ISSN 1880-8468
Technical Report ofInternational Development Engineering࿖㓙㐿⊒Ꮏቇႎ๔TRIDE-2008- 02

February 8, 2008

Abstracts of Master Theses
Presented in February 2008

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

Preface
Master theses of Department of International Development Engineering, Tokyo Institute of Technology were presented successfully on July 25, 2008 and February 8, 2008.
This technical report consists of the abstracts of those theses.

iTechnical Report of International Development Engineering
TRIDE-2008-02
Table of Contents
Path loss model considering beam tilt angle of base station antenna formobile communication systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ryoko NAGANO

1

Comparative study of biodiesel production by alkaline transesteriﬁcationfrom low-valued feeds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sinthupinyo PATIMA

5Moving picture coding using wavelet transform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osamu SAKURAI

9

Expansion of non-negative matrix factorization using Fisher’s discriminant
and its application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naoya KOIDE

13

Parameter determination method for constitutive model of sand basedon relative density relation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takanori AOKI

17

An applicability of DR-MEAM parameters for interfacial energy calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Takao ABE

21

Application of information and communication technology in cultural world
heritage site; case of Luang Prabang, Lao PDR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shingo ENOKI

25

Fundamental research on the hydration reaction of steel slag hydratiod matrix(SSHM). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ippei SUZUKI
calibration method for angle-of-arrival estimation in multipath
environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daiji TOMITA

29

Array

33

Selective catalytic reduction of nitrogen monoxide by CeO2 -added Nb/TiO2
catalysts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Megumu MAKII
ii

37

Ca leaching deterioration behavior in cement-based materials by electrochemical acceleration method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Masaya MATSUDO

41

Outdoor urban scale model experiments on the eﬀects of building geometry on
the urban atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Takanobu MORIIZUMI45

Pattern recognition by kernel Wiener ﬁlter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hirokazu YOSHINO

49

Eﬀect of stiﬀness and shape on release mechanism mimicking gecko foothair. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junichi WATANABE

53

A study about the parameters of the non-linear contractancy expressionfunction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NI Wei

57

Development of a visible light responsive photocatalyst for PCE treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhiwei WU

61

Plasma enhancement of hydrogen permeation through metal membrane. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuejia ZHU
63
Enhancement of permeation for emulsion liquid membrane separation of coaltar absorption oil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dejin BI

67

Strength of clay seam along the potential failure plane in soft rock slopes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sokbil HENG

iii

71

Path Loss Model considering Beam Tilt Angle of Base Station
Antenna for Mobile Communication Systems
Student Number: 05M18107

Name: Ryoko NAGANOSupervisor: Jun-ichi TAKADA

Abstract
携帯電話のサービスを安定して供給するには携帯電話端末機での受信信号電力や希望信号と干渉信号の比を十分考慮したセル設計が必要となる。通常、受信信号電力の大きさを予測するには奥村-秦モデルのような伝搬損失の距離特性モデルが使用される。伝搬損失モデルはアンテナ利得を含んでいないため、実際に受信信号電力を予測する際にはアンテナ指向性が与える影響を合わせて考慮する必要が生じる。そこで本研究では、基地局におけるアンテナ利得が伝搬損失の距離特性モデ
ルに与える影響を市街地における実験データを使用して検討した。

1

Introductionis the Okumura-Hata model [1, 2] which is widelyused to predict path loss because of its simplicity
and the applicable range of the parameters. The
Okumura-Hata model is expressed in a closed formof the antenna heights, the carrier frequency and the
distance between the BS and mobile station (MS ).
The Okumura-Hata equation for path loss in dB iswritten as

Wideband Code Division Multiple Access (WCDMA)is a type of third-generation (3G) mobile system
which provides wide-area wireless voice telephonyand broadband wireless data communication to mobile phones. Services such as web browsing, audiostreaming, and video-streaming are signiﬁcant examples of broadband data communications.
However, these services might be limited due toattenuation and ﬂuctuation of the received signal
strength at the mobile terminal, on which the installation of base station (BS ) could have a big impact.
Therefore, in cell planning, path loss prediction isused in order to minimize dead spots avoiding poor
quality-of-service (QoS ). Path loss is deﬁned as thepower density reduction through propagation whichis aﬀected by free space loss, reﬂection, diﬀraction,
scattering, and absorption. The simplest path loss
model is Friis’ law which describes free-space propagation. Friis’ law on the decibell isλPRX = PTX + GTX + GRX + 20 log(),
4πd

P L = 46.3 + 33.9 log(f ) − 13.82 log(hBS ) − a(hMS )+(44.9 − 6.55 log(hBS )) log(d) + Cm , (2)where, P L denotes the path loss, f is the frequency
in MHz, d is the distance between the BS and MS
antennas in km, hBS and hMS is the BS and MS
antenna height above ground level in meter respectively, and Cm is deﬁned as 0 dB for a suburban
or rural (ﬂat) environment, and 3 dB for an urban
environment. The parameter a(hMS ) for an urban
environment is deﬁned asa(hMS ) = 3.2(log(11.75hMS ))2 − 4.97

(1)

(3)

when f > 400 MHz, while for suburban and rural(ﬂat) environments,

where PRX and PTX denote the received power andthe transmit power respectively, GTX is the transmit
antenna gain to the direction of the receive antenna,
λ)GRX is the receive antenna gain, and 20 log( 4πdrepresents the free-space path loss.
Several path loss models have been proposed formobile communications. Some were derived analytically considering the laws that govern electromagnetic wave propagation. These models require geometrical information such as the building height,
the street width, etc. Others were derived empirically based on ﬁeld measurements and observation.
These empirical models usually include small-scalefading and large-scale-fading. For macrocell planning, one of the popular empirical path loss modelsa(hMS ) = (1.1 log(f ) − 0.7)hMS − (1.56 log(f ) − 0.8).
(4)
In order to predict the Rx power, the eﬀect of the
BS antenna gain is also to be designed considering
the predicted path loss. The design of the eﬀect of
the BS antenna is important as most of the BS antennas are directional, because this saves radiation
power and aids in interference suppression. In thecase of line-of-sight (LOS ) propagation, the antenna
gain in the direction to the receive antenna is simply
added to the transmit power. However, in macrocell
scenario, the transmit signal usually travels along
various paths to the receiver. This propagation iscalled multipath propagation. Thus, the eﬀect of BS
1
1

3

Table 1: Measurement Information

BSMS

Area class
Sectors per cell
Carrier frequency
Transmit power
Antenna height
Antenna gain
Elevation beamwidthAzimuth angle (peak)
Beam tilt angle (electronical)
Cable loss
antenna height

Regression is a method to describe the relationshipbetween the dependent variable (path loss in thisstudy) and the independent variables (BS-MS distance, antenna height, etc. in this study). The dependent variable is assumed to be determined by the
independent variables. When the relationships between the variables can be presented by linear functions, it is called linear regression. The linear regression equation is written as

Urban
2 or 3
2.2 GHz
30 dBm30–47.2 m
16.95 dB
120 deg
60 deg
1–9 deg
1.3–3.9 dB
1.7 mŷ = α + β1 x1 + β2 x2 + · · · + βp xp ,b = (XT X)−1 XT y,
where
yT =

1
 1

X= .
 ..

(

···

(7)

yn

)

y1

y2

x11
x21
..
.

x12
x22
..
.

···
···
..
.

x1p
x2p 

..  ,. 

1 xn1 xn2(b = α β 1 β2

···

xnp

···

βp

T

,

)



.

(8)

(9)

(10)

The smaller the variance of the residual is, thebetter the regression model describes the relationship between the variables. The critical of the coefﬁcient of determination (R2 ) is often used in order
to examine the goodness of ﬁt in the resulting equations. The coeﬃcient of determination is deﬁned as

Path Loss Measurement

The ﬁeld data were measured by driving throughthe urban service areas in western Japan. Table 1
lists the basic measurement parameters. The BS
antenna directivity was tilted electronically not mechanically and thus, the antenna beam pattern was
simply shifted down. The data at a measurement
point consists of the Rx power and the latitude andlongitude of the measured location. Thus, assumingthat the receive antenna gain is zero, the path loss
minus the transmit antenna gain is determined ateach measurement point as we know the value of Tx
power and cable loss. The equation is
P L − GTX = PTX − LC − PRX(6)where ŷ denotes the dependent variable, each xi denotes the independent variables, α and βi denotes
the intercept and coeﬃcient of the regression model,
respectively, and p denotes the number of the independent variables. The coeﬃcients and intercept aresolved using least squares which is expressed asantenna gain is not just viewed to go straight to thereceiver but to be averaged over multipath propagation.
Therefore, in this study, how the BS antenna directivity aﬀects the path loss prediction in multipath
environment, has been examined.
Section 2 presents path loss data collection. Then,in section 4, two models are ﬁtted by regression analysis using the path loss data. One model is the same
form as Okumura-Hata model. The other model includes an additional term that could describe theeﬀect of the BS antenna gain if it exists. Afterwards
the two models are compared in terms of goodnessof ﬁt.

2

Linear Regression

R2 =SStot − SSreg, 0 ≤ R2 ≤ 1,
SStot

where
SStot =n∑(yi − y¯i )2 ,

(11)

(12)

i=1

SSreg =n∑(yi − yˆi )2 ,

(13)

i=1

SStot stands for the total sum of squares, SSreg
stands for error sum of squares, yˆi denotes the expected value of yi , and y¯i denotes the average of yi .
The closer R2 is to 1.0, the better the simple linear regression model explains the relationship of thevariables.
However, it is not straightforward to choose thebest model based on R2 for the models which contain diﬀerent numbers of independent variables. By
adding independent variables to a model, R2 is sure
to increase, though it may be quite small. Hencethe adjusted R2 (R¯2 ) [7] is used to adjust the number of independent variables in the model. Unlike(5)

where LC denotes the cable loss. If the path loss
and the GTX data sets can be proven to be independent from GTX , GTX can be neglected in Rx power
prediction. And then we can conclude that the effect of the BS antenna gain can be smoothed overmultipath propagation.

2
2

Table 2: Path Loss Models
Same form as the Okumura-Hata model
P L = (159 log(hBS ) − 223) log(d)−12.0 log(hBS ) + 138
Same form as O-H model + term of X
P L = (118 log(hBS ) − 154) log(d)−31.3 log(hBS ) + 170 − 1.08(X − 3.03)2
Figure 1: Deﬁnition of X as the elevation angle difference between the antenna main lobe and the LOS
between the BS and MS
Path Loss [dB]

(14)

140
120
100

60
2

4

6

8

10

X [degree]

Figure 2: Second regression model of the path loss
with the eﬀect of the antenna gain

Path Loss Models

Two path loss models are calculated by linear regression to compare their goodness of ﬁt.
The ﬁrst is the regression model which takes thesame form as Okumura-Hata model. Since the carrier frequency and the MS height of the data areconstant, the intercept of the model includes them.
The model consists of the distance and the BS heightterms, which are all in logarithm to the base 10 scale.
P L = (a log(hBS ) + b) log(d) + c log(hBS ).

0.20

80

where P is the total number of independent variables, and N is the sample size.

4

0.17
adj.R2160

R2 , adjusted R2 increases only if the additional term
explains the model better than would be expectedby chance. The adjusted R2 is deﬁned as(N − 1),R¯2 = 1 − (1 − R2 )(N − P − 1)

adj.R2term of X shows better agreement to the measured
path loss data with respect to the adjusted R2 . Also
the signiﬁcance of X is proved by hypothesis testing
using the conditional F-test [8]. Since the path loss
is dependent on X, its inclusion to the conventional
path loss model is a signiﬁcant supplement.
Figure 2 is a graph of the second regression model.
The color level of regression model denotes the pathloss in terms of the distance between the BS and
MS. The graph presents the dependency of the second regression model on X. The path loss decreases
about 8 dB, i.e. Rx power increases 8 dB, when Xchanges from 5 to 1 degrees. This shift of the levelis similar to the vertical antenna gain pattern in therange of X : 5-1 degrees (see Fig. 3). Although thisgraph shows the vertical antenna gain pattern in acertain azimuth direction, the other antenna gain
patterns are assumed to be approximately similar.
This similarity of the shifts of the path loss and vertical antenna gain against X (5-1 degrees), suggeststhat X in the path loss model can possibly describe
the neglected eﬀect of the BS antenna gain and thus,
give better prediction of the Rx power.

(15)

The second model is the same form as the OkumuraHata model plus a term, e(X + g)2 .
P L = (a log(hBS )+b) log(d)+c log(hBS )+e(X +g)2 .
(16)
where X is the elevation angle diﬀerence between
the antenna main lobe and line-of-sight (LOS ) between the BS and MS (see Fig. 1) and is deﬁnedashBS − hMS
X = θtilt − arctan(
,)
(17)
d
where θtilt denotes the BS antenna beam tilt angle,MS) represents the elevation angle lookarctan( hBS −hding from the MS side to the BS. Parameter X wasintroduced to the path loss model because BS antenna gain depends on the parameter X and hence,
it is expected that the eﬀect of antenna gain in apath loss model can be expressed more precisely byadding the parameter X appropriately if it exists.
Table 2 lists the regression result and the adjusted coeﬃcient of determination of each model.
The result of the second model which includes the5Conclusion

A modiﬁed path loss model based on Okumura-Hatamodel considering the eﬀect of BS antenna gain was
examined to predict Rx power more precisely.
The result shows that the path loss data that33

[7] J.G. Liao , D. McGee, “Adjusted Coeﬃcientsof Determination for Logistic Regression”,The
American Statistician, vol.57, 2003
[8] W. L. Carlson and B. Thorne, Applied Statistical
Methods, pp. 724, 1997

Figure 3: Vertical antenna gain pattern versus X
includes the eﬀect of the BS antenna gain is dependent on the angle X. Thus, the eﬀect of the BS
antenna gain seems to be describable by including
X which is associated with antenna gain. Therefore
this study proposes to present path loss excluding
the BS antenna gain as
P L−GTX = (a log(hBS )+b) log(d)+c log(hBS )+e(X+g)2(18)
by adding the term, e(X + g)2 to the conventional
form of the Okumura-Hata model. It should be concerned that the coeﬃcient of X would change depending on the BS antenna gain pattern.

References[1] M. Hata, “Propagation loss prediction models
for land mobile communications”, Microwave
and Millimeter Wave Technology Proceedings,
ICMMT, 1988.
[2] M. Hata, “Empirical formula for propagation
loss in land mobile radio services”, IEEE Transactions on Vehicular Technology, vol. VT-29,
no.3, pp. 317-325, Aug. 1980.
[3] COST Action 231, “Digital mobile radio towards
future generation systems, ﬁnal report”, European Communities, EUR 18957, 1999.
[4] Y. Okumura, E. Ohmori, T. Kawatoko, and K.
Fukuda, “Field strength and its variability in
UHF and VHF land-mobile radio service”, Review of Electrical Communications Laboratory,
vol.16, no.9-10, pp. 825-873, 1968.
[5] J. Chambers, W. Cleveland, B. Kleiner, and P.
Tukey, Graphical Method for Data Analysis,
Chapman and Hall, 1983.
[6] J. H. Zar,Biostatistical Analysis, Prentice-Hall
Inc., pp.82-84, 1974.
4
4

Comparative Study of Biodiesel Production by Alkaline
Transesterification from Low-valued Feeds
Student ID: 05M51263

Name: Sinthupinyo Patima

Supervisor: Egashira Ryuichi

アルカリ触媒エステル交換によるバイオディーゼル製造に対する低価格原料油の比較
シントュピンヨ

パティマー

まず，トリパルミチン(脂肪酸基C16)およびトリオレイン(C18)からなる2成分モデル混合物を原料としてメタノールおよび水酸化ナトリウム触媒によりバイオディーゼルを合成した。原料中のC16の分率の増加等によりバイオディーゼルの収率は低下し純度は向上した。C16とC18の間の水への溶解性および乳化性の差異によるものと考えられる。ついで，粗パーム油，粗ジャトロファ油，および廃食油を原料としてバイオディーゼルを合成した。粗ジャトロファ油および廃食油に比較して，粗パーム油中のC16の分率は高かった。粗パーム油を原料とした場合の収率は低く，純度は高く，モデル原料の場合の結果と一致した。粗ジャトロファ油中の不純物の分率は最も高く，前処理において多量の試薬を必要とした。
and oxidation from air. After the transesterification, oiland glycerol phases were separated into each other bydecantation funnel. The oil phase was freed ofmethanol, soaps and glycerol by washing with warm
water (20 % mass of oil phase), was dried overmagnesium sulphate, and was filtered to remove thesolid drying agent.2) Methyl ester content in oil phase
was determined by analysis using a gas chromatograph(G-3000, Hitachi Co. Ltd).
Table 2-1 shows the principal experimental
conditions. The composition of the feed oil, namely,
the mass ratio of C16/C18, F, mass percentage of
sodium hydroxide to the feed oil, C, molar ratio of
methanol relative to the feed oil, M, and reaction
temperature, T, were varied first to know roughly theeffects of these variables on the biodiesel yield andpurity. The ranges of M and C widely used were
employed here. The range of T was selected based on
the boiling point of methanol (64.7 °C) and sufficienttemperature to equilibrate the reaction system within
one hour (aprrox. 45 °C~). Next, in the ranges
allowing high conversion of methyl ester, the effectsof the respective variables on the biodiesel yield andpurity were analyzed by means of a simple
mathematical way3).

1. Introduction
Biodiesel is one of the alternative energy
outstanding on the forefront of energy business due toits biodegradability, renewability, an excellent
lubricity in low-sulfur diesel, high cetane number,
etc.1) Moreover, biodiesel production itself also shows
the outstanding reduction in CO2 generation
drastically. Nonetheless, biodiesel commercialization
has not been effective due to its high cost of
production and limitation on feed cost and supply.
This study comparatively investigates differencesamong low-valued oils used as feedstock for biodiesel
production in order to minimize cost of biodiesel
production and to increase feed supply and flexibilityof biodiesel production. In the first phase of this study,
a binary model feed oil was transesterified to examine
the relations among feed oil composition, the required
reaction conditions, the biodiesel yield, and purity.
Then, the real feed oils were applied in the second
phase of study. The characterization of the feed oils,
the pretreatment to remove impurities in the feeds, andbiodiesel syntheses from the pretreated feeds wereconducted and their relations were discussed.
2. Transesterification of Binary Model Mixture
2.1. Experimental
A binary mixture of pure tripalmitin (C16) andtriolein (C18) (Wako Pure Chemical Industries, ltd.)
was selected as a model feed oil to be transesterified tobiodiesel (methyl ester form). The reaction was carriedout in a 50 cm3 three-necked flask, which was
equipped with reflux condenser, and temperaturecontrolled bath oil. After 30 ml of the oil was heated toa specified temperature in the reactor, the mixture ofmethanol and sodium hydroxide, the catalyst, wasadded to the oil. The transesterification was a set ofsimultaneous reactions and is heterogeneous during
reactions. Therefore, the liquids in the reactor were
well mixed by a magnetic stirrer and this state was
kept for one hour. During the reaction, nitrogen gas
was purged inside to avoid moisture contamination

Table 2-1: Principal experimental conditions
F [–]
C [%]
M [–]
T [°C]
0.25~1.25
3~9
45~65
0~

2.2. Results and Discussion
Biodiesel yield (Y) was defined as the mass ratioof methyl esters in the obtained biodiesel phaserelative to the feed oil and biodiesel purity (P), as themass fraction of methyl esters in the biodiesel phase.
The experimental results obtained under thevarious conditions of Table 2-1 were narrowed down
experimentally based on the ranges, which gave thegreat yield and purity. Except that of F, which was
based on typical fatty acid composition in vegetableoil.

5

As the result, the optimum ranges of conditions
were obtained and shown in Table 2-2

where the coefficients on the variables were obtainedby the method of least squares with the experimental
results. The influences of F, C, M, and T on thebiodiesel yield and purity were evaluated using theseequations.
Figure 2-1 shows the relations between one of theencoded variables, XF, XC, XM, and XT, and thebiodiesel yield, Y, or purity, P, when the otherencoded variables were fixed at 0, the center point. Y
decreased with F, or T and increased with M. The
effect of F was explained as follows: low-molecularweight oil has higher saponification value and isconverted into soaps more; the formed soap increases
triglyceride loss, lowers the reaction rate by foamingwith gas in the reactor, and interferes the separation
between methyl ester and glycerol. P increased, as
either F, M, or T increased. Glycerides with shortchain of fatty acid remaining not converted into methyl
esters would have higher solubility in glycerol phasethan that with long-chain, so that the higher F gave theTable 2-2: Optimum experimental conditions
F [–]C [%] M [–] T [°C]
0.7 ~ 1.3 0.5~1.0 5~7 50~60

These ranges of F, C, M, and T were transformed
into those of encoded variables, XF, XC, XM and XT,
valued from −1 to 1 for each variable. Y and P could,
thus, be expressed by simple quadratic equations of theencoded variables as,Y  (99.54  0.39 X F  1.05X C  0.44X M  0.45X T  0.11X F2
2
 0.80X C2  0.35X M
 0.04X T2  0.42X F X C  0.05X F X M

 0.38X F X T  0.01X C X M  0.39X C X T  0.05X M X T ) / 100 (1)P  (99.01 0.27 X F  3.24X C  1.04X M  0.89 X T  0.59 X F2
2
 2.83X C2  0.42X M
 0.05X T2  0.11X F X C  0.10X F X M

 0.26X F X T  0.11X C X M  0.32X C X T  0.01X M X T ) / 100

(2)

1

P [-]

Y [-]

0.99

X
X
XM
XT

XFF

0.98

X
XC
XM
XT

XF F

XC C

XC

XM

XM

XT

XT

0.97
-1.0

0.0

1.0

-1.0

0.0

1.0

XF, XC, XM, XT [-]XF, XC, XM, XT [-](b)(a)
Figure 2-1: Relations between XF, XC, XM, XT, and: (a) Y; (b) P
1

XC [-]

Y [-]

0.99
0.98
0.97
Series1
F
Series2

X = -1
XF = 1

0.96

X = -1
XF = 1

X = -1
XF = 1

Series1
F
Series2

Series1
F
Series2

0.95
-1.0

0.0

1.0 -1.0

0.0

XC [-]

1.0 -1.0

XM [-]

(b)

(a)

0.0

1.0

XT [-]

(c)

1.00

P [-]

0.98
0.96
0.94

X = -1
XF = 1

X = -1
XF = 1

Series1
F
Series2

Series1
F
Series2

X = -1
XF = 1

0.92

Series1
F
Series2

0.90
-1.0

0.0

1.0 -1.0

0.0

1.0 -1.0

0.0

XC [-]XM [-]

XT [-]

(d)

(e)

(f)

Figure 2-2: Interactions between XF and other variables6

1.0

higher biodiesel purity. Y had the maximum over M.
Due to reversibility of transesterification, lower
amount of methanol will reduce drive of forward
reaction and have a negative effect on reaction yield,
while higher amount of methanol increased side
reaction and solubility of methyl ester into glycerol aswell. Y and P had the maxima over C, namely, toohigh C gave low Y and P. This is due to saponification
side-reactionoftriglyceride
insteadoftransesterification under too high C. This side-reaction
led to the triglyceride loss and the formation of soaps.
The effects of C on both Y and P were the strongest
among those of the studied variables.
The interactions between the effects of XF and theother variables on biodiesel yield, Y, and purity, P,
were presented in Figure 2-2, where XF was fixed at 1
or −1 while the other variables were varied from −1 to
1 for the both value of XF. The influences of C and T
on Y were more significant at higher F, while those of
M were quite the same irrespective of XF. In otherwords, it was presumed that the appropriate amount of
catalyst should be chosen for each of feed oil, since thecatalyst amount giving the maximum yield varied withthe feed oil composition. The influences of all thevariables on P were the same regardless of XF.

ester to know fatty acid chain compositions.
Phosphorus and water contents were analyzed by an
inductively coupled plasma spectrometer (SPS 7800,
Seiko Instruments Co. Ltd.) and a Karl-Fisher titrator(758 KFD Titrino, Metrohm Co. Ltd.), respectively.
Acid value was determined according to ASTM D
664. The average molecular weight of fatty acid andtriglyceride were calculated from the composition ofoil and the content of free fatty acid. Concentration of
methyl ester in the biodiesel product was determinedby the gas chromatograph.
The real feed oils contain impurities unfavorable
for biodiesel production, e.g., phosphorus, free fatty
acid, moisture. These impurities not only contaminate
in biodiesel product but reduce biodiesel yield as well.
The feed oils were, thus, pretreated to remove
phosphorus (degumming, DG), free fatty acid(deacidification, DA), and water (drying), before sent
to transesterification. Some or all of these
pretreatments were carried out, in order to investigate
the influences of the respective pretreatments on thebiodiesel yield and purity.
The experimental apparatus and procedure for thetransesterification were the same as those with themodel feed oil as described in Section 2.1. Intransesterification, the methanol/oil ratio, M, and thecatalyst concentration, C, were varied from 3 to 6 and
0.5 % to 1.5 %, respectively.
3.3. Results and discussion
Fatty acid compositions, free fatty acid contents(%FFA), moisture contents, and phosphorus contentsof the fresh feed oils are shown in Table 3-1 together
with the previous result 4)-6). All of the feed oils
contained fatty acid chains mainly of C16 and C18 and
CPO had more C16 chains than CJO and UFO.
If there was no pretreatment, biodiesel yielddecreased from 99 % and 96 % to 60 % and 71 % in
the case of CPO and UFO, respectively, and jelly-soap
instead of biodiesel formed entirely in the reactor in
the case of CJO. Biodiesel could not be produced in
the case of CJO and the biodiesel yield considerably
decreased in the case of CPO, without deacidification(DA). DA was the most significant of all the
pretreatments in this study. Degumming was necessary
only in the case of CJO, of which phosphorus content
was the highest.

3. Biodiesel Production from Low-valued Feeds
3.1. Feed oil selection
Crude palm oil (CPO), crude jatropha oil (CJO),
and used frying oil (UFO) were selected as low-valued
feed in this study. The merits of CPO is a high scale ofproduction, lower price than any other vegetable oils
such as soy bean oil, rapeseed oil, sunflower oil, etc..
CJO is superior in respect of inedibility, i.e., no oilcompetition with food use, drought-resistant perennial,
and growing well in even poor soil. UFO is consideredas the costless oil, recycle and value-add the un-use
vegetable oil.
3.2. Experimental
CPO and CJO were obtained from Malaysia andThailand, respectively. UFO was obtained from acompany treating used oil in Japan (Someya Shouten
Ltd., Sumida, Tokyo).
The feed oils were analyzed by the same gas
chromatograph as mentioned above after sufficienttransesterification of triglycerides in the oils to methyl
Table 3-1: Properties of Feed Oil
Myrstic acid (C14:0) [%]Palmitic acid (C16:0) [%]Stearic acid (C18:0) [ %]Oleic acid (C18:1) [%]Linoleic acid (C18:2) [%]Avg. fa tty acids MW
Avg. oil MW
Free fatty acid [%]Acid val ue [mg-KOH/g-oil]
Phosphorus [pp m]
Water [%]CPOCJOThis study Maycock 5) This study Azam4)
1.13
1.08
nil
1.4
35.02
44.0
10.25
15.6
4.63
4.5
13.84
9.7
42.11
39.2
42.7
40.8
16.60
10.1
26.26
32.1
272
267
277
276
854
840
869
866
5
N/A
11
N/A
10.3
N/A
22.3
N/A
9.01
N/A
40.15
N/A
0.47
N/A
2.3
N/A

7

UFO
This study Merve 6)
nil
0.23
7.72
11.93
3.98
3.80
34.42
31.25
53.89
50.76
280
276
877
866
0.62
N/A
1.24
N/A
8.93
N/A
0.72
N/A

1 010Y[-]
Biodiesel Yield [%]

90
0.9

80
0.8

NaOH 1.5%
NaOH1%

70
0.7

NaOH 0.5%

0.6
60

0

3

6

9

0

3

6

Methanol/oil molar ratio

9

0

3

6

9

M[-]
Met hanol/oil
io(b)(c) molar rat
Figure 3-1: Effect of the mass ratio of methanol to feed oil, M, on the biodiesel yield, Y: (a)in the case of CPO; (b) CJO; (c) UFO

Methanol/oil(a) molar ratio

100
1

P[-]
Biodiesel Purity [%]

90
0.9
80
0.8
70
0.7

NaOH 1.5%

0.6
60

NaOH 0.5%

NaOH 1%

0.5
50

0

3

6

9

Met hanol/oil molar rat io

(d)

0

3

6

9

Methanol/oil molar ratio

(e)

0

3

6

9

M[-]
Methanol/oil molar ratio(f)Figure 3-2: Effect of the mass ratio of methanol to feed oil, M, on the biodiesel purity, P: (a)in the case of CPO; (b) CJO; (c) UFO

Biodiesel yield comparison among CPO, CJO, andUFO in the case with all pretreatments is shown in
Figure 3-1. The biodiesel yields of 99 % or more could
be obtained in the cases of CJO and UFO, whichcontained C16 less, whereas the yield from CPO,
whose C16 content was higher, was lower. The
biodiesel yield had the maximum over themethanol/oil ratio, M. The emulsification of glycerol
and methanol due to soap formation become serious, if
the amount of methanol was too high or too low. Toomuch catalyst lowered the biodiesel yields.
Biodiesel purity comparison among CPO, CJO,
and UFO in the case with all pretreatments is shown in
Figure 3-2. All of the feed oils gave biodiesel purity
more than 99 %. The highest purity was obtained from
CPO, which contained C16 more than the other oils, thepurities in the cases of the other oils were slightly
lower. The highest biodiesel purities from all feed oils
increased with methanol/oil ratio, M, and came toplateau around M=6. The biodiesel purity increased
with amount of catalyst.
All of these results obtained with the real feedoils, CPO, CJO, and UFO, were similar to those withthe binary model feed oil.

process with low-valued feed oils.
Literature Cited
1) Vicente, G.; Martinez, M.; Aracil. J. Bioresource. Tecnol. 2004, 92,
297-305.
2) Dhruv, T.; Dennis, W.; Cole, G. Process Modeling Approach for
Evaluating the Economic Feasibility of Biodiesel Production. 2004.
3) Gemma, V.; Mercedes, M.; Jose, A. Optimisation of Integrated
Biodiesel Production. Bioresource. Tecnol. 2007, 98, 1724-1733.
4) Azam, M. M.; Amtul W.; Nahar N.M. Biomass & Bioenergy. 2005,
29, 293-302.
5) Maycock, J.H. Extraction of Crude Palm Oil, in Palm Oil, edited by
F.D. Gunstone, C. Rep. Appi. Chem., John Wiley & Sons, New
York. 1987, 15, 29-38.
6) Merve C.; Filiz K. Energy & Fuels. 2004, 18, 1888-1895.

4. Conclusion
Firstly, the relations among feed oil composition,
the required reaction conditions, the biodiesel yield,
and purity were clarified by the transesterification run
with a binary model feed oil. In the next, with the real
feed oils, the requirement for the pretreatments of thefeed oil was correlated with the impurities in the feed
oil and the results with the model feed oil was
confirmed. These results will provide useful
information for design of the biodiesel production

8

Moving picture coding using wavelet transform
Student Number：05M18060

Name：Osamu SAKURAI

Super visor：Yukihiko YAMASHITA

ウェーブレット変換を用いた動画像符号化に関する研究
櫻井 牧
MPEG に代表されるブロック単位の動画像圧縮方式では，復号した画像にブロック歪が現われるという問題が生じる．この問題を解決するために「ウェーブレット変換を用いた動画像符号化」を提案する．この手法はブロック歪を減らすだけでなく，動きベクトルの正確な検出により，より効率的な符号化の実現が期待できる．

1 Introduction

moving picture coding with 2×2 pixel motion compensation prediction and wavelet. The wavelet coding which
reduces block distortion is applied to our video coding
method. Furthermore, motion compensation with 2×2

Recently, the information communication technology
is rapidly developed. Not only document but also picture, a sound, etc. are widely used for communication.

[pixel] block is applied. Thereby, the problem of block
distortion is vanished. Moreover, various motions rota-

However, the amount of information of the multimedia data which generally contains digitized pictures andtion, expansion, etc. which were not able to be usedconventionally can be applied more correctly. Sincecorrelation of data becomes high, it enables to perform

sounds is huge. Therefore, in order to treat the information, the broad band method and the mass storage
medium are necessary. Then, research on reduction of

efﬁcient compression. Therefore, decoding which suppresses quality-of-image degradation is realized in small

the information by the data compression of pictures orsounds so-called compression coding has come to beamount of data. This thesis explains the algorithm of the
popular for the purpose of efﬁcient use. Although theproposed method. Next, the advantages are conﬁrmed
broadband communication can be used in wide area,by comparing with MPEG which is a video coding stannarrowband communication is also used. Then, efﬁdard by using a computer experiment.
ciency of image coding has to be increased.
Video coding is the method of compressing by reducing the redundancy included in video. There are twokinds of the redundancy in video data. One is the spatial
redundancy and the other is temporal redundancy. The

2 MPEG

former is mainly used for still picture coding. JPEG
(Joint Photographic Experts Group) which is the inter-coding for accumulation media, broadcast, communication, etc. It mainly consists of three regulations, such as
the regulation on the coding method of a video signal,

The MPEG standard is a standard of the multimedia

national standard of still picture coding is used very
widely. And the algorithm called motion compensa- the regulation on the coding method of an audio signal,tion prediction is used for the latter reduction. This is and the integration method for both.
Video data is realized by set of the still picture loused in MPEG (Moving Picture Experts Group) whichis the international-standard system of video coding. It cated in a line on the time-axis. Each picture is called
enables to code and compress video data at high efﬁ- a frame. MPEG performs compression coding by reciency by these two techniques compared with only the ducing those spatial redundancy and time redundancy.
former. However, such algorithm had the problem in a Reduction of spatial redundancy is called the coding
decoding picture. Visual degradation called block dis- in a frame, and performs DCT (discrete cosine transform), quantization, and coding for every 8×8[pixel]
block. Moreover, reduction of time redundancy is called

tortion is produced from process on block.
In order to solve this problem, we propose a new

9the coding between frames, and is performed using thetechnique of motion compensation prediction. This is
extracting and treating the motion information on a cer-

block matching method used by MPEG, the problem of
producing block distortion which is visual lattice-likedegradation appears in the decoded video. In order totain domain in a picture between two near frames in reduce block distortion using wavelet, the motion comtime. It reduces the redundancy of video. Generally, pensation prediction which uses block matching is notblock matching is performed for every 16×16[pixel]
block. And the motion vector which is motion information is extracted. The general procedure of motion

proper. Then, we propose the motion compensation prediction which extracts the information on a motion in
2×2[pixel] block.

compensation prediction is shown below.
1. Extraction of motion vector by comparing between 3.1
a target frame and a reference frames
2. Generation of the prediction frame by the motion
vector and the reference frame

2×2 pixel motion compensation prediction

Using a pixel for the macro block, the motion compensation with a pixel unit was realized. However, thismethod lacks in reliability. Therefore, we propose thedifference between the prediction and the target 2×2[pixel] motion compensation prediction, which uses
the recursive algorithm. In order to prevent a motion
framesvector dispersing, a smooth portion is detected in the
4. Coding the motion vector and the difference frame picture and processing suitable for the portion is per3. Generation of the picture of the difference by theformed. The algorithm of 2×2[pixel] motion compensation prediction is as follows.

5. Execution of 1. to the following two frames
The main coding parts of MPEG are realized with the
combination of the coding between frames and within a

1. A target block is compared to a reference frame
using the block of 16×16[pixel] and extract a motion vector. The variance of pixel values of theframe. First of all as a basic procedure of MPEG, theframe of the beginning of video or the frame used as

block is calculated, and when the value is below
a ﬁxed value, the block is not divided any more.

a starting point performs only the coding in a frame.
These frames are called Intra-coded frame or I-frame.
I-frame which had conversion-quantization performed
here is inverse-transformed by the local decoder, andis temporarily memorized by the frame memory. Next,

2. Four divisions (these are called a mini blocks)of the target block are carried out. In each mini
block, block matching is applied and motion vec-

frames other than I-frame perform the coding between
frames which use motion compensation prediction. Thereare called P-frame and B-frame. P-frame refers to a pre-tors are extracted. The variance of pixel values ofthe mini blocks is calculated, and when the value

vious frame and B-frame refers to both previous and future frames.

is below a ﬁxed value, the block is not divided
any more.
3. For each mini block, if the block size is not 2
pixel, go to Step 2. When a target block becomes
2×2[pixel], go to Step 4.

3 Motion compensation prediction
with 2×2 pixel

4. Compare them with the 2×2[pixel] block ﬁnally,
and the ﬁnal motion vector is calculated.

In the ﬁeld of still picture coding, the coding which
reduces block distortion using wavelet transform andsubband conversion as a method has been examined. 3.2 Wavelet coding
Then, in order to reduce block distortion produced in
Recently wavelet is used for the compression coding.
video, we try to apply the coding (wavelet coding) whichused wavelet transform. However we have another prob- When energy inlines toward low frequence bands, it islem with motion compensation prediction. Using the10known that very efﬁcient coding is possible. The coding method called SPIHT (Set Partitioning In Hierarchical Trees) which is specialized in the tree structureof wavelet. This coding method is used for the coding
part of the proposal technique. Correlation of the 2dimensional motion vectors will be high supposing motion vectors obtained by block matching are extracted
correctly correctly. Therefore, information inclines toward lower frequency domain. Moreover, the absolutevalues of the pixels in the difference frames become
very small. Therefore, both information can be compressed at high efﬁciency using the above-mentioned
wavelet coding.

Fig. 1: Intersection
34
Proposed
MPEG2

33

4

Image coding experiment
PSNR[dB]32

In experiments, two frames of the standard video sequence for assessment are used for I-frame and P-frame,
respectively. And coding and decoding were performed

31

30

by the proposal technique. The example of a frame isshown in Fig.1. Moreover, it is compared with MPEG

29
0.4

used as codec of Hi-Vision or standard quality-of-image
television. And quality-of-image degradation of a decoding picture was evaluated numerically. PSNR is used

√
MSE =

255
MSE

−1 H−102∑W
x=0 ∑y=0 ( f (x, y) − f (x, y))

WH

0.5

0.55

0.6
0.65
bitrate of I frame[bit/pel]

0.7

0.75

0.8

0.85

Fig. 2: PSNR for different bitrates (I frame)

for numerical assessment. PSNR is given by the following formulas here.
PSNR(dB) = 20 log

0.45

in Fig. 5. In this ﬁgure, x-axis stands for bitrate of motion vector and y-axis stands for PSNR of P frame. Each
result of the proposed method can not overcome MPEG.

(1)

The proposed method has caused remarkable qualityof-image degradation especially around the white line(2)of a crossing paved road or a track in a target frame.
This is because matching of such a portion is incorrectand correlation of a motion vector becomes low in thef and f 0 stand for values of the original image and thedecoded image. W and H stand for width and heightof the image. In coding of I-frame, the block distortion

case of 2×2[pixel] block motion compensation prediction.

of the shape of a lattice is produced by MPEG which
performs block processing in a decoding picture. However, block distortion is not produced by the proposed

5 Conclusions

method. In comparison of PSNR, the decoded image by
the proposed method outperforms one by MPEG in the

In this paper, the video coding method using wavelet
and 2×2[pixel] block motion compensation prediction
was proposed, and the computer experiment was con-same bitrate. This result is shown in Fig. 2. In this ﬁgure, x-axis stands for bitrate of I frame and y-axis stands
for its PSNR. This result shows the proposed method

ducted. Moreover, the advantage was shown by comparing with MPEG which is an international image cod-overcomes MPEG.
In coding of P-frame, the motion vector of the hor-

ing standard. For future work, we have to develop theizontal direction expressed by tone of before and after 2×2[pixel] motion detection method that provides morecoding are shown in Fig. 3 and 4. Moreover, the com- highly correlated motion vectors, and a more efﬁcientparison with MPEG by numerical assessment is shown11

coding method for the difference image. Furthermore,calculation speed have to be increased.

References[1] Yoshinori Sakai，Toshiyuki Yoshida：“Image information encoding”, Ohmsha, 2001.
[2] Sadayasu

Ono，Junji

Suzuki：“Achievement

method of comprehensible
Ohmsha, 1995.

JPEG/MPEG2”，[3] Susumu Sakakibara：“Wavelet beginner’s guide”，
Tokyo Electrical Engineering College Publications
Service，1995.

Fig. 3: Before encoding[4] Amir Said, William A.Pearlman：“A New Fast andEfﬁcient Image Codec Based on Set Partitioningin Hierarchical Trees”，IEEE Trans. Circuits andSystems for Video Technology, vol.6, pp.243-250,
June. 1996.

Fig. 4: After decoding

33
I frame=0.82[bpp],P frame=0.17[bpp]I frame=0.72[bpp],P frame=0.27[bpp]I frame=0.62[bpp],P frame=0.37[bpp]I frame=0.52[bpp],P frame=0.47[bpp]I frame=0.42[bpp],P frame=0.57[bpp]
MPEG2 (I frame=0.72[bpp],P frame=0.27[bpp])

32

PSNR[dB]

31

30

29

28

0

0.05

0.1

0.15
bitrate of MV[bit/pel]

0.2

0.25

0.3

Fig. 5: PSNR for different bitrates (P frame)12Expansion of Non-negative Matrix Factorization
using Fisher’s Discriminant and its Application
Student Number: 05M51257

Name: Naoya KOIDE

Supervisor: Yukihiko YAMASHITA

Fisher 識別器を利用した NMF の拡張とその応用
小出直矢
本論文では非負値行列分解 (NMF) に Fisher 線形識別器の評価式を導入した FisherNMFを提案し，手書き漢字を用いた実験によりその性能を評価する．通常の NMF は非負値行列
を 2 つの非負値行列の積に分解する手法である．しかしながら，得られる特徴ベクトルに一意性はなくパターン認識には適していない．提案する FisherNMF は特徴ベクトルのカテゴリ間の線形分離性を高めることができるため，より良い性能を発揮できる．

1

Introduction

Pattern recognition is a process to distribute observed patterns to known categories. For example, in recognition of alphabet characters, we should discriminate observed patterns among 26 categories. Patterns are given by two-dimensional images,
three-dimensional objects, signals in time series like sound signals, and so on.
Non-negative Matrix Factorization(NMF)
is a method for extracting features. Although
NMF decomposes a non-negative matrix into
two non-negative matrices, a base matrix anda feature matrix obtained by NMF is notunique. Therefore NMF is not suitable forpattern recognition.
In this paper We propose FisherNMF,
which is a method that Fisher’s Discriminatis introdeced into NMF. and we evaluate theperformance of FisherNMF by experiments.

2

Non-negative
Factorization

Matrixmatrix into two non-negative matrices.
PCA(Principle Component Analysis)
proves the most suitable basis for approximate patterns. But those bases contain negative values, and meanings as images are lost.
On the contrary, bases given by NMF are notso suitable for approximation, but NMF cangive common parts of images as features since
NMF gives non-negative values. NMF equation is deﬁned by eq.(1).
V ≈ WH(1)

W is a base matrix, and it gives images ofcommon parts. H is a feature matrix, and it
gives feature quantities of each data.
Since we can’t determine a base matrix W
and a feature matrix H uniquely, we should
calculates repeatedly to obtain a solution.
There are several ways to measure the distance between V and WH. The most easiest
way is to measure by Frobenius norm. Thisestimation criterion is obtained as eq.(2).
min

W,H

D(V, WH) = kV − WHk2F(2)

Wia ≥ 0, Hbj ≥ 0, ∀i,j,a,b∑
Non-negative Matrix Factorization(NMF, Notice that kAk2F = i,j A2ij . We should ﬁnd
Paatero and Tapper[1]; Lee and Seung[2],[3]) a base matrix W, and a feature matrix H
is a method which decomposes a non-negative which minimize eq.(2). We can obtain thesubject to13

solution with a rule eq.(3), (4).
(VHT )ij(WHHT )ij
(WT V)ij
Hij ← Hij
(WT WH)ij
Wij ← Wij

3

FisherNMF

FisherNMF minimizes not only kV − WHkF
but SSWB . By minimizing Fisher Discrimi(3) nant term, known training patterns are separated in each category in feature space, so(4) that FisherNMF is more suitable than normal NMF for discrimination.
Let J = log SSWB , the update rule of FisherNMF is obtained by the next equation.

Feature vectors obtained by NMF may not
be separatable in each category in a feature
space. This is because no constraint is given
to a base matrix W and a feature matrix H.
Therefore, it is diﬃcult to classify unknown
input pattern to known categories by using
NMF, and it is not suitable to apply NMF topattern recognition.
In order to solve this problem, We propose a new method, FisherNMF. Fisher’s Discriminant is a well-known method as a 2-classlinear classiﬁer. Its criterion is to minimize
within class variance and maximize between
class variance. In FisherNMF, by introducing
the Fisher’s Discriminant term to the criterion of NMF, the discrimination performanceis improved.
At ﬁrst, within class variance and between
class variance are deﬁned as eq.(5), (6).
SW :=

(8)
(9)

Note that the derivatives of Fisher’s Discriminant term is eq.(10).
(
)
∑
2
∂J
hk ∈Ωx Hpk
M M Hpq −= ∑C ∑
T∂Hpq
i=1
h ∈Ω (hk − µi ) (hk − µi ))( k∑ i
∑C ∑
4
H
−
H
C
2
pk
pk
hk ∈Ωxi=1
hk ∈Ωi
M
−
∑C ∑C
T
i=1
j=1 (µi − µj ) (µi − µj )(10)

In recognition step, the update rule to feature vector h is the same as eq.(11). because
we cannot debate about the categories of unknown input patterns.
hi ← hiC∑∑1(hk − µi )T (hk − µi ),CM i=1 h ∈Ω

(WT v)i(WT Wh)i(11)Note that v is an input pattern and h is a feature vector of an input pattern. The category(5) which an input pattern belongs to is decided
C ∑C
by nearest neightbor method.
∑(µi − µj )T (µi − µj ),

k

SB :=

(VHT )pq(WHHT )pq
(WT V)pq
Hpq ←Hpq∂J(WT WH)pq + α ∂Hpq

Wpq ←Wpqi

1
C(C − 1) i=1 j=1

1 ∑where µi :=hk
M h ∈Ω(6)

4

Experiment

At ﬁrst, I conduct experiments on educaiktional kanji images of diﬀerent 16 fonts. 40
The criterion of FisherNMF is deﬁned by random selected characters are used. The sizeeq.(7).
of images are 32 × 32. 12 fonts of 16 are used
SW as training patterns, and 4 fonts are used asmin
D(V, WH) = kV − WHk2F + α log
W,HSB test patterns. For the dimension of feature(7) vector r is 16 and 64 are used. α is a paramsubject to
Wia ≥ 0, Hbj ≥ 0, ∀i,j,a,beter of FisherNMF.

14

The results are shown in Table 2. In thiscase, FisherNMF doesn’t show better performance than NMF. The convergences of norm,kV − WHkF , on each step is shown in Figure
2. In case of FisherNMF, the iteration converged to a local minimum point. Since thecriterion of FisherNMF is more complex, theiteration falls into a local minimum.

The results are shown in Table 1. α is asshown in the table, FisherNMF gives better
recognition results than normal NMF.
Obtained bases W are shown in Figure
1. Although FisherNMF gives a better result, images of basis by FisherNMF are are
not clearer than images of NMF. It is because
the criterion of FisherNMF has a constraintthat minimizes Fisher’s Discriminant term.

320000
NMF norm
FisherNMF norm
300000280000260000norm

Table 1: Result for educational kanji
Methodα Error Rate(%)
r = 16 r = 64
NMF
– 12.50
5.00
0.5 9.38
4.38
2.50
FisherNMF 1.0 13.75
2.0 8.13
3.75

240000220000200000180000160000
0

20

40

60

80

100
iteration

120

140

160

180

200

Figure 2: Convergence of kV − WHkF (r =16)
On some characters. I selected 30 characters which look like (Table 3). These characters have a very similar part with each other.
Therefore pattern recognition becomes diﬃcult for them.

FisherNMF

NMF

Figure 1: Obtained Base W(r = 16)

In the next experiment, ETL data set isused. The size of character images was re- Table 3: Characters Used on the Experiment
duced to 16 × 16. There are 160 patterns foreach character. 120 patterns of 160 patterns 位 依 億 化 仮 泳 液 演 温 河
are used for training data, and 40 patterns for 横 械 機 橋 極 絵 級 給 経 結
test data. In this experiment, the dimension 課 議 語 誤 護 鏡 銀 鉄 銅 録
of feature vectors W are set to 16, 64, 256.
Table 4: Result
Methodα Error Rate(%)
r = 16 r = 64
NMF
– 15.83 13.33
0.5 11.67
5.83
FisherNMF 1.0 11.67
8.33
2.0 10.83 10.00

Table 2: Result for ETL data set
Method

α

NMF

0.5
1.0
2.0

FisherNMF

Error Rate(%)
r = 16 r = 64 r = 256
67.40 31.68
29.43
72.70 33.54
30.65
71.60 36.19
32.72
70.90 33.31
31.01

15

The obtained bases are shown in FigThe results are shown in Table 4. It is obvious that FisherNMF is more eﬃcient in this ure 4. Although NMF does not distinguishany parts of characters, FisherNMF emphaexperiment.
sizes main parts of characters which is called
bushu. This is because FisherNMF clariﬁes
the diﬀerence between each categories.

5

Conclusions

In this paper, We proposed FisherNMF,
which is a method that Fisher’s Discriminant
is introduced into NMF. Furthermore, we
evaluated performance of FisherNMF by experiments on kanji images, comparing NMF
and FisherNMF. As a result, FisherNMF
showed well on some conditions. We have toimprove convergence of FisherNMF and apply it to various ﬁelds.

NMF

References[1] Paatero, Tapper Positive matrix factorization: A non-negative factor modelwith optimal utilization of error estimatesof data values, Environmetrics, vol.5,
pp.111-126, 1994.
[2] D.D.Lee and H.S.Seung. Learning theparts of objects with nonnegative matrix factorization, Nature, 401:pp788-791,
1999.
[3] D.D.Lee and H.S.Seung. Algorithms for
non-negative matrix factorization, Advanced in Neural Information Processing
Systems 13, pp.556-562. MIT Press, 2001.

FisherNMF
Figure 3: Obtained Base W(r = 64)

16

Parameter determination method for constitutive modelof sand based on relative density relation
Student Number: 06M18010 Name: Takanori AOKI Supervisor: Thirapong PIPATPONGSA

密度の相違を考慮した砂の構成モデルとそのパラメータの決定法
青木孝憲

砂質土のせん断挙動に関して最も支配的なのは初期状態における密度の違いである．この相違を統一的に表現できる構成モデルについては既に提案されているが，それらに関わるパラメータの多くは明確に決定する手法が現時点で存在しないため，実務レベルでの活用に至っていない．本研究では，全てのパラメータを客観的に決定できることを主眼におき，下負荷面の発展則を導入した構成モデルを用いた．密度の違いを構成モデル内における降伏応力の違いとして考え，それを定量的に評価できる手法を提案する．またその妥当性を，初期密度の異なる砂の非排水三軸試験結果と比較して検討を行っている．
the Sys-Cam clay model, EC model4) proposed by Ohno
et al.(2006) including Sub-loading surface5)(Hashiguchi, 1989), was employed in this research.
This model can more or less express sandy soil behavior
at a certain level. Besides, all parameters can be
determined obviously.

1. Introduction
The most influential property of sandy soil on elastoplastic behavior is the degree of density. Shearing
behavior of sandy soil does not entirely depend only on
current stress condition and stress history, but also
relative density.
500
400

3. Parameters determination method

600
Stress path on p'-q space

C.S.L

300
200
100

Deviatoric stress, q (kPa)

Deviatoric stress, q (kPa)600

00 100 200 300 400 500 600
Effective mean stress, p' (kPa)e0=0.933 (Dr=14.8%)
e0=0.889 (Dr=26.6%)

500

Stress- strain relation

3.1 Parameter which interpret “degree of density”
In order to represent “the degree of density” inconstitutive model, the parameter which can govern arelative density is necessary. However,typicalconstitutive models; the modified Cam clay, theSekiguchi-Ohta, EC models and so on, do not have suchparameter, because these model were rooted in theexperimental results of clays, not sand.
On the other hand, the characteristics under drained andundrained shear condition of over-consolidated clay
and dense sand are similar. Therefore, in this research,the degree of density is assumed to associate with theyielding stress parameter, p0′ . Fig.2 shows the concept
of Dr relation with p0′ .

400
300
200
100
0
0

10
20
Axial strain, εa (%)
e0=0.904 (Dr=22.6%)
e0=0.872 (Dr=31.2%)

Fig.1 Experimental result of undrained shear test
Left: Stress path on p’-q space
Right: Stress-strain relationshipFig.1 shows the experimental results of undrained shear
test for various initial relative density ( Dr ) conductedby Kato, Ishihara and Towhata (2001)1).

q
Initial normal
yield surface

2. Previous researches

Density larger →Initial stress

Many researchers have proposed constitutive models
which can express shearing behavior of sandy soil.
Asaoka et al. (2002) proposed the Sys-Cam clay
model2) which can suitably express shearing behavior ofboth dense and loose sand3). However, parameters
determination method has not been specifically definedyet. That is why, this model is not used in practicalanalyses. Due to setback of parameter determination forp′0 p′i

p′0

p′0

p′0

p′

Fig.2 Illustration of Dr − p0′ relation

1
17

Stress path on p'-q space

150
Initial normal
yield surface

C.S.L

100
Initial sub-

50 loading surface
0
0
50
100
150
Effective mean stress, p' (kPa)

Deviatoric stress, q (kPa)

Deviatoric stress, q (kPa)

3.2 Sub-loading surface model
In fact, over-consolidated state is represented as thestress bounded within the yield surface. In this case, thesimulated behavior of soil becomes elastic, as far as thestress remains inside the yield surface by using typicalconstitutive model. In this research, a degree of density
is interpreted to a degree of over-consolidation by theconstitutive model. As a consequence, in the case ofundrained shearing simulation for dense sand,elasto-plastic response does not exhibit at the initialstage. On the other hand, realistic range of elastic
response for sand carried out under undrained shear test
is very small, ( ε ≈ 10−6 ∼ 10−5 at strain level). Thus,

Stress- strain relation

150
100
50

Initial OCR=3000.1
Shear strain, εs

0.2

Simulated without Sub-loading surface model
Simulated with Sub-loading surface modelFig.4 Simulated result with sub-loading surface model& without sub-loading surface model
Left: Stress path on p’-q space
Right: Stress-strain relationshiptypical constitutive model cannot satisfactorily describe
realistic behavior of sand (See black lines in Fig.3).

3.3 Method for estimation of p0′
In order to estimate the realistic p0′ , experimental result

Sub-loading surface model was proposed by
Hashiguchi (1989). In this model, sub-loading surface
always passes through a current stress point in the stage
not only loading but also unloading process while
keeping a similarity to the normal-yield surface.
Besides, it is assumed that the sub-loading surface
approaches asymptotically to the normal-yield surface
in a loading process, causing a decrease of plastic
modulus (See Fig.3).

is required. Through the observation of Fig.1, shear
behavior among each initial density can be categorized
to 3 types:① Dense: q is monotonically increasing.
② Intermediate: q becomes stable at critical state.

③

Loose: q becomes decreasing after critical state.

On the other hand, the behavior of undrained shear
simulation on normally consolidated soil is similar totype ② . Therefore, the realistic value of p0′ can beq
σ′classified into the following Table (1).
Table (1)plastic yielding

p′

0

Type
①
②
③

subloading surface
normal yield surface

Experimental result
Constitutive model
Relative density,
Yield stress, p'0
Degree of density
Dr(%) in Fig.1
p0′ > pi′ ( 0 < R < 1)
Dense
26.6, 31.2
p0′ = pi′ ( R = 1)
Intermediate
22.6
p0′ < pi′ (1 < R )
Loose
14.8

Fig.3 Illustration of the Sub-loading surface model

In Fig.1, behavior of loose sand (type ③, Dr =14.8%)
cannot be described by using sub-loading surface
model, because the similarity ratio of sub-loading
surface must be defined in the range 0 < R ≤ 1 . This
is why, the modeling of loose sand is out of scope inthis study.
Generally, the value of yielding stress is obtained by
e-lnp’ relationship. Therefore, compression andswelling indexes of sand are required. Sakajo et al.
(1995) performed isotropic consolidation/swelling tests
on sand with different grain size distribution by using
triaxial apparatus6). Tests ware carried out for various
relative density at the initial states. Fig. 5 shows therelation between compressing and swelling indices, λ ,κ and the initial relative density Dr before loading. It
is obvious that λ and κ do not depend on
characteristics of grain size distribution but depend on
initial relative density, i.e., the larger initial density is,

Expansion ratio of sub-loading surface to normal yieldsurface, Rɺ (where 0 < R ≤ 1 ) is defined by thefollowing Eq. (1).
Rɺ = m ln R εɺ p(1)where, R : similarity ratio of sub-loading surfacem : expansion rate parameter
ε p : plastic strain tensor
By using this model, it is possible to describe
elasto-plastic response for the state of stress within thenormal yield surface (See red lines in Fig.4). It is
obvious that for over-consolidated soil, shear behavior
simulated by using sub-loading surface model is far
more realistic than that simulated without using
sub-loading surface model.

218

The intersecting point of λref line from eref with

Compression / Swelling index,
λ/κ

the smaller λ and κ are observed.
0.05

κ1 , κ 2 line from e1 , e2 is supposed to estimate p0′ .

0.04

λ = 0.313Dr −0.63

0.03

3.4 Parameters determination proceduresFig.7 shows the flow chart of parameters determination.
The proposed procedure is divided into 2 sections.
At section 1: “reference state”, eref ( Drref ) will be

0.02
0.01
0
0

−0.45
Toyoura sandLeigton Buzard sand κ = 0.1026 Dr
undisturbed sandfound based on the experiment result of undrained shear
test. This state is interpreted as the “normal
consolidated state” in constitutive model.
At section 2: “dense state”,
p0′ related to ei

20 40 60 80 100
Relative density, Dr(%)

Fig.5 Relationship between λ , κ and initial Dr( ei < eref ) will be estimated by using the determined

According to these experimental results, the concept for
estimating p0′ is proposed. Fig. 6 shows the illustrationof the relationship between initial void ratio, eparameters obtained from section1. In addition, dense
state is considered as “over-consolidated state” inconstitutive model. Therefore, sub-loading surface
parameter, m is required. This parameter will be
determined by curve fitting with experimental results;
stress path and stress-strain relation.

which can directly related to the relative density, andto p0′ . This idea has no objection with the typicalcharacteristics of soil under compression loading. The
initial void ratio of sand in corresponding to the initialconsolidated pressure pi′ refers to the various degree of
density. Among of them ( eref , e1 , e2 in Fig.6),【Section 1: “reference state”】

Experimental result(Un-drained shear test)

eref defined as the intermediate state (type ②) can beobtained from the process of undrained shear test. In
regarding to the state compressed from eref ,compression index is adopted as λref , because eref
( Drref ) is considered as the normal consolidated
condition in the constitutive model. λref can beobtained from Eq. (2).
λref = λ = 0.313Dr −0.63(2)

Dense sandOver consolidated state
in constitutive modele1

Dr ( ei )Λ(5)pi′ ( = p0′ )D(4)( p′f , q f )
nE

(2)

κ

(3)

Λ

D

Section 1

λref
(6)

p0′

(5)

e0

(7)

eref

M
Curve fitting with stress strain
Experimental result
path on dense sand(Un-drained shear test)

κ1κ2(1) Compression index(2) Swelling index

λref
pi′

κ

pi′

Normal consolidated line

e2

(2)

(3)

Drref ( eref )【Section 2: “dense state”】

e

eref

λref

M

Denser state ( e1 , e2 in Fig.6) is considered to be “over-consolidated state” in constitutive model. Thus, inregarding to the state compressed from e1 , e2 ,compression index is adopted as κ which can beobtained from Eq. (3).
κ = 0.1026Dr −0.45(3)Reference state
Drref

(1)

′
p01

′
p02

(3) Irreversible ratio

λref = 0.313Dr −0.63
κ = 0.1026 Dr −0.45
Λ = 1−κ λ((4) Fitting parameter in EC model nE = Λ ln p0′ p′fln p′(5) Dilatancy parameter(6) Yield stress parameter

Dense sand → OCR larger (OCR>1)

Fig.6 Illustration of the relation density to p0′(7) Void ratio at yieldingm)

D = λΛ ( M (1 + e0 ) )p0′ = pi′ exp ( eref − ei ) ( λref − κ ) 
e0 = ei − κ ln ( p0′ pi′ )Fig.7 Flowchart of parameters determination

319

4. Simulated results

5. Conclusion

To validate the proposed determination method, 5
simulations of undrained shear test were carried out tocompare with the reported experimental results. Herein,
the simulated result for Toyoura sand is presented.

In this research, simulations of undrained shear test for
5 kinds of sand are carried out. Conclusions are
discussed as followed:
・ The simulated results are agreed well withexperimental result when the initial Dr is close to
Drref .

Section 1: “reference state”
According to Fig.1, Drref is determined as 22.6%. By

・ However, the discrepancy increases as the degreeof relative density becomes larger. The applicablerange of Dr seems to be more or less Drref +15

using flowchart defined in section 1, parameters arecalculated as shown in Table (2).
Table (2)nE p'0 (kPa)
Dr (%)
MΛ
D
22.6
1.2 0.4254 0.00817
1.4
294.0

～25%.
・ The reason why simulated result does not coincidewith experimental result, for which is very densesand, the estimated p0′ is significantly larger than
the appropriate value.
・ Simulated results of ”reference state” defined in
section 1 which do not coincide with experimental
results were found in other kind of sand.
Conceivable reasons are thought as followed:① The constitutive model employed in thisresearch, EC model including sub-loading surface,
still has a limitation to represent the realistic
behaviorofsand.
② Parameter λ , κ obtained from the empirical
Eq. (2) & (3), which take an important role in the
proposed parameter determination method, are notcoincide with whole kinds of sand.

Section 2: “dense state”
According to Fig.1, Dr at dense state are 26.6% and31.2%. By using flowchart at section 2, parameters arecalculated as shown in Table (3).
Table (3)Dr (%) M
26.6
1.2
31.2

Λ
D
0.4661 0.00911
0.5030 0.01000

nE

m

1.4

0.2

p'0 (kPa)
608.4
1251.7

600

Stress path on p'-q space
500 (Experimental result)Stress strain relation
500 (Experimental result)

400

C.S.L

300
200
100

Deviatoric stress, q (kPa)

Deviatoric stress, q (kPa)600

00 100 200 300 400 500 600
Effective mean stress, p' (kPa)

300
200
100
10
Axial strain, εa (%)

600

600

Stress path on p'-q space
500 (Simulated result)Stress-strain relation
500 (Simulated result)

400

C.S.L

300
200
100
0
0 100 200 300 400 500 600
Effective mean stress, p' (kPa)
Relative density, Dr

e0=0.904 (Dr=22.6%)
e0=0.889 (Dr=26.6%)
e0=0.872 (Dr=31.2%)

References

400

0
0

Deviatoric stress, q (kPa)

Deviatoric stress, q (kPa)

Simulated result

Experimental result

Fig. 8 shows the simulated result of undrained shear
test for each relative density. Each simulated resultshows a good agreement with experimental results.

20

400
300
200
100
0
0

10
Shear strain, εs (%)

20

Yield stress parameter, p′0p'0=294.0 (kPa)
p'0=608.4 (kPa)p'0=1251.7 (kPa)

Fig.8 Simulated result
Upper: Experimental results, Lower: Simulated results
Left: Stress path on p’-q space
Right: Stress-strain relationship4201) Kato, S., Ishihara, K. and Towhata, I. (2001).
Undrained shear characteristic of saturated sandunder anisotropic consolidation, Soils andFoundations, Vol. 41, No. 1, 1-11.
2) Asaoka, A., Noda, T., Yamada, E., Kaneda, K. andNakano, M. (2002). “An elasto-plastic descriptionof two distinct volume change mechanisms ofsoils”, Soils and Foundations, Vol.42, No.5, 47.
3) 中井健太郎,浅岡顕,中野正樹,野田利弘,金田一広 (2003). 粒径分布の異なる砂の締固め特性に関する上負荷面カムクレイモデルに基づく一考 察 , 学 術 講 演 会 講 演 論 文 集 , Vol. 52(20030516), 343-344
4) Ohno, S., Iizuka, A. and Ohta, H. (2006) : Two
categories of new constitutive model derived from
non-linear description of soil contractancy, Journalof Applied Mechanics, JSCE, Vol.9, pp.407-414.
5) Hashiguchi, K. (1989). Subloading surface modelin unconventional plasticity, Int. J. Solids Struct.,Vol.25, 917-945.
6) 阪上最一, 柳浦良行, 山田真一, 榎本雅夫 (1995).
三軸圧縮条件下における細かな粒径の力学特性, 土木学会第 50 回年次学術講演集, Ⅲ-145,
290-291.

An applicability of DR-MEAM parameters for interfacial energy calculations
Student Number: 06M18027 Name: Takao ABE Supervisor: Kunio TAKAHASHI, Satoshi KOJIMA

界面エネルギー計算における DR-MEAM パラメーターの適用性
阿部喬夫
半経験的原子間ポテンシャル計算法 DR-MEAM は理想的バルク構造からクラスター構造まで幅広い
適用性を持つとされる。しかしそのパラメーターセットの物性値への適用性を評価した例はあまりない。
本研究では積層欠陥エネルギーを計算し、実験値あるいは計算結果同士の比較をすることにより適用性を調べた。計算の結果、既存のパラメーターの中では徳丸のパラメーターよりも殷のパラメーターの方が優れた適用性を示した。

1

Introduction

following 3 material properties using exiting 14 parameter sets for Cu, as shown in Table 1,2.

Modified Embedded Atom Method (MEAM) is a series of semi-empirical interatomic potential for calculating material properties of a large-scale molecular model.
MEAM92, developed by Baskes [1], is very popular andparameter sets for 26 elements are published by thismethod. MEAMs show good applicability and reliable
for bulk systems. However, the applicability of MEAMs
to non-bulk systems is not well understood. Takahashi etal. has expanded the applicability of the MEAMs to nonbulk systems, which is called Dimer Reference Modified
Embedded Atom Method (DR-MEAM) [3–5]. The reference structure used in this method has been changed
from nearest neighbors of fcc to Dimer.
In order to calculate material properties of elements
using MEAM calculations, it is needed to determine appropriate parameter set sets, e.g. equilibrium binding energy, exponential decay factors for the atomic densities,
etc., included in the calculation. In DR-MEAM, parameter sets are determined by fitting both to bulk and cluster
properties aiming for wide applicabilities of calculations.
For DR-MEAM parameter sets, Yin [5] and Tokumaru[4] have calculated and published parameter sets for Copper. They used diﬀerent determining method: Yin focus
on properties of cluster systems like Cu3 Triangle anduse results of DFT calculations, Tokumaru placed emphasis on bulk properties and using results of calculations by MEAM92. Additionally they published lists ofcandidate parameter sets as shown in Table 1,2. Since
they published many candidate parameter sets for Cu, itis needed to select good parameter set among them into
use based on applicabilities. But there are less previous
works on evaluating the applicabilities of parameter sets,such as calculated results of material properties.
In this research, we calculate the stacking fault energy
and relative properties using Yin’s and Tokumaru’s parameter sets, and evaluate their applicabilities for interfacial energy calculations.

2

1. The stacking fault energy of Copper
2. Energy variation in slip deformation
3. Most stable structure among fcc, bcc and hcp
Stacking fault is a one of simple and typical interface
structures. Experimental results of stacking fault energy
can be easily obtained to refer. So the stacking fault energy are calculated as representative of interface. In addition I calculate energy variations in slip deformationfrom equilibrium structure to stacking fault. Stable structures are basic properties and relating above 2 properties,
but it is not entertained enough. So it need to review.
Following sections, calculating methods and results ofabove 3 material properties are shown. For convenience
to discuss, parameter sets in Table 1,2 are tagged withID, as Y01-04 and T01-10.

3Calculating method and results

3.1

Stacking fault energy

3.1.1

Atomic model for calculating Stacking Fault
EnergyThe stacking fault is described as a interfacial defect ofstacked (111) planes of fcc. The schematic illustration of
the stacking fault is shown Fig.1
The stacking fault energy γ s f is defined as follows:γs f =

E s f − E0
S

(1)

Here E0 is the total energy of the equilibrium bulk structure, E s f is the total energy of the structure involving thestacking fault, and S is the interface area of the stacking
fault.
As the atomic modelfor calculating the stacking fault,
18 (111) planes contain 2 × 2 atoms per each, whichstacked on ⟨111⟩ direction are used. E s f is calculated
as a sum of energies of these 72 atoms with slippage deformation in the middle of the stack, and E0 is a sum ofenergies without defects.
For avoiding influences of surfaces on interfacial energy calculations, we apply periodic boundary conditions

Purpose of research

To evaluate the applicabilities of DR-MEAM parameter
sets for interfacial energy calculations, we calculate the21Table 1: DR-MEAM parameters for Cu by Y.Yin [5]
id

E0

R0

a

A

b(0)

b(1)

b(2)

b(3)

w(0)

w(1)

w(2)

w(3)

Y01
Y02
Y03
Y04

1.01
1.01
1.01
1.01

2.22
2.22
2.22
2.22

4.42
4.42
4.42
4.42

0.48
0.48
0.52
0.49

3.88
3.88
3.95
3.88

3.00
3.00
4.80
4.40

3.95
3.85
3.35
2.97

2.95
2.75
3.95
2.95

1.0
1.0
1.0
1.0

1.1
1.6
3.1
2.1

2.1
2.3
2.1
2.1

1.1
1.4
2.1
1.1

Table 2: DR-MEAM parameters for Cu by K.Tokumaru [4]
id

E0

R0

a

A

b(0)

b(1)

b(2)

b(3)

w(0)

w(1)

w(2)

w(3)

T01
T02
T03
T04
T05
T06
T07
T08
T09
T10

1.01
1.01
1.01
1.01
1.01
1.01
1.01
1.01
1.01
1.01

2.22
2.22
2.22
2.22
2.22
2.22
2.22
2.22
2.22
2.22

4.42
4.42
4.42
4.42
4.42
4.42
4.42
4.42
4.42
4.42

0.64
0.64
0.65
0.65
0.65
0.64
0.65
0.64
0.65
0.64

4.77
4.77
4.80
4.80
4.80
4.77
4.80
4.77
4.80
4.77

4.35
4.31
4.35
4.35
4.31
4.29
4.35
4.33
4.35
4.29

5.25
5.25
5.21
5.25
5.25
5.23
5.17
5.25
5.25
5.25

5.25
5.25
5.25
5.25
5.25
5.25
5.21
5.25
5.25
5.25

1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

1.09
1.09
1.12
1.12
1.11
1.09
1.12
1.12
1.11
1.09

1.09
1.09
1.12
1.12
1.12
1.09
1.12
1.09
1.12
1.09

1.11
1.11
1.12
1.12
1.12
1.10
1.12
1.09
1.12
1.10

in parallel directions to the interface. But the stacking
fault is not periodic along the perpendicular direction to
the interfacial plane. So this model has fixed boundaries
on both ends of the perpendicular direction to the stacking fault. Schematic illustrations of the model for calculating the stacking fault energy are shown as Fig.2.

Top Surface

Rbarrier

RbarrierFixed

7 layers

Top surface
7 layers

MovableInterface

Interface(Stacking Fault)

2 layers
2 layers
7 layers

Bottom surface

displacement

Fixed

Center cell
2×2×32[ 211]

Mirror cells
80 (9×9 - 1)

Affected by
Top Surface

Rbarrier
Affectedby Interface
Energycalculationarea

7 layers Affected by
Bottom Surface

Bottom Surface

Rbarrier

Total 10368 atoms

Stacking Fault

Fig. 2: Schematic Illustrations of periodic boundary conditions and fixed atom layers
stacked(111) planes

3.1.2

Precisions of calculated results

In principle, we can calculate energies with expected significant figures using DR-MEAM, because equations of
DR-MEAM consist of elementary functions. But limitations of numerical calculations reduce the significant
figures. In calculating the stacking fault energy, the limitaions can be considered as follows: First, the significant
figures of the DOUBLE PRECISION in FORTRAN isabout 15 digits. Next, energy is calculated by summing
interactions reduce the significant figures of the result.
Because DR-MEAM has no screening functions or cutoﬀ functions, all pair interactions between one atom andthe others, i.e. 10367 atoms in this model, must be calculated. In addition, E s f and E0 are the sum of energy of
72 atoms. Therefore, the significant figures would be reduced to 8 digits. Finally subtraction in Eq.(1) decrease
the significant figures to 5 digits.

Fig. 1: Schematic illustration of the stacking fault
Rbarrier in Fig.2 is the distance between surfaces ofthe model and energy calculation area. To prevent influences of surfaces on calculated results of energy, Rbarrier
should be chosen as large as possible. but in the aspectof calculation amount, smaller Rbarrier should be considered to reduce the number of atoms in the calculation
model. From our preliminary study, 6R, 6 times of the
distance between nearest neighbor atoms in fcc, are chosen as Rbarrier .
Consequently, the number of atoms in the center cell is128. There are 4 mirror cells in each parallel directions
to the interface, The total number of atoms included in
the model is 10368.

22

3.1.3Calculating process: initial displacement andrelaxation

As shown Fig.1, the stacking fault are modelled by displacement of upper half of stacked (111) planes to 13 [21̄1̄]
directions. And after this initial displacement, there arerelaxation processes in which interfacial planes are reconstructed to more stable structure depending on DRMEAM parameter sets. In the relaxation process, positions of 16 atoms upon and beneath of the stacking
fault and the distance of interface, in z-direction, arechanged using steepest descent method for minimize thesummation energy of 72 atoms. Taking into accountthe precisions, calculations of steepest descent method
are terminated when the amount of energy change in 1
step is less than 10−11 (eV). In calculation of gradient,∆x = ∆y = ∆z = 10−15 (m) are used as the diﬀerence of
positions and distance to avoid local minimum.

3.1.4

3.2

Energy variation in slip deformation

3.2.1

Calculation model and method

The total energy of the atomic model is calculated step bystep as the structure performs the slip deformation fromequilibrium bulk structure to stacking fault. Regardless of initial displacement, calculation methods such as
boundary conditions, the number of atoms or relaxation
methods are the same with the calculations of the stacking fault energy.
3.2.2

Calculated results of energy variation

Results of energy variation are clearly classified into 2
groups, Y01-Y04 and T01-T10. As representative results, calculated results of Y01 and T01 are plotted in
Fig.3, 4, respectively. The vertical axis is the total energy, and the horizontal axis is the ratio of displacement,slide to 13 [21̄1̄] direction. When the ratio equals 1.0, it isstacking fault.
The most important characteristics of these graphs isthe energy barrier between ideal equilibrium and stacking fault. In other words, stacking fault is stable structurein Fig.3, but not stable in Fig. 4. However, it’s diﬃcult to discuss the applicabilities from the characteristics
about the energy barrier because it’s available that there
are influences of relaxation conditions, i.e. no strains, noexternal pressures.

Results of the stacking fault energy

Calculated values of the stacking fault energy of Copper
are shown in column 1 of Table 3. Experimental values
are about 30 ∼ 150(mJ/m2 ), depending on experimental
methods, environments and etc. [2]. Calculated values ofthe stacking fault energy by all parameter sets agree withthe experimental values. It means all parameter sets areapplicable for stacking fault energy calculations.

3.3

Stable structure of parameter sets

3.3.1

Calculation model

Total Energy (eV)-250.35

EnergyBarrier-250.4For determining the stable structure we calculate andcompare equilibrium binding energies and distances between nearest neighbor atoms of fcc, bcc and hcp.
Searching equilibrium structures, energies of structures,
which have the same geometries as fcc, bcc or hcp anddiﬀerent distance between nearest neighbor atoms, arecalculated. For avoiding the eﬀect of surfaces we use
1289 atoms for fcc, 1243 atoms for bcc and 1261 atoms
for hcp. The equilibrium distance is the distance which
minimize the energy of the atom in the center of themodel.
In these models we need to sum the interaction terms
between the center atom and the others, about 1200
atoms. We can obtain the results of energy with the significant figures of 12 digits.

-250.45

Ideal
Bulk

Stacking
Fault
Energy-250.5

Stacking
Fault-250.55
00.5

1

Ratio of displacement

Fig. 3: Calculation result of Y01

Total energy (eV)-252.45

No energy barrier
Stacking
Fault-252.50

Ideal bulk

0.5

Results of calculating stable structure

Results of all parameter sets are shown in column 3-5
of Table 3. Here Req
f cc is equilibrium distance between
nearest neighbor atoms of fcc, E eq
f cc is equilibrium binding energy per atom of fcc, ∆Ebcc− f cc and ∆Ehcp− f cc
are structural energy diﬀerence. If ∆Ebcc− f cc > 0 and∆Ehcp− f cc > 0 then fcc is the most stable.
eq
From experimental results, Req
f cc = 2.56(Å), E f cc =3.54(eV) and the stable structure of Copper is fcc. But results of T01-T10 show that the stable structures of theseparameter sets are bcc and obviously disagree with the

Stacking
Fault
Energy-252.55

0

3.3.2

1

Ratio of displacement

Fig. 4: Calculation result of T01

23

ID

γs f
(mJ/m2 )Energy Barrier

Y01
Y02
Y03
Y04
T01
T02
T03
T04
T05
T06
T07
T08
T09
T10

67.31084.077147.9467.86798.25297.91698.61598.57298.041101.4699.21596.65998.31696.889AppearAppearAppearAppearNot appear
Not appear
Not appear
Not appear
Not appear
Not appear
Not appear
Not appear
Not appear
Not appear

Table 3: Results of calculations
f cc
f cc
Req
Eeq
(m)
(eV)
2.5074373E-10
2.4948876E-10
2.5449382E-10
2.5165486E-10
2.5603344E-10
2.5612400E-10
2.5670537E-10
2.5671845E-10
2.5685560E-10
2.5623170E-10
2.5667833E-10
2.5602480E-10
2.5678393E-10
2.5623869E-10-3.47966495717-3.52949482140-2.95737380959-3.28715212792-3.50808138984-3.50382063515-3.45190782186-3.45034029914-3.44556520975-3.50087565242-3.45503249615-3.50773976657-3.44841020439-3.49998253802

experimental results. In addition, Y03 is doubtful, because E eq
f cc diﬀers from experimental results too much.
Theese suggest that T01-T10 and Y03 have less applicabilities than the others.

4∆Ebcc− f cc
(eV)

∆Ehcp− f cc
(eV)

1.141184643E-2
1.368218936E-2
0.873000644E-2
1.242962083E-2-2.024075324E-2-2.020328896E-2-2.139767365E-2-2.158684096E-2-2.154102968E-2-2.006043339E-2-2.102147897E-2-2.016548630E-2-2.158602665E-2-2.016125718E-2

1.158708470E-2
1.432977696E-2
2.633663546E-2
35.611491374E-2
1.799456819E-2
1.794176288E-2
1.816454452E-2
1.816041618E-2
1.808228157E-2
1.772346210E-2
1.826629808E-2
1.767586163E-2
1.812724290E-2
1.772127887E-2

with the experimental results. It confirm the predicted
limitation of Yin’s method in ref. [5].
There are not so large diﬀerence between Y01, Y02
and Y04. ∆Ehcp− f cc of Y04 are 10 times larger, but thestacking fault energy do not so diﬀerent from the others.
It needs other calculated results to discuss about this difference in more details.

Discussions

Results of stable structures show that the stable structureof T01-T10 are bcc, and imply that parameter sets by
Tokumaru have less applicabilities than by Yin. The difference of stable structure should leads to the results ofenergy variation in slip deformations in which the stacking fault is unstable. Tokumaru had used 2 properties ofbulk, equilibrium energy (eV) and equilibrium distance
between nearest neighbor atoms (m) of ideal fcc bulk,
and 6 properties of clusters, binding energy (eV) and distance (m) of Cu3 , Cu4 Tetragon and Cu4 Tetrahedron, as
the fitting values in determining DR-MEAM parameter
sets. On the other hand, Yin had used equilibrium energy (eV) , equilibrium distance between nearest neighbors (m), bulk modulus of fcc bulk (N/m2 ) and binding
energy (eV) distance (m) and bond angle (degree) of Cu3
Liner, Cu3 Triangle and Cu4 Tetrahedron. Comparing
these lists of properties used in determining parameterssuggests that the lack of properties including dimensions
relating geometries of crystallines, i.e. bulk modulus orangle, leads changing the stable structures of parameter
sets by Tokumaru, which determined by fitting equilibrium energies and distances of fcc bulk and clusters, tobcc.
From another point of view, the relations between theproperties used in determining parameters and the stablestructures are very interesting in point of expanding applications to alloys or compounds. But for more discussions, we need another research about DR-MEAM parameters determining methods.
In parameter sets by Yin, Y03 is less applicable thanthe others. The equilibrium distance between nearest
neighbors of Y03 are the most close to the experimental
value. However, the equilibrium energy clearly disagree

5Conclusion

The parameter sets by Tokumaru have less applicabilities
for interfacial energy calculations than the parameter sets
by Yin. One parameter set by Yin, i.e. Y03, has lessapplicability than the others.
Applicabilities of Y01, Y02 and Y04 for calculatinginterfacial energy are almost the same. For more details, it is needed to calculate other properties and evaluate them.

Reference[1] M. Baskes. Modified embedded-atom potentials for
cubic materials and impurities. Physical Review B,
46(5):2727–2742, 1992.
[2] P. Gallagher. The influence of alloying, temperature,and related eﬀects on the stacking fault energy. Metall. Trans., 1(9):2429–2459, 1970.
[3] K. Takahashi. Dimer Reference Embedded Atom
Method. Journal of Computational and TheoreticalNanoscience, 1(2):221–226, 2004.
[4] K. Tokumaru. A procedure of determining parameters by adapting to the results calculated bymeam’92. Master’s thesis, Tokyo Institute of Technology, 2007.
[5] Y. Yingchen. Development of a Modified Embedded
Atom Method for Copper Clusters. Doctoral dissertation, Tokyo Institute of Technology, 2006.

24

Application of Information and Communication Technology inCultural World Heritage Site; Case of Luang Prabang, Lao PDRStudent Number: 06M18040 Name: Shingo ENOKI Supervisor: Jun-ichi TAKADA, Shinobu YAMAGUCHI

ラオス・ルアンパバーンの世界文化遺産地域における情報通信技術の導入
榎慎吾

近年世界文化遺産地域において，効率的な遺産保護及び地域開発のために，複数分野の融合的開発アプローチが注目されている。本論文ではその具体例として，ラオス・ルアンパバーンにおける情報通信技術の地域開発への応用例を示し，世界遺産地域の保存と開発の具体的活動の詳細を報告、議論した。さらにプロジェクト実施を通じ観察された発見および問題点を紹介し，国際開発プロジェクトの持続可能性に関連する要因について考察を行った。

1

Introduction

dom which possesses variety of traditional temples
and colonial buildings have been built. Luang Prabang was inscribed in the list of the world cultural
heritage sites in 1995 with its valuable mixture of
Lao tradition and French colonial culture in its peculiar landscape. Since the inscription, the numberof tourists has continuously increased and the town
has been experiencing both positive and negative effects of its development.

The World Heritage Sites attract a lot of attention inrecent years. It promotes the economic development
through increased tourism, but at the same time,
it can cause problems such as destruction of environment due to rapid development. To control suchproblems and promote regional development throughmaximizing positive eﬀect of becoming world heritage site, a lot of discussions have been conducted.
Among such discussion, introducing Information
and Communication Technology (ICT) to promotesustainable development of world heritage site gained
attention in 2004 in Lao P.D.R. Tokyo Institute ofTechnology in cooperation with Lao government and
UNESCO conducted need assessment and ﬁve main
activity components were identiﬁed based on its needassessment result. As the third year of the project,
the author participated in the activities since spring
2007, particularly in the following two important activities; 1) Development of building authorization
database of the local institution, 2) Setup of Internet
server for the heritage database application. These
activities have particular signiﬁcance since they areof the urgent needs of the local government and community, and thus the successful end results are expected to contribute to the development and management of the town of Luang Prabang through introduction of ICT. At the same time, these activities
are planned and implemented as a form of trainingof local ICT team, contributing to the local humanresources development.

2

Background

2.1

Project Site

2.2

Project Background and Signiﬁcance of the Study

Tokyo Tech has established tri-partite collaboration
with UNESCO and Lao government since 2004, aiming at contributing to the sustainable regional development from technical aspect. During the initialneed assessment at the site, introduction of ICT was
considered as one of the most needed and missing
areas of intervention at the World Heritage site ofLuang Prabang. And thus, with the request of UNESCO, the leading agency of world heritage preservation and management and experts of Lao government, Tokyo Tech team together with newly established local ICT team conducted extensive needs assessment for the project. In this context, this research project is the local needs based and its development has been recognized and appreciated by the
Lao government. The speciﬁc activities are planned
and implemented particularly in close cooperation
with MdP (La Maison du Patrimoine, Heritage House).
MdP is a local governmental institution particularly
responsible for the conservation and management of
heritage zone of the town Luang Prabang.

2.3

Lao People’s Democratic Republic, or Laos, is a developing country located in south-east Asia. The
country’s population is about 6.5 million and GDP
per capita is around 1,900 USD[1]. ICT infrastructure and its utilization are still under developmentin Laos. The project site, Luang Prabang is a regional center of northern Laos, with a population of
65,000. The town used to be the capital of king-

Basic Needs in ICT in Luang Prabang

In earlier stage of the project, based on the resultof need assessment mentioned above, the following
areas of needs regarding ICT were identiﬁed. These
needs are considered underlying signiﬁcance to planand implement the project activities.

25

Need for long-term preservation of data: In
Luang Prabang, abundant useful information on heritage was accumulated during the decade in the formof photos and drawings. Such paper-based information needed to be preserved as digital data to avoid
quality deterioration.

types of training. The training covered the creationof the static website, database application programming. Prototype database application was also created in the training. In order to avoid illegal useof software together with local availability, Free andOpen Source Software (FOSS) were utilized in the
project activities.
In addition, the ﬁrst local ICT center was established in July 2006, as an important outcome of the
project. In the ICT center, database application andwebsite which are the product of series of training
and the project activities are available for visitors,
both to tourists and local residents.

Need for an eﬀective data utilization in daily
operation: Accumulated important data was under utilized due to absence of database system. Initial eﬀort of creating database was found at individual basis and thus, institutional database system wasin serious need to promote an eﬀective data utilization.

3.2
Need for information sharing within and among
local institutions: Information sharing by introducing client/server system enables to manage thedata by concentrating on to the server which waspreviously stored on individual PCs. This systempromotes the consistency of data.

Authorization Database

This section focuses on the creation of authorization
database. Since the beginning of the project, ourcounterpart institution, MdP found one of the mostimportant task is to regulate the development of thetown in line with management plan of the world heritage site. In case of Luang Prabang, its landscape
together with natural environment is of particularimportance as the heritage site and thus, monitoring
and regulating rapid changes of the landscape due toconstructions and unwanted restoration is the vital
task of responsible institution.

Need for information dissemination for general public: Traditionally speaking information was
not to be disseminated but to be kept in the insitutions. Given this situation, town of Luang Prabang had no oﬃcial information center nor website
for public and heritage information. This limits activities for the tourists as well as the residents, andthus promoting information dissemination was foundto be urgent.

1) Signiﬁcance of Authorization Process
Authorization process of MdP is an operation to give
authorization for the construction or restoration of
the buildings located in heritage conservation areas.
The owners and/or residents who wish to build or restore the building have to apply to local government
for authorization. MdP provides approvals and/or
recommendations regarding building plan to ﬁt to
heritage townscape by following the heritage regulations.

Need for local human resources development
in ICT ﬁeld: As one of the most urgent and important needs was human resource development at
local institutions for eﬀective and sustainable implementation of ICT activities. Such human resource
was undoubtedly needed to promote locally applicable and sustainable ICT utilization.

2) Process of Development

3

Project Implementation

The development of authorization database was conducted in close cooperation with Architecture and
Urban Development section of MdP. The local ICTteam has played a major role in development. Tokyo
Tech team including the author has provided continuous assistance in the development process.
Workﬂow of authorization process was identiﬁed
through discussion with relevant staﬀ members. Thenthe database table structure was designed by using
database normalization scheme. Subsequently, thelocal ICT team started development of UI and basic functions and had a periodic meeting with MdP
staﬀs. Currently, most of the fundamental functions
have been already implemented.

Given the important and urgent needs in ICT areas,
the project identiﬁed ﬁve major project components
as speciﬁc activities: 1) Establishment of databasemanagement system; 2) Establishing network among
institutions; 3) Creating website for town of Luang
Prabang; 4) Establishment and management of local ICT Center; and 5) Training of local human resources. As the third year of the project activities,
and as a member of Tokyo Tech team, the author
has been involved especially in the development of
database system and setup of Internet server.

3.1

Summary of Initial Stage of the
Activities

3) Impact of Authorization Database
The authorization database provides the way of longterm preservation of accumulated paper documents.
Storage space will be also saved by digitization. In

At the initial stage, local ICT team was organized
to develop future experts in ICT ﬁeld in Luang Prabang. Tokyo Tech team provided need-based various

26

addition, the authorization database can provide identical sets of data to the users. It also enhances theeﬃciency of search and analysis process with customized User Interface (UI). Creation of the database
will show a signiﬁcant presence of local ICT team. It
also means that the team has reached certain levelof skills in ICT so as to develop the mission-critical
database application. Since the authorization databaseis a pilot database for practical operation, it is expected to stimulate further utilization of ICT for the
daily operation. Moreover, this database application
is expected to promote information sharing between
institutions.

3.3

to disseminate valuable information. Furthermore,through participating WHN, it is expected to share
the knowledge and experiences with ICT related professionals in other world heritage sites.

4

Findings and Issues

Through intensive participation to the project activities with local ICT team in introducing ICT in heritage town of Luang Prabang, the following ﬁndings
and issues may be highlighted.
Continuous training provided local ICT teammembers motivation to acquire skills: It is evident that the local ICT team members have highmotivation in learning and acquiring new skills. They
understand the importance of learning to make project
activities sustainable. As leading ICT members in
the town, they are trying to become the leading local
human resource for ICT. It is understood that compared to the beginning of the training activities, theirunderstanding and utilization of English has greatly
improved. The team leader visited Tokyo Tech tolearn more speciﬁc skills this year. The youngest
member has challenged to take exam to be enrolled
in Masters’ program in Japanese university. These
examples well illustrate high motivation for further
learning.

Internet Server Set-up

Throughout the project activities, accumulated information in MdP was compiled into the databaseserver. However, currently the server is accessible
only in ICT center due to cost and limited bandwidth of Internet. Therefore Internet server setup
is highly required to disseminate the information to
general public.
1) World Heritage Network
The Internet server is planned to be set up in World
Heritage Network (WHN)[2].
WHN is a website for world heritage communities, site managers and other conservation professionals. The purpose of WHN is to provide a meeting
place for sharing knowledge about heritage management and as we all opportunities to learn and experiment with information technologies.

ICT introduction to Luang Prabang receives
attention and thus promotes ICT utilization:
The project activity has been gaining attention frommass media and external organizations for its locallydriven approach and eﬀective utilization of ICT. For
example, local brochure ”Stay Another Day - Laos”
has given an account of the ICT center. Many foreign organizations also visited and provided positive
comments for local ICT Center. Further, as the internet environment rapidly develops in Luang Prabang, business owners pay more attention to internet as an advertisement tool. This gives the ICTteam small business opportunities through creating
database system for guesthouses management. Another signiﬁcance of this ﬁnding is that it can promote spread of ICT utilization.

2) Technical Issues
Server will be set up in following processes; 1) Software installation and conﬁguration, 2) Data upload
to WHN server, 3) Test and publishment.
Regarding process 1, there were technical issues
caused by the diﬀerence in Operating System (OS)and software version of servers in WHN and ICT
center. Currently the software version was upgraded
and the setup process of the server in FreeBSD has
been established by the author. The installation will
be done by remote access through Internet.
Process 2, Data upload is another technical issue.
Upload from ICT center is not realistic because thebandwidth is 64 kbps, though data size is up to 5 GB.
The realistic way is high speed Internet or sending
storage media such as DVD.
Since the WHN server is published in Internet,
security is the important issue. To conduct a sufﬁcient management for security, additional training
will be provided to the local ICT team.

Support for Lao language is improving in ICT
ﬁeld: Previously, OS did not support direct inputof Lao. To input Lao, additional software such as”Lao Script for Windows” was needed[4]. But recently, several Linux distributions and Windows Vista
supports direct input of Lao. Lao localized oﬃce
software was also developed by Science, Technology
and Environment Agency (STEA), Lao government.
Thus, ICT situation in Laos is rapidly improving.
Under this situation, MdP staﬀ members seem to be
motivated more and more to make maximum use of
the database system for their daily operation. At individual basis, it is observed that purchase and useof PC for study and business are increasing rapidly.

3) Impact of Internet Server Set-up
As mentioned above, it has an impact that the general public from all over the world can access tothe database application. This is a powerful way

27

Language training is a must for acquisition of
new technical skills: Most of the information regarding FOSS is available in Internet, only in English. Particularly updated information is seldom
available in Internet in Lao and the local team learned
from English documents. Although their English
skills are progressing, understanding new concepts
with technical expressions are still challenging. Continuous training for English along with ICT trainingis necessary to make maximum eﬀects.

ability of computer equipments is drastically improving compared to the beginning period of the project.
Further, as mentioned earlier, support of Lao language is available more and more when introducing
FOSS. It is important to pay attention to the rapidchange in available technology locally. Careful analysis of available infrastructure and technology, realistic decisions can be made to recommend and introduce most reasonable application.
Cultural aspect: Cultural aspect is also signiﬁcant. Laotians are considered to prefer harmony
avoiding argument. Therefore, it is sometimes difﬁcult to confront with project counterpart when encountering the problems. Understanding their culture to promote a better communication is highly
required for proper and sustainable implementation.
Further, it is worth mentioning that Ministry of information was called as Ministry of Propaganda only
until a decade ago.

Instability of basic ICT infrastructure prevented smooth shift from paper-based system tocomputer-based system: In the town of Luang
Prabang, instability of infrastructure such as electricity and Internet connectivity is often observed.
Electricity failure still occurs at least once a week.
Internet connectivity is not stable throughout theday. Due to such a situation, the training and otheractivities were delayed or suspended from time totime. This is one of the reasons that shifting paperbased system to computer-based system has diﬃculty. Oﬀ course, other reason such as ﬁnancial difﬁculty causes insuﬃcient computer installation andthus, prevents the computer-based system to be themain system. Considering this situation, combined
usage of both systems seems to be realistic at current
stage of Luang Prabang.

5

6

Conclusion

Based on local need assessment, important ﬁve areas are identiﬁed in introducing ICT in the world
heritage site of Luang Prabang. Among continuous research and project activities since 2004, thisthesis highlighted two main activity components: 1)
establishing authorization database; and 2) internet
server set-up. Both components are based on urgent
needs of Luang Prabang to promote its sustainable
development. Further such activities are within theframework of local human resources development using FOSS for its operation. The author’s intensive
experience of working closely with local ICT team indeveloping above mentioned activities, identiﬁed major ﬁndings as well as issues to be tackled for further
sustainable operation of ICT application in Luang
Prabang. The study also discussed four important
aspects, namely, human resources, ﬁnancial, technical and cultural aspects as signiﬁcant factors in introducing and sustaining ICT activities to contributeto the development and management of Luang Prabang.

Discussion on Sustainability

Sustainability question of the project is always important since any kind of activities need to be transferred to the local institutions as localized activities. This section touches upon important aspects
when considering sustainability of this development
project. This analysis is based on experiences of theauthor.
Human resource aspect: Human resource development is always a key factor for project sustainability. In this project, the local ICT team was continuously trained for three years. With such experiences
the team is expected to be converted as the Communication Unit of MdP. This indicates that skillsof local ICT members are recognized by local institutions, and thus expected to continue forthcoming
ICT related activities.

References[1] CIA,”The
World
Factbook-Laos-”,
https://www.cia.gov/library/publications/theworld-factbook/geos/la.html, last updated on Jan
17, 2008.

Financial aspect: To make the activity sustainable after the initial stage of the project with external
support, suﬃcient funding is crucial. This question
was considered important from the beginning of thelocal ICT Center operation. Income from the ICTCenter covers its running cost. Besides, governmental support is also expected since the team will be
kept as Communication Unit of MdP.

[2] Center
for
Information
Media
and
Society,”WorldHeritage
Network”,
http://worldheritagenetwork.org/[3] ”Lao
Scriptfor
Windows”,
http://www.laoscript.net/, last updated on Dec 6,
2007.

Technical aspect: Technical aspect such as stability of electricity, Internet connectivity and avail-28

Fundamental Research on the Hydration Reaction of Steel Slag
Hydratiod Matrix (SSHM)
Student Number: 06M18056 Name: Ippei SUZUKI Supervisor: Nobuaki OTSUKI
鉄鋼スラグ水和固化体の水和反応に関する基礎的研究
鈴木 一平
鉄鋼生産において副産物として生じる製鋼スラグと高炉スラグ微粉末、火力発電所から副産物として産出されるフライアッシュを主要原料とし、アルカリ刺激材として消石灰を添加した、環境低負荷の鉄鋼スラグ水和固化体（スラグ固化体）が土木建設材料として開発された。スラグ固化体に使用されている高炉スラグ微粉末やフライアッシュの反応は温度依存性を有し、養生温度により異なった性能が認められることが報告されており、夏季や温暖な地域での施行には注意が必要である。しかしながら、養生温度がスラグ固化体の強度特性に及ぼす影響や、消石灰、フライアッシュおよび高炉スラグ微粉末が共存する際の水和反応機構に関する研究はほとんど見られない。そこで本研究では、養生温度がスラグ固化体の強度特性に与える影響を把握し、強度発
現のメカニズムを構成化合物の反応機構と関連付けて明らかにすることを目的とした。
are main ingredients for SSHM have temperaturedependence. These materials are reported todemonstratio different strength performance
depending on the curing temperature. Therefore its
use in tropical regions like Southeast Asia is
noteworthy. However, the researches that concerns
the strength characteristics of SSHM at different
curing temperature and hydration reaction
mechanism in situations in which CH, FA, and BFS
coexist are not yet known. Therefore it is the aim of
this research to understand the influence of curing
temperature to the strength characteristics of SSHM
and to clarify the relationship of strength to thereaction mechanisms of the composition compounds.

1. Introduction
Recently, the effective use of by-products and thedepletion of natural resources such as lime stones,
quartz rocks, and other aggregates are serious social
issues in the engineering and construction field. With
this as background, the steel slag hydratiod matrix(SSHM) was developed. It is made from steel slag andblast furnace slag fine powder (BFS) which areby-products from ironworks, and fly ash (FA) which
are by-products from thermal power plants. SSHM issolidified substance that is made from slag aggregatesand uses slaked lime (CH), FA and BFS as bonding
materials. Because of this, its use as an alternative toconcrete is examined.
There are some researches about FA and BFS
because their use is increasing as a concrete mixture
material. Osawa1) made a study about the reaction
ratio of FA by the selective dissolution method thatuses hydrochloric acid-sodium carbonate solution. It
was reported that when the FA curing temperatureand fineness are high and a low replacement ratio isused, the reaction ratio increases. Kondo2) on the other
hand, made a study about the reaction ratio of BFS by
the selective dissolution method that uses salicylic
and methanol mixture. It is reported that when the
BFS curing temperature and fineness are high and a
low replacement ratio is used, the reaction ratio
increases.
As stated above, the reactions of BFS and FA which

2. Outline of experiments

2.1 Materials used
Table2.1 and 2.2 shows the chemical composition of
FA and BFS.
CaO

SiO2

Al2O3

FeO

MgO

TiO2

17.2

38.8

27.5

9.9

2.8

3.9

Table 2.1 Chemical composition of FA (unit:%)
CaO

SiO2

Al2O3

MgO

S

43.9

32.4

13.9

6.4

1.6

Table 2.2 Chemical composition of BFS (unit:%)29

2.2 Method of manufacturing the test specimen

the curing temperature 20℃ and 30℃, compressive
strength increased even on the 56th day. And the caseof 30℃ curing temperature shows higher compressive
strength than the case of 20℃ curing temperature.
However, an increase in compressive strength
stagnated in the 40℃ curing temperature after the
14th day, and since then compressive strength values
are way below the compressive strength of those
cured at 20℃, though the compressive strength at 1st
and 3rd day is equal to the that of 30℃ curing
temperature.

In this research, two types of specimens prepared.
Table 2.3 and 2.4 shows the mix proportion of thespecimens. The water/powder ratio was fixed at 40%.
The mixing was done at 20℃. Distilled water wasused as mixing fluid. Mixing was done using two-liter
mortar mixer. After all the ingredients were added,
mixing was done at low speed for 30 seconds and waselevated to high speed for 150 seconds. Styrene bottles(50cc) were then filled with the mixture and sealed.
After 72 hours curing time, the hardened samples
were demolded and cured underwater until the target
ages were reached. The curing temperature was set
at three levels (20℃, 30℃, and 40℃).
After curing time was reached, a test piece was thentaken from the centers of the samples and was soakedin acetone for 24 hours. This is done to stop thehydration within the test piece. The test piece wasthen crushed to produce powder passing through the
150μm sieve. This powder becomes the hydration test
specimen.
W/P

CH

BFS

FA

W

Slag

40％

86

484

163

296

1466

3.2 Measurement of pore structure
It is said that there are a close relationship between
compressive strength and pore structure. Therefore
the pore structure of SSHM on the 28th day wasmeasured by using mercury porosimetry. Fig 3.2
shows the results. Similar to the results ofcompressive strength, it is confirmed that in the case
40℃ temperature, both amounts of total pore andminute pore size increased compared with the case of
the 20℃ and 30℃ curing temperature.
25

Compressive strength (MPa)

圧縮強度

Table 2.3 mix proportion of specimen (For the measurement ofcompressive strength)

20
15

20℃

W/P

CH

BFS

FA

W

（MPa）

40％

151

845

284

518

10

Table 2.4 mix proportion of specimen (For the investigation of hydration
mechanism)

30℃

5

40℃

0
0

3. Investigation of the influence of temperature on
the strength characteristics of SSHM

7

14

21

28
35
Time
(day)
材令（日）

42

49

56

63

Fig 3.1 Compressive strength of SSHM at different curing temperature

140.0

Pore volume （mm3/g）

The compressive strength and pore structure of
SSHM cured at different temperature were measured.
These results are concerned from the view points ofhydration products and reaction ration of composition
compounds in chapter 4 and 5.

3.1 Measurement of compressive strength

120.0

20℃ 28day

100.0

30℃ 28day

80.0

40℃ 28day

60.0
40.0
20.0
.0
.0010

The cylindrical test piece of ø50mm×100mm (paste+ slag aggregate) is made and the compression test
following JIS A1108 "Concrete compression
examination" was performed. The test piece was thenset up in the compression strength machine (500kN
compression examination machine AYC-500P), andthen applied with load (0.6±0.4 N/mm2 per second).
Fig 3.1 shows the results of the compression test. At

.010

.10

1.0

10.0

Minute hole diameter （μm）
Fig 3.2 Pore structure of SSHM

4. Investigation of the hydration products of SSHM
First, to clarify the results of strength characteristics,

30

the hydration products of SSHM were investigated.

reaction ratio of CH increases as curing time increases,
and also as curing temperature rises.

4.1 Identification of hydration products of SSHM.

5.2 Measurement of reaction ratio of FA

X-ray diffraction instrument (XRD) was used for
identification of the hydration products. The range of
5°〜70° was done and qualitative analysis wasdone at operation speed 2°/min.
Fig 4.1 shows the hydration products generatiod in
the SSHM. The peak that appears at about 9.1° is
Ettringite（3CaO・Al2O3・3CaSO4・32H2O）, and theneedle crystal generatiod by the hydration of theordinary Portland cement. The peak that appears at
about 9.9° is Monosulfate (3CaO・Al2O3・CaSO4・
12H2O）that shifts from Ettringite. The peak at about
11.5° is Calcium Aluminate (Ⅳ) hydratio (4CaO・
Al2O3・13H2O), and the hydration product that can begeneratiod even with the hydration of the ordinary
Portland cement. The peak that appears at about
10.6° is thought to be a solid solution for the
Monosulfate and Calcium Aluminate (Ⅳ) hydratio 4).
Moreover, the broad peak at about 29.4 ° was
confirmed to be C-S-H. At different curing
temperature, there was no difference in the kind of
the hydration products and peak intensity.
CrystalChemical formula

In the measurement of the reaction ratio of FA, theapplication of the selective dissolution method thatused hydrochloric acid-sodium carbonate solution by
Osawa1) was adopted. The procedure of this method isshown below.（Fig5.2）BFS, CH and the hydration
products dissolved while FA was hardly dissolved
using this method. Therefore the reaction ratio of FA
was determined by measuring the amount of theresidue extracted.
The Fig 5.3 shows the reaction ratio of FA. The
reaction ratio of FA increases as curing timeincreases, and also as curing temperature rises.

5.3 Measurement of reaction ratio of BFS
Salicylic and methanol mixture previously
mentioned are generally used for the measurement of
the reaction ratio of BFS. However, because FA andBFS coexist in the SSHM mixture, therefore an
accuratio fixed quantity would be difficult to bemeasure. Therefore in this research, the application of
the method of measuring the BFS by DSC used by
Imoto3) was adopted. The enthalpy change (generationof heat) from the glass phase to Melilite (solid solution
for Gehlenite and Akermanite) by crystallization
happens at about 900℃. This heat generation wasmeasured by DSC.
Fig 5.4 shows the reaction ratio of BFS. At 20℃ and
30℃ curing temperature, the reaction ratio increases
as curing time increases with 30℃ showing higher
reaction ratio than 20℃. However, at 40℃ curing
temperature, the reaction ratio of BFS stagnates after
14th day and the values are less than those cured at
30℃.

Diffraction angle(deg, CuKα)Ettringite

3CaO・Al2O3・3CaSO4・32H2O

9.1

Monosulfate

3CaO・Al2O3・CaSO4・12H2O

9.9

C4AH13

4CaO・Al2O3・13H2O

11.5

Solid solution
C4AH13 -Monosulfate

4CaO・Al2O3・13H2O+ 3CaO・Al 2O3・CaSO4・12H2O

10.6

Fig 4.1 Hydration products generatiod in SSHM

5. Investigation of the reaction ratio of composition
compounds of SSHM
Next, to clarify the results of the strengthcharacteristics, the reaction ratio of composition
compounds were investigated. However, the reactions
in situation in which three kinds of composition
compounds coexist have never been investigated.
Therefore the reaction ratio was measured byfollowing proposed methods.

5.4 Influence of temperature on the reaction ratio ofcomposition compounds.
To summarize the phenomenon happed at 40℃
curing temperature, it is assumed that following
phenomenon happed.
At 40℃ curing temperature, the reaction of FA is
promoted thus consuming the CH priority. The
reaction of BFS making stagnated after 14th day,because CH is insufficient. Therefore, the pore
structure become less tight and compressive strength

5.1 Measurement of reaction ratio of Slaked Lime
The reaction ratio of CH was determined using
Differential Scanning Calorimetry (DSC). The
amount of CH (calcium hydroxide) was measured bydehydration between 400℃〜500℃.
The Fig 5.1 shows the reaction ratio of CH. The

31

dose not increased after 14th day.
To avoid these situations, using a low heat Portland
cement (LPC) which can supply Ca(OH)2 for a longterm as alkali stimulant is suggested.

6. Conclusions[1] Compressive strength of the SSHM at curing
temperature of 20℃ and 30℃ shows more than
20MPa at 28th day, and the compressive strength isincreased even after the 28th day. However,compressive strength at the 40 ℃ curing
temperature is not increased after the 14th day andfall well below the compressive strength of those
cured at 20℃, though the compressive strength at
the 1st and 3rd day was equal to that of 30℃.
[2] The hydration products generatiod in the SSHM
were Ettringite, Monosulfate,Calcium
Aluminate (Ⅳ) hydratio, solid solution for the
Monosulfate and Calcium Aluminate (Ⅳ) hydratio,
and C-S-H. At different temperature, there was nodifference in the kind of hydration products.
[3] The reaction ratio of FA and CH is increased as
curing temperature rises. The reaction ratio of
BFS is increased at 20℃ and 30℃. The reaction
ratio at 30℃ curing temperature shows higher
value than that at 20 ℃ curing temperature.
However, at 40℃ curing temperature, the reaction
ratio is stagnated after the 14th day. The value ofthe reaction ratio fall well below that at 20℃
curing temperature after the 28th day. Therefore, at
40℃ curing temperature, it is recommended touse a low heat cement as alkali stimulant.

90
80

Reaction
Reaction ratio
rate (%)

70
60
50

20℃

40

30℃

30
20

40℃

10
0
0

7

14

21

28

35

42

49

56

63

Time (day)Fig 5.1 Reaction ratio of CH
Centrifugal
separation

2Ｎ HCl30ml

Centrifuge
tube

Water bath：60℃，15min

3000-4000rpm
30sec

Weighting
Sample1g

Washing(3times, using hot water)
Centrifugal
separation

Drying at
110℃
Weighting

3000-4000rpm

Water bath：80℃，20min 5% Na2CO3
30ml

30sec
Washing
3timesFig 5.2 Flow chart of the measurement for unreacted FA
25

References[1] Eiya Oosawa,: Reaction ratio of fly ash in thehydration of fly ash-cement system，Cement concrete
thesis collection，No.53, pp.96-101, 1999
[2] Renichi Kondo:Research on quation ratio slag in
fixed quantity and cement of blast furnace slag,
Pottery industry society magazine ， Vo.77, No.2,
pp.39-46, 1969
[3] Harutake Imoto:Measurement of blast furnace
slag substitution ratio of cement with DSC, Concrete
engineering annual thesis collection, Vol.28, No.1,
Page29-34, 2006
[4] A.Lagosz, J.Malolepszy, S.Garrault：Hydration oftricalcium alminate in the presence of various
amounts of calcium sulphite hemihydratio, Cement
and Concrete Research Vol.36, pp.1016-1022, 2006

Reaction
Reaction rate
ratio(%)
(%)

20
15
10

20℃
30℃

5

40℃
0
0

7

14

21

28

35

42

49

56

63

56

63

Time (day)Fig 5.3 Reaction ratio of FA
45
40

rate (%)
Reaction ratio
(%)

35
30
25
20

20℃

15

30℃

10

40℃

5
0
0

7

14

21

28
35
Time (day)42

49

Fig 5.3 Reaction ratio of BFS

32

Array Calibration Method for Angle-of-Arrival Estimation in Multipath
Environment
Student Number: 06M18062 Name: Daiji TOMITA Supervisor: Jun-ichi TAKADA

多重波伝搬環境における到来方向推定のためのアレーアンテナ較正法富田大滋

近年, アレーアンテナを用いた電波の到来方向推定は, レーダ等の電波監視, 伝搬チャネル推定に加え, 無線通信シ
ステムやセンサネットワークにおける端末の位置および方位推定手法としての応用も進められている. このようなさ
まざまなシステムへ各種の到来方向推定法を導入する上で, 理論値とは異なる実際のアンテナの特性を知る必要があ
る. そこで本研究では, アレーアンテナの特性を到来角推定に反映することができる, いわゆるアレーアンテナ較正法を取り上げた. その較正法の妥当性をビームフォーミングで確認し, 測定データへの適用方法を示した.

1

Introduction

Modern multi-antenna communication systems are highly
dependent on the spatial characteristics of the propagation channel. Consequently, it is necessary to knowa detailed knowledge of the propagation channel asa prerequisite in order to evaluate the performanceof systems. This can be achieved by channel sounding assisted by high resolution estimation algorithms.
By employing high resolution algorithms, one tries toestimate multipath parameters such as multipath delay, doppler shift, angle-of-arrival (AoA) and angle-ofdeparture (AoD). The resolution and reliability of algorithms are highly depended on the presounding calibration which aims to provide a precise knowledge ofthe measurement device. Obviously for the precise estimation of AoA and AoD, calibration of the antenna
array has the prime importance.
In this paper, array response can be measured in an
anechoic chamber using a reference wave. Since thecontinuous data is not available, the measurement data
is interpolated by eﬀective aperture distribution function (EADF). Later, how to apply those response data
will be described.

2

Signal model

The received signal model of array antenna is shownin Eq.1.
x(t) = a(θ, φ)s(t) + n(t)(1)

where x(t) is received signal, a(θ, φ) is array response,s(t) is transmitted signal and n(t) is noise. Equation1means a single path model.
Multipath model is shown in Eq.2.
x(t) =K∑a(θk , φk )sk (t) + n(t) = As(t) + n(t)(2)

Fig. 1: The deﬁnition of AoA.

3

Sounder system

The properties of sounder system are described in thissection and the parameters are shown in Table.1.
Table 1: The parameters of system
Center frequency
2.2255 [GHz]
Chip rate
30 [Mbps]
Communication system Spread spectrum(M-sequences)
Modulation system
BPSK
Transmitter antenna
Horn antenna (10 [dBi])
Antenna input power-7.7 [dBm]8-element linear array
Receiver antenna
Baseband output
I, Q
Dynamic range-60 ∼ -110 [dBm]
Array element interval
0.5 wavelength

3.1

Requirements for channel sounding

k=1

where A = [a(θ1 , φ1 ), a(θ2 , φ2 ), · · · , a(θK , φK )], s(t) =[s1 (t), s2 (t), · · · , sK (t)]T and K is the path number.
The angles, θ and φ are deﬁned as Fig.1.

33

Channel model is based on measurement data. For
stochastic channel models, parameter values can be obtained from extensive measurement campaigns, while
for deterministic models, the prediction qualities canbe checked by comparisons with measured data. The
measurement property, impulse response of wirelessFig. 3: Transmitter antenna (Tx).
Fig. 2: Model of BPSK system[1].
channels, better known as channel sounding, is thus
a fundamental task for wireless communications[2].

3.2Correlative sounders

A time domain measurement directly measures the impulse response. Then, the measured impulse responseu(t) is the convolution of the true channel impulse response h(t) with the impulse response of the sounderR(t, τ ):u(t) = h(t) ∗ R(t, τ )(3)where the eﬀective sounder impulse response R(t, τ ) is
the convolution of the transmitted pulse shape pTx (t)and the Rx ﬁlter impulse response pRx (t, τ ):R(t, τ ) = pTx (t) ∗ pRx (t, τ )(4)where τ is a time shift of the sliding correlator. R(t, τ )is similar to the delta function, therefore the impulse
response and the transfer function can be obtained using Eq.3. The model of sounder system is shown as
Fig.2. In practice, pseudo noise (PN) sequence is utilized in this sounding sequence which is well-known
from code division multiple access (CDMA) using maximum length PN-sequences created by means of a shift
register with feedback[2].

3.3Transmitter and receiver antennas

In this measurement, the horn antenna is used astransmitter antenna shown in Fig.3 and the linear array antenna is used as receiver antenna shown in Fig.4.
Especially, the coordinate of receiver antenna is described in Fig.1. The receiver anttena can not estimate the horizontal angle since the vertical antenna isutilized. Therefore, linear array data need to be compound such as cylindirical antenna when the azimuth
angle is required to be estimated.

Fig. 4: Receiver antenna (Rx).
statistical ﬂuctuations in the measured data due to theprecision limitations of the measurement device. Random errors usually result from the experimenter’s ability not to take the same measurement in exactly thesame way to get exactly the same number. Systematic errors, by contrast, are reproducible inaccuracies
that are consistently in the same direction. Systematic errors are often due to a problem which persists
throughout the entire experiment. Systematic errorsof antenna array measurement can be excluded by calibration.

4.2

Necessity of the transfer function

The transfer function h̃(f ) or impulse response h(t)of propagation path is one of the most fundamental
and most important quantities for equalizing the distortions caused by multipath propagation. The former,
transfer function is the frequency domain samples. In
this measurement, a wideband signal(refer to Table.1)
has many frequency bins and array response can alsochange with frequency. Therefore it is necessary to use
transfer function as narrow band.

4.3

Array response

The MIMO (Multiple Input Multiple Output) signal
model can also be described like Eq.5 using the transfer
matrix H̃(f ).

4

Calibration theory

4.1

Random errors and systematic errors

Most of experimental uncertainty is due to either random errors or systematic errors. Random errors are34

x̃(f )= H̃(f )s̃(f ) + ñ(f )(5)where x̃(f ) is the received signal, H̃(f ) is the transfer
matrix, s̃(f ) is the transmitted signal, and ñ(f ) is noisefunction[4].
The l-th array array response ãr (θ, φ, l) is stored in
the matrix Ã. The periodicity of the array response
can be ensured in both dimentions. If the 2D-periodic
array response deﬁned as Ãr which can be as follows.
Using the discrete Fourier transform, the elevationtransformation matrix can be deﬁned as00

D1 = e−j2πf1 θ = e−j2πµ1 ∆f1 n1 ∆θ = e−j2πµ1 n1 /N1 (9)12π, ∆f1 =
, θ0 = θ − π
(10)
∆θ0 =
N1
2π
N1
N1
µT1 = n1 = [− , · · · ,
− 1]
(11)
2
2
where N10 = π/∆θ + 1, N2 = 2π/∆φ and N1 = 2(N10 −1). The azimuth transformation matrix is also deﬁned
asFig. 5: Array response measurement.
in frequency domain respectively.
Then, the transfer function H̃(f ) can be described like
Eq.6.
H̃(f ) =ζ̃(f )ãt (θ, φ)ãTr (θ, φ)(6)where ζ̃(f ) is the complex amplitude, ãt (θ, φ) is thearray response of transmitter antenna and ãr (θ, φ) isthe array response of receiver antenna in frequency domain. These two array responses are unique. Since,
the array antenna is utilized only at receiver side inthis measurement, ζ̃(f ) and ãt (θ, φ) are regarded ascoeﬃcient α like below.
h̃(f ) = αãr (θ, φ) + ñ(f ).

D2 = e−j2πφf2 = e−j2πn2 ∆φµ2 ∆f2 = e−j2πn2 µ2 /N2 (12)
2π
1
∆φ =
, ∆f2 =
(13)
N2
2π
N2
N2
− 1].
(14)
µ2 = nT2 = [− , · · · ,2
2
Therefore, the EADF of the l-th antenna can be obtained as
1
El = √
D1 Ãr D2 .
(15)
N1 N2
The complex exponentials of EADF after transforming to the beam domain are deﬁned for elevation andazimuth as0

db1 (θ0 ) = ejµ1 θ
db2 (φ) = ejφµ2
Na1 − 1Na1 − 1µ1 = [−,···,]
22
Na2 − 1Na2 − 1 T
µ2 = [−
,···,
]
2
2

(7)

If SNR (Signal to Noise Ratio) is suﬃciently high thatnoise can be neglected (ñ(f ) ' 0), the array responseãr (θ, φ) is settled like Eq.8.
h̃(f ) ∝ ãr (θ, φ).

(8)Therefore, the transfer function h̃(f ) has same eﬀect
as the array response ãr (θ, φ)[3].

4.4

4.5

ãr (θ, φ, l) = √Eﬀective aperture distribution function (EADF)

Eﬀective aperture distribution function (EADF) represents how to model the array response of antenna
arrays as a function of the azimuth and elevation angles of incoming waves. An antenna array response
due to a far ﬁeld source can be modeled by measuring
the directional characteristic of antenna in an anechoic
chamber. The array response can be measured by moving the antenna array. The array response can be expressed by an Inverse Fast Fourier Transform (IFFT)of the previous measured data. This Fourier series
are referred to as the eﬀective aperture distribution
35

(18)
(19)

where Na1 , Na2 are the required number of samples inEl .
The l-th array array response ãr (θ, φ, l) are deﬁned in
the following equation,

Array response measurement

It is necessary to measure two dimensional array response, azimuth and elevation. The range of azimuthand elevation are 63◦ to 117◦ by 9◦ , −90◦ to 90◦ by
5◦ respectively. The schematic ﬁgure about array response measurement is described in Fig.5.

(16)
(17)

1
Na1 Na2db1 El db2 .

(20)

Calibration data ãr (θ, φ, l) is substituted for ãr (θ, φ)in
Eq.7.

5

Numerical example

5.1

Calculation of the transfer function

In this research, since the wideband signal is used,
the transfer function of array antenna is required. The
impulse response is not ideal since the PN is utilized.
Therefore, u(t) should be retured to the data before
going to the correlator. Essentially, u(t) must be theimpulse response h(t) in Eq.3, however autocorrelation
function R(t) is not equal to one since PN series are
utilized. Therefore, Eq.1 can be transformed in frequency domain using Fourier transform.
F{u(t)} = F{h(t) ∗ R(t, τ )}ũ(f ) = h̃(f )S(f ).

(21)

Fig. 6: The array response after interpolation.

Fig. 8: Beamforing output of broad side path.
quency bin, the beamforming output is converted totime domain by IFFT. The beamforming result is shownin Fig.8 which presents a broadside test(φ = 90◦ ). The
path is received in θ = 90◦ (elevation=0◦ ), therefore,
the interpolation methodology can be concluded to beappropriate.

6

Fig. 7: The phase data after interpolation
S(f ) is the output of back to back connection ũdir (f ).
Then, the desired transfer function h̃(f ) can be obtained when ũ(f ) is divided by ũdir (f ).

5.2

Reconstruction of array response using EADF.

Transfer function known as the discontinuous measurement data can be transformed to the continuousfunction using EADF wherein the calculation was discribed in 5.1. Figure.6,7 can be calculated by themethod in 4.5 and also mean desired array response of
amplitude and phase respectively. These ﬁgures show
EADF can be interpolated in two dimentions. Therefore, reconstruction data ãr (θ, φ, l) can be substituted
for ãr (θ, φ) in Eq.7. When it is necessary to concentrate to small values, it can be realized by increasing
the number of samples Na1 , Na2 in El (refer to 4.5).

5.3

Beamforming using reconstructed array response

In this section, beamformimg result is presented. Inorder to provide the reliability of reconstructed array
response, beamforming is implemented. Wideband signal is used in this measurement, therefore each frequency bin is processed by a narrowband processor
structure. After narrowband processing on each fre36

Conclusion

The methodology of the array antenna calibration forthe multipath environment was described. Since the
wideband signal was used in the measurement, the array calibration was applied to each narrow band of
the transfer function. Essentially, this methodology
aims to be utilized for high resolution AoA estimation, therefore the angle interval in the array response
measurement needs to be concentrated to very smallvalues. However, this measurement can not be implemented since it is time consuming. Therefore, theresponse is measured discontinuously. Then, the discontinuous array response could be interpolated using
EADF. Calibration data ãr (θ, φ, l) in Fig.6 can be used
for ãr (θ, φ) in Eq.7. Then, the beamforming output result is shown in Fig.8 and it is proved that the reconstructed array response was appropriate to be utilized
for the AoA estimation.

References[1] S. Haykin, ”Communication systems 4th edition”,
Wiley, 2001
[2] A. F. Molisch, ”Wireless communications”, John
Wile & Sons Ltd, 2005
[3] 山田 寛喜, ”高分解能到来波推定法の基礎と実際”
アンテナ · 伝搬における設計 · 解析手法のワークショップ（第 33 回）, 2006 年 10 月
[4] F. Belloni, A. Richter, V. Koivunen, ”Reducing
excess variance in beamspace methods for uniform
circular array”, IEEE CNF, Statistical Signal Processing, 2005 IEEE/SP 13th Workshop, pp.940943, July.2005

Selective Catalytic Reduction of Nitrogen Monoxide
by CeO2-added Nb/TiO2 Catalysts
Student Number: 06M18091 Name: Megumu MAKII Supervisor: Hirofumi HINODE

CeO2 添加 Nb/TiO2 触媒による NO 選択還元牧井 恵
本研究では、過剰酸素存在下でプロピレンを還元剤とする NO 選択還元を目的とした触媒の開発を行った。
チタニア担持ニオブ触媒は、350C 付近で約 60％NO を N2 へと還元した。また、チタニア担持ニオブ触媒へ
酸化セリウムを機械的に混合したところ、活性が向上し、320C 付近で約 80％NO を N2 へと還元できた。反応系に水蒸気が存在しても同様の転化率が得られ、水蒸気による失活は観測されなかった。NO が酸化して生成した NO2 とプロピレンが反応して N2 を生じる反応機構が推定される。

Experimental

1. Introduction

2.

Air pollution by nitrogen oxides (NOx) emitted
from mobile and stationary sources is one of serious
environmental problems to be solved because NOxcontribute to the formation of acid rain, global warming,
photochemical smog, and depletion of ozone layer.
Lean-burn gasoline or diesel engines have highly
attracted attention due to their fuel efficiency. However,the presence of excess O2 in exhaust gas from theseengines suppresses NO reduction activity. Therefore, ahigher conversion of NO to N2 in the presence of O2 isdesirable. Among several methods that can be used,
selective catalytic reduction with hydrocarbons (HCSCR) is currently the most studied technology for NO
reduction.
For this purpose, supported noble metals, zeolitebased catalysts, and alumina-based catalysts have beenreported [1-3]. Activity of various metals supported on
TiO2 has been investigated [4-6]. In this study, thecatalytic activity of Nb-supported TiO2 (Nb/TiO2)
catalyst and the effect of support for HC-SCR of NO by
C3H6 in the presence of O2 was investigated.
It was also suggested that NO reduction possibly
occurred via the formation of NO2 on M/TiO2 catalysts[4-6]. Metal oxide was thought to accelerate theformation of NO2. Therefore, research aiming at
increasing activity of the catalyst further by adding metaloxide to catalyst has been performed. Mn2O3 waseffective in promoting Nb/TiO2 or Cu-ZSM-5 catalyst
activities [5,7]. D. Niakolas et al reported that addition of
CeO2 on Au/Al2O3 resulted in the highest catalytic
activity improvement when compared with several othermetal oxides (Cr2O3, Co3O4, Mn2O3, CuO, Fe2O3, SnO2
NiO, ZnO, and V2O5) [8]. Based on these reports, CeO2
was selected as oxide that would be added to the catalyst,
and the activity of developed catalyst was tested.
CeO2-added Nb/TiO2 catalyst was prepared by several
methods, i.e., co-impregnation, consecutive impregnation,and mechanically mixing.

2.1 Materials
Nb/TiO2 catalysts were prepared by impregnation
method from reference catalysts supplied by Catalysis
Society of Japan, which were JRC-TIO-4 (TiO2 ➃ ),JRC-TIO-6 (TiO2 ➅ ), and JRC-TIO-7 (TiO2 ➆ ).
NH4[NbO(C2O4)2(H2O)2](H2O)n was supplied by Companhia
Brasileira de Metalurgia e Mineração (Brazil). The
precursors were added into 300 mL of ion-exchanged
water and stirred at room temperature for 24 hours. Thenthe impregnated catalyst was dried up at about 70C for
24 hours, and calcined at 550C for 5 hours under air flow.
(Ce+Nb)/TiO2 was prepared by co-impregnation
method from TiO2 ⑦ . NH4[NbO(C2O4)2(H2O)2](H2O)n andCe(NO3)36H2O were used as precursors to impregnate
TiO2 with Nb and Ce simultaneously.
(Ce-Nb)/TiO2 was prepared by consecutive
impregnation method from TiO2⑦. Nb/TiO2 was preparedfirst, then Nb/TiO2 was impregnated with Ce by using
Ce(NO3)3・6H2O as precursor.
CeO2 + Nb/TiO2 was prepared by mixing CeO2 andNb/TiO2 mechanically. CeO2 was prepared by calcinationof Ce(NO3)3 ・6H2O (WACO Chemical Inc., Japan) at
550C for 5 hours under air flow. CeO2 and Nb/TiO2 weremechanically mixed, then calcined at 550C for 3 hours
under air flow.
All catalysts were pelletized, crushed, and sieved to
0.71-1.00 mm.

2.2 Catalytic Activity Measurements
The catalytic activity was measured with a fixed-bed
flow reactor by passing a reactant gas containing 1500
ppm of NO, 10% of O2, 1500 ppm of C3H6, and helium asbalance gas with a total flow rate of around 4.0 mL/s over1.0 g of catalyst. The analysis of NO and NO2 were madeby NOx analyzer (NOA-305A, Shimadzu Corp.). N2O and
CO2 were analyzed by gas chromatographs (GC-8A,
Shimadzu Corp. and GC-390, GL Science Co.,37

prepared from TiO2 ⑥, which has mainly rutile form.
From this result, it is concluded that the crystalline
property of support (TiO2) does not have an important
effect on the catalytic activity.

respectively). The reaction temperature was increased
with a step of 50C from 150C to 550C.
The influence of water vapor on the activity ofcatalyst was investigated by introducing 5 vol.% of water
vapor during the reaction. Gas composition and analysis
apparatus were the same as stated above.

NO conversion to N2 [%]80

2.3 XRD Characterization
Characterizations of supports and catalysts weredone by XRD analysis (Multi Flex, Rigaku Corp.). The
mass fraction of anatase structure of each TiO2 support
was determined by the following equation [9],

XA 

100
11.26 I R I A [%]

TiO2 only
Nb 3%
Nb 7%
Nb 10%
Nb 15%
Nb 20%

60

40

20

0
100

where XA is the mass fraction of anatase, and IA and IR are
the intensities of anatase (101) reflection and rutile (110)reflection, respectively.

200

300

400

Temperature [℃]

500

600

Fig. 1 Influence of Nb loading levels on Nb/TiO2➆

3.

Results and Discussion

3.1

XRD Characterization

Figure 2 shows the conversion of NO to N2, N2O,
and NO2, and the conversion of C3H6 to CO2 overNb(15wt.%) /TiO2➆. All catalysts prepared from TiO2➃,
TiO2 ➅ , and TiO2 ➆ showed similar behavior. Small
amount of N2O was produced only in the low temperature
region (T400C). NO2 existed throughout thetemperature range used in this experiment, although theconcentration remained very low in the temperature atwhich the conversion of NO to N2 at the highest. This
result suggests that NO was oxidized to NO2 before it
subsequently reduced to N2. Up to 550C, the conversion
of C3H6 to CO2 showed similar tendency as the conversion
of NO to N2 in which the conversion increased withincreasing temperature up to 350C and decreased at
higher temperature.

Each TiO2 support has different mass fraction ofanatase structure (Table 1). TiO2 has three crystalline
forms, which are anatase, rutile, and brookite. However,no evidence of brookite presence was observed in these
supports.
Table 1 Mass fraction of TiO2 crystalline forms ofused supports
Supports
TiO2➃
TiO2➅
TiO2➆

3.2

Anatase percentage [%] Rutile percentage [%]
74
26
1
99
97
3

Catalytic Activity of Nb/TiO2

80
NO conversion to N2, NO2, N2O [%]C3H6 converion to CO2 [%]Figure 1 shows the effect of Nb loading level on the
catalytic activity of Nb/TiO2➆ catalyst for NO reduction
to N2. Catalysts loaded with Nb showed higher catalytic
activity than that of TiO2 only, and the temperature atwhich conversion of NO to N2 reached the highest value
shifted to lower temperature (from 400C to 350C).
Optimum loading level of Nb for TiO2➆ was 15wt.%,
and the highest conversion of NO to N2 for this catalyst
was 63% at 350C.
Catalysts prepared from TiO2 ➃ and TiO2 ➅showed similar behavior as those prepared from TiO2➆.
Optimum loading levels of Nb for TiO2➃ and TiO2➅
were 7wt.% and 5wt.%, respectively, and the highest
conversions of NO to N2 for these catalysts were 64% and58%, respectively (at 350C). Catalyst prepared from
TiO2 ➃ or TiO2 ⑦ , which has mainly anatase form,
showed a slightly higher catalytic activity than that

N2
N2O

60

NO2
CO2

40

20

0
100

200

300

400
Temperature [℃]

500

600

Fig. 2 NO conversion to N2O, N2, and NO2, and C3H6
conversion to CO2 over Nb(15wt.%)/TiO2➆

38

3.3 Catalytic Activity(Ce-Nb)/TiO2

of(Ce+Nb)/TiO2

ability of CeO2. Equilibrium between NO2 and NO
suppressed NO2 formation at temperature higher than450C. Up to 300C, the conversion of C3H6 to CO2
showed similar tendency with the conversion of NO to N2
in which the conversion increased with increasing
temperature (mountain-shape peak). It means C3H6 wasused as reductant to produce N2. At temperature higher
than 300C, the conversion of C3H6 to CO2 increased
linearly, and then reached 100% at 400C. In this region,
C3H6 was probably just oxidized by reaction with O2.
Compared with Nb/TiO2 catalyst, C3H6 conversion andNO2 formation of CeO2+Nb/TiO2(1:1) catalyst werehigher. The higher formation of NO2 was thought to be thecause of higher catalytic activity.
Additionally, the two-bed catalyst in which CeO2
and Nb/TiO2 were packed separately was also tested. For
two-bed catalyst, CeO2 was packed in the first bed tooxidize NO to NO2, and Nb/TiO2 in the second bed toreduce NO2 to N2 selectively. The result showed a much
lower activity for the two-bed catalyst. From this finding,it was thought that NO2 molecule in gas-phase is not the
key intermediate, and the contact between CeO2 andNb/TiO2 is necessary for higher activity.

andInfluence of Ce loading levels on the catalytic
activities were investigated for co-impregnated(Ce+Nb)/TiO2
andconsecutively
impregnated(Ce-Nb)/TiO2 catalysts, and it was found that the optimum
loading level of Ce was 5 wt.% for both catalysts.
However catalytic activities of (Ce5%+ Nb15%) /TiO2and (Ce5%-Nb15%) /TiO2 with the best performance for
co-impregnated catalyst and consecutively impregnatedcatalyst, respectively, could not reach that of Nb/TiO2 only
(shown in Figure 3). It is possible that CeO2 covered Nb
active site by impregnation and oxidized C3H6 to CO2
before C3H6 could function as reductant. This could leadto the decrease of catalytic activity.
100(Ce5%-Nb15%)/TiO2
Nb(15%)/TiO2

60

40

100

20

0
100

200

300
400
Temperature [℃]

500

600

Fig. 3 Comparison of (Ce+Nb)/TiO2, (Ce-Nb)/TiO2,
and Nb/TiO2 catalyst activities

3.4

CeO2+Nb/TiO2 (2:1)
CeO2+Nb/TiO2 (1:1)

NO conversion to N2 [%]NO conversion to N2 [%](Ce5%+Nb15%)/TiO2

80

Catalytic Activity of CeO2+Nb/TiO2

Figure 4 shows the conversion of NO to N2 overmechanically mixed CeO2+Nb(15wt.%)/TiO2 catalyst.
Catalysts mixed with CeO2 showed higher catalytic
activity than that of Nb/TiO2. Maximum conversion of
NO to N2 reached 80% on mechanically mixed CeO2 +Nb/TiO2 (2:1), (1:1), and (1:4) catalysts. The temperature
at which NO converted to N2 reached the highest value
was 350C for CeO2 + Nb/TiO2 (1:4) catalyst, and the
maximum conversion shifted to lower temperature forcatalysts with higher CeO2 ratio such as CeO2 + Nb/TiO2(2:1) and (1:1) catalysts.
Figure 5 shows the conversion of NO to N2, N2O,
and NO2, and the conversion of C3H6 to CO2 overmechanically mixed CeO2+Nb/TiO2(1:1) catalyst. Small
amount of N2O was produced only in the low temperature
region (T  400C). NO2 formation decreased in thetemperature region where the conversion of NO to N2 washigh, and increased again in higher temperature region (T> 350C) where the conversion of NO to N2 decreased.
This result suggests that NO was oxidized to NO2 before it
was subsequently reduced to N2. NO2 formation was
accelerated at higher temperature because of the oxidation

CeO2+Nb/TiO2 (1:4)

80

CeO2+Nb/TiO2 (1:9)
Nb/TiO2

60
40
20
0
100

200

300
400
Temperature [℃]

500

600

Fig.4 Influence of CeO2：Nb/TiO2 ratio on mechanically
mixed CeO2+Nb/TiO2 catalyst

NO conversion to N2, N2O, NO2 [%]andC3H6 conversion to CO2 [%]

100
N2

80

N2O
NO2

60

CO2

40
20
0
100

200

300

400

500

Temperature [℃]Fig. 5 NO conversion to N2O, N2, and NO2, and C3H6
conversion to CO2 over CeO2+Nb/TiO2(1:1)

39

600

Catalyst should be resistant to water vapor forpractical use because exhaust gas normally contains water
vapor. Figure 6 shows comparison of mechanically mixed
CeO2+Nb/TiO2(1:1) catalyst activity with or without thepresence of water vapor. Because of the water vapor, theconversion of NO to N2 shifted to higher temperatureslightly although no serious activity inhibition wasobserved. For mechanically mixed Mn2O3+Nb/TiO2
catalyst, it is reported that water vapor promoted thecatalytic activity by the inhibition effect of water vapor on
C3H6 oxidation [5]. However, in this experiment, C3H6
combustion started at lower temperature in the presence ofwater vapor. Water vapor caused no promotion effect on
the activity over CeO2 +Nb/TiO2(1:1) catalyst. It is also
known that the effect of water vapor depends not only on
the catalyst but also strongly on the reductant and thereaction temperature [10]. NO conversions to N2O andNO2 profile (not shown in the figure) were similar
regardless of the existence of water vapor.

320C. Although containing the same component asmechanically mixed catalyst, two-bed catalyst showedmuch lower activity. It means the contact between CeO2
and Nb/TiO2 is necessary to promote activity. The key
intermediates must be short-lived compounds such as
R-ONO and R-NO2, and not the NO2 molecule ingas-phase. Mechanically mixed CeO2+Nb/TiO2(1:1)
catalyst showed resistance against the deactivation caused
by water vapor.

References

NO conversion to N2 [%]andC3H6 conversion to CO2 [%]100
N2N2 (with water)
CO2
CO2 (with water)

80
60
40
20
0
100

200

300
400
Temperature [℃]

500

600

Fig.6 Influence of water vapor on mechanically mixed
CeO2 + Nb/TiO2 (1:1) catalyst

4. Conclusions
Nb/TiO2 exhibited the selective catalytic reductionof NO to N2 by C3H6 in the presence of O2. Nb loaded
catalyst showed higher catalytic activity than that of TiO 2
only. The highest conversion of NO to N2 for Nb/TiO2 wasabout 60% at 350C. The crystalline forms of TiO2
support did not influence the activity of the catalysts
significantly.
The effect of CeO2 addition to Nb/TiO2 catalyst
depends on the preparation method. For co-impregnated(Ce+Nb)/TiO2
andconsecutively
impregnated(Ce-Nb)/TiO2 catalysts, catalytic activities were lower
than that of Nb/TiO2 catalyst. CeO2 added byimpregnation was thought to cover active site of Nb/TiO 2.
On the other hand, mechanically mixed CeO2+Nb/TiO2
catalyst promoted Nb/TiO2 catalyst activity and the
maximum conversion of NO to N2 was 80% at around40[1] M. Haneda et al; “Infrared of catalytic of NO bypropene over Ag/TiO2-ZrO2,” Catal. Today, 42,
127-135 (1998)[2] M. Iwamoto et al; “Influence of sulfur dioxide on
catalytic removal of nitric oxide over copper
ion-exchanged ZSM-5 zeolite,” Appl. Catal., 69,
L15-L19 (1991)[3] H. Hamada et al; “Transition metal-promoted silica
and alumina catalysts for the selective reduction of
NO with propane,” Appl. Catal., 75, L1-L8 (1991)[4] J. Mitadera; Master Thesis, Tokyo Institute ofTechnology (2003)[5] H. Kawai; Master Thesis, Tokyo Institute ofTechnology (2004)[6] C. Tang, Master Thesis, Tokyo Institute ofTechnology (2006)[7] M. Misono et al, “Reduction of nitrogen oxides withhydrocarbons catalyzed by bifunctional catalysts,”
Catal. Today, 38, 157-162 (1997)[8] D. Niakolas et al “Influence of metal oxides on the
catalytic behavior of Au/Al2O3 for the selective
reduction of NOx by hydrocarbons,” Catal. Today,
112, 184-187 (2006)[9] R.A.Spurr, H.Myers; “Quantitative analysis ofanatase-rutile mixtures with X-ray diffractometer,”Anal. Chem., 29, 760 (1957)[10] K. Sato et al, “Catalytic performance of silver ionexchanged saponite for the selective reduction of
nitrogen monoxide in the presence of excess
oxygen,” Appl. Catal. B, 13, 27-33 (1997)

Ca leaching Deterioration Behavior in Cement-Based Materials by electro-chemical
acceleration method
Student number: 06M18100 Name: Masaya MATSUDO Supervisor: Nobuaki OTSUKI
電気的促進法によるセメント系材料の Ca 溶脱特性
松土真也
セメント系材料からのCa溶脱現象による劣化は100 年単位に亘る生じるため，促進手法が必要となる．
本研究では電気的促進法と実現象に近い拡散法を繰り返し行う手法を採用した．本手法では促進倍率を実時間に換算できるという利点を有し，100 年の供試体について，本手法の妥当性が物理的変質により示されている．しかしながら，電気的促進法は電場でCa 溶脱を促進するため，実際の拡散場での溶脱現象とは異なる．そのため，電気的促進法を適用した供試体について，化学的・物理的変質を測定し，電気的促進法の検証を行なった．その結果，長期的(本研究では拡散換算期間 20 年)には電気的手法は拡散場による溶脱に近づくことが示された．
1. Introduction
Table.1. The specimens are cured for 56 days undersealed condition with temperature of 50℃. The
specimen setup is shown in Fig.1.

Deterioration due to calcium(Ca) leaching from
cement-based materials progress very slowly
compared to other types of deteriorations. However,in the design of special concrete structures withservice life of more than a hundred years such as
dams and nuclear waste repositories, it is important
to consider the effect of Ca leaching from the view
point of durability.
To investigate the mechanism of Ca leaching, there
is a need to propose a technique. The proposed
method performs repeated Electro-chemical and
Diffusion Method (called EDM from here on). The
merit of EDM not only accelerates deterioration of
Ca leaching but also converts acceleration time into
real time (called Diffusion Conversion Time).
However, the electro-chemical method in EDM
uses electric field for Ca leaching. So it is different
from diffusion test which is closer to the real
phenomenon. From those backgrounds, theobjectives in this study are as follows: (1) toinvestigate the differences between EDM anddiffusion only method in 1 year specimen. (2) toinvestigate the specimens deterioration after
diffusion conversion time reach 20years by EDM.

Table.1. mix proportions
kg/m3

Specimens

Specimennumbernumber

W

C

S

W/C

s/c

1

OPC

360 1200 1200

0.30

1.0

2

OPC

457 1062 1062

0.43

1.0

3

OPC

500 1000 1000

0.50

1.0

4

OPC

554

0.60

1.0

Titanium mesh（Cathode）

923

923

Exposure surface

2.5cm
12.0cmEpoxy coating except
for exposure surface

1.5cm
4.0cm

Titanium mesh（Anode）

Fig.1. The images of specimens① Specimen number2 in Table.1 was adopted forthe diffusion test for 1 year. In the diffusion test,
specimens are soaked in distilled water. The water
is changed every two weeks, and the amount of the
Ca that leached in water is measured. On the other
hand, another specimen with the same proportion
was accelerated by EDM. The method waschanged every two weeks. Both specimens were set
to have the same amount of Ca that leached out.
The specimens were measured at points every

2. Outline of experiment
2.1 Test methodology
In this section, the outline of the experiments for the
study is presented.
The mix proportions in this study are shown in41

2mm from exposure surface.
② Ca leaching for the specimens in Table.1 wereaccelerated by EDM. By EDM, it is assumed thatleaching rate during electro-chemical method isequal to the average of the back and forth leachingrate during diffusion test. With this, the acceleration
time can be converted into real time. In this study,
the specimens specified in Table1 were accelerated
by electro-chemical method until real time reached
20 years by EDM. The specimens were measured
at points every 5mm from the exposure surface
until 2cm depth is reached.

Vickers Hardness(HV)parts, outer part(0-4mm), middle part(4-20mm)
and deeper part(20-25mm).
Outer part

Middle part deeper part

0-4mm

4-20mm200
160
120
Cycle of electro-chemical anddiffusion

80

Diffusion only40

2.2. Items of investigation

00

Chemical deterioration was evaluated by thefollowing methods:
・Ca leaching ratio: ion chromatography・Ca(OH)2: differential thermal analysis
・Ettringite, monosulfate, non reactive alite, belite:
XRD
・Ca/Si ratio: EDX
Physical deterioration was evaluated by thefollowing methods:
・Total Porosity
・Porosity(0.01μm～10μm):Mercury press fit
method・Minute compressive strength20-25mm

5

10

15

20

25

Depth from exposure surface (mm)Fig.2. Vickers

Hardness

The results of porosity test are shown in Fig.3, 4. In
diffusion method, at the part near the exposure
surface, porosity is larger. It is the same
phenomenon that is already known. However, theporosity in EDM and diffusion are different except
for the outer part. In outer part, the porosity became
large. And it was quite similar to the results ofdiffusion only specimen. However, in the middle
part, the porosity became smaller than initial value.
In the deeper part, total porosity was same,
however a part of porosity became large.
Thus it was confirmed that the outer part can beapplied with the electro-chemical method. However,in the middle and outer part, there is a need toinvestigate more about the specimens.

3. Results and Discussion
3-1. Investigation on investigate the differences
between EDM and diffusion only method in 1year
Specimen number 2 in Table.1 was used to
conduct both EDM and diffusion test. The two
specimens were compared with each other. In thereal phenomenon, it is known that Ca leachingoccurs using the diffusion field, and deterioration
due to Ca leaching occurs near the surface. Also,
deterioration gradually progresses to the deeper
part. Therefore this section focuses on thespecimens that underwent EDM and diffusion
methods.
The result of Vickers Hardness is shown in Fig.2.
Near the exposure surface (0-4mm), the values ofVickers Hardness for both methods are quite
similar to each other. However, in the middle part
(4-20mm), EDM is stronger than diffusion
specimens. In the deeper part (20-25mm), thevalues become similar. Thus it confirmed that in the
EDM specimen, the result can be divided into three

Total pore volume (mm3/g)

Diffusion only
100
80

These value is from surface.

60
40
0-2mm

20
0

initial

4-6mm

6-10mm

1years 拡散

10-25mm

0.01

0.1

1.0

Pore size
Fig.3. Pore size distribution

42

10(μm)

3-2. Investigation on the specimens deterioration after
diffusion conversion time reaching 20years by EDM

Cycling of Electro-chemical
acceleration and diffusion
Total pore volume (mm3/g)

100

0-2mm
80
60
40
20

4-6mm
6-10mm23-25mminitial
1years 電気

0
0.1

0.01

1.0

10(μm)

Pore size

Fig.4. Pore size distribution

The results of reaction rate of alite and belite areshown in Fig5. It shows that the reaction rate using
EDM at the middle part is larger than diffusion
specimen. Thus in the middle part, hydration
occurred and the porosity was decreased.

100

100
Reaction ratio (%)

200

diffusion method
initial95

initial

98
98

90

97
97
96
96

0-5mm

W/C30 20years

electro chemical method

diffusion method

W/C 30%250

The reaction rate of belite （C２S）
100
100electro chemical method

99
99

.
Total pore volume (mm3/g)

The reaction rate of alite （C3S）

Initially, the result between EDM and diffusion
method in 1year was compared. However, actual
deterioration occurs in longer time. Therefore, there
is a need to confirm mortar deterioration in longertime.
The specimen in Table.1 was used to conduct the
EDM until real time reached 20 years. The resultsof porosity are shown in Fig.7. Regardless of water
cement ratio, the porosity is increased near thesurface. These tendencies are quite similar todiffusion field which is similar to actual
phenomenon

5-10mm
10-15mm150

15-20mm100

initial

50
0
250

0-5mm
5-10mm200

10-15mm150

15-20mminitial

85
85

100

80
80

50

95
95
94
94
93
93

エーライト

0

75
5

10

15

20

25

0 5
10 15 20 25
Depth form the surface (mm)

W/C50 20years

0

ビーライト

75

00

55 10
10 15
15 20
20 25
25
Depth form the surface (mm)W/C 50%Fig.5. reaction rate of alite and beliteFig.7 Pore size distribution

The result of porosity of mortar (W/C60%) is shownin Fig.8. The first one is the diffusion conversion
time of 12 years and the other is 20 years. It
confirmed that if time is longer, the porosity is larger
near the exposure surface. The tendency is alsosimilar to the real diffusion phenomenon.

In the deeper part (20-25mm)

4

Total pore volume (mm3/g)

Ettringite after Ca leaching / Ettringite initial

The results of ettringite are shown in Fig6. A
generation of ettringite was confirmed in the deeper
part. It caused the SO42- ion to move to the anode inmortar, and monosulfate changed into ettringite in
the outer part. In the outer part, it was difficult touse EDM.

3.5
3
2.5
2
1.5
1

W/C 60%Diffusion Conversion time is 12 years

W/C60 20years

0-5mm
5-10mm
10-15mm15-20mminitial

200
150
100

0-5mm
5-10mm
10-15mm15-20mminitial50
0W/C60% 6months

0.01

0.1

1.0

Pore size

initialDiffusion Conversion time is 20 years

250

10 0.010.1

1.0

10(μm)

Pore size

Fig.8 Pore size distribution

0.5
AFt

0
0

5

10

15

20

The hydration rate of alite and belite are shown in
Table.2. It is confirmed that the hydration rate ofboth alite and belite became nearly 100% in every
part. In Objective1, specimens in 1year still have

25 (mm)Exposure from the surface

Fig.6. Ettringite

43

non reactive alite and belite. Thus hydration
occurred while Ca leaching occurs. However, in 20
years specimen, alite and belite are almost
exhausted. Thus it is thought that in 20 years
specimen the Ca leaching deterioration becomessimilar to real diffusion specimen.

strength of prediction specimen corresponds withthe real concrete member. It is confirmed that the
EDM is applicable in long term prediction.

Real concrete member (contact with water for 100 years)
Prediction specimen (Diffusion conversion time = 101 years)

W/C 60%0-5

5-10 10-15 15-20 (mm)

alite

99.4

99.6

99.9

99.7 (%)

belite

99.8

99.8

95.3

98.8 (%)

Uniaxial compressive strength[MPa]

Table.2. reaction rate of alite and belite

Other deteriorations are shown in Fig.9. All graphs
show that near the surface, the deterioration
became bigger. It was also similar to real diffusion
specimen.
W/C30%
W/C43%
W/C50%
W/C60%

Ca(OH)2

0.04
0.03

W/C series
Minute diffusion test

2.0E-07

W/C30%
W/C43%
W/C50%
W/C60%

1.5E-07
1.0E-07
0.5E-07
0
0-5

5-10

Compression strength

Diffusion coefficient of Cl-

2.5E-07

10-15 15-20 (mm)

10

15

20

Summary

0.02
0.01(mol/cm2/s)

5

Fig.10. Uniacial compressive strength

0.05

30～60％

0Distance from exposure surface [mm](g/g)

Water/cement ratio

16
14
12
10
8
6
4
2
0

0
0-5 5-10(MPa)
180
W/C Series
150

10-15 15-20 (mm)Minute compression test

120
90
60

W/C30%
W/C43%
W/C50%
W/C60%

30
0
0-5

5-10

10-15 15-20 (mm)Fig.9. deteriorations due to Ca leaching

However, it is known that when Ca leachingoccurred, first, chemical deterioration occurs likethat of Ca(OH)2. Then porosity became large andfinally the deterioration of compression strengthand diffusion coefficient occurs. Thus if the porosity
is known, these deterioration can be predicted.
Therefore the porosity is important. And in 20 years
specimen, the porosity became similar to the real
phenomenon, thus other deteriorations also became
similar to the real phenomenon. Finally it isestimated that deterioration by EDM becomessimilar to diffusion method in 20 years.

① It was confirmed that EDM provides a close
simulation of the real diffusion deterioration
phenomenon near the exposure surface(0-4mm) in 1year specimen. However, in
middle part (4-20mm), the hydration of alite
and belite occurs during EDM. Thus theporosity is decreased compared to specimensthat underwent diffusion.
② It is estimated that even in the middle part,
deterioration became similar to diffusion
method in 20years. The porosities of EDM
were quite similar to real diffusion
deterioration regardless of water cement ratio.
This is evidenced by exhaustion of alite andbelite. Thus it is thought that in 100years
specimen by EDM, the physical deterioration
became similar to real concrete members.

References[1]Hiroshi MINAGAWA, Doctoral thesis in Tokyo
Technology of Institute 2002
[2]Masaki DAIMON et, al., Cement Technique
report, No.41, pp221-224, 1987.12[3]Takeshi YAMAMOTO et, al., Report of Central
Research Institute of Electric Power Industry p.2,
2007

3-3. 100years specimen
The 100 years uniaxial compressive strength
results of concrete deterioration due to Ca leaching
are shown in Fig.10. The real concrete member of awaterworks structures in 100 years is compared to
specimen with 101 years Ca leaching by EDM.
This graph shows that the uniaxial compressive

44

OUTDOOR URBAN SCALE MODEL EXPERIMENTS
ON THE EFFECTS OF BUILDING GEOMETRY ON THE URBAN ATMOSPHERE
Student Number: 06M18116 Name: Takanobu MORIIZUMI Supervisor: Manabu KANDA

屋外模型都市を用いた建物幾何形状が都市大気に及ぼす影響の検討
森泉 孝信
屋外スケールモデルを用いて都市を構成する建物の形状やその配列の違いが熱や運動量の輸送特性に与える影響を検討した．その結果，運動量輸送には建物高さの分散や密度などの幾何形状が大きく関与している一方，熱輸送は概してその影響を受けにくいことが分かった．これは熱輸送に地表面熱収支が関与していること，また粗度要素近傍での輸送メカニズムが運動量と異なることが主要因に挙げられる．

1

Introduction

Dominant seasonal winds are southeasterly in summer, andnorthwesterly in winter. The scale models were designed
such that the x axes (street line) were roughly on anorthwest-southeast line along which the wind direction(WD) was defined. Positive WD is a direction anticlockwisefrom this axis. COSMO has two different size scale models,
and this paper concentrates on the smaller one because there
are two advantages to use the smaller models; one is thegood workability to arrange buildings in various kinds ofarrays, and the other is smaller site requires smaller lot sothat it is possible to compare with different surface
conditions simultaneously. Two arrays of concrete blocks are
set up to be compared with each other (Figure 1). Each arrayis on 12-m-square concrete basement. A total of threeexperiments were done with the combination of two
different arrays.
The first experiment was to investigate the effect ofbuilding height variability on the momentum and heattransfer characteristics. Cubic, solid concrete blocks 15cm
per side composed the one array (Figure 2(a)). The blocks
were distributed regularly such that the plane area density(λp) was 0.25 which is the normal value in European
residential areas. This arrangement set is called hereinafter as“HS” (Homogeneous and Standard-dense city). The otherarray was composed of 2 types of blocks having the samebottom size, but different heights: 15cm × 15cm × 7.5cm and15cm × 15cm × 22.5cm (breadth-width-height) (Figure 2(b)).
These blocks were distributed alternately both in the x and y
direction, keeping the same λp and average building height(= 1H) as that of HS. This array is called hereafter “VS”
(Variable heights, and Standard-dense city).
The second experiment was to investigate the effect ofclustering buildings. 3-times-length blocks, 15cm × 45cm ×15cm, described the cluster housings with λp = 0.25 (Figure2(c)). This is called “AS” (Apartment array of Standard
density). HS was left for the comparison.
The third experiment was to investigate the effect of thedifference of λp. One array was again HS, while the otherwas the similar arrangement of 15 cm cubes but with λp =0.44 (Figure 2(d)) which assumes normal Japanese
residential area. I call the latter setting “HD” (Homogeneousand Dense city). See Table 1 for each observation period.

Understanding the transport process of energy and scalarin the turbulent boundary layer over cities is an ongoing
important issue in meteorological study. Thanks to manylaboratory and numerical studies, aerodynamic properties
over urban-like roughness are being systematicallyunderstood. However, these knowledge is applicable only tohighly-simplified geometries i.e., arrays of uniform building,under indoor environments with less fluctuated wind and nosolar radiation. Meanwhile, real cities have never such
uniform surface geometry but have a lot of buildings ofvarious shapes. Also the real cities are under much fluctuatedwind field and sunlit-sunshade distribution. This might cause
more complex transport process than in wind channel.
The outdoor reduced urban scale model experiment is asuitable way in order to clarify such problem. Observed data
are free of various uncertainties in real cities. We canimplement detailed measurement within and above thecanopy with real synoptic conditions of sunshine and windfluctuations. In addition, it is easy to change the building
array and height. This study is based on the Comprehensive
Outdoor Scale Model (COSMO) data.
So far basic features on regular (aligned) array of
COSMO have been well investigated: the surface energybalance by Kawai and Kanda [1], and the turbulent flow
characteristics and similarity (e.g. Inagaki and Kanda [2]).
This paper focuses on the effect of different geometries onsuch features.
The purpose of this study is to investigate the effect ofirregularity of building geometry both on momentum
transfer and energy balance. The surface geometry focusedon includes (1) the existence of building height variation, (2)distinct characteristic arrays in cities: cluster housings anddetached buildings, and (3) packing density.

2

Outdoor Urban Scale Model Experiment

2.1 Site Description
COSMO is located on a portion of the campus of Nippon
Institute of Technology, Saitama Prefecture, Japan (39º04’N,
139º07’E). Gentle terrain extends at least several tens ofkilometers in all direction from the site. Surrounding land
use is paddy field in summer and bare soil in winter.

2.2 Instrumentation

45

Main wind direction
in winter

(a)

(b)

(c)

(d)

y
3m

x

1m

4m
(B)

(B)

(A)
12m

(A)
(B’)
12m

Large-scale model
11m

(B’)
13m

12m

47º
100m

Main wind direction
in summer

50m

Figure 1: Plane view of the two scale models and the adjacentlarge-scale model. (A) indicate the location of radiometers. (B) and(B’) indicate the measurement points of 3 dimensional wind andvertical air temperatures in summer and winter, respectively. Largemodel has 1.5m cubic concrete blocks in an aligned array with λp
being 0.25.
Table 1

Table 2

Observation period and the usage of two sites.

Observation period'06.10/10'07.07/17'07.09/26

Figure 2: Photograph of each site; (a): HS, (b): VS, (c): AS, (d): HD.

~ '07.07/09~ '07.09/25~ '07.12/26

Use of the east site

Use of the west site

HS
HS
HS

VS
AS
HD

Equipped instruments and observation conditions.
Instrument

Installed height Sampling rate

Ultrasonic anemometer Kaijo TR90-AH

2H (30cm)

50 Hz

Radiometer

3H (45cm)

0.5 Hzmultiple points*

0.5 HzEko MR-40

Thermocouple (Type-E) (my own making)* 0.5H, 1H, 1.5H, 1.75H, 2H, 2.25H, 2.5H, 3H, 3.5H, 4H

Table 3 Relative magnitudes of momentum fluxes, heat fluxes, bulk
transfer coefficients for momentum and heat.

I measured wind speeds, air temperatures, and radiations
at both sites. Fluctuations of wind speeds and airtemperatures were obtained by three-dimensional sonicanemometers. Upward and downward short- and long-wave
radiations were measured separately by radiometers. I
equipped thermocouple tower to get information of vertical
air temperature. See Table 2 with detailed observational
condition.

τsensible heat flux

QH

bulk transfer coefficient for momentum C D
bulk transfer coefficient for heat

CH

VS/HS

AS/HS

HD/HS

1.3
1.1
2.3
1.6

1.1
1.0
1.1
0.84

0.75
1.0
0.65
0.85

suffer less drag force. In contrast to the clear difference byheight variation and densely packing, cluster buildings
exhibited similar momentum transfer.
The sensible heat flux showed the least effect among thestatistics given in the table. Only the height variance brought
1.1-times larger heat transfer. This augmentation is, on the
one hand, a big difference from the viewpoint of thermal
environment within the canopy; actually the air temperaturein the height variant canopy layer becomes lower than that inhomogeneous canopy in daytime due to the enhanced
sensible heat transfer. However it is, on the other hand, asmall change in comparison with the momentum
enhancement. This reflects the fact that heat transfer isdetermined by the surface energy balance rather than the
mixing intensity.
Although the different geometry brought similar sensible
heat transfer, it is not the case for the heat transfer efficiency(CH) because CH is directly influenced by the mixing
intensity. The height variant city showed large increase as
was the case for CD. The dense city exhibited smaller
efficiency in response to the weakened momentum transfer.
The apartment city showed decreased transfer efficient. One
possible reason why CH decreased despite the increase of CD
is that the wide wind street in AS lowered the height ofinternal layer, and the thermocouple was exposed to the

2.3 Data Processing
Momentum and sensible heat fluxes were evaluated by
the eddy correlation method every 30 minutes. The data wasscreened out when precipitation occurred within past 24
hours, WD was more than ±45º, or mean wind speed wasless than 0.1ms-1, thereby optimizing data quality.

3

momentum flux

Results and Discussion

3.1 Fluxes and Transfer Efficiencies
Table 3 represents the observed momentum and sensible
heat fluxes, and transfer efficiencies for momentum and heatin VS, AS and HD relative to those of HS.
At first we can see that a change in building geometry is
the quite important factor in momentum transfer. The largest
impact of all was made by the height variation in buildings,
yielding much enhanced mixing. Such obvious difference
can also be seen in bulk transfer coefficient for momentum(CD); the height variant city produced much efficient
momentum transfer 2.3 times larger than the canonical city’s.
Kanda [3] also reported that the height variation enhanced
CD by large eddy simulation (LES). The second largest
impact was found in an opposite sense in the dense city. The
packed buildings made the flow skim over themselves and

46

3.0

VS

HD

3

HD
Roth - u

2

De Bruin et al. - uRoth - w

1

Rural

2.5

AS
VS

σw/u*

2.0

De Bruin et al. - w

L

1.5

0
0.01

0.1

1

HD

10

- z'/L

0.00001

Figure 3: Normalized standard deviations of mean flow velocity (σu/u*)
and vertical velocity (σw/u*) versus atmospheric stability. The upper andlower plots in the figure correspond to σu/u* and σw/u*, respectively. Twolines for respective components are the empirical fits of real urban cities(Roth [4]) and flat terrains (De Bruin et al. [5]).

HS
VS
AS
HD
Ma (aligned)
Ma (staggered)

0.4
0.3
0.2

-15

0

15

Wind direction [deg ]

30

45

0.01

zm / zi

20

0.1
HS
VS
AS
HD

0.001

HS
VS
AS
HD
L
COSMO reg
BS82 reg

15
10
5
0
-5

0.00001
-30

Rural

0.001

Figure 4: σu/u* and σw/u* against zm/zi. The present data (HS, VS, AS,
and HD) are shown as average values with standard deviations byerror bars. The other symbols are; L: large scale model experiment in
COSMO, Inagaki and Kanda [2], Urban: Roth [4], Rural: Högström
[6].

0.1
0

AS VS

0.0001(b) 10zT / H

(a)

Urban
HS

1.0

-1

0.001

κB ( = ln (zm / zT ))0.0001

-45

Urban

HS

AS

σi / u *σi / u *LHS4zm / H

σu/u*

3.5

5

-45

-30

-15

0

15

Wind direction [deg ]Figure 5: Roughness lengths plotted against WD; (a): for momentum, and (b):for heat. Each length was normalized by the mean building height (H). Solid anddotted lines in (a) represent the conventional morphometric relationships for thecase of aligned and staggered array, respectively (Macdonald et al. [7]).

30

45

1

10

100

1000

10000

100000Re * ( = z m u * /ν )Figure 6: κB-1 versus Re*. Two lines are from the theoretical
relationship (equation 1); the solid curve (COSMO reg) is afitted one by Kanda et al. [10], and the dotted curve (BS82
reg) was given by Brutsaert [9]. “L” is the results from thelarge-scale model in COSMO.

On the other hand, σw/u* in all the geometries including
the large scale model and real world observations agree well
(Figure 3) because the outer layer contribution on verticalvelocity fluctuation was moderate as seen in Figure 4 that
σw/u* is almost constant with various zm/zi.

outer cool air. Then the temperature gradient became large,
and lowered CH.

3.2 Turbulent Statistics
The standard deviations of wind speeds normalized by
friction velocity (σi/u*, i = u, v, w) are the universal functionsof atmospheric stability regardless of the surface geometry
according to Monin-Obukhov Similarity Theory (MOST).
However, some researchers reported that σu/u* and σv/u* in
urban cities are somewhat different from those of anothersurfaces.
Figure 3 shows σu/u* and σw/u* against the atmosphericstability. The present data except for VS showed larger σu/u*
in comparison with the empirical curves given by real city
(Roth [4]) and grass field (De Bruin et al. [5]) experiments.
From the spectra analysis (not shown here), this enlargement
was caused by the strong propagation of outer layer
disturbance, which is the convective motion in atmosphericboundary layer (ABL). Inagaki and Kanda [2] succeeded inquantifying the relative importance of such outer layer
disturbance to inner scale eddies in horizontal velocity
fluctuations by use of zm/zi (zm and zi are the roughness length
for momentum and the ABL height) in neutral conditions;
σu/u* gets smaller with an increase of the relative scale of zm(i.e. the relative contribution of inner layer turbulence)(Figure 4). Four geometries implemented in this study also
show such decreasing trend. For the case of VS, theinstrument was closer to the higher building, and this may
have made u* larger and hence σu/u* lower.

3.3 Roughness Parameters
Figure 5 shows the evaluated roughness length formomentum (zm) and heat (zT) assuming MOST. We can see
the systematic WD dependency of zm; the transfer efficiency
of momentum is variable with respect to the geometricalchange. Two lines involved are the conventional
morphometric relationship for arrays of aligned andstaggered cubes (Macdonald et al. [7]). A staggered array has
not been tested in COSMO, the present result of HS with
WD = 45º can approximate a staggered array with WD = 0º.
zm/H in HS seems to shift from one branch (aligned array) tothe other (staggered), so that the behavior of estimated zm isreasonable. The graph also shows the relative difference of
zm in four sites. The height variant city exhibits much higher
zm so that the momentum transport could be enhanced. The
dense city has smaller value reflecting the weakness of dragforce.
On the other hand, such WD dependency andgeometrical difference can not be found in zT. All the results
have large scatters so that it is difficult to prescribe zT withrespect to certain geometry. However, using constant zT on
numerical simulation would result in significant errors ofheat flux (e.g. Kawai et al. [8]).

47Brutsaert [9] theoretically related the relative ratio
between zm and zT to the roughness Reynolds number Re* (=zmu*/ν; ν: the kinematic viscosity):κB −1 = a ⋅ Re*0.25 − 2.0 ,(4) The ratio of roughness length for momentum to heat
was well prescribed by the roughness Reynolds number
regardless of the building geometry. Thus combining thisrelationship with UCM and MOST seems the better way
for urban energy balance prediction.

(1)

where κB is the logarithm of the ratio between zm and zT (=ln(zm/zT)), and a is an empirical constant. Figure 6 represents
κB-1 against Re* in the case of HS, VS, AS, and HD together
with the results of large scale model experiment. The dottedand solid lines are from equation 1 with different coefficientsa; one is the original curve with a = 2.46 by Brutsaert, andthe other is a best-fit curve with a = 1.29 from the large scalemodel and HS data (not the present data; Kanda et al. [10]).
We can find the good agreement of all the present data (HS,VS, AS, and HD) and the large scale model data on Kanda etal.’s regression curve. The reason why the COSMO data
don’t follow the Brutsaert’s curve is that (1) as Brutsaert
wrote in his paper, his estimate was based on the data only
cover Re* smaller than 1000, and it may not be valid for very
rough surfaces with zm of the order of a meter, and that (2)the boundary conditions were different from the COSMO;
Brutsaert determined the numerical constant a with the dataover water surfaces. In addition, Kawai et al. [11]investigated the discrepancy between real city data and theregression curve (equation 1) with a = 1.29, and revealed
that a was variable with respect to the vegetation ratio.
Therefore, the use of equation 1 in UCM will improve urban
energy balance estimation.
-1

4

Acknowledgment: This research was financially supported by
the Core Research for Evolution Science and Technology
program of the Japanese Science and Technology Cooperation(CREST).

References[1] Kawai, T., and M. Kanda, “Energy balance obtained fromcomprehensive outdoor scale model experiment (I) Basic
feature of the energy balance,” J. Appl. Meteor. (submitted)[2] Inagaki, A., and M. Kanda, “Turbulent flow similarity overan outdoor reduced urban model,” J. Fluid Mech. (inrevision)[3] Kanda, M., “Large eddy simulations on the effects of surface
geometry of building arrays on turbulent organizedstructures,” Bound.-Layer Meteor., vol.118, pp.151-168,
2006.
[4] Roth, M., “Review of atmospheric turbulence over cities,”
Quart. J. Roy. Meteor. Soc., vol.126, pp.941-990, 2000.
[5] De Bruin, H. A. R., W. Kohsiek, and B. J. J. M. Van Den
Hurk, “A verification of some methods to determine thefluxes of momentum, sensible heat, and water vapour using
standard deviation and structure parameter of scalarmeteorological quantities,” Bound.-Layer Meteor., vol.63,
pp.231-257, 1993.
[6] Högström, U., “Analysis of turbulence structure in thesurface layer with a modified similarity formulation for near
neutral conditions,” J. Atmos. Sci., vol.47, pp.1949-1972,
1990.
[7] MacDonald, R. W., R. F. Griffiths, and D. J. Hall, “An
improved method for the estimation of surface roughness of
obstacle arrays,” Atmos. Environ., vol.32, pp.1857-1864,
1998.
[8] Kawai, T., M. Kanda, K. Narita, and A. Hagishima,“Validation of a numerical model for urban energy-exchangeusing outdoor scale-model measurements,” Int. J. Climatol.,
vol.27, pp.1931-1942, 2007.
[9] Brutsaert, W., “Evaporation into the Atmosphere,” D. Reidel
Pub Co., 1982.
[10] Kanda, M., M. Kanega, T. Kawai, R. Moriwaki, and H.
Sugawara, “Roughness lengths for momentum and heat
derived from outdoor urban scale models,” J. Appl. Meteor.,
vol.46, pp.1067-1079, 2007.
[11] Kawai, T., K. R. Mohammad, and M. Kanda, “Evaluationof a simple urban energy balance model using one-year
observed fluxes from two different cities,” J. Appl. Meteor.
(submitted)

Conclusions

The effect of irregularity of building geometry, including(1) the existence of building height variation, (2) distinct
characteristic arrays in cities: cluster housings and detachedbuildings, and (3) packing density, was investigated using
the unique dataset of COSMO. The main findings are as
follows;(1) The momentum flux was obviously affected by thesurface geometry. The sensible heat flux, in contrast, wasless affected due to the limitation of available energy
stored in the concrete materials. There exists a trade-off
relationship between the enhancement (suppression) of
turbulent mixing (i.e. momentum flux) and the descent(ascent) of the surface temperature. Consequently, theoffset of these two factors led to the less sensitive sensible
heat flux.
(2) The bulk transfer coefficients both for momentum andheat showed large variation with geometry. In particular,
height variation made much efficient transport and hence
it is preferable to diffuse particles (e.g. air pollutants). On
the other hand, detached building arrays and apartment
ones exhibited similar transfer characteristics.
(3) The outer scale turbulence influenced on all of the
COSMO data used in this analysis, resulting in thedeparture from MOST. It was confirmed that the relative
scale of outer and inner scale eddy can be the good index
to estimate the inner-scaled horizontal wind velocity
fluctuations.

48Pattern Recognition by Kernel Wiener Filter
Student Number: 06M18122

Name: Hirokazu YOSHINO

Supervisor: Yukihiko YAMASHITA

カーネルウィナーフィルタによるパターン認識吉野 紘和
本論文では，カーネルウィナーフィルタをパターン認識の識別部に応用するこ とを提案する．カーネルウィ
ナーフィルタは画像信号復元の分野で利用されるウィナーフィルタを拡張したもので，高次元特徴空間に写像することによって，信号の非線形処理が可能になっている．また，カーネル関数における非線形写像
関数の一次近似を考え，観測信号空間での距離に基づく正則化を提案する．これにより識別器の汎化性能の向上が期待できる．

1 Introduction
Inverse problem is used in various ﬁelds such as engineering, science and medical science. In the problem we estimate an origin from its results or responses.
Especially linear inverse problem is widely applied tomany difﬁculties in, for example, restoration of deteriorated signals or images.
Let Hs be an original signal space and Ho be an observed signal space. f denotes an original signal and it
is transformed into an element in the observed space by

Fig. 1: Inverse problem

an observation operator A ∈ B(Hs , Ho ). Then a noisen is added to the element and we can obtain an observed
signal g = A f + n in Ho . Here, B(Hs , Ho ) is the total

Recently the kernel method is intensively researchedin the ﬁeld of pattern recognition. In that method, vectors in the input space are mapped into a high dimen-

set of the bounded linear operators between Hs and Ho
and X denotes an estimation operator. An estimated sig-

sional vector space by a nonlinear mapping function.
We can linearly handle them in that space instead ofnal of f is denoted by f̂ . Then, this model is formulated
as below.
g = Af +n(1)(2)

non-linearly in the input space. By kernel trick we justneed to calculate in a space of which dimension is thenumber of samples. Therefore, the kernel method en-

It can be illustrated by Figure 1. Linear inverse problem is to ﬁnd the best-estimated signal f̂ from the ob-ables us to cut the total amount of the calculations andlets us apply it to many problems. However we can ap-

served signal g, in other words to acquire the suitable
X which gives the best-estimated signal f̂ . A variety ofply the kernel trick only to nonlinear functions that arederived from Mercer kernels.
In this paper, we apply the kernel Wiener ﬁlter (KWF)f̂ = Xg

techniques for inverse problem are proposed. Amongthem Wiener ﬁlter (WF) [1] is deﬁned as a linear oper- [2, 3] to the pattern recognition problem. We regard aator that minimizes the squared error between f and f̂ pattern as an observed signal and provide an identical
averaged over f and n. Many other criteria evaluating original vector with the patterns belonging to the samethe accuracy of the restored image in Ho are adopted class. This model is shown in Figure 2. In Figure 2,†
though, WF directly compares f and f̂ in Hs . Moreover f t denotes the original signal of the t-th class and gi jdenotes the j-th observed signal belonging to the i-thclass.

the structure of WF is so simple that it can be widely applied to a variety of ﬁelds.

49

described as eq.(6) when the original signal and noise
are uncorrelated.
XW F = RAT (ARAT + Q)† .

(6)

3 Kernel Wiener Filter
The signals in a space are mapped by a nonlinear
function Φ, which is derived from a Mercer kernel, into
a reproducing kernel Hilbert Space (RKHS). This spaceis called the feature space. Generally speaking the feature space has a higher dimension than the original space.
In this paper we use the following Gausian kernel function k(x, y) as a Mercer kernel for the observed signal
space.

Fig. 2: Observation of original signals()
k(x, y) = exp −γkx − yk2(γ > 0),(7)

Also applying ﬁrst-order approximation of Taylor exand also we deﬁne γ as a positive parameter.
pansion to the nonlinear function in observed signal space,
We provide the framework of inverse problem by thewe propose a regularization method based on the diskernel method. Let Φs be a nonlinear function from thetance in the observed signal space.
original signal space to its feature space Fs and also Φo
We clarify the advantages by experimental results andbe a nonlinear function from the observed signal space
make it clear that changing properly the value of kernel
to its feature space Fo . This model is shown in Figure
function in Hs improves its generalization ability.
3.

2

Wiener Filter(WF)

WF is deﬁned as a linear operator that minimizes thesquared error between an original signal f and a restored signal f̂ averaged over f and noise n. E f and
En denote the average over f and n, respectively. Let
the inner product of f and g be h f , gi and the norm of f√
be k f k = h f , f i. WF is formulated as a liner operator
that minimizes the following criterion JW F .
JW F = E f En k f̂ − f k2 = E f En kXg − f k2 .

(3)

Let R and Q be correlation operators of a original signal
and a noise, respectively.
()
R = Ef f fT ,()
Q = En nnT .

(4)
(5)

Here, AT denotes the transposition of a vector or a ma-

Fig. 3: Inverse problem between Fs and Fotrix A. When an operator B satisﬁes ABA = A，BAB =B，(AB)T = AB and (BA)T = BA, B is called the MoorePenrose generalized inverse matrix of A and it’s denoted

X is a restoration operator between Fs and Fo . Its
estimated signal is given by XΦo (g). f is provided byusing XΦo (g).

by A† . Under these mathematical preparations, WF is

50

3.1

Proposed Method 1: KWF-1

deﬁne inner products concerning with above two nonlinear functions by introducing derivation of the kernel
function. Then ko,i (x, y), koi (x, y) and ko,i j (x, y) are de-

We deﬁne KWF-1. Here we assume that each sample

consists of both an original signal f i and an observed
noted bysignal gi (i = 1, 2, . . . , L). KWF-1 X1 minimizes the sum∂of squared errors between Φo ( f i ) and its restored signal
ko,i (x, y) ≡ hΦo (x), φ o,i (y)i =ko (x, y), (13)∂ yi
X1 Φo (gi ). Then X1 minimizes the following criterion∂
JKW F1 .
koi (x, y) ≡ hφ o,i (x), Φo (y)i =ko (x, y), (14)
∂ xi
1 L
∂2
(8)
JKW F1 = ∑ kΦs ( f i ) − X1 Φo (gi )k2 .
ko (x, y).
ko,i j (x, y) ≡ hφ o,i (x), φ o, j (y)i =L i=1∂ xi ∂ y j(15)
From eq.(8), X1 can be solved as follows.
[]†Eq.(10) and a decision function have to be expressedL

X1 = ∑ Φs ( f i )Φo (gi )T
i=1

3.2

L

∑ Φo (g j )Φo (g j )T.

(9)by the kernel function and its differential forms withoutΦs , Φo and φo,i .
We can deﬁne X2 by eq.(16) from the derivations of

j=1

Proposed Method 2: KWF-2

the kernel function.
L L(N+1)

We introduce a regularization method based on theX2 = ∑

distance of Ho , which is an N dimensional vector space,
for the purpose of avoiding over ﬁtting to learning data.
where
Then we assume that virtual noises e1 , . . . , eL are added
to gi (i = 1, . . . , L) respectively and propose the following criterion to minimize.

νi =

L

i=1

{

∑ αi j (Φs ( f i ) ⊗ ν j ) (αi j ∈ R),

Φo (gi ) (1 ≤ i ≤ L)
ϕ i−L

(L + 1 ≤ i ≤ L(N + 1))
φ o,1(gi ) φ o,2(gi−L )
ϕi =
..


.



φ o,N(gi−(N−1)L )JKW F2 = Ee1 ,...,eL ∑ kΦs ( f i ) − X2 Φo (gi + ei )k2 . (10)i=1

We assume that the observed signals and ei are independent, the average on each element of ei is 0 and ei is
uncorrelated with each other. Furthermore, we assume
that the variance-covariance matrix of ei (∀i) is given by
the same matrix Q = diag(σ12 , σ22 , . . . , σN2 ). Let the j-thelement of a vector xi be xi j .

(16)

j=1

,

(17)

(1 ≤ i ≤ L)
(L + 1 ≤ i ≤ 2L). (18)..
.
((N − 1)L + 1 ≤ i ≤ NL)

3.3 Kernel Function Value

It’s clear we don’t have to calculate the kernel funcIn the original kernel method, sample average overtion in Hs but just need its value.
the virtual noises has to be calculated because of the
Let an angle between Φs ( f †i ) and Φs ( f †j ) in Fs be θi j ,nonlinear function Φo . It needs huge calculation cost
the inner product is obtained as the following eq.(19) byeven if the kernel trick is applied. Here, we assume
deciding on the value of kΦs ( f t† )k and θi j .
that gi is much larger than ei and apply the following
ks ( f †i , f †j ) = Φs ( f †i )T Φs ( f †j )ﬁrst-order approximation to eq.(10) to simplify the thecalculation [4].

= kΦs ( f †i )k · kΦs ( f †j )k cos θi j (19)NΦo (gi + ei ) ' Φo (gi ) + ∑ ei j φ o, j (gi ),

(11)

j=1

4 Experimental result

where φ o,l (gi ) is deﬁned by(12)

4.1 2 Class Problem: Comparison Between
KWF-1 and KWF-2

For both φ o,l (g) and Φo (g) has the same dimension,then it’s difﬁcult to calculate them directory. Here, we

To avoid calculating the (L × (N + 1)) generalized inverse matrix to obtain X2 , which needs huge calculation

φ o,l (gi ) ≡∂ Φo (gi )
.
∂ gil

51

cost, we ignore the effect of ko,i j (x, y) and adapt an approximated KWF-2 X20 . We can construct X20 by letting
K = K̃ and K j = σ j K̃ j .

Table 2: USPS: Error Rate of KWF-2
normkΦs ( f t† )k2=1

Table 1: Error rate of KWF-1 and KWF-2
Data setNamebananabreast cancer
diabetes
ﬂare solar
germanheart
image
ringnorm
splicethyroidtitanic
twonorm
waveform

error rate
SVM
11.53 ± 0.66
26.04 ± 4.47
23.53 ± 1.73
32.43 ± 1.82
23.61 ± 2.07
15.95 ± 3.26
2.96 ± 0.60
1.66 ± 0.12
10.88 ± 0.66
4.80 ± 2.19
22.42 ± 1.02
2.96 ± 0.23
9.88 ± 0.43

error rate
KWF-1
10.38 ± 0.45
25.05 ± 4.04
23.04 ± 1.79
33.48 ± 1.62
23.50 ± 2.19
16.38 ± 3.59
2.79 ± 0.56
4.00 ± 0.51
10.85 ± 0.70
4.13 ± 2.04
22.65 ± 0.73
2.38 ± 0.13
9.54 ± 0.49

error rate
KWF-2
10.29 ± 0.43
24.49 ± 4.29
22.97 ± 1.83
33.26 ± 1.76
23.63 ± 2.24
15.53 ± 3.12
2.80 ± 0.52
3.23 ± 0.50
10.83 ± 0.67
4.13 ± 2.26
22.36 ± 1.00
2.35 ± 0.11
9.50 ± 0.35kΦs ( f t† )k2=2kΦs ( f t† )k2
=t

θi j
π/4
π/3
π/2
π/4
π/3
π/2
π/4
π/3
π/2
2π/3

test error
4.43
4.43
4.43
4.43
4.43
4.43
69.91
24.71
5.48
4.68

cv error
1.62
1.62
1.62
1.62
1.62
1.62
49.93
3.88
2.30
4.44

Table 3: USPS: Error Rate of Other Methods
MethodRBF SVM
Polynomial SVM
k-means RBF

test error
4.20
4.00
6.70

We assume σi = σ (∀ i) and determine γ ，ε and σ
by 5-fold cross validation. In the comparison of 2-classproblem between KWF-1 and KWF-2, the inner productof the nonlinear functions in Fs is provided by

way to reduce the amount of calculation when data set
is large.

kΦs ( f t† )k2 = 1 and θ1 2 = π.
From Table 1 we can see KWFs have lower error rates

References

than SVM on 11 of the 13 data sets. Furthermore KWF2, which is regularized on the distance in Ho , is superior
to KWF-1 on 10 data sets and for remaining 3 data sets

[1] H. Ogawa and E. Oja, Projection ﬁlter, Wiener ﬁlter and
Karhunen-Loeve subspace in digital image restoration,
J. Math. Anal. Appl., vol.114, no.1, Feb. 1986, pp.37-51.

it has almost the same ability to KWF-1.

4.2[2] M. Yamada and M. R. Azimi-Sadjadi, Kernel Wiener
Filter using Canonical Correlation Analysis Framework, IEEE Workshop on Statistical Signal Processing
2005 (SSP’05), Bordeaux, France, July 17-20, 2005.

Multi-Class Problem: KWF-2[3] Y. Washizawa and Y. Yamashita, Kernel Wiener ﬁlter,
JIEICE 6workshop in computer information-theoretic
learning theory, Nov. 2003, pp.143-148.

Table 2 shows an experimental result on USPS dataset, which consists of 10 classes. Other experimental
conditions were the same as the ones of 2 class problem.

[4] S. Akaho, SVM that Maximizes the Margin in the Input
Space, JIEICE magazine，vol.J86-D-II, no.7, July 2003,
pp.934-942. (In Japanese)

General abilities of other methods previously proposed
are also shown in Table 3. KWF-2’s performance wasalmost as good as other methods shown in Table 3.

5[5] S. Abe，Support Vector Machine for Pattern Classiﬁcation，(Springer-Verlag, 2005).
[6] Y. Washizawa and Y. Yamashita, Non-linear Wiener
ﬁlter in reproducing kernel Hilbert space, 18th International Conference on Pattern Recognition(ICPR’06),
2006, pp.967-970.

Conclusion

In this paper, we proposed that we apply KWF topattern recognition. By doing regularization based on[7] Y. Yamashita and H. Ogawa, Relative Karhunen-Loeve
Transform, IEEE TRANSACTIONS ONSIGNAL PROCESSING, vol.44, no.2, Feb. 1996.

the distance to KWF, its general ability was improved.
These facts proved that KWF is effective for not only

[8] Young-Chul SONG and Doo-Hyun CHOI, Kernel Adjusted Wiener Filter for Image Enhancement, Japanese
Journal of Applied Physics., Part 2 Letters, vol.44, issue.6A, 2005, pp3996-3999.

signal restoration but also pattern recognition. For future works, it’s necessary to develop a method to determine the best parameters of the kernel function and a52

Effect of stiffness and shape on release mechanism mimicking gecko foot-hairStudent Number: 06M18139

Name: Junichi WATANABE

Supervisor: Kunio TAKAHASHI

ヤモリ脚部の微小毛構造を模倣した脱離機構に対する硬さと形状の影響
渡邉絢一
ヤモリは壁や天井で素早く動き回り、強い凝着力を有しながらも容易に足を脱離させる脱離機構を有していると考えられる。本研究ではこの脱離機構に注目し、ヤモリが足の裏に有している微小毛構造を大変形する梁構造であると仮定し、脱離に必要な水平方向と垂直方向の力の関係を求める。そして梁の硬さや形状が脱離機構に与える影響を検討する。

1

Introduction

It is known that geckos can walk on vertical andinverted surfaces quickly using adhesion force generated by a huge number of foot-hair under theirfeet[1]. Recently, Autumn measured gecko’s adhesion force, and reported that the adhesion force isdue to Van Der Waals interaction[2][3].The adhesion
force of the gecko foot-hair is considered to be independent to the surface’s conditions, hence some researches on the application of the mechanism of adhesion have been carried out. Takahashi proposeda release mechanism for gecko foot-hair to explain
their quick step[4]. He suggested that gecko cancontrol the normal force for detachment by giving
the tangential force. In our former work, we consequently used a straight and hard beam, expressing a single gecko foot-hair, to analyze the relation
between the normal and tangential force which arenecessary for the detachment process. But actually,
gecko foot-hair is soft, and not straight. Therefore,
it is possible that the stiffness and shape of the beam
influence the release mechanism.
In this study, in order to clarify those effects, asoft beam as shown in Fig.1 is introduced. Numerical and experimental study, to obtain the force atdetachment using various kinds of beams, offer us
a new understanding of the stiffness and shape’s effects on the release mechanism.

2

Fig. 1: Model of gecko foot-hairis constant. When a normal force Fn and tangential force Ft are given to the upper end of the beam,
the normal stress is generated at the adhesion area.
The normal stress can be expressed as the sum of
the stress generated by the normal force Fn and thebending stress generated by the moment M at thelower end of the beam. If the maximum stress at theadhesion area σmax exceeds the critical stress σcr , thebeam detaches from the object’s surface. So, the criterion for detachment can be expressed as eq.(1).
Fn
|M |
+
= σmax > σcr
S
Z

(1)

Where, S is the area of the adhesion area, and Z
is the section modulus of the adhesion area, and Fn ,Ft , and M is assumed positive in the direction of thearrows shown in Fig.1.
From the equations of the beam, the boundary condition (θl and θ0 are fixed), and the criterion for
detachment, Fn and Ft at detachment can be solved
numerically.
Fig.2 is an example of the numerical result. Theline plotted with circle points depicts the Fn and Ft
needed to detach the beam from the object’s surface
in M > 0 direction, and the line plotted with triangle
points depicts the Fn and Ft needed to detach thebeam in M < 0 direction. The region surroundedby these two lines denotes the gripping region, i.e.
the beam does not detach. In otherwise region, thebeam detaches from the surface.

Model of gecko foot-hair andnumerical method

Fig.1 shows a 2-dimensional model of a single
gecko foot-hair using an elastic beam, with lengthl, Young’s modulus E, and cross-sectional momentof inertia I. The beam is assumed that, it can belargely deformed, the mass is ignored, both ends can
not rotate around, i.e. the root angle θl and the top
angle θ0 in Fig.1 are fixed, and it detaches from theobject’s surface without sliding. We define the contact area which contacts with the object’s surface atthe lower end of the beam, as adhesion area. The
critical stress for detachment at the adhesion area53θl = θ0 = π / 6
E / σ c r =10 1
l/d =61500
N o rm al fo rce F n [g]N orm alized N orm a l fo rce F n / F cr

1

θ0 = π / 2
l = 65 [m m ]d = 6 [m m ]

1000

0.5

500

0

0
0

-40

0.5
1
1.5
N orm alized Tangential force F t / F cr

Fig. 2: Example of the numerical result

-20
0
20
Tangential force F t [g]40Fig. 4: Experiment result and numerical result when
the beam is deformed slightly (acrylic)

Measurement of normal andtangential force at detachment

sim ulation result
experim ental result
error range of E

600

N o rm al fo rce F n [g]3

sim ulation result
experim ental result

To verify the validity of the numerical simulation,
we carried out a measurement of the normal andtangential force acting on a straight beam at detachment. Both θl and θ0 are 90[degree]. The original
experiment model is shown in Fig.3(a).
To simplify the experiment model in Fig.3(a)without losing its generality, we use an equivalentmodel with a half length and the upper end is free,
as shown in Fig.3(b).
Stress generated at the adhesion area is the sameas in Fig.3(a). So, the normal force and tangential
force at detachment are identical. Fn and Ft are
applied to the upper end of the beam, and gradually increased. When the beam begins to detach, werecord the values of Fn and Ft . In this experiment,
we used an acrylic (E = 3234[MPa]) and a rubber(E = 5.751[MPa]) cylindrical beams, whose results
are shown in Fig.4 and Fig.5.
Fig.4 and Fig.5 show numerical results, solved by
the method described in previous section, and experimental results. The numerical results are in good
agreement with the experimental ones. So, the validity of the numerical procedure explained in previous

θ0 = π / 2
l = 20 [m m]d = 5 [mm ]E = 5.751[M P a]

400

200

0
-40

-20
0
20
Tangential force F t [g]40Fig. 5: Experiment result and numerical result when
the beam is deformed largely (rubber)
section is verified.

4

Effect of stiffness and shapeof the beam on the releasemechanism

We simulate Fn and Ft required to detach aslanted beam shown in Fig.1, by changing the stiffness and shape of the beam.
First, Fig.6 shows the numerical results when thenormalized Young’s modulus σEcr varies, whereas, theshape of the beam is unchanged. From Fig.6, it isclear that the gripping region increases as the beam
is softer. This is advantageous for gripping, and also
advantageous for avoiding the detachment from asudden disturbance.
Second, Fig.7 shows the numerical result when
the root angle of the beam is changed. From Fig.7, itis clear that, as the root angle of the beam is smaller,
the gripping region becomes larger when M is negative compared to that when M is positive. So, thebigger positive tangential force can be held. This isalso the advantageous point of the soft beam.
Third, for a single bent beam shown in Fig.8, we
simulated and compared the result with those of astraight beam, shown as Fig.9. The area of the ad-

Fig. 3: Experimental model. (a) straight beam, θ0 =
θl = π2 . (b) equivalent model of (a), the beam has
half length the upper end of the beam is free.

54E / σ cr = 10 4
E / σ cr = 10 2
E / σ cr = 10 1

No rm alized N orm al force F n / F cr

1

θ0 = π / 6
l/d=6
0.5

0
0
0.5
1
1.5
Norm alized Tan gen tial force F t / F cr

Fig. 8: Illustration of a bent beam. u is modulus todecide the bending position of the beam. (0 ≤ u ≤1)Fig. 6: Effect of stiffness of the beam on releasemechanism.
θ l = θ0 = π / 3
θ l = θ0 = π / 4
θ l = θ0 = π / 6
θ l = θ0 = π / 7

1
N o rm alize d N orm al fo rce F n / F cr

N orm alized N orm a l fo rce F n / F cr

1

E / σ cr =10 1
l/ d = 6
0.5

be nde d be aml / d = 7 .0 5 3 , u = 0 .7
θl = π / 9 , θ0 = π / 2
straight be aml / d = 7 .0 5 3
θ l = θ 0 = π / 2 .10 1( = 8 5 .6 7 [de gre e])

E / σ cr = 10 1

0.5

0

0
0

-0.05

1
2
N orm alized Tangential force F t / F cr

0
0.05
0.1
0.15
N orm alized Ta ngential force F t / F cr

Fig. 7: Effect of gradient of the beam on releasemechanism.

Fig. 9: Effect of shape of the beam on release mechanism.

hesion area S and the section modulus of adhesionarea Z of the bent beam and the straight beam are
the same. Two kinds of beams are designed as the
maximum normal force is identical. From Fig.9, it isclear that when no Fn exerted, bigger Ft is needed
to detach the bent beam compared to the straightbeam. It is also clear that the gripping region isshifted to the positive direction of Ft , compared tothat of the straight beam. |Ft +Fn | needed to detachthe bent beam in M > 0 direction, i.e. the line withblack circles in Fig.9, becomes smaller than that of
the straight beam. So, detachment can occur withsmaller force. This is advantageous for quick step.

But if the criterion for detachment can be acquiredby measurement, the normal and tangential forces
for detachment can be solved.
We assumed two kinds of beams as shown in
Fig.10. Both of them are straight, hard enough. The
same magnets of circular shape and thin enough are
fixed to the lower end of both beams (S and Z are
the same). When the same magnets are used to thebeams under the same condition, the criterion for detachment would be the same. So, if Fn is the same,
M is the same, hence, we can get the following expression.

Fn cos θ0′ − ll′ cos θ0 + Ft ll′ sin θ0
Ft′ =
(2)
sin θ0′5

If Fn and Ft at detachment using a magnet are measured, we can estimate the relations of the force atdetachment for the other beam using the same magnet from eq.(2). Fig.11 and Fig.12 show the experiment results. In both experiments, the estimatedresults are in good agreement with the experimental
results.
We also do the same experiment while changing the maximum normal force by inserting one or
two thin plastic sheets between the magnet and themagnetic body. Fig.13 shows the result of the relation of the normalized normal force and normalized
moment. From Fig.13, it is clear that the relation

Possibility for release mechanism using magnetic force

As an application, we replaced the adhesion force
by the magnetic force. To solve the force for detachment, the equations of the beam, the boundary condition, and the criterion for detachment have to beconsidered. Since the equations of the beam and theboundary condition, depending on the beam, havebeen previously studied, here, we focus just on thecriterion for detachment. In this case, theoreticallyanalyzing the criterion for detachment is difficult.

55

N o rm al fo rce F n [g]150e xperim ental re sulttransfo rm e d re sult
θ0 = π / 2(fro m l = 7 0 [m m ] into l = 1 0 0 [m m ])l = 7 0 [m m ]d = 5 [m m ]e xperim ental re sult
θ0 = π / 2
l = 1 0 0 [m m ]d = 5 [m m ]

100

50

0
-10

Fig. 10: Two kinds of beams having different lengthand root angle.

N orm alized No rm al fo rce F n / F cr

N o rm al fo rce F n [g]e xperim ental re sult
θ0 = π / 4
l = 7 0 [mm ]d = 5 [m m ]

300

200

100
transfo rm e d re sult(fro m θ 0 = π / 2 into θ 0 = π / 4 )

0
0

100
200
300
Tangential force F t [g]10Fig. 12: Experimental result when the length of thebeam is changed.

e xperim ental re sult
θ0 = π / 2
l = 7 0 [mm ]d = 5 [m m ]

400

-5
0
5
Tangential force F t [g]400l = 7 0 [m m ] F cr = 4 0 3 .5 4 [g] (no she e t)
l = 7 0 [m m ] F cr = 1 4 7 .8 7 [g] (1 she e t)
l = 7 0 [m m ] F cr = 6 7 .4 7 [g] (2 she e ts)
l = 1 0 0 [m m ] F cr = 1 5 0 .4 7 [g] (1 she e t)
σ cr = c o nstant1

0.5

θ0 = π / 2
d = 5 [m m]E = 3 234[M P a]00

Fig. 11: Experimental result when the root angle ofthe beam is changed.

12
3
N orm alize d M om ent S|M | / ZF cr

Fig. 13: Relation between normal force and moment
generated at adhesion area when maximum normal
force and length of the beam are changed.

of normalized normal force and normalized moment
generated at the adhesion area is almost the sameeven if the maximum normal force are changed. So,
the criterion for detachment seems unchanged even if
the magnet is slightly away from the magnetic body.

the magnet is slightly apart from the magnetic body.
These knowledge are helpful for applying the releasemechanism in engineering.

References
6

Conclusion[1] Walker.G. Adhesion to smooth surfaces by insects - a review, Int. J. adhesion and adhesive
13: 3-7. 1993.

We assumed a single gecko foot-hair with a large
deforming beam, and carried out a numerical calculation and experiment to obtain the normal andtangential force at detachment. As the results, we
can conclude as follows: first, the gripping region
increases as the beam is softer. Second, as theroot angle of the beam is smaller, the gripping region becomes larger when the moment generated atadhesion area is negative compared to that whenthe moment generated at adhesion area is positive.
Third, in case of a bent beam, the gripping region isshifted to the positive direction of tangential force,
and when no normal force exerted, bigger tangential
force is needed to detach the bent beam comparedto the straight beam. When magnetic force is usedinstead of adhesion force, if we know the criterion
for detachment by measurement, the relation of theforce at detachment can be calculated. The criterion for detachment is possibly unchanged even if[2] K.Autumn, W.P.Chang, R.Fearing, T.Hsieh,
T.Kenny, L.Liang, W.Zesch, R.J.Full. Adhesive
force of a single gecko foot-hair. Nature 405:681-685. 2000.
[3] K.Autumn, M.Sitti, A.M.Peattie, W.Hansen,
S.Sponberg, Y.A.Liang, T.Kenny, R.Fearing,
J.N.Israelachvili, R.J.Full. Evidence for van der
Waals adhesion in gecko setae. PNAS 99(19):12252-12256. 2002.
[4] K.Takahashi. Effect of surface roughness on adhesion of multi-hair structure. Computer Methods and Experimental Measurements for Surface Effects and Contact Mechanics VII. 49:
141-150. 2005.

56

A STUDY ABOUT THE PARAMETERS OF THE NON-LINEAR CONTRACTANCYEXPRESSION FUNCTION
Student Number: 06M18151

Name: NI WEISupervisor: Hideki OHTA

非線形コントラクタンシー表現関数のパラメータに関する研究倪

微大野ら(2006)により開発された粘土に対する構成モデル(EC/LC model)の非線形コントラクタン
シー表現関数のパラメータ nE , nL を調節しながら、EC/LC model が組み込まれたプログラム(DACSAR)で正規圧密粘土の非排水せん断試験をシミュレートした。解析結果を実験結果と比較することによって各粘土に対する nE , nL 値の取り得る範囲を調べた。さらに、Ariake clay が広く分布している地盤で建設された武雄北方 I.C.盛土工事のシミュレーションを行うことにより、 nE , nL 値の信頼性を検証した。
developed. EC/LC model has two new parameters
nE and nL called fitting parameters in it. Withdifferent nE and nL value, EC/LC model canconnote the representative constitutive equations.
EC/LC model is also considered more usefully in
prediction of ground deformation by finite element
analysis. For that it is important to define the rangeof nE and nL value for the different object clay
to improve the prediction accuracy. In fact, therange of value or determination method of nE andnL have not been proposed yet.

1. Introduction

(a)

10

Amagasaki clay

8

Fukuoka clay
London clay
Weald clay

6
4
2
0
0

0.5

1

Stress ratio,

1.5

Volumetric strain, εv (%)

Volumetric strain, εv (%)

Many constitutive equations of the natural clay
deposit have been proposed before. As therepresentative examples there are Original
Cam-Clay model (1963), Modified Cam-Claymodel (1968), Ohta-Hata model(1971) andSekiguchi-Ohta model(1977).
(Volumetricv(stress ratio) have been used tostrain) anddescribe
thecontractansy(negative
dilatancy)-response. And the experimental data ofhas been plotted by Shibata (1963). Inv andOhta-Hata model, the relationship between them isconsidered as

In this study, define the range of nE and nL for
different clay is the object. To make the range
clearer, many simulations of K0-consolidation tests
under undrained condition for both compression
and extension by finite element analysis was carriedout. Then, the analysis was compared with theexperimental results to determine the possible range
value of nE and nL . At last, the applicability of
the range defined for Ariake clay was examined bysimulating the movement during the fillconstruction process of the highway embankment.

(b)

10

Amagasaki clay

8

Fukuoka clay
London clay
Weald clay

6
4
2
0
0

0.5

1

1.5

Stress ratio,

Fig.1.1 Modeling the cotractancy-response as (a)linear and (b) non-linear

2. The constitutive equation of EC/LC model

linear (Fig.1.1(a)). Ohno (2006) proposed that therelationship between them also can be considered as
non-linear (Fig1.2 (b)). By introducing exponentialfunction and logarithmic function to describe thecontractancy-response, new constitutive models forthe clay deposit called EC/LC model has been

Under the axi-symmetric stress condition

1
57

nE

EC mod el : f p , q, vp

MD ln

p
p0

MD
nE M

LC mod el : f p , q, vp

MD ln

p
p0

M nL
2MD
ln
nL
M nL

p
v

0
nL
p
v

0

When nE 1.0 the EC model corresponds to theOriginal Cam-Clay model. When nL 2.0 the LC
model corresponds to the Modified Cam-Claymodel.
Extend to general stress statenE

EC mod el : f σ , vp

MD ln

p
p0

MD *
nE M

LC mod el : f σ , vp

MD ln

p
p0

* L
2 MD
M nL
ln
nL
nL
M

p
v

0
n

p
v

0

When nE 1.0 the EC model corresponds to the
Sekiguchi-Ohta model. When nL 2.0 the LC
model corresponds to the Modified Cam-Claymodel which is modified by Hashiguchi, Chen
(1998) and Asaoka (2002). The shapes of yieldsurface of EC/LC model are shown as Fig2.1.
EC modelFig.3.1 Sampling sites for clays dealt in this
study
1

1

Stress-strain curve
0.8

0.6

LC modelStress path

Pusan

Yamashita

Bothkennar

q/ 'c

q/ ' c0.2

0.4

Louiseveille

Drammen

K0 Line

K0 Line

-0.2

nL=1

nE=1

p'

q

nE=3
nE=4
nE=5
C.S.L

nL=2
nL=3
nL=4
nL=5

0.2
0

0

nE=2

Bothkelnnar

C.S.L

C.S.L

q

0.6

Pusan

Bangkok
Singapore

0.4

Ariake0.8

AriakeYamashita-0.2

DrammenLouisevilleSingapore
-0.4

-0.4

Bangkok

p'

-0.6
0

3

6

9

12

15

-0.6
0

0.2

0.4

0.6

0.8

p'/ ' c(%)

Fig3.2. Stress-strain curves and stress paths
observed in CK0NCUC and CK0NCUE tests forundisturbed clays

C.S.L

Fig2.1. Shapes of yield surfaces of EC/LC model

4. Input parameters

3. Experimental results

Table 4.1. Input parameters for DACSAR
Parameter

Watabe (2003) carried out a series ofK0-consolidation tests in a triaxial cell for 8 kindsof marine clay collected from different areas in theworld (Fig.3.1). Ariake clay and Yamashita clay
from Japan, Bothkennar clay from England,
Drammen clay from Norway, Louiseville clay from
Canada, Singapore clay from Singapore, Pusan clay
from Korea, Bangkok clay from Thailand. For allthese 8 kinds of clay, after a K0-consolidation testwith a specimen trimmed to 35mm in diameter and
85mm in height, the K0-normally consolidated
specimen was sheared under undrained condition
for both compression and extension. Relationshipbetween (a) deviator stress and axial strain ( q)and (b) deviator stress and effective mean stress(q p ) normalized by the consolidation stress for
K0-normally consolidated undisturbed clays areshown in Fig.3.2.

D

M
v
v0

K0

vi

Ki

v&0

Description
Coefficient of Dilatancy proposed by Shibata(1963)
Irreversibility Ratio exp ressed byCritical State Parameter
Effective Poission's Ratio
Preconsolidation Pressure
Coefficient of Earth Pressure at rest
immediately after K 0 -normal consolidation (reference state)
Effective Overburden Pressure in-situ
Coefficient of In-situ Earth Pressure at rest
Coefficient of Secondary Compression
expressed by
d v /(d (ln t )) at time tc
Initial Volumetric Strain Rate at reference state expressed by
v&0/ tc , where tc is the time at end of primary consolidation

Compression Index in the e ln( p / p0 ) relationship0.434Cc (Cc is compression index in the e-logp' relationship)e0

Void Ratio corresponding to v 0 at reference state(if you don't know the value of e0 , specify e0 = 0 )nL , nE

fitting parameter to describe non-linear contractancy

In this study, the simulation was carried out by
DACSAR (Deformation Analysis Considering

2
58

1

Stress Anisotropy and Reorientation). The
descriptions about the input parameters are shownas Table 4.1. And the values of input parameters areshown as Table 4.2.
Table 4.2. The value of input parametersv0( kPa )

K0

Drammen

0.031

0.88

1.35

0.32

336.8

0.47

Bothkennar

0.096

0.95

1.61

0.30

240.0

0.43

Louiseville

0.112

0.93

1.32

0.35

408.4

0.53

Singapore

0.158

0.83

0.81

0.39

734.7

0.66

Bangkok

0.143

0.84

1.09

0.39

279.9

0.64

Pusan

0.098

0.91

1.38

0.32

213.3

0.47

Ariake

0.064

0.92

1.67

0.30

150.0

0.43

Yamashita

0.072

0.92

1.58

0.31

941.2

0.45

clay

Ki

( kPa)

e0

Drammen

0.47

0.002

0.002

0.10

336.8

1.12

Bothkennar

0.43

0.008

0.008

0.45

240.0

1.79

Louiseville

0.53

0.008

0.008

0.45

408.4

1.79

Singapore

0.66

0.008

0.008

0.36

734.7

1.34

Bangkok

0.64

0.009

0.009

0.49

279.9

1.62

v&0

vi

0.6
0.4
0.2
0

-0.2

6
Axial strain,

9

12

15

0.007

0.39

213.3

1.63

Ariake

0.43

0.006

0.006

0.55

150.0

3.82

Yamashita

0.45

0.006

0.006

0.44

941.2

1

1
0.8

0.6
0.4
0.2
0

-0.2

-0.6
0

2.54

0.2
0

-0.4

3

6
Axial strain,

9

12

15

-0.6
0

a

1

1
0.8

0.6
0.4
0.2
0

0.2 0.4 0.6 0.8
1
Effective mean stress, p' / 'cnL=1.1nL=1.2nL=1.3nL=1.4nL=1.5experimental result

0.6
0.4
0.2
0

-0.2
-0.4

3

6
Axial strain,

9

12

15

-0.6
0

a

1

1

0.8

0.8

Deviatoric stress, ( 'a - 'r)/ 'c

1.3〜1.5
1.3〜1.5
1.6〜1.7
1.4〜1.6
1.4〜1.6
1.4〜1.6
1.3〜1.5
1.4〜1.6

ElastoVisco
Plastic
1.1〜1.3
1.1〜1.3
1.2〜1.4
1.2〜1.4
1.2〜1.4
1.2〜1.3
1.2〜1.4
1.1〜1.3

Deviatoric stress, ( 'a - 'r)/ 'c

1.0〜1.1
1.0〜1.1
1.1〜1.3
1.1〜1.3
1.1〜1.3
1.1〜1.2
1.1〜1.3
1.0〜1.2

LC model nL

0.6
0.4
0.2
0

-0.2

0.2 0.4 0.6 0.8
1
Effective mean stress, p' / 'c-0.6
0nL=1.1nL=1.2nL=1.3nL=1.4nL=1.5experimental result

0.6
0.4
0.2
0

-0.2

-0.4

-0.4

3

6
Axial strain,

9

12

15

a

-0.6
0

0.2 0.4 0.6 0.8
1
Effective mean stress, p' / 'c

Fig.5.4. Using LC model considering viscosity0

sattlement (m)DrammenBothkennar
LouisevilleSingapore
Bangkok
Pusan
AriakeYamashita

0.4

Fig.5.3. Using LC model without considering
viscosity

Table 5.1. Range for fitting parameters

ElastoPlastic

0.6

0.8

-0.6
0

ElastoVisco
Plastic
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0

nE=1.0nE=1.1nE=1.2experimental result

-0.2

-0.4

ElastoPlastic

0.2 0.4 0.6 0.8
1
Effective mean stress, p' / 'c

Fig.5.2. Using EC model considering viscosity

5. Analysis results

Clay

-0.6
0

0.8

-0.2

EC model nE

0a

Deviatoric stress, ( 'a - 'r)/ 'c

0.007

0.2

Fig.5.1. Using EC model without considering
viscosity

Deviatoric stress, ( 'a - 'r)/ 'c

0.47

0.4

-0.4

3

-0.4

Pusan

0.6

-0.2

-0.4
-0.6
0

nE=1.0nE=1.1nE=1.2nE=1.3nE=1.4nE=1.5nE=1.6experimental result

The simulation of K0-consolidation tests under
undrained condition for 8 kinds of marine clays was
carried out and the analysis result for Ariake clay
was plotted as Fig. 5.1~5.4. Consequently, nE andnL value for the other clays are listed in Table 5.1.
For Ariake clay, the movement analysis during thefill construction process was carried out with therange value nE 1.0 ~ 1.3 and nL 1.2 ~ 1.5 .
Then, compare the analysis results to the field0

Observational datanE=1.0nE=1.1nE=1.2nE=1.31
2
3

5

pore water pressure (kN/m2)

150

Observational data
Analysis result

100

depth (m)

M

1
0.8

Deviatoric stress, ( 'a - 'r)/ 'cD

Deviatoric stress, ( 'a - 'r)/ 'c

clay

1
0.8

Deviatoric stress, ( 'a - 'r)/ 'c

Deviatoric stress, ( 'a - 'r)/ 'cmeasurement data to examine the applicability of
the nE and nL value defined (Fig.5.5 andFig.5.6).

50
0
150

Observational data
Analysis result

100

Observational datan E=1.0
n E=1.1
n E=1.2
n E=1.3

10

50
0

200

400

time (day)

600

800

0

10

20

30

40

50

60

lateral movement (cm)Fig.5.5. Movement simulation using EC model

359

sattlement (m)0[3]Tomohide TAKEYAMA, ANALYSIS ABOUTMECHANICAL SECULAR VARIATION OF THE
FILL STRUCTURE ON THE SOFT GROUND,
Graduate Thesis of Tokyo Institute Technology, 2001.
[4]Shintaro OHNO, Atsushi IIZUKA and Hideki OHTA,
DEVELOPMENT OF THE ELASTO-PLASTICCONSTITUTIVE MODEL FOR SOILS BY USINGNEWDESCRIPTIVEFUNCTIONOFCONTRSCTANCY, Technical report, 2006.
[5]Takenobu FUJIKAWA and Masateru TAKAYAMA,PHYSICAL PROPERTIES OF ARIAKE MARINE
CLAY-GEOTECHNICALPROPERTIESOFARIAKE MARINE CLAY(1)-, Transactions of The
Japanese society of Irrigation Drainage and RuralEngineering, 1980.
[6]Takenobu FUJIKAWA and Masateru TAKAYAMA,COMPRESSION INDEX OF ARIAKE MARINE
CLAY-GEOTECHNICALPROPERTIESOFARIAKE MARINE CLAY(2)-, Transactions of The
Japanese society of Irrigation Drainage and RuralEngineering, 1980.
[7]Takenobu FUJIKAWA and Masateru TAKAYAMA,
Cu/p
VALUE
OF
ARIAKE
MARINE
CLAY-GEOTECHNICALPROPERTIESOFARIAKE MARINE CLAY(3)-, Transactions of The
Japanese society of Irrigation Drainage and RuralEngineering, 1980.
[8]Kenta MIZUNO, Takashi TSUCHIDA, Masaki
KOBAYASHI and Yoichi WATABE, NEW
CONSTITUTIVE EQUATION OF CLAY AND THE
PREDICTION OF HORIZONAL DEFORMATIONSOF LARGE SCALE EMBANKMENTS, Journals of
the Japan Society of Civil Engineers Vol.63 No.4,
936-953. 2007.11.
[9]Hiroyuki TANAKA, Jacques Locat, Dinesh Raj
Shiwakoti and Masanori TANAKA, DIFFERENCES
IN PHYSICAL AND CHEMICAL PROPERTIES
OFJAPANESE AND NON-JAPANESE CLAYS, The
Japanese Geotechnical Society, 2001.
[10]Shintaro
OHNO,
Tomohide
TAKEYAMA,
Thirapong PIPATPONGSA, Hideki OHTA andAtsushi IIZUKA, ANALYSIS OF EMBANKMENTBYNONLINEAR
CONTRACTANCYDESCRIPTION, Proc. 13th Asian Regional
Conference on Soil Mechanics and GeotechnicalEngineering, Kolkata, India, 2007.
[11]Hashiguchi, K. and Chen, Z.P., ELASTOPLASTICCONSTITUTIVE EQUATION OF SOIL WITH THE
SUBLOADING SURFACE AND ROTATIONAL
HARDENING, International Journal for Numericaland Analysis Method in Geomechanics, Vol.22,
197-277.
[12]Asaoka, A., Noda, T., Yamada, E., Kaneda, K. andNakano, M.. AN ELASTO-PLASTIC DESCRPTION
OF TWO DISTINCT VOLUME CHANGEMECHANISMS OF SOILS, Soils and Foundations,Vol.42, No.5, 47-57.

0

Observational data
nL=1.2nL=1.3nL=1.4nL=1.51
2
3pore water pressure (kN/m2)

Observational data
Analysis result

100

depth (m)

5

150

50
0
150

Observational data
Analysis result

100

Observational datan L=1.2
n L=1.3
n L=1.4
n L=1.5

10

50
0

200

400

600

800

0

time (day)

10

20

30

40

50

60

lateralmovement (cm)Fig.5.6. Movement simulation using LC model

6. Conclusions
For the 8 kinds of marine clays the range values ofnE and nL was approximately defined. The
values for Ariake clay was nE 1.0 ~ 1.3 andnL 1.2 ~ 1.5 . The applicability for those values
also was examined. Consequently, the reliability is
75% when simulated by EC model, on the other
hand it is 80% when use LC model. The adjustment
ratio plotted in Fig6.1 shows that the best values offitting parameters are nE 1.2 and nL 1.5 .

1

0.5

1.5

EC modelAdjustment ratio

Adjustment ratio

1.5

1.0

1.1
1.2
Fitting Parameter n E

1.3

LC model

1

0.5

1.2

1.3

1.4

1.5

Fitting Parameter n L

Fig.6.1. The adjustment ratio of EC/LC model
References[1]Yoichi WATABE, Masanori TANAK, Hiroyuki
TANAKAand
Takahashi
TUCHIDA,
K0-CONSOLIDATION IN A TRIAXIAL CELL AND
EVALUSATION OF IN-SITU K0 FOR MARINE
CLAYS WITH VARIOUS CHARACTERISTICS,
SOIL AND FOUNDATIONS Vol. 43, No. 1, 1-20,
February 2003 Japanese Geotechnical Society, 2003.
[2]Shintaro
OHNO,
ELASTO-PLASTICCONSTITUTIVE MODELS DERIVED FROMNON-LINEAR
DESCRIPTIONOF
SOIL
CONTRACTANCY, Doctor’s Thesis of Tokyo
Institute Technology Graduate School of Science andEngineering, 2006.

460

Development of a visible light responsive photocatalyst
for PCE treatment
Student Number: 06M18168

Name: Zhiwei Wu

Supervisor: Hirofumi Hinode

現在テトラクロロエチレン(PCE)などの有機塩素化合物による地下水の汚染が発生している。このような化学物
質は産業活動を継続発展させるために現在は不可欠な物質でもある。そのため、これらの化学物質の高度処理システムの開発が急務である。本研究では有機塩素化合物である PCE を除去するための簡易かつ省エネルギーな方法
で、太陽光が利用できる可視光応答型触媒多層カーボンナノチューブと酸化チタンの複合触媒(TiO2-MNWT)を開発し、PCE 光分解実験によって評価を行った。実験の結果、TiO2-MNWT 触媒にシナジー効果が現れ、可視光照射
で TiO2- MNWT 複合触媒を用いた PCE も光分解ができた。

carried out in a glass immersion photochemical reactor.
The reactor was equipped with a UV and visible lightlamp (100W, 300nm~600nm) located axially and held
in a quartz immersion, and a UV-cut film whichallows only visible light (400nm~600nm) to pass. The
UV photodegradation experiment was carried out
without the UV-cut film and the visible light
photodegradation experiment was carried out by
applying the UV-cut film on the lamp. The
experiments was done in a closed system. Before
irradiation, the PCE solution with photocatalyst wasstirred in a dark condition for 60 minutes to establish
an adsorption-desorption equilibrium.

1.

Introduction
Chlorinatedorganic
compounds
suchastetrachloroethylene (PCE) and tricholoroethylene(TCE) have been widely used as industrial solvents fordegreasing metals and dry cleaning. However, they arecommonly found to cause water, soil and air pollution
due to their low biodegradability, toxicity and highpersistence in the environment. In water environment,
contamination is usually found in groundwater. The
treatment of groundwater contaminated with PCE will
be very costly and requires long treatment time due tolarge amount of groundwater that must be treated.
Although some effective PCE treatment methods havebeen established for industrial purposes, there is still
no practical method for groundwater treatment. Somemethods such as air pershing or bioremediation havebeen researched recently, however these methods haveproblems such as secondary treatment or long
treatment period. Previous study of treatment method
using photocatalysis by titanium dioxide (TiO2)
supported on carbon nanotube under ultraviolet light(UV) showed promising results in adsorptive ability
and photoactivity of the catalyst [1]. Using thisphotocatalyst, low energy cost, short treatment period
and simple treatment process could be achieved.
In this study, the photodegradation of PCE undervisible light irradiation using the composite of TiO2
and multi-walled carbon nanotube (MWNT)
photocatalyst was investigated.

3.

Experimental
Preparation and characterization
TiO2-MWNT was synthesized by sol-gel method.
The precursor, titanium isopropoxide (TIP), washydrolized to form TiO2 and deposited on MWNT withthe aid of supersonic treatment [2].
Catalyst are named as X-TiO2-MWNT, where X (1,10, 20 and 30) corresponds to the weight ratio of
MWNT to a 100 weight basis of TiO2-MWNT (ex. 10TiO2-MWNT means 100(TiO2-MWNT):10(MWNT),
w/w)
The TiO2-MWNT composites were characterized by
TEM, XRD, TG, BET and UV-vis spectroscopy.
Photodegradation experiments
The evaluation of TiO2-MWNT photocatalytic
activity for degradation of PCE was conducted under
UV and visible light irradiation. The experiments were

61(a) MWNT (graphite)

Intensity (a.u.)2.

Result and Discussion
Characterization
The XRD results of TiO2-MWNT are shown in
Figure 1. Only titania in anatase structure were foundfrom the XRD patterns of TiO2 and TiO2-MWNT. This
structure is known to contribute to the photoactivity.
The intensity of MWNT in TiO2-MWNT was lower
than that in MWNT. And the peak of TiO2 shiftedslightly with the addition of MWNT. This could
indeicate a change in crystal structure of TiO2. The
sizes of catalysts (dcatalyst) calculated from XRD arelisted in Table 1. The size of TiO2-MWNT increased
with the ratio of MWNT in TiO2-MWNT.
The carbon contents of TiO2-MWNT calculated
from the TG curves (CTG) are listed in Table 1.
The results from BET surface area measurements
are also given in Table 1. The surface areas of
TiO2-MWNT (BET) were higher than those of
estimated theoretically based on the amount of TiO2
and MWNT (BETcal). It can be attributed to theformation of new mesopores between TiO2 and
MWNT.

(b) TiO2 (anatase)(c) 1-TiO2-MWNT(d) 10-TiO2-MWNT(e) 20-TiO2-MWNT(f) 30-TiO2-MWNT

10

20

30

40

2θ (deg.)
Fig. 1 XRD results

50

60

Table 1
Carbon content determined from TG (CTG), Calculated BET surface
area (BETcal) and determined BET surface area (BET),
TiO2-MWNT size (d catalyst)
Catalyst
CTG(%) BET (m2/g) BETcal(m2/g) d catalyst(nm)－MWNT
1-TiO2-MWNT
10-TiO2-MWNT
20-TiO2-MWNT
30-TiO2-MWNT

100

127.32

－

8.28

90

－

20.62

－

－

80

1.84
8.95
21.39
31.57

128.99130.14131.18136.90126.65116.65105.98
95.31

9.24
9.49
10.48
11.79

70

Conversion of PCE (%)

TiO2

range. Then PCE adsorbed on TiO2-MWNT could be
degraded by the radicals [4].

Figure 2 shows UV-vis spectroscopy results of TiO2
and TiO2-MWNT. As expected TiO2 has low
absorption toward visible light range (400nm~600nm).
On the other hand, the composite catalysts with
MWNT could absorb more at wavelength higher thanthat of TiO2 even starting at low MWNT ratio (1TiO2-MWNT).

60
50
40
30
20
10
0
0

60

Conversion of PCE (%)

2

1.5

1

◇ Blank80□ TiO270□ MWNT◇ 1-TiO2-MWNT

60△ 10-TiO2-MWNT

50○ 20-TiO2-MWNT

40◇ 30-TiO2-MWNT

30

1030-TiO2-MWNT

20-TiO2-MWNT
1-TiO2-MWNT
TiO2

0
0

0
400

360

20

MWNT
10-TiO2-MWNT

300

300

100
90

0.5

120
180
240
UV-VIS irradiation time (minute)Fig.3 PCE degradation under UV irradiation

2.5

Abs (a.u)◇ Blank□ TiO2□ MWNT◇ 1-TiO2-MWNT△ 10-TiO2-MWNT○ 20-TiO2-MWNT◇ 30-TiO2-MWNT

500

600

wavelength (nm)

700

800

60

120
180
240
Visible light irradation time (minute)

300

360

Fig.4 PCE degradation under visible light irradiation

Fig.2 UV-vis spectroscopy

Photoactivity for PCE degradation
Figure 3 shows the PCE degradation byphotocatalysts under UV irradiation. No improvement
os PCE conversion could be observed when using
MWNT only.
High conversions of PCE wereobserved for 1-TiO2-MWNT, 10-TiO2-MWNT and
TiO2 only. Samles with MWNT ratio than
10-TiO2-MWNT showed a decrease in conversion of
PCE. High amount of MWNT could cover TiO2 thus
reduced the photoactivity of the TiO2-MWNT
composite photocatalyst.
Figure 4 shows the PCE degradation under visible
light irradiation with photocatalysts. MWNT showedno photoactivity toward visible light. 10-TiO2-MWNT
gave the highest PCE conversion which was almost
twice as high as TiO2 only. This result indicated asynergetic effect between TiO2 and MWNT which
could lower the energy band gap of TiO2 toward visible
light range.
The PCE photodegradation mechanism could be
explained as followed. Firstly, PCE was adsorbed on
MWNT, then transfered to TiO2 surface. The driving
force for this transfer was probably the difference in the
PCE concentration between MWNT and TiO2 [3].
MWNT could also act as electron donor by injecting
electrons into the TiO2 conduction band and lower TiO2
energy band gap so that it could be photoactive andgenerate highly active hydroxyl radicals in visible light62

4. Conclusion
TiO2-MWNT photocatalyst was prepared by amodified sol–gel method. The PCE degradation using
TiO2-MWNT photocatalyst under visible lightirradiation was confirmed with 10-TiO2-MWNT
showing the highest photoactivity under visible lightirradiation (35% PCE converted).
From the result, a synergetic effect was found whichmakes TiO2-MWNT to be activated under visible lightirradiation. MWNT could act as electron donor by
injecting electrons into the TiO2 conduction band andlower TiO2 energy band gap so that it could be
photoactive and generate highly active hydroxyl radicals
in visible light range.
Reference:[1] Tomonori, OGATA, the Dissertation for Master
Degree of Engineering, 2005.
[2] G An, W Ma, Z Sun, Z Liu: Carbon, 45, 9,(8.2007) 1795-1801.
[3]
J. Matos, J. Laine and J.-M. Herrmann, Appl.
Catal. B 18 (1998), p. 281.
[4]
A. Kongkanand and P. V. Kamat: ACS Nano,
1(1), 13(2007)

Plasma Enhancement of Hydrogen Permeation throughMetal Membrane
Student Number: 06M1817-4

Name: Xuejia Zhu

Supervisor: Masaaki SUZUKI

プラズマによる金属膜水素透過の促進
朱 学佳
本研究では、金属膜の水素透過現象に及ぼすプラズマの効果を明らかにすることを目的に実験的研究を行った。水素透過量は膜温度に強く依存するため、プラズマの有無に係わらず一定温度で実験できるよう、テスト部
外側に電気炉を配し、この制御によって温度条件を一定にした。また、水素透過量は膜表面の不純物によって大きな影響を受けるため、積極的に不純物（メタノール、一酸化炭素、水）を導入して、不純物存在下におけるプラズマの効果について調べる実験を行った。その結果、メタノール導入時にはプラズマ反応で CO が生成することにより水素透過を著しく阻害するが、水蒸気導入時には、プラズマが水素透過を促進することが分かった。

1 Introduction

equipped with mass flow controllers, pressuregauges and mass flow meter is used to measure thepermeation of hydrogen through the membranes.
One of the ends of membrane tube is connected tothe outlet tube, the other one is cemented together
with a thermal-couple, whose o.d. is 0.25mm. And
it is placed in the center of an electric furnace which
is used as a heater to change the outside temperature
for comparisons between the permeations with orwithout plasma. Hydrogen or /and helium gas is
introduced into the membrane tube from outer shellthrough the mass flow controller and the gas
permeated through the membrane is led to a mass
flow meter to measure the flow rate.CH3OH, CO,
H2O is introduced as a contamination source fromthe left bypass which is shown in the Fig. 1. Both in
the “Thermal” condition and the “Plasma” condition,
the membrane temperature are all controlled at
190℃. The pressure of feed side is 1 atmosphericpressure and the pressure of permeated side isvacuum.

The properties of hydrogen in metals are importantin describing many phenomena such asheterogeneous catalysis, purification of hydrogen bymembranes, and storage of hydrogen in metal
hydrides [1–6]. However, a detailed understandingof metal–hydrogen systems is required, in particular,
for the development of membrane reactor and fuelcell technologies. It is well known that Pd has a
special tendency to absorb hydrogen compared tomany other metals and hence Pd–H system has been
investigated extensively [1–6]. On the other hand,
Pd and its alloys have been known for theirhydrogen separation. Of the Pd alloys, Pd–Ag is by
far the most studied [7]. While Pd readily absorbs
large amounts of hydrogen at room temperature buthydrogen is practically insoluble in Ag. Despite this,the solubility of hydrogen is larger in Pd–Ag alloys.
Further, Pd–Ag alloy membranes havehigh-selectivity for hydrogen permeation and hence
suitable for hydrogen selective membranes. In thisregard, the understanding of hydrogen permeationin Pd–Ag alloy is extremely important.
Through a large amount of work has been aimed
at the preparation of thin palladium or palladiumalloy layers, there is little known about the
influences to that of plasma.
In this thesis, considering that plasma has
possibilities of permeation enhancement due to its
dissociation at low temperature, the plasma effectonhydrogenpermeation
throughthinsilver-palladium membranes has been investigatedexperimentally.

2.2 Electrodes arrangement and Membranes
The metal membrane which was used in thisresearch is a kind of composite palladium/silver
alloy. In Table 1 the detail information is listed.
Then, the reactors is connected onto the ground to
make it played as separator and the inner electrode.
A piece of the commercial aluminum film ishelically band around a silica glass of which the o.d.
is 6mm as the outer electrode, and the length of itis 80 ~ 70mm approximately. As soon as power is
supplied, the plasma generated between the outermembrane and the silica glass. Fig. 2 shows theelectrodes arrangement and membranes. At themembrane’s temperature 150oC, the primary current
was 0.10 ~ 0.11 [A], and the voltage was 34 ~ 35[V], while at that of 190oC, it was 0.13 ~ 0.14 [A]and 38 ~ 40 [V], respectively.

2 Experimental
2.1 Gas permeation measurements
A shell-and-tube apparatus shown in Fig. 1

163

3 Results and discussion

Draft

In this study, basically, hydrogen permeation
experiments were carried out at the same membrane
temperature in spite of existence of plasma. As
plasma generates heat during discharge by itself,
membrane surface temperature was controlled byusing additional furnace heating. Therefore, twokinds of heating conditions were in the research.
One is the test section is heated by only furnace
heating and the other is heated by both of heatgenerated due to plasma discharge and heat byadditional furnace heating. In the following
discussion and figures, “thermal” means the former
condition and “plasma” means the latter condition.

Electric furnace
H2, HeH2, HeElectric furnace

P1

P2
Thermocouple

CH3OH,CO,
H2O

Mass flow
meter

Mass flow
controller

H2

He

Rotarypump

Q- mass

3.1 Plasma Enhancement of Hydrogen Permeate-onFig. 2. Equipment used for gas permeation experiments of the membrane.

Figure 3 shows Hydrogen permeability through thepalladium/silver alloy membrane under two heating
conditions “thermal” and “plasma”.

Fig. 1. Equipment used for gas permeation experimentsof the membrane.

3 -2 -2 -1
133cm
Perm eability [cm
2 m inmin]
Permeability[cm]
Permeability [cm
/cmcm
/min]

0.45

Membrane Reactor

φ6mm silica glass

[

]

[

]

[

] [

outer electrodeφ0.25mm Temperature Controller

80mm

0.4
0.35
0.3

Thermal

0.25
0.2

Plasma

0.15
0.1
0.05
0
0

200mm

[

]

Fig.

with porous support

×

40

60

80

100

120

140

3.

Hydrogen permeability through thepalladium/silver alloy membrane under two
heating conditions “thermal” and “plasma”.

From this figure, it may be seemed that thehydrogen permeations can be enhanced by plasma
ignition. However, the absolute value of
permeability is very low and data including error
are unstable without reproducibility.

Table 1 The information about the palladium/silver
alloy tube

percent of Pd (%)
o.d. (mm)i.d. (mm)thickness (μm)
length (mm)operating length (mm)20Time[min]
[min]
Time
Time
[min]

Fig. 2. The electrodes arrangement and membranes.

Ag-Pd alloy
75
1.4
1.25
75
330
65-85

070711(1)070711(2)070711(3)Thermal

3.2 Effect of Air-treatment
The effect of air-treatment was shown in Fig. 4.
Air-treatment is the operation that the system isopened to the atmosphere and kept at 190oC for 16
hours.
From Fig. 4, it can be seen that permeability
increases significantly compared with the one
before treatment. This effect can be understood by

2
64

4.5

3

2
1.5

--- after air treatment --- 2nd--- after air treatment --- 1st

1

H2
3

H2 +
CH3OH H2 +
CH3OH

2.5

H2

H2

H2 +
CH3OH

H2 +
CH3OH

H2

H2

3.5

H2

H2

H2 +
CH3OH

2

080128-1 Thermal H2 (1)080128-1 Thermal H2+CH3OH (1)080128-1 Thermal H2 (2)080128-1 Thermal H2+CH3OH (2)080128-1 Thermal H2 (3)080128-1 Thermal H2+CH3OH (3)080128-1 Thermal H2 (4)cooling down with He
080128-1 Plas ma H2 (1)080128-1 Plas ma H2+ CH3OH (1)080128-1 Plas ma H2 (2)080128-1 Plas ma H2+ CH3OH (2)080128-1 Plas ma H2 (3)080128-1 Plas ma H2+ CH3OH (3)080128-1 Plas ma H2 (4)

He

1.5

1

H2 +
CH3OH

Thermal

0.5

Plasma00--- before air treatment

50

100

150

200

80

100

120

140

160

180

200

300

350

400

450

500

Time [min][min]
Time

0.5
0

250

Time [min]Fig. 5. The comparison between Hydrogen permeations
through the palladium/silver alloy layer by thermal
and plasma with adsorbed CH3OH molecules.

oTemp.membrane [ C]

3.4 Effect of Adsorbed Carbon MonoxideFig. 4. Hydrogen permeations in thermal condition
before and after air-treatment.

Using the same method as that used in Section 3.3,
carbon monoxide was supplied together withhydrogen to membrane’s surface. The ratio of CO to
H2 is 1.2:100.
The result is shown in Fig. 6. In the figure, purple
circle means the data when not supplying carbon
monoxide and black circle means the one when
supplying carbon monoxide, in the “thermal”
experiments. Also, pink circle means the data when
not supplying carbon monoxide and dark purple
circle means the one when supplying carbon
monoxide, in the “plasma” experiments. Green line
shows with no additional heating.

3.3 Effect of Adsorbed Methanol

4.5

P e r mPermeability
e a b ilit y [[cmc mcmc mmin m] in ]Figure 5 shows that of the effect of adsorbed
methanol on the surface of the membrane. The ratioof CH3OH to H2 is 15:100.
In order to compare the effect of adsorbed
methanol under the two kinds of heating conditions,the procedure was taken as following: first, the“thermal” experiments with and without water werecarried out 3 times, and then, stopped the furnace
heating to cooling down until the temperature ofmembrane becomes to the room temperature. After
that, the “plasma” experiments with and withoutwater were carried out 3 times.
In the figure, blue circle means the data when notsupplying methanol vapor and pink circle means theone when supplying methanol vapor, in the“thermal” experiments. Also, greenish-blue circle
means the data when not supplying methanol vapor
and red circle means the one when supplying
methanol vapor, in the “plasma” experiments.
Green line shows with no additional heating.
From Fig. 5, it is clearly that the adsorbed
methanol on the membrane’s surface suffocated thepermeation of hydrogen, because during thereaction in plasma, carbon monoxide was created.
And carbon monoxide adsorbed chemically withpalladium atoms strongly, and then covered thesurface of it, so that the membrane couldn’t work as
normal [9]. This was confirmed by CO introducing
experiments in Section 3.4.

-2
Permeability [cm33cm
min--11 ]3 -2 - 2-1

3--- after air treatment & after
plasma treatment--- after air treatment --- 3rd

071122 Thermal Increasing
071122 Thermal Decreasing
071126-Round1 Thermal Increasing
071126-Round2 Thermal Decreasing
071126-Round3 Thermal Increasing
071128 Thermal Increasing
071128 Thermal Decreasing
071130 Thermal Increasing
071130 Thermal Decreasing
071203 Thermal Increasing
071203 Thermal Decreasing
071210 Thermal Increasing
071210 Thermal Decreasing-2-1

P e rm e a b ility [c m c m m in ]

2.5

H2

4

-2

-1

-2
1
3 c m -min2 m i n ]- 1 ]P ePermeabilityr m e a Permeabilityb i l i t y [cm[ c m 3cm
[cmcm min ]explanation that the membrane surface, which wascontaminated by water molecule or other, is cleaned
up and the surface area working for permeability
increases. After this treatment, however, plasma
effect was not detected.

ThermalPlasma

4

H2

H2

3.5

H2

H2

H2

H2

H2

H2

3
2.5
2

He
1.5
1

H2+CO

0.5

H2+CO

H2+CO H2+CO

H2+CO

H2+CO

0
0

100

200

300

400

500

Time [min]

Time
Time
[min][min]Fig. 6. The comparison between Hydrogen permeations
through the palladium/silver alloy layer by thermal
and plasma with adsorbed carbon monoxide.
From this figure, the explanation of Section 3.3
was confirmed. That the reason of the adsorbed
methanol on the membrane’s surface suffocated the365value. The same phenomenon has been reported in
low pressure (about 2 Pa) in the research related tonuclear reactor [8].

permeation of hydrogen is that during the reaction,
carbon monoxide was produced, which can
combine with palladium atoms strongly, and thencovered the surface of it, so that the membrane
couldn’t work as normal [9].

4 Conclusion

3.5 Effect of Adsorbed Water Molecules

The effect of plasma on hydrogen through thinsilver-palladium membranes was studied.

Using the same method, introducing water vapor
together with hydrogen to membrane’s surface wasdone. The ratio of H2O to H2 is 2.4:100.
The result is shown in Fig. 7. In the figure, black
circle means the data when not supplying water
vapor and blue circle means the one when supplying
water vapor, in the “thermal” experiments. Also,
purple circle means the data when not supplying
water vapor and blue circle means the one when
supplying water vapor, in the “plasma” experiments.
Green line shows with no additional heating.

Hydrogen permeations can be enhanced byplasma ignition. However, the absolute valueof permeability is very unstable and include big
error.
Due to the air-treatment, the surface of thepermeability membrane was cleaned up.
Because of the adsorbed water molecules on thesurface of the membrane, the hydrogen
permeation was suffocated with thermal
condition. However, it was enhanced withplasma.
The adsorbed methanol on the membrane’s
surface suffocated the permeation of hydrogen.
Especially, in the plasma condition, carbon
monoxide produced by electron impact
suffocates the permeation.

3
-2
1
-2
PPermeability
e r m ePermeabilitya b il it y [cm[[cm
c m3cm3cm
cmin
m -1- 2]minm in - 1]]

4

3.5

H2
H2

3

H2+H2O
2.5

H2

H2
H2
H2+H2O

H2

2

H2+H2O

References

080124-2 Thermal H2 (1)080124-2 Thermal H2+H2O (1)080124-2 Thermal H2 (2)080124-2 Thermal H2+H2O (2)080124-2 Thermal H2 (3)080124-2 Thermal H2+H2O (3)080124-2 Thermal H2 (4)cooling down with He
080125 Plasma H2 (1)080125 Plasma H2+H2O (1)080125 Plasma H2 (2)080125 Plasma H2+H2O (2)080125 Plasma H2 (3)080125 Plasma H2+H2O (3)080125 Plasma H2 (4)

He

1

0.5

H2+H2OH2

H2+H2O

H2+H2O

1.5

H2

[1]
[2]

PlasmaThermal

[3]

0
0

100

200

300

400

500

600

700

Time [min]

Time
[min]
Time [min]Fig. 7. The comparison between Hydrogen permeations
through the palladium/silver alloy layer bythermal and plasma with adsorbed water molecules.

[4]

From this figure, it can be found that the hydrogen
permeation was suffocated by the water molecules
adsorbed on the surface of the membrane in“thermal” condition. However, it was enhanced in“plasma” condition. At the atmospheric pressure
lots of water molecules adsorb on the membranesurface by introducing water vapor. The watermolecules adsorbed on the membrane suffocate thedissociation of hydrogen molecules and it makes thepermeability lower and lower. In plasma
atmosphere, the hydrogen atoms, which aredissociated by electrons with high energy, can go
through the water film into the membrane, and thewater film is effectively on preventing the dissolvedhydrogen atoms get out and recombine on themembrane’s surface. So in the plasma’s case, thehydrogen permeation can get higher and higher

[5]
[6]
[7]
[8]

[9]

G. Alefeld, J. Völkl, Hydrogen in Metals. I.
Basic Properties, Springer, Berlin, 1978.
G. Alefeld, J. Völkl, Hydrogen in Metals. II.
Applicationoriented Properties, Springer,
Berlin, 1978.
L. Schlapbach (Ed.), Hydrogen inIntermetallic Compounds. I. Electronic,
Thermodynamic,
and
Crystallographic
Properties, Preparation, Springer, Berlin,
1988.
L. Schlapbach (Ed.), Hydrogen inIntermetallic Compounds. II. Surface andDynamic Properties, Applications, Springer,
Berlin, 1992.
Y. Fukai, The Metal–Hydrogen System,
Springer, Berlin, 1993.
H.Wipf, Hydrogen in Metals. III. Properties
and Applications, Springer, Berlin, 1997.
F.A. Lewis, The Palladium–Hydrogen
System, Academic Press, London, 1967.
Masayuki Takizawa, A study about the lowtemperatureplasmadrivehydrogenpermeation phenomenon, Tokyo Institute ofTechnology, 1998. (in Japanese)
Yoshimi Kubo，The application of the fuelcell - Application to a mobile device of adirect methanol type fuel cellhttp://www.jaie.gr.jp/z18_kubo.pdf.

466

Enhancement of Permeation for Emulsion Liquid Membrane Separation of Coal
Tar Absorption Oil
Student Number: 06M18197

Name: Dejin BI

Supervisor: Ryuichi EGASHIRA

コールタール吸収油の乳化液膜分離に対する透過促進畢

徳金

まず、コールタール吸収油とエチレングリコール(EG)、ジエチレングリコール(DEG)、トリエチレングリコール(TEG)の水溶液の液液平衡実験を行い、コールタール吸収油中の含窒素複素環式化合物(キノリン、イソ
キノリン、インドール)と芳香族炭化水素(1-メチルナフタレン、2-メチルナフタレンなど)化合物の分配係数を実測した。O/W/O 乳化液膜法を用いて、コールタール吸収油中含窒素複素環式化合物と芳香族炭化水素化合物など分離させた。膜液中に EG、DEG、TEG を添加するにより原料コールタール吸収油中各成分の膜透過に対して透過促進させた。その中で DEG による透過促進効果は EG、TEG より優れた傾向を示した。
1. Introduction
Coal tar absorption oil (AO) is one of distillation
fractions of coal tar (b.p. =470~550K). There are several
kinds of chemical compounds contained in it, thenitrogen heterocyclic compounds such as quinoline (Q),
isoquinoline (IQ), indole (IL); the homocyclic
compounds such as 1-methylnaphthalene (1MN),
2-methylnaphthalene (2MN), etc. These compounds areuseful as raw materials for agricultural chemicals,
medicines, perfumes, and many other useful chemicals.
Generally, the current method mainly used to separate
these compounds in AO is carried out in two steps:
separation of AO into several fractions by acidic andbasic extraction; and further separation and purificationof these fractions into respective products by othermethods. The separation for this first step in theindustrial fields has some drawbacks, e.g., corrosion of
the equipments and difficulties in solvent recovery. To
solve these problems, several alternative methods, suchas liquid-liquid extraction method and O/W/O emulsion
liquid membrane permeation (LMP) method have been
proposed1)-5). In O/W/O emulsion liquid membrane
system, water is the main material of aqueous membraneliquid phase. Although water showed a rather highly
selective nature in separation but it only allowed quite
slow permeation of compounds through liquid membrane
because the solubilities of above-mentioned components(Q, IQ, IL, 1MN, 2MN etc.) in water are not so high. EG,
DEG, TEG have been widely used as solvent to dissolveorganic compounds and would have a high capacity forthe dissolution of compounds which contained in AO.
Therefore, the addition of one of these substances into an
aqueous membrane solution is expected to enhance thepermeation6)-7).
This work tried to enhance the permeation in separationof AO by O/W/O emulsion liquid membrane. At firstthe distribution coefficients of compounds whichcontained in AO, between EG, DEG, TEG aqueous
solution and AO were measured. Then an aqueous
membrane solution with adding EG, DEG or TEG wasused in O/W/O emulsion liquid membrane separation of
AO. The effects of these three kinds of additives on yieldand separation selectivity were described.
2. Liquid-liquid equilibrium between absorption oil

and aqueous phase2.1. Experimental
AO and aqueous solution of EG, DEG, TEG werebrought into contact to be equilibrated using Erlenmeyer
flask and commercial constant temperature bath. Thenthe solvent phase and raffinate phase were sampled andanalyzed by gas chromatography. The principal
experimental conditions are shown in Table 1.
2.2. Result and Discussion
The mass fractions of the components in absorption oil
were descried as follows: Q 0.08, IQ 0.02, IL 0.04, 1MN
0.10, 2MN 0.28, BP 0.07, DBF 0.14. The mass fractionof 2MN was highest among homocyclic components, sothat 2MN was taken as representative component ofhomocyclic compounds. The distribution coefficient ofcomponent i between aqueous phase and oil phase wasdefined as:
C
(1)
m = i
i,R

xi

where Ci and xi denote the mass fraction of compound iin the extract and raffinate phases, respectively. Figures
1(a)-(d) show the relation between mi,R of Q, IQ, IL,
2MN and concentration of TEG/DEG/EG in aqueous
phase, CA. The mi,R of nitrogen heterocyclic compounds
Q, IQ, IL were larger than that of 2MN and, in all thecases with additives, distribution coefficients were larger
than those without additive. At any case, the mi,R
increased with increasing CA. For example, the maximum
mQ,R was 1.8×10-1 at CTEG=0.5, that was nearly twenty
times of that without TEG, and the maximum m2MN,R was
2.8×10-3 that was fifteen times of that without TEG. In
case with DEG, the maximum mQ,R was 1.1×10-3 andm2MN,R was 2.4×10-3; in case with EG, maximum mQ,R
was 5.0×10-2 and m2MN,R was 1.2×10-3. mi,R increased in
the order of EG, DEG, and TEG cases. This result
Table 1 Experimental condition of liquid-liquid equilibrium
Absorption Oil
Oil phase
0.05
mass, R0 [kg]Aqueous phase
Aqueous solution of EG/DEG/TEG
mass, E0 [kg]0.05
Mass fraction of EG/DEG/TEG in Aqueous phase CA [-] 0-0.05
Frequency of shaking[h-1] 5400
Amplitude of shaking[m] 0.04
Shaking time[h] 72Temperature, T[K] 303

1
67

key

Isoquinoline

Additive

key

TEG

DEG
EG

DEG
EG

mQ,R[-]

10-2

10-2

10-3

10-3

(b)

(a)

0.096

0

0.1

0.2

0.3

0.4

0.5

10-4

0

0.1

0.2

0.3

0.4

0.5
0.008

CA [-]

CA [-]

100

0.02

100

0.009

Indole2-Methylnaphthalene

Additive
key

TEG

DEG
EG

Additive
TEG

10-1

DEG
EG

0.012
0.034

m2MN,R [-]mIL,R[-]

0.09

10-1

0.01

key

0.22

10-4

Additive
TEG

10-1

mIQ,R [-]

10-1

Table 2 Experimental conditions for batch separation with emulsion
liquid membrane
Feed
AO
MembraneAqueous solution of saponin and
EG/DEG/TEG
Solvent
TolueneCs [–]
Ne [h−1]
Np [h−1]
VT [m3]
CA[–]
6
0.03
1.1×1018000, 36000
4.0×10−4 0~0.25
t [h]
T [K]
φOW,0 [–]
φO,0 [–]
0.25
0.5
0~0.044298±5(room temp.)

100

Quinoline

10-2

0.12

100

10-2

0.05

0.037

10-3

0.117

10-3
(d)

(c)
10-4

0

0.1

0.2

0.3

0.4

0.5

10-4

(a)

0

0.1

0.2

0.3

0.4

0.5

CA [-]

CA [-]

Figure 2 Schematic diagrams of apparatuses for batch separation withemulsion liquid membrane (a) emulsifier with high-speed
homogenizer (b) stirring vessel (unit in this figure is meter)

Figure 1 Distribution coefficient of component which contained in AO(a) Quinoline (b) Isoquinoline (c) Indole (d)2-methylnaphthalene

Here, overall permeation coefficient Pi can be expressedby next equation:(3)
Pi = mi , R ⋅ Di ⋅ ρ / δ

suggests that EG, DEG, TEG, added to a liquid membrane
solution, would enhance the permeation of components.
The mi,R/m2MN,R slightly decreased by addition of theadditives and also decreased with increasing
concentration of additives. The effect of the additive
difference was not observed clearly.
3. Batch separation of absorption oil with emulsion
liquid membrane

According to these equations the overall permeation
coefficient, Pi would be influenced qualitatively by both
distribution coefficients, mi,R, and diffusion coefficient Di.
xi can be calculated by mass balance as follows:(4)
R0 xi ,0 + E0 yi 0 = Rxi + Eyi
Here, E and R were obtained from the experimental data.
Yield of component i, Yi, was defined as,(5)
Yi = Eyi / R0 xi 0

3.1. Experimental
AO was used as inner oil phase; aqueous membrane
solution consisted of ion-exchanged water, one of EG,
DEG and TEG as permeation enhancing additive, andsaponin as an emulsifying agent; toluene was used asexternal solvent phase. The feed AO and aqueous
membrane solution were stirred by a commercial
high-speed homogenizer to prepare the emulsion. Thisprepared emulsion was brought into contact with thesolvent by stirring vessel which was made of Pyrex glass
equipped with a six-flat-blade turbine type impeller andfour baffles. Figure 2 shows the schematic diagram ofhigh-speed homogenizer and stirring vessel. Stirring
started to begin a permeation run (t=0) and continued fora specified operating time. This operation was repeated
for the several different times to obtain the time coursesof the compositions in the extract phase etc. Table 2
gives the detailed experiment conditions.

where xi0 and yi denote the mass fraction of component iin the inner oil phase at t=0 and that in the external
extract oil phase after a run, respectively. R0 refers to theinitial mass of inner oil phase and E expresses the massof out oil phase after the run. Separation selectivity ofcomponent i relative to 2MN was represented by,y /y(6)= i 2 MNβi , 2 MN

mi , Emi , Ryi ]xi / x2 MN

3.2.2. Compositions of raffinate and extract
Figures 3 (a)-(d) show the time courses of xi and yi. In
any case, xi decreased and yi increased with stirring time.
In all the cases, yi of the nitrogen heterocyclic
compounds were larger than those of homocyclic
compounds, although xi,0 of homocyclic compounds werehigher than those of nitrogen heterocyclic compounds.
Nitrogen heterocyclic compounds were selectivelytransferred through liquid membrane to extract phase andcould be separated from homocyclic compounds. Figure
3 (d) shows effect of different additives on xQ and yQ. xQ
decreased and yQ increased in the order of EG, TEG, and
DEG cases. mQ,R increased in the order of EG, DEG, andTEG cases, as shown in Figure 1. On the contrary, thediffusion coefficient, DQ, would increase in the order of

3.2. Results and Discussion
3.2.1. Basic Relationships
The permeation rate of component i through ideal one
layer liquid membrane was represented by,
N = Pi ⋅ A ⋅ [ xi −

(b)
0.01

(2)

2
68

1

100

100
Np=36000h-1
Additive=EG

10-2

10-3
i
Q
IQ
IL
2MN

(a)

10

-4

(b)

Np=36000h-1

Np=36000h-1

Additive=DEG

Additive=EG

Additive=DEG

0.01

0

0.02

CA=0
xi
yi

i
Q
IQ
IL
2MN

(b)

0.03

10

0.04

0.5

10-2

10-3

CA=0.25xi
yi

Yi [-]

Yi [-]

10-1

xi, yi [-]xi, yi [-]

10-1

1

(a)

Np=36000h-1

-4

0

0.01

0.02

CA=0
xi
yi

CA=0.25

0.03

0.04

0.5
i CA=0Q
IQ
IL
2MN

CA=0.25

0.03

0.04

CA=0.25
xi
yi

0

0.01

0.02

0

0.01

t [h]

0.03

0.02
t [h]

0.04
1

t [h]

t [h]

i CA=0
Q
IQ
IL
2MN

1

(c)

(d) i=Q

Np=36000h-1

Np=36000h-1

Additive=TEG

Yi [-]i=Q
C A =0.25
Np=3600h -1
Np=36000h-1

10-1

Yi [-]

10-1

100

0.5
i CA=0Q
IQ
IL
2MN

xi, yi [-]xi, yi [-]Additive=TEG

10-2

10-2

0
i
Q
IQ
IL
2MN

(c)

10

-4

0

0.01

CA=0
xi
yi

xi

0.02

0

0.01

0.02

1

1

0.01

0.02

0.03

0.04

0.05

0.04

Additive=DEG

Np=36000h-1

t [h]

0.03

(f) i=Q(e) i=Q

0

Key

t [h]

t [h]

(d)

0.03

0.01

yi

10-3
0.02

t [h]

Additive
TEG
DEG
EG

CA=0.25xi
yi

Additive
TEG
DEG
EG

No Additive

Additive=DEG

Yi [-]

Figure 3 Time course of composition (a) with EG in aqueous
membrane solution (b) with DEG in aqueous membrane
solution (c) with TEG in aqueous membrane solution (d)compare the effect of different additives (EG, DEG, TEG)

Yi [-]

10-3

0.5

CA=0.25

0.5

CA [-]
00.5
0.25

TEG, DEG, and EG cases, based on the Wilke-Changequation, since the viscosity decreased in the order of
TEG, DEG, and EG cases. Both of mQ,R and DQ affected
the permeation rate as Eqs.(1) and (2).
3.2.3 Yield
Figures 4 (a)-(c) are proving that the yields of nitrogen
heterocyclic compounds, Q, IQ, and IL were definitely
larger than those of 2MN. All kinds of additives didenhance the yields of all the components, obviously. The
highest YQ, YIQ, and YIL in the additive cases were almost
as 1.5 times larger than that without additive. These
results were attributed to increase of the distribution
coefficients as shown in Figure 1. The effect of different
additives on YQ was shown in Figure 4 (d). YQ increasedin the order of EG, TEG and DEG cases, because mQ,R
increased and DQ decreased as mentioned above. Figure
4 (e) indicates the relation between YQ and CA. The YQ
increased with increasing CA. This mainly resulted fromthe increase of distribution coefficients as shown in
Figure 1. The relation between yields and stirring
velocity was shown in Figure 4 (f). Higher stirring
velocity gave higher yield, since the interfacial contact
area between emulsion and external phase, A, increased.
3.2.4 Separation selectivity
Figures 5 (a)-(c) show the effects of additives EG, DEG,
and TEG on separation selectivities of nitrogen
heterocyclic compounds. Any kind of additive lowered
these separation selectivities, because mi/m2MN decreased
with adding the additives. Figures 5 (b)-(d) indicate therelation between additive concentration and separation
selectivity of i. In all the cases separation selectivity of

0

0.01

0.02
t [h]

0.03

0.5

CA [-]
0
0
0.25
0.25

Key

0.04

0

0.01

0.02

-1

Np[h ]
18000
36000
18000
36000

0.03

Key

0.04

t [h]

Figure 4 Time course of yields (a) with the EG in aqueous membrane
solution (b) with the DEG in aqueous membrane solution (c)with the TEG in aqueous membrane solution (d) compare theeffect of different additives (TEG, DEG, EG) (e) relation
between yields of Q and additive concentration in aqueous
membrane solution (f) relationship between yield of Q andstirring velocity of permeation

component i decreased with increasing concentration of
additives in aqueous membrane solution, because themi/m2MN decreased.
The relation between stirring velocity and separation
selectivity is shown in Figure 5 (e). The separation
selectivity of component i increased with stirring velocity.
The emulsion drop size and, then, the number of internal
oil droplet in a emulsion drop decreased, so that theunfavorable concentration distribution in a emulsion
drop disappeared8).
4. Conclusions
The distribution coefficients of the components
contained in absorption oil between absorption oil andaqueous phases increased by addition and withincreasing concentration of the additives, EG, DEG, andTEG. This suggested that these additives would be added
into the membrane liquid to improve the performance ofemulsion liquid membrane separation. The permeation
rates of the components in absorption oil were increased
by addition of the additives into the membrane liquid inbatch emulsion liquid membrane permeation runs, aspredicted from the above liquid-liquid equilibrium.

3
69

102

(b) i=Q

CA=0.25

Np=36000h-1

βi, 2MN [-]βi, 2MN [-]N p=36000h-1

iNo
Additiveaddit EG TEG EDG

Q
IQ
IL

101

0

0.01

i

0.02

0.03

0.04

0

0.05

CA=0.05

EG
CA=025DEG
CA=0.05 CA=0.25

TEG

CA=0.05

CA=0.25

Q
IQ
IL

101

0.01

0.02

0.03

0.04

0.05

t [h]

t [h]

102

102

(d) i=IL(c) i=IQ

Np=36000h-1

Np=36000h-1

Keys are shown in (b)βi, 2MN [-]βi, 2MN [-]Keys are shown in (b)101i= component iA= additiveQ= quinoline
IQ= Isoquinoline
IL= Indole
1MN = 1-methylnaphthalene
2MN = 2-methylnaphthaleneBP= Biphenyl
DBF = Dibenzofuran
EG= Ethylene glycol
DEG = Diethylene glycol
TEG = Triethylene glycol
Reference
1) Hara, N.; “Permeation of Nitrogen Heterocyclic
Compounds through Supported Liquid Membrane,”
Bachelor Thesis, Tokyo Tech (2001)
2) Hara, N.; “Improvement of Rough Separation with
O/W/O Liquid Membrane from Coal Tar Absorption
Oil,” Master Thesis, Tokyo Tech (2003)
3) Nagai, M.; “Separation of Nitrogen Heterocyclic
Compounds Contained in Coal Tar Absorption Oil
Fraction by Extraction,” Bachelor Thesis, Tokyo
Tech (1999)
4) Nagai, M.; “Rough Separation of Heterocyclic
Nitrogen Compounds Contained in Coal Tar
Absorption Oil Fraction,” Master Thesis, Tokyo Tech
(2001)
5) Saito J.; “Separation of absorption oil and tar light oil
by solvent extraction method,” Bachelor Thesis,
Tokyo Tech (2004)
6) Bi, D.; “Separation of Coal Tar Absorption with
O/W/O Emulsion liquid Membrane-Effect ofAdditive in Membrane Liquid-”Bachelor Thesis,
Tokyo Tech (2006)
7) Kato, S. and J. Kawasaki; “Enhanced Permeation of
Hydrocarbons through Liquid Membranes by
Lipophilic surfactants,” J. Chem. Eng. Japan, 20, (2),140-1458) Egashira, R., H. Tanno, S. Kato and J. Kawasaki; “A
Simple way for the Improvement of Separation
Selectivity of Hydrocarbons by O/W/O Emulsion
Liquid membrane,” J. Chem. Eng. Japan, 28, 38-45(1995b)

102

(a)

0

0.01

0.02

0.03

0.04

0.05

101

0

0.01

0.02

0.03

0.04

0.05

t [h]

t [h]

10 2

(e)Additive= DEG

β i, 2MN [-]CA =0.25

i
Q
IQ
IL

10 1

0

0.01

N P=18000 h -1 N P=36000 h -1

0.02

0.03
t [h]

0.04

0.05

Figure 5 Time course of separation selectivity (a) effect of different
additives on separation selectivity (b) relation between
additive concentrations and separation selectivity of Q(c)relation between additive concentration and selectivity of IQ(d) relationship between additive concentration andseparation selectivity of IL (e) effect of stirring velocity on
separation selectivity of Q

NomenclaturesA= contact area[m2]
= mass fraction of the additive in aqueousCA
membrane phase[-]= mass fraction of the saponin in aqueousCsmembrane phase[-]= mass fraction in extract phase
[-]
Ci
Di
= diffusivity of component i in liquid membrane[-]E= mass of extraction[kg]m= distribution coefficient[-]M= mass of aqueous phase[kg]= stirring velocity at emulsification[h-1]
Ne= stirring velocity in permeation vessel[h-1]
Np= permeation coefficient of component i[kgpi
R= mass of raffinate phase[g]= total volume of liquid in permeation vessel [m3]VTx= mass fraction in raffinate phase[-]y= mass fraction in extract phase[-]
βi,2MN = separation selectivity of component i relative to
2-methylnaphthalene[-]<Subscript>

470

STRENGTH OF CLAY SEAM ALONG THE POTENTIAL FAILURE
PLANE IN SOFT ROCK SLOPES
Student Number: 06M18205 Name: Sokbil HENG Supervisor: Hideki OHTA

軟岩斜面の滑り面における粘土薄層の強度特性
ヘン

ソクビル

多くの岩盤斜面の破壊においては，粘土薄層の存在によって滑り面が形成される．本研究は軟岩斜面の滑り面における粘土薄層の強度特性を調査するために定体積一面せん断実験を行った．不撹乱試料に現れた粘着力は水中でせん断すると消失するが，室内試験において不飽和・乾燥させたら再び現れることが判明した．従い，現場における強度を室内試験において再現できるのは，内部摩擦角のみであるという理由から，実用的な方法として不撹乱試料の代わりに練返し試料から強度定数がある程度得られる．実験を通して提案されたせん断抵抗特定の決定法を用いて，実際に現場で発生した斜面破壊の安定計算を実行し、その妥当性を確認した．

discussed amoung experts in geotechnical fields. An
advantage come from their interpretation will be a
backbone of final recommendation.
• Mohr-Coulomb’s theory (Terzaghi 1943)
Shear strength of soil: τ = c′ + σ ′ tan φ ′(2.1)

1. INTRODUCTION
Temperature rises in global warming issue making
the tropical ocean over warm, and subsequently
appearing year by year a series of miserable natural
disasters. Rockslide is one amoung the disasters
occuring at the hill or the mountain area, provoked theenvironmental destruction and damaged lives andproperties on the high land as well as along the valley.
The failure, triggered by heavy rain fall, earthquake andexcavation, often occurred at the potential slip planewhere the weak layer of clay seam reposed with its
relatively thin layer. Even thought the issue is not new,the catastrophe remains severe and difficult to predict.

• Terzaghi and Hvorslav’s interpretation (1937)
Shear strength of soil: s = ce + σ ′ tan φe(2.2)• Taylor’s interpretation (1948)
Shear strength of soil: τ = dz σ ′ + σ ′ tan φ ′
dx

(2.3)

dz
rate of dilataion, resulting an apparent interlocking
dx• Schofield opposed to Terzaghi and Hvorslav
Schofield (2006) refered to shear strength of soil
has no true cohesion, only apparent interlocking
obsearves at dry side.
Observation showed that a presence of cohesion insafety factor design made the safety factor
overestimated to safe side, whereas in reality,
unstability and failure occured.

Understanding the behaviour of clay seam is very
crucial for the safety designed factor in slope stability
analysis. In recent year, many research works areundertaken, aiming to demonstrate the appropriate andmore reliable strength parameter determination of slope
materials. The collection of undisturbed samples of clay
seam, although difficult, is believed important by many
researchers.
This study focuses particularly on the strength
behaviour of clay seam at the failure plane in soft rockslope. Constant volume shear box test was used in theexperimental work in order to investigate cohesion andfriction angle of the specimen colleted from Shizuoka,
Japan, where cutting slopes for the new Tomei
Highway’s construction are under operation. The
experiment work had also been conducted on disturbed
sample of clay seam from Guinsaugon, Philippines
where experienced the rock slope failure.
Shear strength parameters of clay seam obtained
from laboratory tests will be used in safety factor
analysis of this study.

3. METHODOLOGY
In general intact sample of clay seam collected at a
weak plane is best to represent the properties of material
at a potential failure plane. It shall be taken at somelocations along the plane and at natural condition to beanalyzed especially the potential state at which soft rockmight possible to slide. Rock slope failure occurred at
the weak plane where laying on the clay seam with its
variety of thickness. Most observation cases haveindicated that rockslide happened after a huge soft rockmass saturated in a following weeks after heavy rainfall.
The rainstorm induces an increase of sliding force andto weaken the shear strength of clay seam. Therefore thesample which could be best to represented the criticalstate of clay seam shall be taken at the submerged state.
However, to collecte sample at dangerious moment is
not practical.

2. LITERATURE REVIEWThe shear strength parameters of soil known ascohesion c and internal friction angle φ have been
171This experiment was carried aiming at defining theunit weight of soil which is necessary in nearly allpressure, settlement, and especially stability problems
in soil engineering.

(1) Site sampling operation
Plasticine
60mm

20mm

Specific gravity of clay seam
The specific gravity of a soil is often used in relating
a weight of soil to its volume. Therefore, knowning theunit wight of a moisture soil can be computed when
when knowing the void ratio, the degree of saturation,and the specific gravity.
This experiment was carry out in order to obtain theunit weight of soil which is necessary in calculation
over burden spressure in stability analysis.

50mm

75mm

Clay seam
Metal tube

Standard cylinder

Fig.1 Metal tube for taking and storing clay

Shear strength of clay seam
Strength parameter of clay seam can be investigatedby using few laboratory testing methods among them
are triaxial test, simple shear test, direct shear test andconstant volume shear test. Observation shows thatthickness of clay seam is not excess few centimetre andthe condition at failure is free for water to drain out. It isthus drained condition of shear test shall be best to suitthe case. Drained direct shear test using direct shear box
box test is one of simple methods often used atlaboratory. Application of this method consumes somuch time as the shearing speed is very slow in order toassure that there is no excess pore water pressure during
the testing process. Constant volume shear box test,
however, does not require so much time when water
was freely to either stay inside or flow out from thespecimen. Vertical strain is controlled by varying
vertical stress during shearing process. Horizontal
displacement is governed by a constant velocity of
0.2mm per minute. Vertical and horizontal stress anddisplacement were recorded by loadcells and strain
guages connecting to data logger.
Multistage-consolidated shearing process was
performed with the different types of specimens at
different conditions. Stress paths of each sample areplotted and the interpretation of each condition is statedin article 4.

In this study, samples were taken at unsaturated
condition from a weak plane where discontinuity richesof soft material clay; and types of sample wereclassified as undisturbed and reconstituted sample.
Because a plane failure is consisted not only the clay
seam but also of some mixed gravel, the sampling sizeof undisturbed sample shall be greater than enough fit
to the dimension in a shear box. For instance, thesample taken shall have diameter and height larger thana standard cylinder of 60mm diameter and 20mm height(i.e. 75mm diameter and 50mm height). Metal tube isused to penetrate into clay seam layer at least 20mm
depth and in some case additional force need to apply
when this layer is stiff. The metal tube must stand inposition perpendicular to the plane surface regardless tothe vertical. The sample placing in the solid tube needs
to be covered by plastcine and plastic rap in order tokeep its original position (direction of plane) and its
moisture content (see fig. 1).

(2) Laboratory testing method
Consistency of clay seam
Liquid limit (LL) and plastic limit (PL) arenecessarily determined on soils which have had theirnatural structure completely destroyed by kneading or
remoulding. Specimen used for testing has to be fineparticle of diameter not excess 0.425mm. Therefore
sieving process is necessary for clay seam in whichsome gross particles is mixing inside.

(3) Experimental program
Program testing on different specimens’ condition:

Effective friction angle can also be appeocximatedby an empirical relation, (Kenney (1959)).

sin φ ′ = 0.81 − 0.233log PI

Condition of clay
seam specimens
Reconstituted
SampleUndisturbed
Sample(3.1)

Normal condition

where plasticity index PI=LL-PL
Original condition

Specific gravity of clay seam

Pre-consolidated& partly air dry
Pre-consolidated
condition

The specific gravity of a soil is often used inrelating a weight of soil to its volume. Therefore,knowing the unit weight of a moist soil can becomputed when knowing the void ratio, the degree ofsaturation, and the specific gravity.

Submerged
condition

Submersion at
1st phase

Pre-consolidation& submersion at
1st phase

Submersion at
2nd phase

Submersion at
2nd phase

Submerged
condition

Fig.2 Diagram fitting specimens condition
272

observe shear resistance at submerged condition of thesample.
c) Pre-consolidated and partly air dry
The pre-consolidated and party air dry procedure is
the particular case of investigation in the experimental
program. It was initiated in order to clarify how
capillary force acts on the sample during man-hours forundisturbed specimen preparation. Specimen was once
experienced a reconsolidated pressure of 80kPa andreleased by few hours exposing to the air at an open top
caps. This model simulated the physical appearance of
undisturbed sample of which residual effective stressreleases and moisture content diminishes during thetime of sampling preparation.

Model testing operation
Sampling

Sampling
Consolidation

Trimming

Trimming

Shearing

Setting

1st Multistage shearing process

Setting

Preparation process

Submersion
at 1st phase

Preparation process

Consolidation

Consolidation

Shearing

Shearing

Multistage shearing process

Submersion
at 2nd phase

Consolidation
Shearing

Multistage shearing process

2nd Multistage shearing process

Fig.3 Testing operation for undisturbed sample
Sampling

Sampling

Reconstituting

Reconstituting

moulding

moulding
Pre-consolidated& partly air dry

Shearing
Multistage shearing process

Preparation process

Pre-consolidation& submersion at
1st phase

Shearingd) Multistage shearing processes
Consolidation process: Constant volume shear test
is operated in accordance with specification as
described in a manual of Testing of Soil Mechanics,
produced by The Japanese Geotechnical Society(2001). Clay seam specimen was trimmed and set into
the shear box apparatus, then started to consolidate withnormal pressure of 9.8kPa, in case of submersion, water
was poured around the metal device inside the acrylic
rectangular box and kept for 2 to 3 hours before starting
consolidation. Consolidation time was estimated byusing square root of time ( t ) method or three time(3t) method.
Shearing
process:Once
after
finishingconsolidation, shearing force is applied horizontallywith a constant speed of 0.2mm per minute until thehorizontal displacement reaches to 7mm and theshearing ends at that time (35mns stated from the settingtime). The specimen then forced to return to thebeginning position and reconsolidated with doublepressure before the other shearing stage restarts. The
entire shearing process comprises 6stages with the sameprocedure, consolidation then shearing.

1st Multistage shearing process

Submersion
at 2nd phase

Consolidation

Consolidation

Shearing

Shearing

Multistage shearing process

Consolidation

moulding
Preparation process

Preparation process

Consolidation

Sampling
Reconstituting

Multistage shearing process

Consolidation
Shearing
2nd Multistage shearing process

Fig.4 Testing operation for reconstituted sample

Laboratory testing proceduresa) Preparation process
Trimming and setting of undisturbed sample: Oncethe samples manipulated to the laboratory, the trimming
process can be operated on the undisturbed sample inorder to have an appropriated geometric form which isjust fit to the shear box apparatus. By carefully andpatiently removing the sample covered with plasticine
little by little from metal tube, spatula or cuter is used toslice laterally without much disturbance to the structureof sample. Specimen sunk into a pattern cylinder of
60mm diameter and 20mm height when its dimension
just a little fit to the cylinder. Finally, only both surfacesof lower and upper clay seam sample are needed to betrimmed and flattened. The specimen in the cylinder isjust having a good geometrical form as its container andlater released to the shear box apparatus.
Reconstituting and remoulding of a sample:
Disturbed clay seam collected from the site or someresidual amount from trimming of undisturbed
specimen can be reused to reconstitute a new sample bymixing the clay with some water, making it softer or
paste-like material. In certain cases, granular ofdiameter bigger than 2mm, contained in clay seam isneeded to remove by letting the slurry flow through thesieve of 2mm diameter. That slurry then moulded in ashear box apparatus which covered at upper and lower
cylinder with porous stones through which water candrain out.

4. EXPERIMENTAL RESULTS
Constant volume shear stress τ (kPa)220-- reconstituted consolidation and partly air dry-- unstaturated undisturbed
180-- submersion at 1st phase
160 -- submersion at 2nd phase140 -- reconstituted
200

120
100
80
60
40
20
0

b) Submersion processes
This process is conducted over unsaturated sample(intact condition or reconstituted condition) at the
beginning of shearing process (submersion at 1st phase,or over unsaturated sample at the end of 1st multistage
shearing process (submersion at 2nd phase,) aiming to

0

20

40

60

80 100 120 140 160 180 200 220 240 260 280 300 320 340

Vertical effective stressσ' (kPa)

Fig.5 Stress paths during the constant volume shear

Experimental results obtained from 5 unsaturated
undisturbed samples showed a considerable cohesion
value similar to a model reconstituted sample under3
73

samples, friction angle at kink level resistes with thesafety factor less than one, and that result agreed withthe site respond that slope failed under that condition.
On the other hand, by including cohesion in safety
factor design, the result showed safety factor equal 1.85
and 8.34 for strength parameter of submersion at 2ndphase ( c′ = 6kPa, φ ′ = 23.5o ) and of non-submerged

preconsolidation pressure and partly air dry condition.
In submerged condition of the same sample, strss paths
showed a relatively small value or negative apparent
cohesion. Similar to this case the reconstituted sample
is comparable to the submerged condtion.
Figure 5 showed the stress paths of sample from
Nakanogou at different conditions. At intact state,apparent cohesion reached to 40kPa and at submersion
2nd phase, little apparent of c′ = 6kPa . The othersamples taken at the same site Nakanogou, areconstituted sample and the clay seam sample atfailure plane showed negative apparence of cohesion
with angle at kink 24.6o and 20o respectively. All thecase of testing conditions are subject to verify in backanalysis in following article.

intact sample ( c′ = 40kPa, φ ′=20.1o ) respectively.
6. CONCLUSIONi) apparent cohesion obtained from laboratory test isresulted from the effect of capillary force forming
during the sampling at site and laboratory process
only and it does not represent the real condition of
the site behaviour.
ii) apparent cohesion will disappear partly or totally
after submerging or re-submerging into the water.
Using this cohesion in safety factor analysis
showing risk of lower estimation.
iii) to avoid capillary action, shear test in submerged
condition is recommended, otherwise apparent
cohesion obtained from non-fully saturated sample
shall be exempted.
iv) reconstituted sample, of which major cohesion
disappear and only its friction angle is comparable
to that of the submerged undisturbed samples shallbe able to represent the shale sample of clay seam at
submerged state. Advantages of reconstituted
sample gain through this finding.
v) effective friction angle represented by a line
crossing at kinks agrees well in safety factor design.

5. BACK ANALYSIS
A case of slope failure occurred in Nakanogou, 2004
has its records as presented in the figure below:
2.2m

1

4.7m

)ψ f

)ψ

1.2
Weak plane
Clay seam
N72E62Ns

Fig. 6 Model of 2D Plane failure of Nakanogou site
3

Total unit weigth of material: γ t = 22.51 kN m
Weigth of massmovement:
W f = 74.49kN
Normal component of weight:
Fn = W f cosψ f = 74.49 × cos 27.38o = 66.15kN
Tangential component of weight:

REFERENCES
1)o

Ft = W f sinψ f = 74.49 × sin 27.38 = 34.25kN
According to equation (2.2), c′ = 0
Resistance force: FR = Fn tan φ ′ = 66.15 tan φ ′(kN )Safety factor against slidding:F
shear resistance
66.15 tan φ ′= R == 1.931tan φ ′mobilized shear stress Ft
34.25
Factor of safety F s

Fs =

2
1.5
1
0.5
0
0

5

10

15

20

25

30

35

Internal friction angle

φ'

40

45

Fig.7 Relation between safety factor and friction angleof Nakanogou soil with γ t = 22.51 kN m3

Regarless the cohesion in safety design with, figure 7
shows the factor of safety against sliding for
Nakanogou clay seam. For all conditions of soil
474

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