced by the other more polar component. This trend was
also reported in the previous studies[8].

3.3 Extraction of model multicomponent feed oil system
The results with the multicomponent system are shown in
Fig.5, when 1MeN was used as Non-hetero compound. The 𝑚𝑖of Hetero components were larger than 𝑚𝑖 of Non-hetero in 2
components systems, because total initial mass fraction of
Hetero component in multicomponent system was larger thanthat of Hetero component in 2components systems. So, thetendency of 2 components systems was same as that ofmulticomponent system.

Fig. 5 Effect of CN for distribution ratio in multicomponent
system

4. Conclusion

Batch Equilibrium of nitrogen heterocyclic compounds,
alkanes and aromatics were measured on 2 component systems.
As CN increased, distribution ratio of Hetero and alkanes
decreased. Distribution ratio of CBZs were smaller with eachNon- hetero components than that of INs and Qs. Distribution
ratio of Hetero was larger than that of aromatics,
and that of aromatics was larger than that of alkanes. The
selectivity of Hetero against alkanes was largest, followed bythose with BuBEZ and 1MeN.
The distribution ratio of Hetero was smaller witharomatics than that of Hetero with alkanes. The solubility ofless polar component was enhanced by the other more polar
component.
The liquid-liquid equilibrium in 2 compounds systems andmulticomponent system was almost comparable with eachother.

Nomenclature

CN = carbon number of compounds, 𝑥𝑖 = Mass fraction of 𝑖 in
raffinate phase, 𝑦𝑖 = Mass fraction of 𝑖 in extract phase, 𝑚𝑖 =Distribution ratio of 𝑖
1MeN 1= 1-methylnaphthalene, BuBEZ = Buthylbenzene, C10 =Decane, C11 = Undecane, C12 = Dodecane, tC13 = Tridecane ,C14r= Tetradecane, Q = Quinline, DMeQt= 2,6-dimethylquinline,
MeQ t= 2-methylquinoline,TPhPYR1= 4-phenylpyridine, BeQ t=
benzo[h]quinline, IN = Indole, MeINtt= 2-methylindole, EtINt= 7ethylindole, t DMeINt= 2,3-dimethylindole, PhIN = 2-phenylindole,
CBZ = Carbazole, MeCBZ = 9-methylcarbazole, EtCBZtt= 9ethylcarbazole, MeOH1= Methanol, W = Water, Qs = Quinoline
compounds, INs = Indole compounds, CBZs = Carbazole
compounds, Hetero = Heterocyclic compounds, Non-hetero =alkanes and aromatics, AL = alkanes

Subscript

𝑖 : component 𝑖,

Reference

0 : at initial state, 1 : at equilibrium state[1] Nagai.M 東京工業大学 1999 年卒業論文[2] Yoshimura.Y Solvent Extraction Research and Development,
Japan, 20, 169~174 (April, 2013)[3] Chris. S 東京工業大学大学院 2000 年卒業論文[4] Hasegawa.H, 東京工業大学 2014 年卒業論文[5]石油学会;石油精製プロセス,第五章,接触分解 125-138[6] Kim. C.J, Won.D.B, Fluid Phase Equilibria 193 (2002) 217–227[7] George.W.M, Dennis.R.H, Ind. Eng. Chem. Res. (2008), 47, 28672875[8] Chris.S et al, J.Jpn. PetInst, 48, 60-66 (2005)

Fig. 4 Effect of mass fraction excluding solvent in extract
phase for distribution ratio

32

The effect of non-policy factors on the diffusion of EV
Student Number: 11_01714

Name: Junya Ishida

1. Introduction

factors on the EV diffusion, which are often out of
our control, we can improve the policy strategies
such as realignment of area-targeting to realize
more effective subsidy scheme.

Outside temperature (℃)

Electric vehicle (EV) is one of the key options forthe automobile sector to save energy consumption
and reduce emissions mainly because it uses
electricity as its power source. Anticipating thefuture where renewable energy will account for
greater portion of electricity source, further
contribution from EV in creating a cleaner
environment is expected. Thus, the diffusion of
EV is a great concern and interest in the world.
Despite of its promising role, the diffusion of EV
is still at an early stage. Previously, someresearchers tried to find out the key factors of EV
diffusion. Sierzchula et al. (2014) revealed
positive effects of financial incentives, charging
infrastructure,
and
local
manufacturers.
Tagashira et al. (2013) pointed out thecomplaints made by EV drivers including shortcruising range and the inconvenience of charging
that is done away from home, but there are also
positive points of EV such as low running costand charging at home. Looking back at theformer EV’s failed diffusion movements, Tsuchiya
et al. (2010) claimed that it is important to create
scenarios that are suitable with consumers’
lifestyle and needs.
The primal cause of the short cruising range isthat the capacity of battery is small. However,there are also other factors that can worse
cruising range. These are temperature and slope.
Using an air conditioner and driving EV on asteep slope require extra consumption ofelectricity. Although these do not affect only to
EV, its short cruising range makes it more
serious for EV. Moreover, since Japan is astretched and mountainous island country from
north to south and mountainous, those factors
possibly have stronger effects in some specificregions. Especially for EV users in cold andmountainous districts, running out of battery
electricity could be a bigger anxiety due to safety
reasons. It is assumed that temperature andslope affect not only actual cruising range but
also consumer’s sense of danger. By deepening
the understanding of the effect of non-policy

2. Purpose

	  The purpose of this paper is to investigate
whether or not non-policy factors, such astemperature and slope, affect the actual diffusionof EV. People in some districts could havedisadvantages in terms of cruising range.
However, there is no research that focuses on
those local differential impacts for the actual
diffusion of EV. In this paper, the hypothesis isthat low temperature and steep slope would
negatively affect consumer preferences andhinder the actual diffusion of EV in cold or
mountainous districts.

Figure 2: Concept of this study

3. M ethodology

Multiple regression analysis was conducted,
targeting municipalities in Japan. Dependent
variables (y) and independent variables (x, z, w)
are set as below.

y1
y2
x1
x2
x3
x4
x5
x6
x7
x8

AC: Air conditioner
Without
AC

22.2

With
using ACfor cooling

35
-6.7
0

0.1

0.2

Supervisor: Naoya Abe

x9
z1
z2
w1
w2
w3
w4
w5

With
using ACforheating

0.3

Net Energy Consumption (DC kWh/km)	
 Figure 1: Urban Dynamometer Driving Schedule(UDDS) Test for Nissan Leaf 2013 SV. Source: [1]33Table1: Variables
The number of retained EV (cars)
The number of retained EV per 1,000 persons(cars/1,000 persons)
The average temperature (℃)
The average highest temperature (℃)
The average lowest temperature (℃)
The average temperature in summer (℃)
The average temperature in winter (℃)
The annual average number of days whose
maximum temperature is more than 30 ℃
(days/year)
The annual average number of days whose
maximum temperature is less than 0 ℃(days/year)
The annual average number of days whose
maximum temperature is more than 25 ℃
(days/year)
The annual average number of days whose
minimum temperature is less than 0 ℃(days/year)
The average slope of residential areas (°)
The average slope of nonresidential areas (°)
The number of households (household)
The number of person per habitable area
(person/km2)
The income index
The number of charging station per habitablearea (station/km2)
The ratio of detached house against all houses

w6
w7
w8
w9
w10

Table 1: Normalized beta coefficients

The number of passenger cars per household(cars/household)Electricity price in midnight (JPY/kWh)
Gasoline price (JPY/L)
Subsidy (Dummy)
EV・PHV town （Dummy）

Model 1

Here, “EV” is a car classified into standard-sized
passenger car, and the data of EV is as of March
2014. [2] Temperature factors (x) are the average
from 1981 to 2010. [3] The data of slope wassummarized in 2005. [4] The data of household
and population is as of January 2014. The data ofcharging station is as of March 2014. The data ofhouse is as of 2008. Electricity price is theaverage from 2010 to 2012. Gasoline price is theaverage from 2010 to 2013. Subsidy is what was
enforced from 2012 JFY to 2013 JFY.
Although there are approximately 2,000
municipalities in Japan, the sample number is 85
due to the limited availability. There are 9
variables in the temperature category and 2
variables in the slope category. One variable waschosen from each category. All of the combination
was simulated in each model. y1, y2, and x1 wereapplied to logarithmic transformation to copewith heteroskedasticity of the residuals. Most of
the independent variables follow the normal
distribution. Model 2 does not include x3 because
y2 is ratio against people.
𝑀𝑜𝑑𝑒𝑙  1: 𝑙𝑛 𝑦! = 𝛽! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑙𝑛 𝑥! + 𝛽! 𝑥!
+ 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥!
+ 𝛽!" 𝑥!" + 𝛽!! 𝑥!! + 𝛽!" 𝑥!" + 𝜀  𝑀𝑜𝑑𝑒𝑙  2:  𝑙𝑛 𝑦! = 𝛽! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥!
+ 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥! + 𝛽! 𝑥!
+ 𝛽!" 𝑥!" + 𝛽!! 𝑥!! + 𝛽!" 𝑥!" + 𝜀(βis coefficient, andεis residual, assumed as tofollow the normal distribution.)

Model 2

VARIABLES

1

2

1

2

x1

0.145***

0.134***

0.363***

0.331***

z1-0.0464z2-0.168-0.0179

w1

0.990***

1.00***

w2-0.178*-0.160-0.0354-0.422*-0.331w3

0.08490.08970.242

0.274*

w4

0.138**

0.121**

0.376***

0.323**

w5

0.00566

0.000241

0.0165-0.00587

w6

0.259***

0.276***

0.335*

0.393**

w7-0.0766-0.0803*-0.161-0.176w8

0.028

0.0225

0.123

0.101

w9

-0.0273-0.025-0.0497-0.0401w10-0.0345-0.0305-0.176*-0.168

adj R2

0.868

0.867

0.311

0.289

F-value

47.11***

46.53***

4.441***

4.106****** p<0.01, ** p<0.05, * p<0.1

slope did not have effect on the diffusion of EV.
Now, it is suggested that the local differential
effect derived from temperature should beconsidered for better diffusion strategies of EV.
	  Example of policies incorporating temperatureeffects are as follow: ①Cold districts should be
supported more strongly than the other regions.
② Since EV is not appropriate for cold districts,
other types of vehicle, such as fuel cell vehicle,
should be promoted in these areas. ③ In cold
districts, EV may survive in specific business oractivities, for example, postal service and bus
service.
There are some limitations in this study. It does
not consider consumers’ individuality. People in a
municipality are regarded as the same. In
addition, as written above, the diffusion is still at
an early stage. Therefore, the next phase mightshow a different trend. The transition should be
followed.

4. Results and areas for further study

Table 2 shows the regression analysis results.
The adjusted R2 of model 1 is much higher thanthat of model 2. Temperature factors, except forx9, have negative effect on the diffusion and arestatistically significant at 1% level in all cases.
The number of charging station per habitablearea has positive effect on the dependent variable
and is statistically significant at 5% level in all
the cases of model 1 when z1 was chosen. In themodel 2, it is significant whether z1 or z2 waschosen. The number of passenger cars perhousehold has positive effect and is statisticallysignificant at 5% level in all cases of model 1. The
other factors hardly show statistical significance.
Now, the effects of some variables in those models
are understood. When temperature factors andthe number of charging station per habitablearea increase, there are more the number ofretained EV and the number of retained EV per
1,000 persons. When the number of passenger
cars per household increases, the number ofretained EV enlarges. In addition, the impacts oftemperature factors and the density of charging
station is almost the same.

References
Sierzchula et al., (2014), The influence offinancial incentives and other socio-economic
factors on electric vehicle adoption
Tagashira et al., (2013), A User Survey of Plug-in
Hybrid and Battery Electric Vehicles
Tsuchiya et al., (2010), Transition of the
Diffusion Policies and their Impacts with theMarket on Electric Vehicle in Japan[1] Argonne National Laboratory Transportation
Technology R&D Center, Advanced Powertrain
Research Facility Downloadable Dynamometer(D3)[2] Automobile Inspection & Registration
Information Association[3] Japan Meteorological Agency, The pastmeteorological downloadable database[4] Zaiki et al., (2005), “Relationship between
Distribution of Japanese Residential Areas andTopography”

5. Conclusion

In this paper, we statistically found that low
temperature negatively affected the actual
diffusion of EV in Japan. On the other hand,

34

DISSIPAITION ENERGY IN THE PROCESS OF ADHESION AND DETACHMENTBETWEEN ELASTIC BEAM AND RIGID BODY
Student Number: 11-08604

Name: Takeru KUSAKARISupervisor: Kunio TAKAHASHI

1 Introduction
Hysteresis is frequently observed in adhesion phenomena.
It is significant and essential to understand themechanism of the hysteresis, in the design of devices,such as grip-and-release devices.
The hysteresis is the difference between loading andunloading process. Based on a point contact model, Beak
[1] suggested that energy dissipation is never observed in
the loading processes and dissipation energy appears in
the unloading processes. Furthermore the dissipationenergy is proportional to the contact edge length. In theexperiments [1], the contact edge length varies every
moment, so, complicated calculation is required toconsider the mechanism of energy dissipation using.
In the present study, the contact model between an elastic
beam and rigid body is used to fix the contact edge width
to simplify the geometry. And the dissipation energy ismeasured to discuss the mechanism of the hysteresis.

Figure.1 Schematic illustration of the contact model

2 Method of Calculating Dissipation
EnergyFig.1 shows the model used in the present study. 𝑓𝑧 , isthe force and 𝑑 is the displacement, and ∆𝛾 is thework of adhesion between the beam and the substrate. 𝑙is the length of non-contact area of the beam, which has
the angle 𝜃, the width 𝑊, length 𝐿, Young's modulus
𝐸, and the moment of inertia 𝐼. In order to calculate thedissipation energy, the equation (1) can be used,∆𝑈𝑑𝑖𝑠. = ∆𝑈𝑒𝑥𝑡. − ∆𝑈𝑒𝑙. − ∆𝑈𝑖𝑛𝑡.
(1)
where ∆𝑈𝑑𝑖𝑠. , is the dissipation energy, ∆𝑈𝑒𝑥𝑡. is theexternal work from the outside, ∆𝑈𝑒𝑙. is the elastic
energy stored in the elastic beam, ∆𝑈𝑖𝑛𝑡. is the energy
come from the ∆𝛾. These energy can be calculated byusing equations (2)~(4)[2].
𝑈ext. = ∫ 𝑓𝑧 d 𝑑𝑈el. =

2
6𝐸𝐼 𝑑 1
𝐸𝐼
(
+
sin
𝜃)
+ tan2 𝜃
2𝑙 cos 𝜃 𝑙 2
2𝑙

𝑈int. = −𝑊(𝐿 − 𝑙)∆𝛾 = 𝑊 ∙ ∆𝑙 ∙ ∆𝛾

Figure.2: Schematic illustration of the experimental
system

3 Experiment to Measure Dissipation
EnergyIn order to calculate dissipation energy by using
equations (2)~(4), it is necessary to measure the force 𝑓𝑧 ,the displacement 𝑑, and the length of not-contact area 𝑙experimentally. In the presant study, an experimental
system like Fig.2 is used for measuring these parameters.
The stepping motor is operated to load and unload thebeam by 50 m per step. The displacement is set to zero
when the beam starts contacting the rigid body. There are

(2)
(3)
(4)

35

0.02
First experiment
Second experiment
Third experiment

0.04

Loading Process0Udis./l [J/m]f z [N]

0.02

Unloading Process

-0.02
-0.04

0.01
Unloadong Process0
Loading Process-0.004-0.003d [m]-0.002-0.001-0.01

Figure.3 Experimental results of relation between theforce and the displacement

0.01

0.011

0.012 0.013
l [m]

0.014

0.015

Figure.5 Experimental results of the dissipation energy
as a relation of variation in the not-contact area ∆𝑙 in
the whole process

Unloading Process

L-l [m]0.004

nearly zero in the loading process. And the dissipationenergy in the unloading process is nearly fixed. We canthink that, in the unloading process, some constant force
works to the contact edge, and this force causes theconstant energy dissipation. But from the results, themechanism of this force has not been revealed yet. So wehave to reveal it experimentally in future. In order toreveal the mechanism, this beam model can be useful,because the length of the contact edge where this force
works is fixed in this model, although it is various in the
point contact model.

0.002

Loading Process
0-0.004-0.003-0.002d [m]-0.001Figure.4 Experimental results of relation between thelength of contact area and the displacement

60 steps of loading process and we unload the beam until
it detaches from the rigid body. The duration of waitingtime between each step is 60 seconds. By using
electronic scale, the force 𝑓𝑧 is measured. By using
microscope, the length of not-contact area 𝑙 is observed.
In this study, we use PDMS as the elastic beam and slide
glass as the rigid body. And in this study, because thefriction force between the beam and the rigid body isneglected, steel balls which diameter is 5 mm are set
under the rigid body.

5 Conclusions
The dissipation energy in the process of adhesion anddetachment between elastic beam and rigid body ismeasured experimentally. And because the energy
dissipated constantly in the unloading process, wesuggest that some force works to the contact edge in theunloading process. But the mechanism of this force
hasn’t been revealed. In order to consider the mechanism,
this model is useful.

4 Results and Discussion
The relation between the force and the displacement ismeasured like Fig.3. And then, the length of contact area
is measured like Fig.4. By using these measured
parameters, the dissipation energy can be calculated. In
the present study, the same experiment was conducted
three times and the calculation results of the energy
dissipation per variation in the length of non-contact area
are shown Fig.5 As Fig.5 shows, the dissipation energy is

References[1] D.Beak, et al., a master thesis of Tokyo Institute ofTechnology(2013)[2] Y.Sekiguchi, et al., International Journal of Adhesion& Adhesive 49(2014)36

方向適応変換による画像符号化のための係数予測に関する研究
学籍番号: 11 12445

1

名前: 朱正奇

はじめに

指導教員: 山下幸彦

ブロックが隣接しているため，ターゲットブロックの OAVEKLT 基底を延長を延長して求めたその隣接

本研究の画像符号化のための係数予測はブロックの画素値 ⃗g と隣接ブロックの本当の画素値との差は小さいはずである。つまり予測係数を a⃗′ とする

OAVEKLT (Orientation Adaptive Karhunen-Loevei

Transform)[3] をもとにし，現在処理中の画像ブロッ
クを処理済みのブロックの画素値を使って予測する方法である。自然画像において隣接するブロック同士は場合，隣接のブロックの本当の画素ベクトル ⃗g とター
ゲットブロックの予測係数と延長した変換基底 v⃗i を∑ ′使って求めた g⃗′ =a v⃗i との差が最小になる必要がi

互いに強い相関関係を持っているため，ターゲットブiある。従って，||⃗g − g⃗′ ||2 の最小二乗解 a⃗′i を求めればロックの OAVEKLT 係数はその隣接のブロックの境
界の画素値との最小二乗解によって予測することがでよい。すなわち，V = [v1 , v2 ...vi ...] とおけば，予測係
数は a⃗′ = (V T V )−1 V )⃗g で求まる [4]。

きる。この方法によって，エントロピー符号器に送る

普通の OAVEKLT の符号器は OAVEKLT を行っものをターゲットブロックのもとの OAVEKLT 係数

た 後 ，OAVEKLT 係 数 を 量 子 化 し ，直 接 エ ン ト ロと予測係数の差にできる。予測が正確であれば，このピー符号化を行うが，係数予測を取り入れた符号器差が小さくなり，符号化において要する符号量が少な

は ，OAVEKLT 係 数 を 量 子 化 し た 後 ，逆 量 子 化 とくなる [4]。従って理論的には係数予測によって符号化効率が向上する。しかし，係数予測を画像符号化の手

OAVEKLT の逆変換を使い，次のブロックの予測に供する隣接ブロックの画素ベクトル ⃗g を求める。そして法として実用化するためには，符号量の効率が実際に係数予測を行い，もう一回量子化を行う。最後にエン向上したか，どのくらい向上したか，予測がどのくら

トロピー符号化器に送るものはもとの OAVEKLT 係

い正確であるか，予測がどのようなところで正確であ数と予測係数の差である。予測が正確であるほど，こるか，予測は OAVEKLT のパラメータによってどうの差が小さくなり，少ないビット数で符号化できる。

変化するのかという様々な課題に答えなければならな

復号器においても同様な処理を行い，全体的に符号化い。本研究はこのような課題に答えるために，３種類の効率を向上させることができる。

の実験を複数の画像を用いて行い，係数予測の効果を
調べた。

3

符号化実験
実験１では予測精度と送信符号量の差の関係を定量

2

係数予測

的に分析する。各ブロックにおいての予測誤差と送信符号量変化を抽出し，その関係を比較した。実験２では

OAVEKLT は ，DCT (Discrete Cosine
Transform)[1] の 変 換 基 底 を い く つ か の 角 度 で 回予測の精度に影響する要因を分析する。予測の精度に転し，得られた基底の中で，ブロックごとに低い周波

よってそれらの仮定を検証した。仮説１：ターゲットブ数の領域で最も画像情報量 [2] が多く保たれている基ロックの画素値の分散が小さいほど，その隣接のブロッ底を選択し，変換を行う方法である [3]。この方法によクの相関関係が強い。仮説２：隣接のブロックの境界のり画像の各方向のエッジを適切に符号化することがで

画素ベクトルの相関係数が大きいほど，隣接のブロッ

きる。

クの相関関係が強い。実験３では OAVEKLT のパラ

影響する要因として以下のように２つ仮定し，実験に画像符号化を行う際，一般的には画像をいくつかのメータによる違いを比較する。係数予測は OAVEKLT

ブロックに分けて，右ブロックから左へ順番にブロッに基づく方法であるため，本実験は重要なパラメータ

クごとに処理を行う。ターゲットブロックを処理するである Bit Rate と OAVEKLT の変換基底の種類数 K

際，その隣接のブロックの画素ベクトル ⃗g は取得するが係数予測にどのような影響を与えるかを分析した上ことができる。係数予測は処理済みのブロックの画素

で，係数予測の精度を向上させるための OAVEKLT のベクトル ⃗g を用いてターゲットブロックの OAVEKLT

パラメータ設定を調べた。この 3 種類の実験から得ら係数を予測する方法である [4]。

れた結果の一部を以下の図で示す。

37

0.02
Code Saved by Prediction[%]6
Average Code Difference[bit]

4
2
0
-2
-4
-6

K=8
K=16
K=32

0.015
0.01
0.005
0
-0.005

-8
-0.01

-10

0

0.2

-12
0

14

2

4

6
8
Prediction Error

10

12

14

0.6

0.8
1
1.2
Bit Rate[bpp]

1.4

1.6

1.8

図 4: Bit Rate と減少した符号量%

図 1: 予測誤差と符号量差の平均の関係

Bit Rate が低い領域では，係数予測の精度は K の値によってあまり変化しなかった。しかし Bit Rate が大き12Prediction Error

0.4

くなると，係数予測精度の差が開いた。Bit Rate がお10

よそ 0.3-0.4 のところでピークを迎え，それからまた小8さくなった。K=32 の時，係数予測の精度が一番よい

6が，計算時間もかかる。今回の実験によって，中程度

4の画質の画像を出力する場合，K の値を高く設定する

2

ことが効果的であることがわかった。

0
500

600

700

800
900 1000 1100
Average Block Variance

1200

1300

4

おわりに

Prediction Error

図 2: ブロック分散の平均と予測誤差の関係
14

今回は符号化実験において多数の視点から係数予測12の精度を分析した。係数予測の精度をさらに向上させ

10るには，実験２の結果に基づき，ブログの分散を閾値とする係数予測の選択法を開発すること，さらに実験

8３の結果に基づき，出力画像の画質，符号量，計算時6間の３者のバランスについても研究するこることが必

4

要である。

2
0
0.35

0.4

0.45
0.5
0.55
0.6
0.65
Average Block Boundary Correlation

参考文献

0.7

[1] N. Ahmed, T. Natarajan, and K.R. Rao, Discrete cosine transform, IEEE Transactions on

図 3: ブロック境界相関係数の平均と予測誤差の関係

Computers, vol. COM-25, pp. 90-93, January 1974

図 1 より予測誤差と符号量の差の平均の間に単調増加が示された。他の実験画像についても同様な結果[2] H. Ogawa,”Karhunen-Loeve subspace,” Proc.

が得られたため，予測精度を向上させることは符号化

12th Int. Conf. on Pattern Recognition, vol.2
pp.75-78, Hague, Netherlands,1992の効率の向上につながることがわかる。図 2 より，ブロックの分散の平均と予測誤差の間に単調増加が示さ[3] T.tanaka and Y.Yamashita, ”Vector-Embeded
Karhunen-Loeve Transform and its application in

れた。他の実験画像についても同様な結果が得られた
ため，入力画像において平坦な領域で予測精度が正確

Orientation Adaptive Coding of Image,” vol.E83A,No.6, pp. 1257-1266, june 2000になることがわかる。図 3 より，ブロックの境界画素
ベクトルの相関係数の平均と予測誤差の間は簡単な形がなく，他の実験画像においても，同様な結果が得ら[4] Gaudencio G.BanSIL,Jr.,Dr.Yukihiko Yamashita,”Image Coding by Orientation Adaptive Trans-

れることが多い。しかし，図 3 の点は散らばらず，断続的に単調増加と単調減少が交互に示された。このよ
うな特性が現れた理由は今後の課題である。図 4 より，

38

form with Prediction,” Master Thesis, Tokyo
Tech, 2014

Diffusion of Electric Vehicles and Charging Infrastructures:
The Current Condition in Japan
Student Number: 10_01958

Name: Masaaki Ichimura

1 Introduction
Electric Vehicles (EVs) is a technological innovationthat is thought to be a valid measure to reducegreenhouse gas emissions. However, the current EV
adoption rate is very low in Japan. One of the possible
barriers of EV diffusion is its limited cruising range.
While some studies (Neubauer and Wood, 2014) had
shown that substantial charging infrastructures allow
to overcome barriers to EV adoption, it is also assumed
that charging infrastructures are necessary conditions
to increase the number of EVs. That is, diffusion of EVs
and charging stations are in a dilemma. To solve thisproblem, there is a need to clarify the relationships
between EV diffusion and increase of charging stations.
Not only the number of charging stations, but its
placement is also a big deal on EV diffusion. Actually
there are many studies about optimal placement ofcharging stations (Central Research Institute ofElectric Power and Kozo Keikaku Engineering, 2011).
However, these studies had not compared with theideal placement and the actual situation. There arepossible gaps between ideal and real placement. If gaps
exist, there is a need to consider how to overcome thegaps.
Based on these backgrounds, the objectives of thisstudy are:1) To clarify the influence of increase in the number ofcharging stations to support EV diffusion
2) To clarify the actual state of charging stations
placement
In Objective 1, this study targets Japan’s all
prefecture, therefore, the number of samples is N=47.
And then, in Objective 2, this study had researched
Kanagawa Prefecture, which is most successful
prefecture in terms of diffusion state of charging
station. This study had collected data of the normal andfast charger. The sample number of the normal and fast
chargers are N=264 and N=234 respectively.
2 Literature Review
Sierzchula et al. (2014) had studied the effect of
economic and social factors on EV adoption rate with
Ordinary Least Square (OLS) regression model. The
study had shown that the number of charging stations
is significant on EV market share, but the correlation is
weak. The study noted that the substantial charging
station may be effective but does not ensure EV
diffusion.
Central Research Institute of Electric Power and Kozo
Keikaku Engineering (2011) had analyzed optimal
placement by simulating EV driving pattern. The study
had shown that the placement of charging stations
should be more concentrated on the densely populatedarea than on the roadside at the beginning of EV
diffusion.

Supervisor: Naoya Abe

3 Methodology
3.1 Multiple Regression (Objective 1)
In order to estimate the influences of various variables
that are thought to have some influences on EV
diffusion, multiple regression was conducted. The
expression of the MR model is following:
EVshare𝑖 = α + 𝛽1 𝑐ℎ𝑔𝑖𝑛𝑓𝑖 + 𝛽2 𝑖𝑛𝑐𝑜𝑚𝑒𝑖+ 𝛽3 𝐸𝑉𝑐𝑜𝑠𝑡𝑖 + 𝛽4 𝐺𝐸𝑉𝑐𝑜𝑠𝑡𝑖+ 𝛽5 EV_PHVtown𝑖 + 𝛽6 𝑑𝑒𝑛𝑠𝑖𝑡𝑦𝑖+ 𝛽7 𝑐𝑎𝑟𝑝𝑜𝑠𝑒𝑠𝑠𝑖 + 𝜀𝑖𝜀𝑖 : residual, α: intercept, 𝑖: sumple number(1)

By using OLS, we could estimate the regression
coefficient 𝛽𝑘 (𝑘: coefficient number). The definitions of
independent and dependent variables in this model are
shown in the Table 1.
Table 1. The definitions of variables
Variables
Definition
EVshare [%]Chginf[ / 100 𝑘𝑚2 ]Income [¥]EVcost [¥]GEVcost [¥]EV_PHVtown
Density[ / 𝑘𝑚2 ]CarposessMarket share of EV as a percentage
of all cars
The number of charging stations
per 100 𝑘𝑚2
Average monthly income per capita
Estimated annual running cost ofEV
Estimated annual running cost of
Gasoline Engine Vehicle (GEV)Information about participant or
not of Japanese demonstration
project “EV-PHV town”: expressedon 2-way variables (1 or 0)
Population density
The number of car per household

3.2 Quantification Method III (Objective 2)
Quantification Method III (QMIII) is one type of theMultivariate Analysis. QMIII gave score to category andsample respectively. With the scatter diagram ofcategory score or sample scores, we can see thecharacteristics and relationships of the category andsample.
By assessing the location information of the actual
charging stations, this study depicts the characteristics
of charging station location of Kanagawa Prefecture.
This study set 26 categories elaborated in Table 2.
Categories such as “Category 1 low-rise exclusive
residential districts” are the term defined in the “City
Planning Act” law. Again, the sample numbers ofnormal and fast chargers is N=264 and N=234
respectively.

39

Table 2. Categories of the placement of chargers
Categories

concentrate on the roadside. In Figure 1 and 2,
categories locate everywhere, but clustered around thegroup encircled with blue circle. This group is composedof car dealers. That is, charging stations are allocatedeverywhere but concentrate on car dealers. Car dealers
generally locate commercial and industrial district, so
charging stations concentrate on these districts.

Category 1 low-rise exclusive
residential districts
Category 2 low-rise exclusive
Toyota car dealer
residential districts
Mitsubishi car
Category 1 medium-to-high-risedealerexclusive residential districts
Category 2 medium-to-high-rise
Other car dealer
exclusive residential districts
Commercial facility Category 1 residential districts
Convenience store Category 2 residential districts
Parking lot
Quasi-residential districts
Highway service
Neighborhood commercial
area
districts
Company
Commercial districts
Gas stand
Quasi-industrial districts
Distance from Main
Industrial districts
Road: ~250m
Nissan car dealer

Distance from Main
Road: 250~500m

Car dealers

Commercial
Industrial

Figure 1. Category plot of normal chargers placement

Exclusive industrial districts

Distance from Main
Unknown districts
Road: 500m~
4 Results
4.1 Multiple Regression
Table 3 shows the results of the MR model. Model 1 isexpressed by equation (1). In Model 1, income is only asignificant variable. Since some variables seeminsignificant, this study made Model 2 with least
Akaike’s Information Criterion (AIC). Model 2 is given
as following equation:
EVshare𝑖 = α + 𝛽1 𝑐ℎ𝑔𝑖𝑛𝑓𝑖 + 𝛽2 𝑖𝑛𝑐𝑜𝑚𝑒𝑖(2)+ 𝛽4 𝐺𝐸𝑉𝑐𝑜𝑠𝑡𝑖 + 𝜀𝑖

Commercial
Industrial
Car dealers

𝜀𝑖 : residual, α: intercept, 𝑖: sumple number

Figure 2. Category plot of fast chargers placement

In Model 2, all variables are significant but theadjusted R-square is apparently low. That is to say thatthe number of charging stations has a weak influence
on EVshare.

5 Conclusion
5.1 Installation of charging station
This study had clarified the effect of number ofcharging stations on EV adoption. This effect issignificant, but weak. Therefore, installing charging
stations is not much effective on EV diffusion. Thereseems to be needs to help the operation more thaninstallation of charging station.
5.2 Placement of charging station
This study revealed characteristics of charging
stations placement. Charging stations generally locate
roadside and commercial district, industrial district.
These kinds of districts are relatively non-residential.
So there are apparent gaps between actual and ideal
placement.
References[1] Neubauer, J., Wood, E., 2014, The impact of range
anxiety and home, workplace, and public charging
infrastructure on simulated battery electric vehicle
lifetime utility, Journal of Power Sources 257 (2014)[2] Sierzchula, W., Bakker, S., Maat, K., Wee, B., 2014,
The influence of financial incentives and othersocio-economic factors on electric vehicle adoption,
Energy Policy 68 (2014)[3] Central Research Institute of Electric Power andKozo Keikaku Engineering, 2011, Analytical survey ofthe optimal placement of charging stations

Table 3. Result of Regression Model 1 and 2Standardized Beta

Model 1Model 2Chginf

0.377

0.519**

Income-0.651***-0.531***

Carposess

0.19

Density

0.368

EV-PHVtown

0.146

EVcost0.147

GEVcost0.274

0.317**

Adjusted R-square

0.154

0.17

F-value

2.194*

4.138**

AIC-170.04-174.35‘***’ P<0.01, ‘**’ P<0.05, ‘*’ P<0.1
4.2 Quantification Method III (Objective 2)
Figure 1 and 2 display the scatter diagram of category
score of normal and fast chargers placement.
Interpretations of the axes are described with red
characters. Figure 1 and 2 show that charging stations

40

Field survey and numerical analysis for floods in Ho Chi Minh City
Student number: 09_21527

Name: Shintaro Maeda

Supervisor: Hiroshi Takagi(2) After the historical flood on 20th October, 2013, some

1. Introduction
Ho Chi Minh City (HCMC) is the biggest commercial

countermeasures such as raising of the roads and thecity of Vietnam, experiencing rapid economy growth.

repair of drainage systems have been implemented.

HCMC is surrounded by Saigon River and Dong Nai
River. Floods in HCMC show complex patterns
influenced by sea tides in addition to rain falls and riverdischarge particularly in the rainy season from July to
November. Since HCMC expands over the low-lying
floodplain the elevation of which 1 to 3 meters above sea
level, the city essentially has a great risk of the flood. In
addition to this, sea level rise as well as heavy rain, whichis expected to increase due to the global warming, could
further increase the flood risk.
Besides many small floods, there were three large
floods that occurred on 20th October, 7th November, and4thDecember in 2013. Especially, the flood on 20

Fig.1 Investigation placesth

October is considered to be caused due to the highest tide
for the past ten years.
To continue the economic growth, it is necessary forthis city to take measures for reducing economic andsocial loss due to the flood. To reveal mechanisms of floodand find a clue for the countermeasures, the flood in
HCMC was investigated by the field survey and numericalanalysis.

2. Field survey in HCMC
To research the floods in HCMC, we conducted field
Fig.2 Field survey conducted in 2014

survey in HCMC twice in dry (March) and rainy season(November) in 2014, accompanied by the members from

3. Analysis of tides

Ho Chi Minh City University of Technology. We visited
five places where are particularly considered to have highFirst, tides observed at Phu An Station and predictedrisk of the flood and conducted interviews to the residents

astronomical tides were compared to estimate how highand visit Dau Tieng reservoir. Figs.1 and 2 show theocean tides influence the total water levels. It was foundinvestigation sites and the photos during the surveys. The

that actual water level shows 30 to 40cm higher thansummary of the field surveys is as follows:

predicted tides (Fig.2). We investigated atmosphericpressure and precipitation, both of which seem to(1) Flood elevation can be influence by multiple factors

influence water level. However, strong correlations

such as heavy rain, high tide, reservoir operation,

between them were not observed as shown in Fig.4.

and inadequate drainage system.

41

Water Level
[m]

2.5
0.5
-1.5
2013/10/18

2013/10/22

Observed

Predicted

Coefficient R2

Fig.3 Observed tides and Predicted tides
0.04
0.03
0.02
0.01

Fig.6 Two river systems connecting to HCMC

0
0

100

2

200

Water
level [m]Integrated period from the time origin[hr]Fig.4 R2 value between precipitation and adjusted waterlevel considering atmospheric pressure

1
0
-1
-2

A moving average filter was applied to filter out tidal
components from original water levels in order to examine

2013/10/19 0:00

2013/10/21 0:00

2013/10/23 0:00

Filtered

HEC-RAS

Observed

the excess increases caused by the other mechanisms(Fig.5). The figure indicates that the excess component

Fig.7 HEC-RAS water level analysis

reaches up to 80cm. The period of the highest water levelseems to correspond with the timing of the floods in 2013

The regression approximation that enables to estimate

Water level [cm](i.e. 20th October, 7th November, and 4th December).

the water level was also obtained as follows:
𝑍 = 0.001637𝑋1 + 0.001715𝑋2 − 1.16155 (1)100
50

where Z is the excess water level at Phu An associatedwith discharge from reservoir, X1 discharge from Dau

0

Tieng Reservoir, and X2 discharge from Tri An Reservoir.
-50
2013/1/1

2013/4/1

2013/7/1 2013/10/1

5. Conclusion

Fig5. Filtered observed tides

The floods in HCMC would occur by multiple
mechanisms. Among many factors, we concluded that

4. HEC-RAS model

river discharge by reservoir operation particularly affected

To reveal the mechanisms of this excess water level,

the water levels in 2013, in addition to daily ocean tides,

we hypothesized that the operation of two reservoirs in thewhereas it seems that precipitation did not contributeoutskirts of HCMC (Dau Tieng and Tri An) has ansignificantly.

influence on water levels downstream, and performed ariver simulation by using HEC-RAS model, which isReferences

commonly used for one-dimensional flow analysis. The[1]river system developed is shown in Fig.6. The water level

http://floodlist.com/asia/high-tide-ho-chi-minh-

simulated (Fig.7) agrees well with the observed one,city (Accessed on 15th Feb. 2015)

providing the reason of the excess water level about 80cm.
42

High

Tide

Floods

Ho

Chi

Minh

City,

Study of multilingual semi-machine translation based on CollectiveIntelligence
Student Number：11B27085

1

Name：Wuning LI

Supervisor：Yukihiko YAMASHITA

Introduction

environent. It also has problems. For instance, users
have to add a spece between a word and a punctuation
The Wiki system represented by Internet encyclopemark before dividing the original text. In this study,
dia Wikipedia, which anyone can participate anytime,
I develop a new web-based Adam document support
provides useful information continuously by collaboratool by using Ajax.
tion in a wide area from the arts and sciences to life.
Collective Intelligence is the count of the medley of a
However, there is the language barrier to prepare docularge quantity of information by many people, and it isment file in every language in the present Wiki system.
treated as a synonym of ’the wisdom of the crowd’ [2].
Machine translation is a solution in order that people
This concept means that a gathering of many opinions
all over the world can cross the barrier and acquire inis more useful than a conclusion or the information
formation from one document file, but machine transmaking by few authorities, alternatively, leading to alation has a problem that incorrect recognition occurs
right conclusion and prediction.
in syntactic analysis or semantic selection.
Adam document is a document of the XML form that
In order to enable high-accuracy translation, aprovides the information of meaning, construction andtechnique called multilingual semi-machine translation
grammar with tags. Mistakes during syntactic analybased on Collective Intelligence was suggested. By thissis or vocabulary interpretation which are the biggest
technique, an XML document is made using a supfactors of the mistranslation in machine translation disport tool to add the information such as grammar andappears and highly accurate translation is enabled bymeaning, and is translated by an exclusive automaticthe information. A writer who puts a tag on the origtranslator to aim at the construction of the multilininal text is not necessary to be familiar with plural
gual information service system. In this study, I delanguages like a translator. Everyone who is familiar
velop a new web-based support tool to add the inforwith the source language can do it easily.
mation such as parts of speech, grammar and meaning.
Lilith system is a system which can decode tags on

2

the word and translate an Adam document into any
language by using a multilingual dictionary. This mul-Semi-machine translation sys- tilingual dictionary supports plural languages with thetem by Adam document
index of the source language before translation.

Semi-machine translation system by Adam (Auxiliary Description to Attach Meaning) document enables

3

that users add tags with the information to the originaltext on the Web. A computer translates these doucu-

Adam document support tool
by using Ajax

ments into plural languages based on the information
In this chapter, I explain the Adam document supon tags. Then we can read the web page by the multiport tool by using Ajax that I developed.
language. The ducument which is added the tags with
Languages that I used for developing this tool are
information is named Adam document and the maHTML, CSS, JavaScript and PHP. I have installed thischine translation system wthich translates Adam doctool in Web, and anyone can make Adam document
ument into plural languages is named Lilith (LInguaLusing this tool by accessing the following Web address:
Interpreter To Human) system. Ishibashi [1] developed
http://eagle.ide.titech.ac.jp/crocofrog/Adam js/
a support tool to make Adam documents. However thisThe main function of this tool is inputting a source
tool was written in Java and only works on the native
43
1

language (now only English), making an Adam document and saving the Adam document.

have input the original text and the ”MakeTags” button in the lower left is clicked, the original text will

Figure 1 shows the opening screen on the web of this be divided into words and they will be displayed aftool. Concerning that the object of the source language ter ”WORDS”. In addition, the text area will be resetin this study is only English, users who make Adam when ”rest” button is clicked, and the whole screen will
documents can speak English to some extent. For this be reloaded and return to the initial screen when thereason, I designed all the editing screens of this tool in ”reload” button is clicked.
English. In order to make edit areas plain, I divided
The right part of the screen is a domain for giving
it by colors so that users can edit syntactic informa- syntactic information such as base form and parts oftion such as base form and parts of speech, semantic speech, and semantic information and grammatical ininformation and grammatical information easily.
formation. ”BASE FORM” button is for updating theinformation of base form of the word and ”MEAN”
button is for setting semantic information. Users will
give the information of parts of speech when a button
corresponding to parts of speech of the selected wordin the blue domain is clicked. And users will give thegrammatical information when a button corresponding
to the selected phrase or clause is clicked after clicking”GRAMMAR” button in the purple domain.

4

Conclusions
The machine translation is a technique which cancross the language barrier so that people all over theworld can share information with each other. In thisFigure 1: Opening screen on the Web

study, I developed an Adam document support tool byBlack characters ”Auxiloray Description to Attach using Ajax for multilingual semi-machine translation
Meaning” in the gray background on the top shows based on Collective Intelligence which is suggested to”ADAM” which is the title of this tool. And I lo- enable high-accuracy translation. By this tool, we cancated ”SAVEtoSERVER” button and ”SAVEtoLO- make Adam document on Web.
CAL” button under the title. The Adam document
Performance could be improved, by adding a support
is saved to the server when ”SAVEtoSERVER” button function to make Adam document by anyone who is not
is clicked. The file name is given by the clicked time familiar with English, and adding other languages on
automatically to prevent duplication of the file names. the web user interface as well as English, and making
The Adam document is saved on the local directory it possible to read a local file. It is also necessary towhen ”SAVEtoLOCAL” button is clicked. And users make the conversion rules of Lilith system by Collective
can name the file freely.
Intelligence, and letting Lilith system work not only on
The left part of of the screen is a domain for in- the native enviroment but also on the Web.
putting the original text, and checking the tagged document and the Adam document. Left upper ”sentence”
button is gray when it is the initial state, and thecolor shows a chosen state. The text area of ”Input
a sentence” shows a area where users input a origi-

References[1] Takuya ISHIBASHI. Study of multilingual semimachine translation based on Collective Intelli-

nal text. And users can confirm the tagged document
when ”tag” is clicked and the Adam document is dis-gence. Master’s thesis, Tokyo Institute of Technology, 2008.

played when ”xml” is clicked. I used Ajax to develop
this tool so that the document that was carried out isalmost to reflect at the same time and users can con-[2] James Surowiecki. The WISDOM OF CROWDS.
Anchor, Aug. 2005.

firm progress of description here anytime. After users
44
2the Eﬀect of Arc Current and Distance between Electrodes on
Reignition Executable Time in DC Arc Discharge
Student Number: 11 06120

Name: Yuki ORIMO Supervisor: Kunio TAKAHASHI

1Introduction

2

Experiment method and equipment

arc current[A]61.6V. R1 (10 Ω) is used to limit the current at touch
start.
Voltage between electrodes, voltage drop at Ri
Arc discharge is the phenomenon of electric current
andgate-source
voltage of MOSFET are measured byflowing in gas between electrodes. It is applied toaoscilloscope.
welding because high temperature that can melt theelectrodes occurs. Breakdown voltage of gas ranges 3 Experiment resultsfrom a few kV to tens kV, but reignition voltage is
The Change of arc current with time in case thatlower than that voltage[2]. If reignition voltage isreignition is succeeded and failed is shown in Fig. 2,
the same level with voltage between electrodes before
3. Fig. 2 shows in case that arc current is 1.15A,
extinction of arc, it can be reignited easily. In thisdistance between electrodes is 30mm, arc-less time isreport, “reignition executable time” is defined as the
300 µs and reignition are successful. Fig. 3 shows in
range of arc-less time which reignition is executablecase that arc current is 1.15A, distance between elecin.
trodes is 30mm, arc-less time is 350 µs and reignition
In arc welding, anode melts more than cathodeis failed. If successful, arc current is stopped once,
because of emission of the energy equivalent to anand after arc-less time, it started to flow from small
ode’s work function. This causes problems because
current. In this report, “initial current” is defined as
the amount of melt eﬀects on strength and shape of
1.5
welded part. There is a way capable to solve thisproblem by altering polarity of arc discharge considering reignition executable time. Therefore it is1
important to measure reignition executable time andunderstand the occurrence condition of reignition. In
0.5
this research, the eﬀect of arc current and distance
between electrodes is investigated and cleared the oc0
currence condition of reignition is clarified.
-0.5

To investigate the eﬀect of arc current and distance
between electrodes, reignition in control of arc current, distance between electrodes and arc-less time is
attempted. The experiment system and the discharge
circuit are shown in Fig. 1.

-1
0

500 1000 1500 2000 2500 3000 3500
time[µs]

Fig. 2: Change of current between electrodes in caseof arc-less time = 300 µs, arc current = 1.15A, distance between electrodes = 30mm
1.5
1
arc current[A]Fig. 1: Schematic illustration of experiment system
and arc discharge circuit

0.5

Arc discharge is generated by touch start. After
starting arc discharge, arc current, distance between
0electrodes are set to the target value and waited until arc discharge became settle. In stable condition,-0.5
arc current is stopped by MOSFET switch and voltage between electrodes is set to 0V. After arc-less-1
time, reignition is attempted by raising voltage be0 500 1000 1500 2000 2500 3000 3500
tween electrodes to the same level before extinction
time[µs]of arc.
The electrodes is ThO2 -W and its diameter is 1mm. Fig. 3: Change of current between electrodes in case
Shield gas is Ar and its flow rate is 0.55L/min. Con- of arc-less time = 350 µs, arc current = 1.15A, disstant current source is used, its open-circuit voltage is tance between electrodes = 30mm
45

electric field intensity[V/m]this small current. If failed, after arc-less time, initial about arc current and electric field intensity, intercurrent flows but over time it attenuates and finally dependence between arc current and electric field invanishes.
tensity.
Reignition executable time is defined rigorously
arc-less time 400µs
here. In each conditions reignition executable time8000
[successed]
is the range of arc-less time in which reigniton suc[failed]
7000
cess rate is over 50%. Fig. 4 shows relation between reignition executable time, arc current and dis6000
tance between electrodes. From this graph, we can5000
see positive correlation between arc current and reignition executable time and negative correlation be4000
tween distance between electrodes and reignition ex3000
ecutable time.The eﬀect of arc current and distance
between electrodes on reignition executable time are
2000
suggested.
1000

reignition executable time[µs]

1200

0.7

arc current 0.70A
0.85A
1.00A
1.15A
1.30A

1000
800

200
0
35

Fig. 4: Reignition executable time

4

1.1

1.2

1.3

Conclusions

In this report, I measured reignition executable timethrough experiments in control of arc current, distance between electrodes and arc-less time, and investigated the eﬀect of arc current and distance between electrodes. Positive correlation between arc
current and reignition executable time and negative
correlation between distance between electrodes andreignition executable time are clarified. These causes
are considered arc current and distance between electrodes deciding electrodes temperature and electricfield intensity. Additionally thresholds of arc current
an electric field intensity required by reignition became obvious from graphs as shown in Fig. 4, Fig.
5.

400

10
15
20
25
30
distance between electrodes[mm]1Fig. 5: Threshold of arc current and electric field intensity in case of arc-less time = 400 µs

5

5

0.9

arc current[A]

600

0

0.8

Discussion

The diﬀerence between reignition and general discharge is that electrodes is high temperature to glow
by preceding arc discharge. Voltage is applied to cathode in high temperature, so electrons is easy to be
emitted from cathode by thermal electron emission[4],initial current increases. The amount of initial current
contributes to reignition because this causes impact
ionization and gets to discharge again. Electrodes
temperature determines initial current and arc current determines electrodes temperature. It is obvious
from energy consumed at discharge that the more thearc current, the higher the temperature of electrodes.
Hereby I can explain positive correlation between arc
current and reignition executable time.
The longer the distance between electrodes, theweaker the electric field intensity. In the mechanismof starting arc discharge, it is possibly that electricfield intensity eﬀects on acceleration of electrons andcations from impact ionization[1]. If electrons haveenough energy at impact, initial current can cause
impact ionization. That is, the stronger the electricfield intensity, the easier the reignition occurs.
Initial current changes by arc current and arc-lesstime. Accordingly, Fig. 5 shows whether reignition success rate exceeds 50%, being fixed arc-lesstime and being set arc current and electric field intensity(voltage/distance between electrodes) as variables. This indicates the conditions of reignition

6

References[1] Mase HIROSI, “Plasma Production by D. C. Discharge”, J. Plasma Fusion Res., Vol. 3 No. 69 P237242 (1993)[2]Mikio MOTOKI, Masaaki ONO, Yukichi KAMIIDE, “Gap Phenomenon in Discharge Process”, Journal of The Japan Society of Electrical Machining Engineers, Vol. 8 No. 15 P44-53 (1974)[3] Masayoshi OZAWA, Toshikatsu MORITA, Kunio
OMURA, “On the After-Arc Phenomena”, Journal of
JWS, Vol. 24 No. 1 P5-11 (1955)[4] Yoshihiro MUROOKA, K. R. Hearne, “The Effect of Electrode Temperature on Spark Brearkdown
Voltage”, The transactions of the Institute of Electrical Engineers of Japan.A, Vol. 92-A No9 P19-26(1972)

46

Facilitation of methane production from wastewater containing soybean oil by microbial pretreatment
Student Number: 11-15410

Name: Kazumasa TANAKA

1. Introduction

Supervisor: Kiyohiko NAKASAKI

experiment, 400 mL of supernatant was extracted from thereactor, and the same volume of fresh substrate was fed everyday.
The value of pH and ORP was measured by the pH and
ORP meter respectively. A sample of the exhaust gas wascaptured into a plastic bag and gas compositions were
measured by GC-MS. Gas volume was measured using dry
test gas meter.
Table 1 Composition of the BO-0 and BO-25.

Fat is one of the major organic matters in wastewater fromfood processing factory. Fat is a cause of the scum which isdifficult to be treated in the process of the wastewater
treatment. For this reason, the fat contained in the wastewater
is usually separated and discarded as the industrial waste
before the biological treatment stage. However, the fat can bea good substrate for methane fermentation as in theoreticalvalue, 850 mL of methane gas can be produced from 1 g offat, even though 500 mL of methane gas can be produced
from 1 g of protein, and 395 mL of methane gas can beproduced from 1 g of carbohydrate [1]. Furthermore,utilization of the fat for methane fermentation will reduce thecost and environmental damage caused by the disposal of thefat. So the establishment of methane fermentation ofwastewater containing fat is desired.
In this study, degradation pretreatment of the soybean oil by
the microorganism which can degrade the fat wasinvestigated for the facilitation of the methane production
from the wastewater containing fat.

Glucose (g/L)
Sodium acetate (g/L)
Lactic acid (g/L)
Soybean oil contained in
the substrate (g/L)

BO-0
12.5
6.25
5.55

BO-25
9.38
4.69
4.16

0

2.6

2-2. Methane fermentation of soybean oil
pretreated by fat degrading yeast
Two strains of yeast, Pseudozyma antarctica NBRC 10260
and Pseudozyma rugulosa NBRC 10877 were used topretreat the soybean oil. The yeasts were precultivated in 50
mL of YM broth at 25 ºC for 2 days. Then, 0.5 mL of the
preculture was inoculated to 100 mL of Yeast oil broth thatwas composed of 20 g/L of soybean oil, 3 g/L of yeast
extract and inorganic nutrients and cultivated at 25ºC for 7
days. After 7 days cultivation, the culture was used formethane fermentation.
The mixture of the culture of yeasts in Yeast oil broth andglucose, sodium acetate, lactic acid was used as substrate formethane fermentation. Three kinds of the substrate withdifferent mixing ratio of the culture of yeast in Yeast oil brothand glucose, sodium acetate, lactic acid, designated as YO-0,
YO-25 and YO-50 were prepared. The compositions of
those substrates are shown in Table 2. Those substrates were
supplied in a stepwise manner: 0-13 days with YO-0, 14-47
days with YO-25 and 48-64 days with YO-50.
For the startup of the experiment, 1 L of granular sludge
from food processing factory and 1 L of YO-0 were mixed in
the anaerobic reactor. The adjustment of pH, flushing of thereactor by N2, and fermentation condition were the samewith previous experiment. In this experiment, 200 mL ofsupernatant was extracted from the reactor, and the same
volume of fresh substrate was fed every day.
Physicochemical analyses for taken samples were thesame with previous experiment.

2. Materials and methods
2-1. Methane fermentation of soybean oil
pretreated by fat degrading bacteria
Strain T1 isolated as the fat degrading bacteria wasprecultivated in 50 mL of TS broth at 30ºC for 3 days. Then
0.5 mL of the preculture was inoculated to 300 mL of TS oil
broth that was composed of 75 g/L of soybean oil, 25 g/L oftrypticase and inorganic nutrients and cultivated at 30ºC for 5
days. After 5 days cultivation, the culture was used formethane fermentation.
The mixture of the culture of T1 in TS oil broth and glucose,
sodium acetate, lactic acid was used as substrate for methane
fermentation. Two kinds of the substrates with different
mixing ratio of the culture of T1 in TS oil broth and glucose,
sodium acetate, lactic acid, designated as BO-0, BO-25 wereprepared. The compositions of those substrates are shown in
Table 1. Those substrates were supplied in a stepwisemanner: 0-15 days with BO-0, 16-36 days with BO-25.
For the startup of the experiment, 1 L of granular sludge
from food processing factory and 1 L of BO-0 were mixed in
the anaerobic sequencing batch reactor (ASBR). The reactor
was made of Pyrex glass and had a working capacity of 2.5
L. The value of pH of the mixture of granular sludge andBO-0 was adjusted around 8.0 with NaOH, and the reactor
was flushed with N2 for 10 min. The temperature in thereactor was maintained at 39 ºC by water jacket, and thesolution was agitated with a stirrer at 100 rpm. In this47

Yeast oil broth in the YO-25. After changing the substrate toYO-50 at day 48, the gas volume decreased to around 1.5 L,
and did not recover until the end of fermentation.
Long Chain Fatty Acid is produced in the degradation of fat,and it is known that the high concentration of LCFA cause
the inhibition against microorganisms [4]. In this experiment,
it is considered that LCFA was produced during thepretreatment of soybean oil. And YO-50 contained higher
amount of soybean oil than O-25. As a result, YO-50 may
have contained higher amount of LCFA than O-25.
Therefore, it is considered that the concentration of LCFA in
the reactor increased after change of the substrate fromYO-25 to YO-50, which led the decrease of the gas
production.

Table 2 Composition of the YO-0, YO-25 and YO-50.

Glucose (g/L)
Sodium acetate (g/L)
Lactic acid (g/L)
Soybean oil containedin the substrate (g/L)

YO-0
11.86
5.93
5.27
0

YO-25 YO-50
8.9
5.93
4.45
2.97
3.95
2.64
2

4

3. Results and discussion
3-1. Methane fermentation of soybean oil
pretreated by T1
Fig. 1 shows the courses of total gas volume in thefermentation of soybean oil pretreated by T1. After changing
the substrate to BO-25, the total gas volume increased until
day 24. It is considered the trypticase contained in the TS oil
broth of BO-25 was consumed by the microorganisms, andgas was produced consequently. Then, after day 24, the totalgas volume decreased to around 1 L until day 34. It isreported that Bacillus amyloliquefaciens can produce thebiosurfactant called fengycin which is toxic for the
microorganisms [2,3]. It is considered that the culture of T1
in TS oil broth contained fengycin, and accumulation of
fengycin inhibited the methanogen activity.

6

Total volume (L)

5
4
3
2
1
0

10

Total volume (L)#10260#10877

0

10

20

30
40
Time (d)

50

60

70

8

Fig. 2 Courses of total gas volume from YO-0, YO-25 andYO-50.

6

4. Conclusions
The methane could be produced from Yeast oil broth
pretreated by P. antarctica NBRC 10260 and P. rugulosa
NBRC 10877 which produce nontoxic biosurfactant while
the TS oil broth pretreated by T1 could not be used formethane fermentation due to the toxicity of the biosurfactant
produced by T1.

4
2
0

0

10

20
Time (d)3040Reference

Fig. 1 Courses of total gas volume from BO-0 and BO-25.

[1] N. Kataoka et al. : Studies on a Methane Fermentation
Process for Treating Oil and Fat Containing
Food-processing Wastewater, Ebara engineering
review, 212, 3-9 (2006).
[2] S. Lee et al. : Isolation, Purification, andCharacterization of Novel Fengycin Sfrom Bacillus
amyloliquefaciens LSC04 Degrading-Crude Oil,
Biotechnology and Bioprocess Engineering, 15,
246-253 (2010) .
[3] T. Okano : 殺菌と界面活性剤の話, Kao Hygiene
Solution, 7, 24-25 (2004).
[4] I. Angelidaki et al. : Effects of free long-chain fatty
acids on thermophilic anaerobic digestion, Appl
Microbial Biotechnol, 37, 808-812 (1992).

3-2. Methane fermentation of soybean oil
pretreated by yeast
Fig. 2 shows the courses of total gas volume in thefermentation of soybean oil pretreated by yeasts. During 1 to
10 days of fermentation, the gas production was not stable.
Then, the volume became stable from day 10. Afterchanging the substrate to YO-25 at day 14, the gas volume
decreased to around 1.5 L. Then, the volume began toincrease at day 22, and reached at around 2.5 L on day 26
that is almost the same volume with that observed beforechanging the substrate to BO-25. It is considered that during
22 to 26 days, microorganisms were acclimated to utilize the48

Arch action in embankments subjected to subsidence of foundation
Student ID: 11_24247 Name: Yoshito MIYATA Supervisor: Jiro TAKEMURA
conducting subsidence. Each two density pans are
fixed same distance of pressure gauges from centerlineof apparatus.
Silica sand No.8 and No.6 are used to makeembankments. Their physical value is as follows.

１．Introduction
Because of the great east Japan earthquake on
March 11th 2011, many embankments was collapsed.
According to the report of Ministry of Land,
Infrastructure and Transport (MLIT), theseembankments are collapsed by liquefaction due toinundation in the loose part of beneath embankment
before earthquake attacked. But until quite recently,
liquefaction occurs only sand ground which wasformed in natural condition. So, it is important toresearch how internal sand condition goes loose. In
this study, I consider this mechanism by noting thevariation of internal stress which is subjected tosubsidence of foundation. To research this mechanism,a series of 1G physical models of an embankment
inclined at angle of repose was conducted with four
different heights. If we clarify the mechanism
reducing the resistance to liquefaction in this study, it
will contributes to design the embankments whichhave some resistance to liquefaction.

Table 1 Physical value of silica sand No.8

Parameters
Specific gravity
Maximum grain size
Medium grain size
Uniformity coefficient
Minimum density
Maximum density

Values and
Units
2.637
0.250 mm0.090 mm
3.05
1.153 g/cm3
1.554 g/cm3

Table 2 Physical value of silica sand No.6

Parameters
Specific gravity
Maximum grain size
Medium grain size
Uniformity coefficient
Minimum density
Maximum density

２．Physical model
Physical models was conducted by using apparatus
which can make subsidence. This subsidence speed
was controlled 0.041mm/s by actuator without making
any external effect. The value of subsidence isdetermined by using dial gauge fixed under theapparatus. Frame of apparatus was made of Aluminumand surface of apparatus is covered with teflon sheet
to reduce the friction between beneath theembankment and surface of apparatus. To determine
the soil pressure beneath the embankment, fourpressure gauges is fixed on apparatus not making any
difference of height between surface of apparatus andpressure gauges. Each pressure gauge is fixed 30mm,
55mm, 100mm, 150mm from centerline of apparatus
whose value of subsidence is maximum.
Sand spreader was used to make embankments. The
height of spreading sand is 10mm from top ofembankments to make embankments’ density
uniformly. And height of embankment is controlled bychanging the number of sand spreader’s reversal.
Normally, we can make triangle embankment
reversing sand spreader four times. After making
embankment on the apparatus, we made subsidence
and determined the value of pressure by using datalogger. We determined sand density by using density
pan which was made of acryl. Eight density pans was
put on apparatus before making embankment andremoved carefully without giving external effect after

Values and
Units
2.643
0.850 mm0.316 mm1.30
1.241 g/cm3
1.556g/cm3

Fig. 1 Sectional view of experiment３．Experimental theory
I used the concept of Fixed Principal stress Analysis(FPA) to this study. This way assumes the direction ofprincipal stress. In this study, I determined the
direction of principal stress as Fig.2 from
Mohr-Coulomb yield criterion. Next, I divide
embankments into six areas as Fig.3 to decideboundary condition of Lame-Maxwell equation as
follows.

 1
  sin 3
  cos

49

The equation for direction of ξ
The equation for direction of η

1

2v

0.8

3v
4v

0.6

5v

 z( s B  H2  B  H2  )
 z( s B  H3  B  H3  )

0.4

 z( s B  H4  B  H4  )
 z( s B  H5  B  H5  )

Fig.2 The direction of principal stress after subsidence occured

0.2

0

0

0.2

0.4

0.6

0.8

1

s s s s s

Fig.4 The result of Test2 to 5 (subsidence 10mm)
Note: Vertical axis is non-dimensional vertical pressure andhorizontal axis is non-dimensional distance from centerline of
the apparatus. Real line is analysis solution from FPA. Eachpoint is result of experiment. Black line is the result of Test2,
blue line is that of Test3, red line is that of Test4, and pink lineis that of Test5.

I conducted same experiment using silica sand No.8
and No.6 to consider the difference of grain size. Also,
to consider the difference between dry condition andunsaturated condition, I used moist silica sand No.6.
Fig.4 shows that the result of Test2 to 5. Before
subsidence occurred, pressure of the bottom center ofembankment is higher any other point. But after
settlement, we can see the pressure of center decreased.
It is because the direction of principal stress ischanged. Particularly the shape of principal stress islike an arch (arch action). Also we can see the pinkline, that is the result of Test5 near the center ofembankment doesn’t change. So there is something
like the ratio of embankment’s width and height which
determine not to occur the arch action.
But results of Test22 to 25 depend on the way tomaking embankment much, so the way to makeembankment uniformly is my challenging for the
future.

Fig.3 Divided areas of embankment
Note: Angle φ means angle of repose, and angle ψ means
angle of σ1’s rotation from z axis.

4．Result and discussion
Test
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

Table.3 Experimental condition
embankment's height settlement
density
152mm
40mm
1.37g/cm3
148.4mm
20mm
1.31g/cm3
124.8mm
20mm
1.31g/cm3
96.2mm20mm
1.29g/cm3
57.2mm20mm
1.27g/cm3
125.4mm
20mm
1.29g/cm3
82mm
20mm
1.31g/cm3
40.6mm20mm
1.31g/cm3
146.6mm
10mm×31.32g/cm3
85.4mm
10mm×31.31g/cm3
142mm
20mm
1.45g/cm3
118.6mm
20mm
1.42g/cm3
87.4mm20mm
1.46g/cm3
50.2mm20mm
1.44g/cm3
117.8mm
20mm
1.49g/cm3
80.6mm20mm
1.44g/cm3
39.4mm20mm
1.43g/cm3
142mm
10mm×31.43g/cm3
87.4mm
10mm×31.44g/cm3
144.4mm2.5,5,10mm 1.41g/cm3
87.4mm
2.5,5,10mm 1.43g/cm3
150mm
20mm
1.47g/cm3
100mm
20mm
1.47g/cm3
75mm
20mm
1.47g/cm3
50mm
20mm
1.47g/cm3

5.

Conclusion
1.
2.
3.
4.

The pressure of center beneath embankment
decreased rapidly after subsistence started.
The lower embankment is, the lower effect ofarch action occur in the embankment.
The shape of embankment while foundation
subsides doesn’t change uniformly.
The soil pressure beneath embankmentchanged more rapidly in unsaturated
condition than in dry condition.

6. Reference
1) JP. Wittmer, P. Claudin, M.E. Cates, and J.P.
Bouchaud: An explanation for the central stressminimum in sand piles, Nature 382(6589),
pp.336-338, 1996.
2) ピパットポンサー・ティラポン, 竹山智英: 安
息角で形成された平面状斜面における固定主応力軸を仮定した応力解析，第 45 回地盤工学
研究発表会講演集, pp.305-306, 2010.

Note: From Test1 to 10 are conducted by dry silica sand No.8,
from Test 11 to 21 was conducted by dry silica sand No.6 andfrom Test22 to 25 was conducted by moist silica sand No.6
whose water ratio was 10%.

50