Transactions on GIGAKU: Scope and Policy
Nagaoka University of Technology publishes an online, open access journal titled “Transactions on
GIGAKU”, which is focused on the science and technology related to GIGAKU*. The mission ofthis journal is to spread out the concept of GIGAKU and the fruits of GIGAKU to the global world
and to be a strong network for innovations in science and technology and for development of next
generations of high-level human resources. This journal, therefore, covers research and education
activities related to GIGAKU in broad areas.
*

See ‘What is GIGAKU?’ below.

‘What is GIGAKU?’GIGAKU is a term composed of two Japanese word-roots; GI and GAKU. The word GI〔技〕
literally stands for all kinds of arts and technology, and GAKU 〔学〕 stands for scientificdisciplines in general when used as a suffix.
The term was originally coined to describe the fundamental philosophy of education and research of
Nagaoka University of Technology (NUT) when it was established in 1976. Through this term thefounders of NUT intended to express their recognition that all technical challenges in the real world
require a scientific approach. And NUT has a relentlessly pursued GIGAKU since then.
Thirty-five years have passed and all surrounding conditions have changed dramatically during those
years. We are witnessing rapidly globalizing economics and huge scale changes in demographic,
industrial and employment structures. All those changes seem to necessitate the further evolution of
GIGAKU. In response to this, NUT recently announced its new “Growth Plan” and a reneweddefinition of the term is given;
GIGAKU is a science of technologies, which gives us an angle to analyze and reinterpret diverse
technical processes and objects and thus helps us to advance technologies forward. By employing abroad range of knowledge about science and engineering, management, safety, information
technology and life sciences, GIGAKU provides us with workable solution and induces future
innovations.

July 2012
Koichi Niihara, President of Nagaoka Univesrsity of Technology

1Editors (2014 to 2015):
Editor-in-chief:
Prof. Yoshiro Ito
Head of Library, Nagaoka University of Technology, Nagaoka Japan
Co-Editors:
Prof. Nobuhiko Azuma
Nagaoka University of Technology, Nagaoka Japan
Prof. Yoshiki Mikami
Nagaoka University of Technology, Nagaoka Japan
Prof. Yasuo Utsumi
Sendai National College of Technology, Sendai Japan
Prof. K. Bhanu Sankara Rao
Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad India

Submission and Publishing“Transactions on GIGAKU” publishes original papers focusing on education, science, andtechnology related to GIGAKU. Proposals for special issues aligned with conferences are strongly
encouraged*, and guest editors are welcomed. Papers submitted are peer-reviewed before publication.
Publication charge is free for all submitted papers.
* Please contact to Editor-in chief, itoy@mech.nagaokaut.ac.jp
Copyright
The copyright of the papers published in Transactions on GIGAKU is transferred to Nagaoka
University of Technology. (See our copyright transfer form for details.)

Contactjoho-kiban@jcom.nagaokaut.ac.jp(Revised January 2014)2

Editorial board of special issue of 1st International Conference on
Energy, Environment and Human Engineering (ICEEHE), in
Transactions on GIGAKU

Guest Editor
Dr. Masato Aketagawa, Professor, Nagaoka University of Technology, Japan

Guest Co-Editors
Dr. Kunio Watanabe, Professor emeritus, Saitama University, Japan
Dr. Hiroshi Nomura, Professor, Nihon University, Japan
Dr. Yutaka Osawa, Professor, Saitama University, Japan
Dr. Masahiko Osada, Associate Professor, Saitama University, Japan
Dr. Takashi Yukawa, Professor, Nagaoka University of Technology, Japan
Dr. Shigeru Nagasawa, Professor, Nagaoka University of Technology, Japan
Dr. Ashu Marasinghe, Associate Professor, Nagaoka University of Technology, Japan
Dr. Yukio Miyashita, Associate Professor, Nagaoka University of Technology, Japan
Dr. Yuichi Otsuka, Associate Professor, Nagaoka University of Technology, Japan3

Preface of the special issue for the 1st ICEEHE
The 1st International Conference on Energy, Environment and Human
Engineering (ICEEHE) was held at Kandawgyi Palace Hotel, Yangon, Myanmar on
22nd and 23rd December 2013. This international conference was co-organized by
Myanmar Maritime University, Nagaoka University of Technology, Nihon University,
Saitama University and AJMMC (Association of Japan Myanmar Mutual Cooperation).
This conference was supported by Ministry of Transport, Ministry of Environmental
Conservation and Forestry, Myanmar Embassy in Japan, Embassy of Japan in Myanmar,
Japan International Cooperation Agency, Japan External Trade Organization (Yangon
Office), The Overseas Human Resources and Industry Development Association. The
Governor of Yangon and other prominent guests gave warm welcome speeches in the
opening ceremony.
More than 250 participants were attended from Myanmar, Japan, Malaysia,
Thailand, India and other countries including Australia, China, USA, etc. There were
135 oral presentations and 84 poster presentations in various sessions: Renewable
Energy and Energy Management, Information and Communication Technology,
Manufacturing and Design, Environmental Technology and Management, Human
Engineering, Transportation and Logistics, Agricultural and Bio Engineering, Materials
Engineering for Sustainable Society, Sensing and Control Engineering. Among a large
number of the presented papers, the selected and peer-reviewed 18 papers areincluded in this special issue. Hope you enjoy these interesting papers.
The 2nd ICEEHE will be held in August 2015 in Yangon. I would like toencourage you to join the next conference and also enjoy Yangon city with beautiful
Pagodas.
Yoshiharu MUTOH
Chairman of the 1st ICEEHE
Director / Vice President
Nagaoka University of Technology

4Transactions on GIGAKU
Volume 2, No. 2, October 2014
pecial Issue of 1st ICEEHE
The 1st International Conference on Energy, Environment and Human
Engineering.(December 22-23, 2013Kandawgyi Palace Hotel, Yangon, Myanmar)
The 1st International Conference on Energy, Environment and Human Engineering(1st ICEEHE) aims to serve as a platform for international exchange on the latest
engineering development in the fields of energy, environment and human forsustainable society. Engineering is not for the engineering profession but it should
contribute to social, economic and environmental development. In the recent globalized
world, the local or regional problems are immediately corresponding to the global
problems and vice versa. Therefore, it is increasingly of importance to discuss the roleof engineering for sustainable society. Total 72 manuscripts have been submitted and 18
peer-reviewed papers are published in special issue of Transactions on GIGAKU.
Table of contents
1. Pages S01001/1-6
Surface Roughness Measurements of a Narrow Borehole Development of a carriage
Eiki Okuyama, Yuichi Suzuki , Ichiro Yoshida
2. Pages S01002/1-7
Introducing and Identifying the Role of Eye Blinking in the Virtual e-Learning
Environment
Asanka D. Dharmawansa, Katsuko T. Nakahira, Yoshimi Fukumura, Ashu
Marasinghe.
3. Pages S01003/1-5Light Trapping Effect of SnO2Nanoparticles for Solar Cell Application
Cho Cho Thet, Hla Toe, Cho Cho, Aye Aye Myint
4. Pages S01004/1-5
Flywheel Motor Powered by Road User
Aung Naing Win, Khin Khin Moe,Ying Jie Xiao
5. Pages S01005/1-6
Demonstration of Text Similarity Metric for Plagiarism Detection
Ohnmar Htun, Yoshiki Mikami
56. Pages S01006/1-6
Identifying the Associations between Music and Emotion in Developing an Emotion
Based Music Recommendation System
Sugeeswari Lekamge, Ashu Marasinghe
7. Pages S01007/1-6
Application of Unscented Kalman Filter with Non-symmetric Sigma Point Sampling on
the Integrated Navigation System
Ye Chan, Chan Gook Park, Pho Kaung
8. Pages S01008/1-6
Enhancement of ionic conductivity in CuI-Cu2MoO4 superionic conducting glass bynano-structure control
Takao Tsurui
9. Pages S01009/1-7
Feasibility Study on a Stand-Alone Photovoltaic HybridMini-Grid Power Generation
System to Promote the Rural Electrification-Rate in Mandalay Region of Myanmar
Aung Ze Ya
10. Pages S01010/1-7
Hardware In The Loop Integration Design Of MicroAvionics System For AEROMAS
UAV
Hline Htet Win, Moe Kyaw Naing
11. Pages S01011/1-7
The Potential of Coconut Oil Biodiesel Utilization in the Delta Regions of Myanmar
Thet Myo, Eiji Kinoshita
12. Pages S01012/1-6
Measuring and Estimating Quality of Server Side Scripts: PHP
Cho Thet Mon, Khin Mar Myo
13. Pages S01013/1-8
Simultaneous Faults on Medium Voltage Distribution of Myanmar Electric Power
System in Mandalay
Thet Tin
14. Pages S01014/1-6
Optimizing Feature Selection Techniques for Network Intrusion Detection SystemThet
Tin
Phyu Thi Htun, Kyaw Thet Khaing

615. Pages S01015/1-6
Robust Interactive Image Segmentation Integration of Region and Boundary
Information
May Thu Win, Kay Thi Win
16. Pages S01016/1-6
Edge embedded Marker based Watershed Method for image segmentation
Khin Lay Mon, Mie Mie Thaw
17. Pages S01017/1-6
Quantifying and Evaluating of Evolvability Software Quality for Aspect-Oriented
Software
Khine Zar Ne Win
18. Pages S01018/1-9Mission Control over Multi UAVs inthe Real-time Distributed Hardware-In-the-Loop
Environment
Soe Linn Htet, Tin Naing Lat, Ye Chan, Soe Mya Mya Aye, Pho Kaung
19. Pages S01009/1-7
Web Document Clustering Using Cuckoo Search Clustering Algorithm Based On Gauss
Distribution
Moe Moe Zaw, Ei Ei Mon

7

Transactions on GIGAKU 2(2)(2014)S01001/1-6

Surface Roughness Measurements of a Narrow Borehole--- Development of a carriage --Eiki OKUYAMA1*, Yuichi SUZUKI1 and Ichiro YOSHIDA2
1)
Department of Mechanical Engineering, Akita University,
Akita, 010-8502, Japan
2)
Kosaka Laboratory,
Mitaka, Saitama,341-0035, Japan*E-mail: okuyama@ipc.akita-u.ac.jp
In various industrial fields, it is frequently necessary to measure surface
roughness in confined spaces such as boreholes and grooves. However,using a small stylus, the surface roughness of a narrow borehole can bedirectly measured only a few millimeters from its end; alternatively,
destructive measurements must be performed. This major disadvantage of
conventional stylus-based surface profilometers is mainly due to an
inductive pick-up that is connected to the stylus used to detect the surface
roughness. We already proposed a novel surface roughness measurement
sensor. To make the surface roughness sensor small, we used a stylus witha cylindrical mirror and a lensed fiber instead of a conventional inductive
pick-up. The proposed sensor converts the signal obtained by measuring
the surface roughness of a borehole into an optical signal, which istransferred outside the borehole by an optical fiber. Experimental results
demonstrate that this system has a measurement range of 8 m and asensitivity of 19 nm. In this paper, a carriage to measure the surface
roughness in a small borehole is designed. The proposed carriage has twodegree of freedom, displacement along the borehole axis and rotation
around the borehole axis.
1. Introduction
In various industrial fields, it is frequently necessary to measure surface roughness in confined
spaces such as boreholes and grooves. However, using a small stylus, the surface roughness of anarrow borehole can be directly measured only a few millimeters from its end. For example, a typeI5B stylus designed from measuring the surface roughness of small boreholes[1] (Kosaka LaboratoryLtd.) has a height of 0.5 mm and a reach of 4 mm. Therefore, this stylus can measure surface
roughness up to 4 mm from the end of the borehole. When it is necessary to measure the surface
roughness of an area far from the end of the borehole, the borehole must be cut in the axial direction
and destructive measurements performed. This major disadvantage of conventional stylus-based
surface profilometers is mainly due to the inductive pick-up that is connected to the stylus. The
minimum size of an inductive pick-up is approximately 20 mm. When the stylus and inductive
pick-up scan inside a borehole, the smallest measureable borehole is approximately 20 mm indiameter. When only a stylus scans inside a borehole and the inductive pick-up remains outside theborehole, the measureable area is restricted to near the end of the borehole.
We have been seeking to measure the profiles of boreholes with a diameter of less than 2 mm
and a length of over 100 mm. We already proposed a novel surface roughness measurement
sensor[2]. To make the surface roughness sensor small, we employed a stylus with a cylindricalmirror and a lensed fiber, instead of a conventional inductive pick-up. The proposed sensor converts
the signal obtained by measuring the surface roughness of a borehole into an optical signal, which istransferred outside the borehole by an optical fiber.
In this paper, a carriage to measure the surface roughness in a small borehole is proposed.The
proposed carriage has two degree of freedom, displacement along borehole axis and rotation around1

Transactions on GIGAKU 2(2)(2014)S01001/1-6

the borehole axis.

2. Sensing principle
Fig. 1(a) shows a schematic diagram of the surface roughness measurement system, which has astylus with a cylindrical mirror and a lensed fiber. The stylus is connected to a carriage by an elastic
hinge and it is used to measure the surface roughness of a narrow borehole. The cylindrical mirror isfixed on the stylus and the lensed fiber is fixed on the carriage. A laser beam from a laser diode (LD)passes through an optical coupler and the lensed fiber. The laser beam from the lensed fiber isfocused on a cylindrical mirror. A portion of the beam reflected from the cylindrical mirror enters thelensed fiber and is detected by a photodiode (PD). The PD output is transformed into a voltage anddigitized by an A/D converter. As the cylindrical mirror moves in response to the surface roughness
in the borehole, the surface roughness can be converted into an electrical signal.
Fig. 1(b) shows the force and measurement loops[3] of the proposed system. The dashed curve inthis figure indicates the force loop, which passes through the stylus, the carriage, the skids, and themeasured borehole. The dash-dotted curve indicates the measurement loop, which passes through thecylindrical mirror, the lensed fiber, the carriage, and the stylus.

(a) Schematic diagram of surface roughness measurement system in a small borehole(b) Force and measurement loops
Fig. 1 Surface roughness measurement system in a small borehole

3. Fundamental characteristics
Figs. 2(a) and (b) respectively show a schematic diagram and a photograph of the experimental
system of the surface roughness sensor with the stylus having a cylindrical mirror and a lensed fiber.
A stylus (Mitsutoyo, 12AAB406) is used to trace the surface roughness. The V-groove of the stylus
is placed on the knife edge of the carriage. The cylindrical surface of an optical fiber (diameter: 125m; length: 2 mm) coated with Cr is used as the mirror. The cylindrical mirror is fixed on the stylus.
A lensed fiber (Moritex Co., Circletran MST-CT1310G11W320/BS10FAd; focal length: 320 m;
spot diameter: 11 m) is fixed on the carriage using a shallow V-groove formed by a scriber.

2

Transactions on GIGAKU 2(2)(2014)S01001/1-6(a) Schematic diagram(b) PhotographFig. 2 Experimental system
Fig. 3 shows a block diagram of the process used to calibrate the proposed surface roughness
sensor. The sensor is set on a Z-stage (Chuo Precision Industrial Co. Ltd., NPZ-347-S1). The Z-stage
can be finely translated in a 10 m range and it is driven by a PZT actuator. It can be roughly
translated by a micrometer. The tip of the stylus is placed on the surface to be measured. The Z-stage
is driven by a triangular wave (period: 2 s; peak-to-valley amplitude: 8.5 m) (see Fig. 4) generated
by a function generator (Agilent, 33120A) and the output of the stage driver, the displacement of theZ-stage (measured by a strain gauge), and the output of the surface roughness sensor are acquired by
A/D converters (Agilent, 34410A). These A/D converters are double integration type and integration
times are set to 0.02 s to eliminate noise from the power line cycle that has a frequency of 50 Hz.
As the Z-stage has a short fine motion range, it was roughly translated by approximately 5 m by
the micrometer and calibration was performed. This process was repeated five times. Fig. 5 shows
the calibration results, where the abscissa is the displacement in the Z direction and the ordinate is
the output of the surface roughness sensor. It shows that the sensor output changes when thedisplacement is changed. A small amount of hysteresis was observed. The sensor output was small(–5 V) when the laser beam was focused lower side of the cylindrical mirror, whereas when the laser
beam was focused near the center of the cylindrical mirror, the sensor output was large (–7 V).

3

Transactions on GIGAKU 2(2)(2014)S01001/1-6

Furthermore, when the laser beam was focused upper side of the cylindrical mirror, the sensor output
was small (–5 V). The two areas indicated by the dashed ellipses in Fig. 5 are suitable for surface
roughness measurements. Since it is nonlinear, a least-squares by a third-order polynomial is used as
the calibration curve. The measurement range was 8 m. Since the noise was 5.2 mV(2), thesensitivity was 19 nm.

Fig. 3 Block diagram of proposed system

Fig. 4 Schematic diagram of calibration

Fig. 5 Calibration results (wide area)4Transactions on GIGAKU 2(2)(2014)S01001/1-6

4.
Design of carriage
In case of surface roughness measurement system, a skid is used to eliminate lower spatial
frequency component, i.e., surface waviness. In the proposed system, two skids are set to thecarriage and the carriage scans along the borehole axis. Therefore, degree of freedom of the carriage
should be constrained properly. Fig.6 shows the outline of the constraint of the carriage in theborehole. The borehole axis is X-axis. Required motions of the carriage are scanning along X-axis
and rotation around X-axis. Originally, the carriage has six degree of freedom. Then, four degree of
freedom should be constrained. Consequently, number of contact point between the carriage and theborehole is four. Two skids are set at both ends of the carriage and they make two contact points.
Furthermore, two springs are set at upper position of the carriage to make two contact points.

Fig.6 Constraint of the carriage in the boreholea) single viewb) Front viewFig.7 Draught of the carriage with the stylus
Fig.7 shows a draught of the carriage with the stylus and the lensed fiber. Fig.7 a) is a single
view drawing and Fig.7 b) is the front view drawing. Length of the carriage along X-axis is 10mm.
Height is 1.3mm and width is 1.5mm. V-groove is made at the lower part of the carriage and twosteel balls with 0.15mm diameter are set in the V-groove as the skid. The end face of the carriage is
cut as an inclined plane and two small through holes with 0.18mm diameter are made. Furthermore,
two springs are set to the carriage using these through holes. Upper part of the carriage is two stage.
Each stage has a V-groove. A spring is set in the lower V-groove and the stylus is set to the spring.
The lensed fiber is set at the higher V-groove.

5

Transactions on GIGAKU 2(2)(2014)S01001/1-6

5.

Trial manufacture of the carriage

Fig.8 Trial manufacture of the carriage

Fig.9 Trial manufacture of the carriage with the stylus
Fig.8 shows photograph of the trial manufacture of the carriage. Fig.8 is the top view photograph.
A string of an electric guitar, D’Addario 0.07(0.178mm diameter), is used as the spring.
Fig.9 shows the carriage with the stylus. The stylus, Kosaka Laboratory I5B, with cylindricalmirror is connected to the carriage using a guitar string. In the near future, we will try to measure thesurface roughness in a borehole using this carriage system.

6.
Conclusions
The carriage to measure the surface roughness in a small borehole is designed. The proposed
carriage has two degree of freedom, displacement along the borehole axis and rotation around theborehole axis.
Acknowledgments
The authors would like to thank Masahiro Morikawa and Yuki Toku of Akita University for their
help. Further, the authors would like to acknowledge Prof. Kazuhisa Yanagi of Nagaoka of
University of Technology for his advice. A part of this work was supported by a Grant-in-Aid for
Scientific Research (C) (23560112), Japan.
References[1]Suefcorder Formcorder, Accessories & Options Catalogue, Kosaka Laboratory LTD., (2006)[2] Eiki OKUYAMA et.al., Surface Roughness Measurements of a Narrow Borehole--- Development of Stylus with Cylindrical Mirror and Lensed Fiber ---, AMPT(2013)[3]S.T.Smith and D.G.Chetwynd, Foundations of Ultraprecision Mechanism Design, Gordon andBreach Science Publishers(1992)(Received; 23rd December ,2013, Accepted; 5th May, 2014)6

Transactions on GIGAKU 2(2)(2014)S01002/1-7

Introducing and Identifying the Role of Eye Blinking in the Virtuale-Learning Environment
Asanka D. Dharmawansa*, Katsuko T. Nakahira, Yoshimi Fukumura, Marasinghe A.
Department of Management and Information Systems Engineering,
Nagaoka University of Technology
1603-1 Kamitomioka, Nagaoka 940-2188, Japan*E-mail: addharmawansa@yahoo.com
With the improvement of ICT (Information and Communication Technology), thesynchronous e-Learning becomes an attractive method for delivering learning. The
virtual environment is the one way of provideing synchronous e-Learning andavatars are active on behalf of the human users in that environment. The non-verbal
communication, which is very essential in the learning process is covered 82% of
all classroom communication process and it is lack in the virtual class due to poorconnection between the avatar and the real user. The ultimate objective of thisresearch is establishing non-verbal communication in the Virtual e-Learning
Environment (VeLE) and identifing the behaviuor of non-verbal features during thee-Learning activities. The eye blinking which is a major portion of the non-verbal
communication is designated to introduce to the virtual environment. Initially asystem of eye blink detection for the real user is constructed using geometric feature
based method. A human head model is also prepared and exported to the virtualworld to create the avatar with eye blinking. The connection is established between
the real world and the virtual world through a http protocol. When the real user isblinking his eyes, it is appeared in the VeLE through the avatar. An experiment was
conducted with the eye blink visualization system to identify the behavior of eye
blinking in the VeLE. Student tried to control the eye blink to avoid the loss ofcritical information and tried to keep the eyes open when they demanded the visual
attraction during the experiment. The results of the experiment convey that the eyeblink rate depends on the visual attraction and most of the eye blink happened at the
speaking time but at the break points of the speaking. The result conclude that the
behavior of eye blinking changes based on the activities that students engage in thelearning process of the virtual e-Learning.

1. Introduction
The advancement of technology is affected to the each and every field including education.
Simultaneously, internet makes the immense effect to the education with the increment of online users
day by day [1]. As a result of that knowledge can be delivered through the electronic media and that iscalled e-Learning [2]. Currently most of universities conduct both e-Learning and traditional classroom system concurrently. Defense Acquisition University in UK is one of the university provides both
web and resident based learning. The number of graduated students who are following web based
method is growing rapidly in recent years and it is shown that the significant increment in e-Learning
students than the students in traditional class [3].
There are several ways to deliver the knowledge through e-Learning and synchronous virtuale-Learning method is considered in this research. To conduct the synchronous virtual e-Learning,
Second Life is chosen as our virtual world since the Second Life is the leader of compelling,cost-effective virtual education solutions to amplify an existing curriculum or create a new model toengage for collaborative learning [4]. The three dimensional VeLE which is an online area wherestudents and staff can interact synchronously is very attractive and different types of class rooms werebuilt in that environment. Office package, white board, online-resources can be employed to deliver theknowledge and chat, voice are utilized for the communication in the VeLE. Avatar is an online
manifestation of self in the virtual world and it is designed to enhance the interaction of the virtual
space [5]. While using avatars for social networking (Ex: MogiMe, MOOLTO) and entertainment
purposes is now well established, they are used in educational contexts is still in its early stage ofdevelopment [6]. Even though, the avatar represents the real user in VeLE, the avatar cannot make the1Transactions on GIGAKU 2(2)(2014)S01002/1-7

Virtual Class

Teacher(s)Students

Analyzed eye blink

Server & Database

information of student

Transfer student eye blink information
AccessJust connection
Fig. 1 Architecture of the Eye Blinks Visualization System
reasonable representative of the real user because the relationship between the avatar and the real user
is only depending on the control of arrow keys of the keyboard. If there is rich connection between thereal users and the avatar, it will be very useful for the teacher to maintain the class effectively because
teacher can obtain the feedback or the student response via the avatar will contribute for the better
learning environment.
Even though teacher and students can sight their avatars in the virtual classroom, their physicalbodies are located in different places and they don’t know real conditions of the participants. Allavatars are like artificial icon and they couldn’t make a reasonable representation of the human.
Therefore the usefulness of the face to face layout cannot be obtained due to the poor connection
between the real user and the avatar. Non-verbal communication which is essential component of theeducation [7] should be established to enable the effective face to face layout to view their realcondition in the virtual environment.
Facial expressions, eye blinking, gestures including head direction and hand positions are majorityof the non-verbal features that can be established in the VeLE. In this research, the establishment of theeye blinking is discussed because eye is the most expressive part on the face. It is called the window of
the soul [8].
The ultimate objectives of this research are establishing non-verbal communication in the VeLE toraise the connection between the real user and the avatar and identify the behavior of non-verbal
features in the virtual classroom. As an initial step of this target, eye blinking which is an element of
most expressive part in the face is introducing to the virtual learning environment and analyzing the
behavior of eye blink in the virtual class.
2. Proposed System
2.1. Architecture of the System
The layout of the eye blink visualization system and the way of identifying the role of eye blinking
in the virtual learning environment is shown in Fig. 1. Students are directly access the virtual class andthey can engage with the learning activities. The eye blink information of the real user is obtained at thesame time through a web-camera. The extracted data is transferred to the virtual environment through aserver and it is stored in that server. Then the real user eye blink can be visualized in the VeLE. Whenthe real user eye blink happens in the real world it is appeared in the virtual world. The teacher or the
instructor can observe the student non-verbal behavior using direct and indirect methods. The behaviorof the student is feasible to obtain directly through interacting with the virtual class because it isconducted as a face to face layout. Non-verbal behavior information could be extracted from the server
for the indirect observation as a post analyze. The next sections provide more details regarding the wayof fulfilling this design.

2Transactions on GIGAKU 2(2)(2014)S01002/1-7

2.2. Methodology
Mainly there are three steps thatfshould be completed to establish theAeye blink visualization in the virtual
class. The eye blink of the realf
student needs to be captured in a realBtime is considered as the initial step.
Haar-features
The avatar should be modified tof(A,
B, C,..K,..N)
represent the real user eye blinking isCthe second activity of this process
because the avatar in the virtualenvironment hasn’t any way torepresent the human non-verbal
ffeatures. Establishment of a linkN
between the real world and thevirtual world is the last step of theeye blink visualization system. The
development
of
eye
blink
visualization system and the method,Fig.2 Apply Haar-like Classifier Features
which are helps to construct thesystem, is discussed in the next sections.
2.2.1. Algorithm development: Haar like feature
The detection methods of face and eyes of the user are needed to identify the eye blink. In thisresearch, Haar feature-based cascade classifier for object detection is used to detect the face and eyes
because of following factors.
 Detection at any scale
 Face detection at 15 frames per second
288 pixel images
for 384
 90% of objects detected
The Classification need to be developed based on the Haar-features to detect the face and eye. The
procedure to detect the eye is explained here and the same procedure can be used to detect the face. The
appropriate Haar-features need to apply for the positive images which consist of specific eye
component and notify range of filter and threshold value as shown in Table 1. The threshold value (fi) is
determined by subtracting the summation of white-region pixel value from the summation ofdark-region pixel value.
…………………….. (1)fi = ∑ Xi(w) - ∑ Xi(b)Where Xi(w) means white pixel values and Xi(b) means black pixel values.
The same procedure needs to be applied for many positive images and obtained average value forthreshold and filtering range to develop the classification. The classification can be applied to real time
image as shown in Fig.2. To detect the eye area, the relevant features have to be gone through the allarea of image. After apply the relevant Haar-feature in real world application fi value is able todetermined. If the real image fi value is greater than the fi value of classifier, then the relevant eye
feature is available for one Haar-feature.
All the selected Haar-features are needed
Table 1 Train Haar-like Classifier Features
to satisfy to confirm the availability ofCorrespondHaar-features
Range ofThresholdeye components as indicated in Fig.2.
This is the way for detecting the face andfacial part
Filteringvalue
eye of the image.
f
J
2.2.2. Eye blink
A:
A
A
Detection
J
f
B:
When the e-Learner engages withB
B
the learning activities in front of the
J
f
C:
Eye
C
C
computer, the web-camera is located tocapture the frontal view of the user toJfobtain the real-time video. The video
K:
K
K
consists with set of frames. We have toanalyze the frame by frame to catch the
J
f
N:
N
N
user eye information continually andconstantly.
Haar-feature
cascade3

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Accuracy (%)

120

classification is used to detect
the face area of an image.
94 95 95
100
When the face detection is
84 89 86
79 83 81
successful, we can proceed to80
the next step to detect the eyes.
Otherwise procedure should be
60
started from the beginning.
When the face is detected,
40
location of the eyes can beroughly determined based on
20
the human face layout andpossible to define the region of0interest to detect the eyes. Then
Face detection Eye detection
Eye blink
Haar-feature
cascadedetection
classification can be applied to
the location, which is definedJapanese Students
Other Students
Total Performance
as a region of interest. The
result is categorized as eyes are
Fig. 3 Accuracy of the Eye Blink Detection
detected or not detected. Whenthe eyes are not detected, then it is classified as a blink due to the close eyes when the blink is occurring
and at that time face is also detected. When the eyes are detected, further analysis is carried on to
identify whether the eyes open or close. There are two measurements that are available to determine
whether the eyes open or close. The white and black pixels of the eye region and the width and heightratio of the eyes are considered. Threshold values for the both measurements are obtained throughpreviously trained images. When these both measurements are satisfied, the eyes of the candidate areclassified as open and that is indicated as not a blink. Otherwise it is identified as eyes close and that isindicated as a blink. Then the procedure is started from the beginning and continuously updated till thestop command of the user. The eye blink detection procedure is conducted with the software of VisualC++ with OpenCV library.
Accuracy of the detection is evaluated by considering the detection of the face, eyes and blink for
the Japanese and international students as shown in Fig. 3. The last bars of the each section of thatgraph indicate the total performance of the system. Although it is shown that the detection was less inJapanese students than the other students, total performance for the blink detection is more than 80%.
2.2.3. Avatar modification
The avatar who represents the real student in the virtual world is not allowed to the dynamicchanges during the learning activity automatically due to the restrictions of the Second Life software
which is provided an avatar and the three dimensional environment. The mechanism needs to beprepared to change the eye blinking of the avatar based on the real user requirement. The head model
was prepared in the real world using graphics software (Maya) and exported to the virtual world as an
initial step of introducing eye blink to the avatar. Eyes and eyelids were prepared using objects in thevirtual world and attached to the head. The head model was coupled to the avatar and modified torepresent the eye blink.
2.2.4. Visualize eye blink in the virtual learning world
When the avatar eye blink model and the real user eye blink detection are constructed, the union
between these two places needs to be initiated. PHP,
JavaScript and the http request are used to establish alink between the real world and the virtual world. Whenthe real user eye blink information is obtained, it istransferred to the server which is located in our
university and it is conveyed to the virtual world throughthe http request. The eye blink visualization system isoriginated and eye blink is appeared on the avatar faceafter few seconds when the real user blink happens. We
need only a web-camera (3 or higher mega pixel for
better accuracy), a personal computer (Processor: core i5
or higher) with better performance because Second Life
Fig. 4 Work with Eye Blink
and the image processing software for the eye blink
Visualization System

4Transactions on GIGAKU 2(2)(2014)S01002/1-7

detection are graphical software and a person who works with the eye blink visualization system in thevirtual world as shown in Fig. 4.
3. Experiment3.1. Aim
This experiment was conducted to identify the behavior of the eye blinking during an e-Learning
session of the VeLE and the possibility of conducting the experiment with implemented eye blink
visualization system. The behavior of the eye blinking during the activities in the VeLE can berecognized with the frequency of the eye blinking based on the learning activity and measuring theoccasion of the eye blink.
3.2. Hypothesis
The three hypotheses are derived from the previous studies regarding the eye blink of the real world
activities. Eye blinks is controlled to minimizing the loss of important visual information [9]. This leads
us to hypothesize that we try to control the eye to avoid temporal loss of critical visual information.
The second hypothesis is eye blinks occur immediately before or after the task when we are
explicitly give the break time. Eye blinks may occur at the explicit attentional breaks. It does not occur
during the middle of the task due to the demand of the visual attention [10].
The third hypothesis is valid only for the conversation. The higher eye blink rate is appeared during
the speaking in the conversation which is happened in the real world [11]. Therefore we hypothesizethat the higher eye blink rate appears while speaking than the silence during the conversation in thevirtual world. We are going to identify the behavior of eye blinking via the measuring frequency of eye
blink and occasion of eye blink during the e-Learning activities in virtual world.
3.3. Subjects
There were six students that aged from 25 to 35 years voluntaries participated to this study. The
students belong to different nations namely Sri Lanka, India and Nepal. They are following thepostgraduate course and majoring in management and information science engineering.
3.4. Experiment Procedure
The subjects were instructed to attend three e-Learning sessions; 1) a lecture related to operational
research, 2) an individual exercise based on the previously mentioned lecture about operational
research and 3) a group exercise based on the operational research that was taught in the 1st session andall the sessions were utilized the eye blink visualization system. Each student had a web-camera to
detect the eye blink and a personal computer to engage the learning activities in Second Life. The
course materials were provided by the Department of Industrial Management of Wayamba Universityof Sri Lanka. The lecture on operational research was delivered as face-to-face lecture and the power
point slides were used to explain the content in the virtual environment as shown in Fig 5(a). The
second session of the individual exercise was carried on using an excel sheet appears in front of theirtable and the zoomed view of the exercise sheet appear as shown in Fig. 5(b). The discussion werecarried to solve the group exercise and there was only one large excel sheet to enter the answer which
was possible to edit by any student as shown in Fig. 5(c). All subjects experienced all three conditions(within-subjects experiment). Each session was taken roughly about 15-20 minutes to complete. The
voices of the all participants were recorded and the eye blink was already stored with the time via theeye blink visualization system.
4. Result and Discussion
The mean eye blink rate was obtained during the three sessions and the relax time. The minimalvalues are appears during the lecture and the group discussion while higher rate was appeared during

Fig. 5 Students View During the (a) Lecture Session (b) Individual Exercise (c) Group Exercise

5Transactions on GIGAKU 2(2)(2014)S01002/1-7

Eye Blink Rate (Per Min)

20
18
16
14
12
10
8
6
4
2
0

19.04

18.65

23%
(d)

14.77

15.44

14%
(a)
19%
(b)

44%
(c)

(a)Just before the speaking(b)During the speaking
Relax time Lecture

Individual Group
Exercise Exercise

Sessions

Fig. 6 Average Eye Blink Rate of Whole Students(c)During the breaks of the speaking(d)Just after the speaking
Fig. 7 Occurrences of the Eye Blink

6
4
2
0
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281

Eye Blinking
Freequency (per fiveseconds)

Loudnessthe individual exercise as shown in Fig.6. The student views during the three sessions were indicated inFig. 5 respectively. Each and every student can view others in the virtual class and they have to cover
the whole area of the class room including teacher as well as a power point slides through their eyesduring the lecture. Same as the group exercise, they have to cover whole area of the discussion place
including an excel sheet and especially the excel sheet can be edited by any other students. Therefore
they try to keep eyes open and minimize the blink. The result is confirmed that the student had aminimum eye blink rate during lecture and the group exercise sessions. They viewed only the excel
sheet completely throughout the computer monitor during the individual exercise and the excel sheet
cannot be edited by any other user except the owner. The result is indicated that the eye blink rate wenthigh due to the less demand of the visual attraction as shown in Fig. 5(b). The average rate of the eyeblink confirms the first hypothesis that is the student try to control the eye blink to minimize the loss ofvisual information.
Then the occurrence of eye blinks during the speaking is needed to investigate to identify theoccasion of the eye blink. There are mainly four occasions where the eye blink happens during theconversation. Just before or after the speaking, during the speaking and the break point during the
speaking are the occasions that an eye blink can be happened during the conversation. Fig. 7 shows the
allocations of students’ eye blink timing under the major four occasions.
Highest portion (44%) of eye blinking happened during the breakpoints of the speaking time. “Justafter the speaking” obtained the second highest value (23%). During the speaking time, eye blink rate
was very less and it is obtained only 19%.
The eye blink rarely occurred at the speaking time and most of the eye blink occurred at the breakTime (five Seconds)

Fig. 8 Voice and Eye Blinking of a Student

6

Transactions on GIGAKU 2(2)(2014)S01002/1-7

points of the speaking. Therefore the second hypothesis also confirmed that the eye blink does notoccur at the middle of the task and it occurs at the break points of speak.
Eye blink and the voice of the students during the group exercise were analyzed to identify thevariation of eye blinking in the conversation process and the result is appeared in Fig. 8. For thisanalysis, the student information during the group exercise was only considered because the active
conversation among the students was appeared only during the group exercise.
The below chart of that figure indicates that the eye blink rate of a student per five seconds and thevoice loudness of that particular student appears in the above chart. One of the students’ voice and eye
blinking information are included in Fig.8 and other students also showed the same behavior. We canidentify that a higher rate was appeared during the speaking time and a less blink rate was illuminatedduring the listening time. Therefore the last hypothesis is verified that the higher eye blink rate isappeared during the speaking and it is reduced with the silence.
5. Conclusion
The eye blink which is one of the main non-verbal components [8] has been visualized in thevirtual e-Learning environment and an experiment was conducted to identify the behavior of eye
blinking in the virtual e-Learning environment. The result of the experiment showed that the student
tried to control the eye blink to avoid the loss of critical information because the highest eye blink rate
appears during the session of individual exercise which has only an excel sheet to view. Lecture and thegroup exercise showed the higher rate of eye blink with broad area to view that leads us to conclude
that the student try to keep the eyes open when they demand the visual attraction.
Further analysis was conducted to identify the occasions of the eye blinks during the speaking timeand it is confirmed that a highest rate of eye blink happens during the break point of the speaking. Mostof the eye blink does not occur during a speak time. In addition, the rate of the eye blink is higher
during the speaking than the silence time of the conversation. We have identified that the eye blink ratedepends on the visual attraction and most of eye blink happens at the speaking time but at the breakpoints of the speaking leads us to conclude that the behavior of eye blinking changes based on theactivities that students engage in the learning process of the virtual e-Learning.
References[1]. Internet World Stats, History and Growth of the Internet from 1995 till Today, available on
http://www.internetworldstats.com/emarketing.htm, last visited on Nov. 2013.
[2]. I Ketut Trika Adi Ana, Putu Kerti Nitiasih, Teaching Reading through E-learning Website, 3rdInternational Conference on Foreign Language Learning and Teaching, 554-563, (2013).
[3]. Annual Report of the Defence Acquisition University, (2012), page 7.
[4]. Second
Life:
Ready
for
Learning,
Spotlighton
Education,
availableat
http://wiki.secondlife.com/wiki/Second_Life_Education, last visited on March 2014.
[5]. Peterson, M., Learning interaction in an avatar-based virtual environment: a preliminary study.
PacCALL Journal, 1, 29–40, (2005).
[6]. de Freitas, S., Learning in immersive worlds: a review of game-based learning. a report prepared for
the JISC e-Learning Programme, (2006).
[7]. G. Theonas, D. Employing Virtual Lecturers’ Facial Expressions in Virtual Educational
Environments, The International Journal of Virtual Reality, 7(1), 31-44, (2008).
[8]. Priya Irabatti, Study on Increasing Importance of Nonverbal Communication in Retail Industry,
Abhinav Publication, volume no.1, issue no.4, (2012).
[9]. Stern J. A., Walrath L.., Goldstein The endogenous eyeblink. Psychophysiology 21, 22–33, (1984).
[10]. Hall A., The origin and purposes of blinking. Br. J. Opthalmol. 29, 445–467, (1945).
[11]. Perelman, Detecting deception via eyeblink frequency modulation, PeerJ, (2014).
(Received; 23rd December, 2013, Accepted; 30th July, 2014)7

Transactions on GIGAKU 2(2)(2014)S01003/1-5Light Trapping Effect of SnO2Nanoparticles for Solar Cell Application
Cho Cho Thet1,*, Hla Toe2, Cho Cho1, Aye Aye Myint1
1)
Universities’ Research Centre, University of Yangon
2)
Department of Physics, University of Yangon
University Avenue Road, Yangon, Myanmar*E-mail: chochot@gmail.com
Chemical synthesis of SnO2 nanoparticles (NPs) were prepared byco-precipitation method using the stannous chloride (SnCl2.2H2O) as aprecursor and ammonia solution as a precipitating agent. The
as-synthesized SnO2 colloids were centrifuged and dried at 60 °C for 24 h.
The crystal structure of the prepared samples was examined by X-ray
diffraction (XRD) which reveals the structure of SnO2 NPs was
tetragonal structure. The average crystallite size of SnO2 was about 25 nm.
The surface morphology of the agglomerated SnO2 was almost sphericalshape by scanning electron microscope (SEM). The effect of annealing
temperature of the sample was confirmed by Fourier transform infrared
spectrophotometer (FTIR). The as-synthesized SnO2 layers were
deposited onto a polycrystalline p-Si (100) wafer by screen printing
method and SnO2/Si thin films were carried out by thermal diffusion
method at different deposition temperatures 400, 500, 600, 700 and
800 °C for 1 hour (h). The structural characterization of SnO2/Si junction
was observed by XRD. The photovoltaic properties of SnO2/Si were
measured by LUX meter and FLUKE 45 dual display multimeter
respectively. The photovoltaic parameters including resistance, voltage
and current of SnO2/Si thin film were measured depending on the lightintensities. It was found out that the annealing temperature 500 °C was
the optimum temperature for the better trapping properties for lightintensities of SnO2 thin film.

1. Introduction
A light trapping of a semiconductor is the absorption of light by the active layer ofsemiconductor using the incoming photon energy which is less than or equal to the band gap energyof semiconductor. Light trapping can enhance the solar cell efficiency due to creation of electron
hole pairs which generates a better collection of photogenerated current and a higher open circuit
voltage of thin film solar cells. Therefore, the role of semiconductor material using as the active
layer is the important consideration for the better light trapping. The material for light trapping has
been used the transparent conductive oxide (TCO) such as tin-doped indium oxide (ITO), fluorineand antimony-doped tin oxide (FTO, ATO) layers since they are high optical transparency in thevisible region of electromagnetic spectrum, low electrical resistance, high electrical conductivity andhigh reflectivity in infrared range [1]. TCO are applied in numerous applications including lightemitting diodes [2], dye sensitized solar cells [3], electrodes in solar cells and batteries [4, 5], sensors[6], flat panel displays [7]. SnO2 is an n-type semiconductor with a bulk energy band gap (3.7 eV) at
the room temperature and pressure. Various methods have been developed for preparing thetransparent conducting oxide thin films. Some of them are sol gel [8], screen printing [9], thermal
evaporation [10], chemical bath deposition [11], dc and rf sputtering [12, 13], spray pyrolysis [14],pulse laser ablation [15], electrodeposition [16] and spin coating [17].
Since the knowing the absorption coefficients of materials aids engineers in determining whichmaterial to use in their solar cell designs, the synthesized SnO2 NPs has been deposited onto p-Si
wafers to investigate the potential for light harvesting in this paper. Valuable understanding of the
optical properties of NPs has been measured by meters under different illuminations for theinsightful information on how the optical absorption of NPs reflects the properties of the dispersed1Transactions on GIGAKU 2(2)(2014)S01003/1-5

nanoparticles. The aim of this contribution is the use of SnO2 NPs as the absorption layers of thinfilm and study on their fundamental physical and electrical properties on photovoltaic effect.
2. Experiment
0.1 M of stannous chloride dihydrate (SnCl2.2H2O) was dissolved in 100 mL of deionized water.
After complete dissolution, 11 mL of ammonia was added by drop wise method at the room
temperature. The white solution was suddenly changed into milky as soon as the ammonia wasadded. The solution then was vigorously stirred for a certain period until the white precipitates wereobtained. During the stirring, the pH of solution was adjusted until greater than 10. When the
precipitates were clearly separated from the solution, the stirring was stopped and stayed atovernight. The precipitates were centrifuged for 15 minutes at rpm 1500 and washed them bydeionized water three times to remove excess ions and they were dried at 60 °C in air for 24 h. After
that, dried SnO2 were grinded by agate motor in order to get the fine powders. Then, theas-synthesized SnO2 NPs were printed on Si wafer by screen printing method followed by the
SnO2/Si thin film by thermal diffusion method at the different annealing temperatures 400 - 800 °C
for 1 h respectively. While the characterizations of SnO2 films were investigated by XRD, FTIR,SEM and the electrical properties are measured by LUX meter and dual multimeter respectively. The
crystal structure and crystallite size of the samples were confirmed by XRD (model Rigaku Multi
Flux) using CuKα radiation (40 kV, 40 mA) over a 2θ range from 10º to 70º on a powder type X-ray
diffractometer equipped with a diffracted-beam graphite monochromator. The structural changes of
the as-synthesized and calcinated SnO2 samples were analyzed by FTIR. The spectra formation of
SnO2 structure were obtained by FTIR 8400 Shimadzu spectrophotometer by using a KBr palletmethod in the Mid Infrared (MIR) radiation with the wavelength ranges from 4000 cm-1 to 400 cm-1
range with a resolution of 4.0 cm-1. Surface morphology of the samples was investigated by SEM(JEOL 15 kV). Centrifuge machine (Kokusan H-200 series) with maximum 6000 rpm was used toseparate the SnO2 from the colloidal solution.

3. Results and Discussion
The crystal structures of the SnO2 NPs were examined by XRD. The XRD spectra of the SnO2
NPs (Fig. 1a) and SnO2 thin film (Fig. 1b) at the annealing temperatures 600 °C for 1 h each wereshown in Fig 1. The diffraction peaks from SnO2 NPs (110), (101), (111), (211), (220), (002), (310)
and (301) were observed in both Figs. They were broaden due to the crystallite sizes of SnO2
particles in the samples were very small. All diffraction peaks were matched with the tetragonal
structure of SnO2 with the standard library file (ICDD-PDF #88-0287). The average lattice constantof a=4.7365 and c=3.1727. It was noted that the two prominent peaks (311) and (400) were foundonly in Fig. 1b which were attributed to diffraction peaks from Si wafer. Apart from these peaks, noother additional peaks were observed in XRD spectra. This showed that the samples have no more
impurity after annealing. The predicted crystallite size of NPs was 25 nm by using Sherrer’s formula.
DK

 cos (1)

Where λ is the wavelength of the incident X-ray beam (1.5405 Å for CuKα1), K is a constantequal to 0.89 for spherical shape of particle, β is the FWHM (full width at half maximum), θ is thediffraction angle and D is the crystallite size.
FTIR spectra of as-synthesized and calcinated SnO2 NPs were shown in Fig. 2a to study theeffect of annealing temperature on the chemical bonding of SnO2 NPs. The peaks were similar for all
samples. The well configured band at 3400 cm-1 could be seen before the heat treatment on
as-synthesized SnO2 NPs. This was due to absorption of water. After the heat treatment at 600 °C
and 800 °C, this band was not very strong as before because of dehydration / condensation [18].
Figure 2b showed the comparison of the IR spectra of the as-synthesized SnO2 NPs after annealing
at 600 °C and SnO2/Si film at the deposition temperature 600 °C. In the spectrum (a), the observed
two intense bands 663.53 cm-1 and 547.80 cm-1 were attributed to the vibrations of Sn-O [18-20]. In
the spectrum (b), the band at 1107.18 cm-1 was assigned to the vibration mode of Si-O-Si/ O-Si-O
group [21]. These were well known absorption bands of the SiO2 network formed by the surface2

Transactions on GIGAKU 2(2)(2014)S01003/1-5oxidation of Si substrate. Another well absorption band appeared at 620 cm-1 was associated at
Sn-O-Sn covalent bonds vibration [22].
To investigate the surface morphology and film thickness of the SnO2 NPs, the sample was
examined by SEM. Figure 3a shows the SEM micrographs of SnO2 NPs at 600 °C. The size ofobserved particles shows the agglomeration of many single crystallites due to the inhomogeneous
size distribution. This micrograph shows that the agglomeration of NPs has almost spherical shape.
The film thickness of this sample was about 12.6 μm (Fig. 3b) which were measured by crosssection position of film by SEM. For all samples, the measured film thicknesses were more than 10μm.

Fig. 1 XRD diffractograms of (a) SnO2 NPs (b) SnO2 thin film

Fig. 2 FTIR spectra of SnO2
I-V characteristic of each SnO2/Si thin films were measured by using the LUX meter for
illumination and FLUKE 45 dual display multimeter for measuring resistance, voltage and current.

3

Transactions on GIGAKU 2(2)(2014)S01003/1-5

The graphs measuring values of resistance, voltage and current depending on different illuminations
were shown in Fig. 4. The graph revealed that the greater the illuminations, the lower the resistancevalues in all samples. Besides, the more increased the illuminations, the gradually higher values ofvoltage were resulted only for the film annealing at the deposition temperature 500 °C. As a result,the current values were also increased for that sample. In contrast, the voltage and current values
were not sharply increased for other samples although the light intensities were increased. It can beconcluded that the resistance and voltage values were depended on the light intensities which in turndepended on the deposition temperatures during the preparing of SnO2 NPs deposited on Si substrate.
It was observed that SnO2 thin film showed photovoltaic characteristics since the resistance values
were inversely proportional and the voltage values were directly proportional to the light intensities.

Fig. 3 SEM image of (a) surface morphology of SnO2 NPs (b) cross-section of SnO2 film

Fig. 4 The graph of SnO2 /Si films showing (a) resistance, (b) photovoltage and (c) photocurrent
depending on different illuminations

4. Conclusion
SnO2 NPs were successfully synthesized by co-precipitation method. The crystal structure of
SnO2 NPs was tetragonal and the crystallite size was 25 nm by XRD. As-synthesized SnO2 was
deposited onto Si substrate at various diffusion temperatures from 400 C to 800 C by thermal
diffusion method. The characterizations of the films were also investigated by XRD, SEM, FTIR and
optical parameters depending on light intensities were measured by meters. According tomeasurements, SnO2/Si junction resistance decreased with increasing illumination for all samples.
However, the photovoltage values for the film at the diffusion temperature 500 °C were increased
while the other films at the diffusion temperatures 400, 600, 700 and 800 °C were almost constants
when the light intensities were increased. As a result, diffusion temperature 500 ˚C is the optimum
temperature for the highest photovoltaic performances in this work.

4Transactions on GIGAKU 2(2)(2014)S01003/1-5

Since the resistance values were inversely proportional and the photovoltage and photocurrent
were directly proportional to the light intensities, the SnO2/Si thin film behaved as a solar cell. This
could be attributed to the absorption of light energy by SnO2 NPs which converted then into theelectrical energy. Therefore, SnO2 NPs has the light trapping effect which can be applied inphotovoltaic cells.
Acknowledgments
The authors acknowledge Prof Dr Tin Tun, Rector of University of Yangon and Prof Dr Pho
Kaung, Pro-Rector of University of Yangon and Head of Universities' Research Centre (URC) for
their encourages in carrying out the research. We also do thank to Dr Nay Myo Khaing (Associate
Professor), Dr. Myo Aung (Assistant Lecturer) and Dr. San Htoo (Assistant Lecturer) for their help
in the sample characterizations.
References[1] A. V. Moholkar, S. M. Pawar, K. Y. Rajpure, C. H. Bhosale and J. H. Kim, Applied Surface
Science, 255 (2009) 9358.
[2] H. Lee, C. M. Kang, M. Park, J. Kwak and C. Lee, ACS Appl Mater Interfaces, 5 (2013)1977.
[3] T-T. Duong, H-J. Choi, Q-J. He, A.-T. Le and S-G.Yoon, Journal of Alloys and Compounds,
561 (2013) 206.
[4] S. Chappel, S-G Chen and A. Zaban, Langmuir, 18 (2002) 3336.
[5] H. Köse, A.O. Aydin and H. Akbulut, Acta Physic Polonica A , 121 (2012).
[6] S. Chaisitsak, Sensors, 11 (2011) 7127.
[7] T. Isono, T.Fukuda, K. Nakagawa, R. Usui, R. Satoh, E. Morinaga and Y. Mihara, Journal of
the Information Display, 15 (2007) 161.
[8] S. Gnanam and V. Rajendran, Digest Journal of Nanomaterials and Biostructures, 5 (2010) 925.
[9] M. H. S. Abadi, M.N.Hamidon, A. H. Shaari, N. Abdullah and R. Wagiran, Sensors, 11 (2011)7724.
[10] S. J. Ikhmayies, International Journal of Materials and Chemistry, 2 (2012) 173.
[11] H. U. Igwe and E- I. Ugwu, Advances in Applied Science Research, 1 (2010) 240.
[12] S.-W. Ryu, J.-S. Hong, S.-T. Kim, J.-Y. Yang, B.-C. Ahn, W.-P. Hong, S.-H. Park, H.-M. Kim
and J.-J. Kim, Journal of the Korean Physical Society, 50 (2007) 1833.
[13] A. F. Khan, M. Mehmooda, A.M. Rana and M.T. Bhatti, Applied Surface Science, 255 (2009)8562.
[14] D. Tatar and B. Duzgun, Journal of Physics, 79 (2012) 137.
[15] C. Ristoscu, L. Cultrera, A. Dima, A. Perrone, R. Cutting d, H.L. Du, A. Busiakiewicz, Z.
Klusek, P.K. Datta and S.R. Rose, Applied Surface Science, 247 (2005) 95.
[16] J. Jhon M, Vequizo, Jun and M. I. Wang, JJAP, 49(2010) 125502.
[17] F. Gu, S. F. Wang, M. K. Lu, X. F. Cheng, S. W. Liu, G. J. Zhou and D. Xu, D. R. Yuan,
Journal of Crystal Growth, 262 (2004) 182.
[18] A. S. Lanje1, S. J. Sharma , R. B. Pode and R. S. Ningthoujam, Archives of Applied Science
Research, 2 (2010)127.
[19] C. M. Liu, X.T. Zu and W.L .Zhou, J. Phys, Condens. Matter, 18 (2006) 6001.
[20] A. Punnoose, J Hays, A. Thuber, M.H. Engelhard; R.K. Kukkadapu, C. Wang, V.
Shutthanandan and S. Thevuthasan, Phys. Rev.B, 72 (2005) 54402.
[21] A. Sharma, D. Prakaha and K.D.Verma, Optoelectronics and Advanced Materials, 1 (2007)
683.
[22] C. Cobianu, C. Savaniu, O. Buiu, D. Dascalu , M. Zaharescu, C. Parlog , A. van den Berg and
B. Pecz , Sensors and Actuators B, 43 (1997) 114.
(Received; 23rd December, 2013, Accepted; 15th July, 2014)5

Transactions on GIGAKU 2(2)(2014)S01004/1-5

Flywheel Motor Powered by Road User

Aung Naing Win1,2,*, Khin Khin Moe1,*, Prof. Ying Jie Xiao3,*,
1)
Lecturer, Myanmar Maritime University, Yangon, Myanmar
2)
Ph.D Student, Shanghai Maritime University, Shanghai, China
3)
Dean, Merchant Marine College, Shanghai Maritime University, Shanghai, China*E-mail: aungnaingwin29@hotmail.com, khinkhinmoe1@gmail.com

Extracting energy from several sources such as solar, wind, hydro and allavailable sources ,which does not impact the environment so much and issustainable, is the most popular and thirsty technology of the world. Severalof experts all over the world have been trying to get the more and more
superior technologies of the production of energy which is environmentallyfriendly and sustainable. The paper presents the conceptual idea to recoverenergy from the downward acting forces of the vehicles moving on the road.
Flywheel is a kind of energy storage unit that has been used for a long time. It
is a rotating mechanical device where energy is stored mechanically andtransferred to and from the flywheel by an integrated motor/generator. The
paper proposes a flywheel motor powered by moving truck (road user) on theroad. Energy source places considered in this paper are the places where
speed bumps are installed on the road for speed reduction purpose such as theplaces in front the toll gate where vehicles are needed to stop. Flywheel
energy storage system is used because of the intermittent nature of energy
formation process. Energy will collect from the downward acting force of thevehicle when it passes over the speed bump and the collected energy will be
stored in the flywheel to drive the integrated motor. In this paper, the basic
concept of the proposed machine is presented with a simple schematic
diagram and the required materials are also listed. Feasibility of theinstallation of this kind of machine is also discussed in all aspects.
The proposed machine will give comfort of driving to the road user in spiteof giving them discomfort because of installing the machine. Although theproposed idea mainly based on intuition without any design data, the machine
would be functionally feasible and economically viable. The idea discussed
in this paper is only the simple way but it is believed to be one of the waysthat can contribute some amount to the future green energy production
process.
Keywords: Flywheel, Motor/Generator, Speed bump, Sustainable energy,
Energy storage system.

1. Introduction
Whenever a car pass over a speed bump, it must use some energy to climb up the bump because
it is designed as a fixed bar and is protruding on the road bed (3 to 4 inches). Off course it is need to
protrude on the road bed to make driver getting scene to slowdown his car but making as a fixed bar
make vehicle to use more energy and let driver lost comfort of driving. In spite of installing as afixed bump, if it is installed as an up and down movable bump, it will give not only comfort ofdriving and lesser use of energy but also can collect some energy from up and down moving force ofthe bump.
Actually to collect this kind of energy, no more high technology is needed. It can be done onlywith basic mechanical knowledge. The problem is the absent of initiator. With the aim of initiating to1

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recovery energy from this kind of source, the simple flywheel motor powered by moving truck on
the road is presented in this paper. It is hoped that from this conceptual idea, more and more deeper
research will be carried out in future in regard of energy recovery contribution.

2. Basic Concept of Machine
Schematic arrangement of the proposed flywheel motor is shown in figure .1. In this proposedmachine system, there include three sub-unit: (1) the energy unit, (2) the transmission unit, and (3)the process unit.
Flywheel
ClutchGear Pair

Crank
Gear Box
Generator

Hydraulic cylinders

Fig. 1 Schematic Arrangement of the Proposed Machine
Energy unit consists of a hydraulic force collecting mechanism, a speed rise gears pair withappropriate gears ratio and a flywheel. Force collecting mechanism contains two hydraulic cylinders.
One cylinder is to accept the downward acting force of the speed bump exerted by the over passing
truck. The other cylinder is to drive the crank with this collected force. The speed rise gears pair is totransfer the torque on the crank to flywheel, a rotating mechanical device where energy is stored
mechanically. The transmission unit consists of appropriate clutch and gear transmission. Because
RPM of the flywheel will vary with the amount of energy stored in it, appropriate gear transmission
is needed to regulate constant output. The process unit is just an electric generator.
Whenever a vehicle passes over the speed bump, it will give a driving force to the crank via
hydraulic assembly. This driving force will give torque to flywheel via the gears where energy willstored mechanically. When the flywheel has stored the required amount of energy, that energy would
be available for actuation of the generator through a suitable clutch and gear transmission.

3. Required Materials for the Proposed Machine
According to the schematic arrange of the proposed machine, the basic required materials arelisted below;
One adjustable up and down moveable speed bump with spring loaded lift2Transactions on GIGAKU 2(2)(2014)S01004/1-5

The purpose of this item is not only to serve as a speed bump, but also to accept the downward
acting force of the passed over vehicle by moving up and down according to the force exerted.
Two hydraulic cylinders,
The purpose of one cylinder is to accept force from up and down movement of speed bump andthe other one is to drive the crank with that accepted force (i.e. giving torque to flywheel). The sizesof the pistons will be chosen based on the crank throw and high of speed bump.
One crank shaft
The purpose of this item is to change reciprocating motion to rotary motion.
One speed rise gears pair
The purpose of this gears pair is to increase the RPM.
One flywheel
This one purposes to store energy.
One set of clutch and gear transmission box
The purpose of this set is to transmit the stored energy with stable output to the generator
One generator
This is for electricity generation
4 pairs of bearing and support frame
These will used in the installation of crank, gears, flywheel and generator.
Example of specifications of major machine parts intended to use in proposed machine prototype
are listed in the following table 1.
No.
1
2
3
4

Table 1 Specification of Major Machine Parts
Name of Component
Major Dimensions
High adjustable spring Length 72” x Width 12” x Height 2.25”
lifted speed bumphydraulic cylinder 1
70 ton capacity, Single Acting
hydraulic cylinder 2
70 ton capacity, Single Acting
CPO = 0.225
Crank shaft

Material
RUBBERSAE 4340 steel alloy

Crank stoke = 107.95 mm
Rod journals = 53.34 mm
Main journals = 66.04 mm

5

Gear (Crank)

6

Pinion (Flywheel shaft)7

Flywheel89Flywheel shaft
Clutch10
11
12

Drive Shaft
Driven Shaft
Pinion (Drive shaft)

13

Gear (Driven shaft)

14

Bearing

15

DynamoPCD = 450 mm, face width = 50 mm
No. of teeth = 91
PCD = 190 mm, face width = 50 mm
No. of teeth = 24
Outer diam = 1000 mm, rim
Width = 1000 mm, no. of arms = 6
Hub diam = 100
Wgt = 50 kg
Length 1050 mm, max. diam = 72 mm
Length of one half = 118 mm
Mean diam = 67 mm
Length 740 mm, max. diam = 72 mm
Length 740 mm, max. diam = 72 mm
PCD = 130 mm, face width = 55 mm
No. of teeth = 24
PCD = 530 mm, face width = 55 mm
No. of teeth = 97
Crank Shaft
Flywheel Shaft
Drive Shaft
Driven Shaft
5 Kw

3Cast Iron
Cast Iron
Cast Iron

SAE 1030 mild steel
Cast SteelSAE 1030 mild steel
SAE 1030 mild steel
Cast Iron
Cast Iron--

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4. Feasibility of the Proposed Machine
The proposed machine is very simple and only need to have basic mechanical knowledge to
evolve this kind of machine. The only idea is to convert the reciprocating motion that generated fromdownward acting force of the truck exerted on speed bump to rotary motion so as to power thegenerator. According to the schematic diagram, if the developer has basic knowledge of hydraulic,crank, gear train, flywheel and motor/generator, it is enough to develop the machine. So it can saythat the proposed machine is technically feasible. Moreover, the parts include in the proposedmachine are very conventional and can easily get from the market with reasonable price. This fact
also pointed out the economic feasibility of the machine.
In environmental point of view, the machine uses no fuel and it is like an energy compensatingmachine. It recovers the energy that truck will used in passing over the speed bump. The energy
generated from this kind of machine can be called free, green and waste compensating energy. The
machine will contribute in saving environment in spite of giving any impact to environment.
Moreover, in driver point of view, they will welcome this kind of machine because of giving them
driving comfort in passing the toll collection.
But a flywheel stores energy in a rotating mass. Depending on the inertia and speed of therotating mass, a given amount of kinetic energy is stored as rotational energy. The kinetic energy
stored in a flywheel is proportional to the mass and to the square of its rotational speed according to
the following equation.
1
IωE
2
Where Ek is kinetic energy stored in the flywheel, I is moment of inertia and ω is the angularvelocity of the flywheel. The moment of inertia for a circular object is a function of its shape and
mass.
1
I
Mr
2
To optimize the energy to mass ratio the flywheel needs to spin at the maximum possible speed.
Kinetic energy only increases linearly with mass but increases at the square of the rotational speed.
On the other hand, for effective storage of energy, long rundown times are also required.
The literature search made did not produce data regarding what should be the optimal gear ratio
and the moment of inertia of the flywheel so that this proposed machine system could store
maximum energy in the flywheel for the shortest possible running time. Obviously the mostcompatible system with the minimum possible energy loss is wanted. Thus the most obvious
drawback or weak point of the proposed system is no design data were available for the energy unit.
5. Conclusion
Although the proposed machine is a simple one, it is sure that more or less energy could be
generated. Several of experts all over the world have been trying to get the more and more superior
technologies of the production of energy which is environmentally friendly and sustainable. Every
day there has millions of several heavy weight trucks and vehicles operating all over the world fortransportation purpose. If a reasonable amount of energy used by these vehicles in speed bump canbe recovered by the proposed system, it would be a contribution to the future green energy
production process. So it is recommend that, this kind of simple and low tech arrangement should
not be underestimated and a deeper research should be carried out. Therefore, future work would be
the development of a prototype and making research for the best arrangement and analysis of how
much energy that this kind machine can generate on an average.
Acknowledgments
Special appreciation goes deeply to supervisor, Prof. Ying Jie Xiao, Dean, Merchant Marine
College, Shanghai Maritime University, Shanghai, China for his kindness to me. And special thanks
to my wife Khin Khin Moe, who gives me encouragement, motivation and advice, moreover gives
me present for poster presentation at 1st ICEEHE.

4

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References[1] J. P. Modak, Design and Development of Manually Energised Process Machines having
relevance to Village/ Agriculture and other Productive Operations, HUMAN POWER, International
Journal of Human Powered Vehicle Association USA, April 2006, No. 57.
[2] J. P. Modak, Human Powered Flywheel Motor Concept, Design, Dynamics and Applications[3] Luís de Sousa, Energy storage – flywheel, The Oil Drum, OCT 5, 2011,
http://www.resilience.org/stories/2011-10-05/energy-storage-flywheel[4] Pedal powered farms and factories: the forgotten future of the stationary bicycle, Low-tech
Magazine, May 25, 201 1, http://www.lowtechmagazine.com/[5] United Nations Environment Programme(UNEP), Energy Efficiency Guide for Industry in Asia,
2006, www.energyefficiencyasia.org[6] B. Bolund et al., Flywheel energy and power storage systems, Renewable and Sustainable
Energy Reviews, November 2007, Pg. 235–258[7] Gabrys CW. High Performance Composite Flywheel, US patent Pub. No.: US 2001/0054856 A1;
27 Dec 2001.
[8] Ojo O. Multiobjective optimum design of electrical machines for variable speed motor drives.
IEEE Conferrence Record 1991 p. 163–168.
[9] Kim WH, Kim JS, Baek JW, Ryoo HJ, Rim GH, Choi SK. Improving efficiency of flywheel
energy storage system with a new system configuration PESC 98 Record. 29th Annual IEEE PowerElectronics Specialists Conference; 1998, p. 24–28.
[10] A. R. Bapat, "Formulation of Generalised Experimental Models for various Dynamic Responses
of a Manually Energised Flywheel Motor". Ph. D. Thesis of Nagpur University, 1996 under thesupervision of Dr. J. P. Modak.
[11] Bitterly JG. Flywheel technology: past, present, and 21st century projections. IEEE Aerospace
Electron Syst Mag 1998;13:13–6.
[12] Taylor P, Johnsson L, Reichert K, DiPietro P, Philip J, Butler P. A summary of the state of theart of superconducting magnetic energy storage systems, flywheel energy storage systems andcompressed air energy storage systems SAND99-1854, unlimited release. Albuquerque, New
Mexico 87185 and Livermore, California 94550: Sandia National Laboratory; 1999.
[13] Aanstoos TA, Kajs JP, Brinkman WG, Liu HP, Ouroua A, Hayes RJ, et al. High voltage stator
for a flywheel energy storage system. IEEE Trans Magn 2001;37:242–7.
[14] Hofmann H, Sanders SR. Synchronous reluctance motor/alternator for flywheel energy storage
systems. IEEE Power Electron Transport 1996;199–206.
[15] Cengelci E, Enjeti P. Modular PM generator/converter topologies, suitable for utility interfaceof wind/micro turbine and flywheel type electromechanical energy conversion systems. Conference
Record of the 2000 IEEE Industry Applications Conference, vol. 4, p. 2269–2276.
[16] Mellor PH, Schofield N, Howe D. Flywheel and supercapacitor peak power buffer technologies,
IEE seminar electric hybrid fuel cell vehicles, p. 8/1–5.
[17] Kan HP, Chau KT, Cheng M. Development of a doubly salient permanent magnet motor
flywheel energy storage for building integrated photovoltaic system APEC 2001, Proceedings of the
16th IEEE conference, p. 314–320.
[18] Yoon-Ho K, Kyoung-Hun L, Young-Hyun C, Young-Keun H. Comparison of harmoniccompensation based on wound/squirrel-cage rotor type induction motors with flywheel. Proceedingsof third international conference on power electronics and motion control; 2000, p. 531–536.
[19] Jiancheng Z, Zhiye C, Lijun C, Yuhua Z. Flywheel energy storage system design for
distribution network. IEEE Conf Proc 2000;4:2619–23.
[20] Truong LV, Wolff FJ, Dravid NV. Simulation of flywheel electrical system for aerospace
applications. Collection of Technical Papers. 35th Intersociety Energy Conversion Engineering
Conference and Exhibit (IECEC) (Cat. No. 00CH37022). vol. 1, pt. 1 2000 p. 601–68.
[21] Bakholdin D, Bosley RW, Rosen HA, Pearson CC, Pano SB. Hub and cylinder design forflywheel for mobile energy storage US Patent No: 6,175,172 B1; 16 Jan 2001.
(Received; 23rd December, 2013, Accepted; 8th May, 2014)5

Demonstration of Text Similarity Metric for Plagiarism Detection
Ohnmar Htun1, Yoshiki Mikami2,*
Department of Management & Information System Science, Nagaoka University of Technology,2)
Department of System Safety, Nagaoka University of Technology,1603-1 Kamitomioka-cho, Nagaoka 940-2188, Japan*E-mail: mikami@kjs.nagaokaut.ac.jp

1)

This paper proposes an enhanced text similarity metric, and presents a detailed
study of its application to plagiarism detection. Text can be plagiarized in many
ways; typically text is reused material (copy & paste) and in addition some partsof the reused text may be changed. Our method combines tree based indexing andstring edit-distance measure algorithms: suffix tree and longest common
subsequence, as well as introducing a text chunking approach to break a given
document into segments that provides a way to efficiently identify the level ofplagiarism. The proposed algorithm consists of three phases: pre-processing, theprocessing, and post processing. The pre-processing phase executes two tasks:
text-chunking and indexing the documents. Then the processing phase performs
string matching task by calculating the longest common subsequence. Finally the
post-processing step carries out a two level local and global analysis, which
identifies the similar text passages that may have been plagiarized. The proposed
algorithm is evaluated by using four artifical plagrized suspicious documents (i.e.,
100%, 50%, 25%, and 0% plagiarized dataset) made from academic papers andscience & technology review works as source. The results were 99.64% accuracy,
94.22% recall, and 99.32% precision, on average. We also found that if 5% oftext in sentences were replaced with synonyms, the precision was adversely
affected. Synonym replacement cannot be detected easily and this can makeplagiarism detection considerably more difficult. The proposed algorithm takes
time O(m2n2ω) to compute LCS, it is required to optimize in future work.

1. Introduction
Text can be plagiarized in many ways; typically text is reused material (copy & paste) and in
addition some parts of the reused text may be changed. The problems of plagiarism are often dealt
with in several domains (e.g., the software and entertainment industries), but it probably occurs mostfrequently in academic institutions [1]. Today, the Internet is a vast information resource foreveryone. Students can easily reproduce one another’s work without acknowledging the source.
These dishonorable activities are not acceptable in the academic community. Research anddevelopment of plagiarism detection methodology is active in the fields of both Natural Language
Processing (NLP) and Information Retrieval (IR). There are many tools available, both commercial
and free GNU (General Public License). These systems use many different sorts of retrieval
techniques: vector space models (VSM), fingerprint, sliding window techniques, etc. The applicationof techniques involving the longest common subsequence (LCS) (i.e., approximate string matchingapproaches) are limited. Each method of plagiarism detection has a basic problem with large
amounts of data, because every usable method is capable of processing only a limited amount of data
within an appropriate time limit [2]. Furthermore, most applications support a particular language
only, so that methods for multi-source and multi-lingual plagiarism detection are currently achallenging issue in information retrieval area. Therefore, our research objective is to develop an
efficient text similarity metric that can be applicable detecting reused/plagiarized texts and finding
source/reference documents of the manuscripts in the academic domain. In addition, using anapproximate string matching approach that can be easily scaled up to linear data processing. In orderto demonstrate this proposed methodology, we applied a powerful dynamic programming techniquethat combines a number of methods: suffix tree, suffix arrays, longest repeated substring (LRS),
longest common subsequence (LCS), and a new improvement of LCS that yields the common string
matching. Experiment is conducted with the four different proportions of text reuse (i.e., 100%, 50%,
25%, and non-plagiarized) in the suspicious documents and the collection of nine source documents(i.e., size 280,180 bytes in total). This paper is organized as follows: the next section gives anoverview of related research and technologies. The proposed methodology is presented in Section 3.
The detail of experiment: data, results, and evaluations are reported in Section 4. Finally, a
conclusion and avenues for future work are presented in Section 5.
2. Related Research
Narayanan et al. (2008) proposes the architecture and design of a copy-paste checking system [3].
Their approach begins with building an index of documents. Text in a source document is broken
down into snippets of 20 words. Then, document similarity is computed based on the extent of thematching fingerprints in a target document. The scope of the system is that it is only to serve as afunction in this particular e-learning system. Kasprzak et al. (2009) presents an external plagiarism
detection system that is a modified version of an existing anti-plagiarism system used in the Czech
National Archive of Graduate Theses [4]. The detection approach of the retrieval method is based on
tokenization, a word-5-gram vector space model (VSM) using Boolean weights, and Jaccard
similarity. If a suspicious document shares at least 20 n-grams with the reference collection
documents, it is taken to have plagiarized sections. Their overall performance result achieved 2ndplace in the PAN Workshop-2009 competition. In pre-processing, a language-dependent step totranslate and stem words is required. Kong et al. (2013) presents plagiarism detection for source
retrieval and text alignment in previous contest at the PAN Workshop-2013 [5]. Source retrieval
method combined TF-IDF (i.e., Term Frequency - Inverse Document Frequency) PatTree, andWeighted TF-IDF to retrieve the keywords to search in suspicious documents. Their text alignment
method is based on sentence similarity: text alignment algorithm and similar sentences merging
algorithm, which is called Bilateral Alternating Merging algorithm. Their results did not perform
well in all detection tasks. Osama et al. (2013) presents an implementation of a plagiarism source
retrieval system in English at the PAN Workshop-2013 [6]. In their approach, the TextTilingalgorithm is used to break a given document into segments, and the KPMiner key phrase extraction
system generates the key phrases from segments. And then these key phrases and segments are usedin generating queries to ChatNoir search engine for finding plagiarism sources in the ClueWeb09
corpus. The proposed system reached the highest number of top performance indicators (recall andF1 score).
In general, plagiarism detection algorithms specifically depend on the knowledge of natural
languages processing (NLP) such as grammatical rules, parsing words, dictionaries, translation, etc.
The performance of plagiarism detection is as important as their underlying algorithms
implementing in dynamic programming. Many string-matching algorithms feature have both merits
and demerits based upon the length of the strings and the technique used. The PAN2013 [7]developed a baseline algorithm using n-gram tokenizer with a filter that breaks tokens into N-grams(50-grams) to identify the length passages of reused text between source and suspicious documents.
Using this tokenizer leads to spaces in the tokens. In the previous literatures, most of the methods
were based on n-gram matching and vector space models. Our approach is based on the approximate
string matching by applying two particular techniques: document level text chunking and suffix
array that is able to reduce the memory problems found in suffix tree construction. In addition,
calculating enhanced common string affirms the reused text passages between source and suspicious
documents accurately.
3. Methodology

Fig. 1 Overview of proposed system: Text Similarity Metrics
The proposed algorithm, enhanced Text Similarity Metric (eTSM) is composed of three phases:
pre-processing, the processing, and post processing. The pre-processing phase executes two tasks:

chunking and indexing the documents. Mid-processing phase performs the string-matching task.
Finally, post-processing determines the similar text passages that may have been plagiarized. Figure
1 depicts an overview of methodology, in this figure, D is a collection of documents in general, dq isa suspicious document (i.e., possibly plagiarized document), ds is the source documents (i.e.,
documents are possible to plagiarize by dq), and dw is the source documents from the Web (i.e., apply
to the future search engine implementation). We will use these notations in the following sections.
3.1 Text chunking
Typically text chunking is the task of dividing sentences/phrases into non-overlapping
segments/chunks [8]. The length of a document can be an arbitrarily large string that must be split
into groups of bytes called chunks. In this case, we chose appropriately the maximum size of achunk to be 1,024 bytes of content-length. This size is chosen an optimal value because the limit of aquery string in Google advanced search is 1,024 bytes1, which is intended to be used in theprocessing phase (i.e., search engine). The chunk of the candidate suspicious document dq is denotedby cq, and the source document ds of chunks is denoted by cs. Generally, the document dq and ds with
nonempty strings can be split into m and n number of chunks as shown below:

Where the length of chunk cq and cs can be 1,024 bytes in maximum.
3.2 Indexing
Then each set of chunks (cq,cs) is built into an index. We applied linearithmic algorithm [9, 10] toconstruct a suffix tree index and convert it into a suffix array index for each chunk. Let the suffix
tree of a candidate suspicious chunk be Tq and a source chunk be Ts, which can be split into a set of
substrings as shown below:
Then the substrings of Tq and Ts are merged andsorted in a suffix array Tw, as shown below:
Building suffix array is able to reduce the memory problems found in suffix tree construction.
3.3 String matching
In the processing phase, we find all possible longest common strings in each suspicious chunk by
applying the LCS algorithm [11, 12], and define Mi to be the longest length of a longest common
subsequence that is found in the suffix array Tw of a suspicious chunk. Mi is given by the following:(1)
Then the filtering process begins to extract the common strings, comparing each |Mi| against athreshold t. According to our observations, we set a parameter of t to a minimum string length of 28
bytes for the length of each common string, |Mi|. If the score of |Mi| is equal to or lower than t, thevalue of the similarity chunk will be disregarded. Alternatively, if |Mi| is greater than t, we assume
that there may be a similar/plagiarized passage in a chunk. In addition, this process reducesmismatching and the unnecessary performance of calculation tasks. After the filtering process, thelongest common string Mi is matched in a chunk of cq. Then again, the scalar value of enhanced
common string eCS of each matching substrings is calculates as follows:
where Lq and Ls are the lengths of suspicious and source chunk.
3.4 Local analysis
Then the degree of similarity (θi) between all i chunks in a suspicious document (i.e., ci in dq)
and all j chunks in a source document (i.e., c j in ds) is calculated based on the enhanced common
string value | eCS ij| as follows:1

This level determines the degree of similarity (i.e., theamount of plagiarized data) between a suspicious document dq
and a source document ds (i.e., one-to-one document).

http://www.googleguide.com/search_forms.html

3.4 Global analysis
Finally, the evaluation of total similarity/reused text (Simdq) between a candidate suspicious
document dq (i.e., for all ci) and all candidate source documents (k) for all ds (one-to-many
document) is calculated as follows:This level gives a final similarity/detection
score of a suspicious document (i.e., dq-to).

4. Experiments
4.1 Data
We developed a simulation dataset, which contains a collection of nine source documents (i.e.,
size 280,180 bytes in total) from academic papers (i.e., conference proceedings, journals, and white
papers) and four suspicious documents (i.e., formed as 100%, 50%, 25%, and 0% non-plagiarized).
The sizes of reused text passages in a suspicious document ranged from 142~722 bytes and weretaken from the collection of source documents. The size of each suspicious document is shown in
Table 1.
In addition, the degree of text reuse amount
Table 1 The size of candidate suspicious
can be changed using synonyms in the sentences.
documents
Therefore, Test-3 data is modified by replacing
Suspicious% of Text
Sizesynonyms in a part of ds4 (229 bytes), then weDocument
Reuse(byte)
evaluate the results between Test-3 andTest-1 (d s1)
100%
7,356
synonyms replacing Test-3/1. Both suspicious
Test-2 (d s2)
50%
6,431
and source text files are encoded in UTF-8
Test-3 (d s3)
25%
6,864
without BOM format. Our proposed method is
Test-4 (d s4)
0%
16,631
expected to cover detection for a group of UTF-8
encoded languages, but the current experimental
test data was only in English.
4.2 Experimental results
In this experiment, we applied seven variable character-level thresholds (i.e., 0, 8, 16, 24, 32, 48,
64, and 128) to evaluate the detection results. Based on these threshold values, the results are listed
in below Figures 5~12. The results for 0 byte (without filtering), 8 byte, and 16 byte thresholds
found more than the actual number of characters of text reuse in each suspicious document. The
results for threshold 24 byte and 32 byte are close to the number of actual reused characters in eachsuspicious document.

Fig. 2 Characters in actual plagiarized anddetected by eTSM with a 0-byte threshold

Fig. 3 Characters in actual plagiarized anddetected by eTSM with a 8-byte threshold

Fig. 4 Characters in actual plagiarized anddetected by eTSM with a 16-byte threshold

Fig. 5 Characters in actual plagiarized anddetected by eTSM with a 24-byte thresholdFig. 6 Characters in actual plagiarized anddetected by eTSM with a 32-byte threshold

Fig. 7 Characters in actual plagiarized anddetected by eTSM with a 48-byte threshold

Fig. 8 Characters in actual plagiarized anddetected by eTSM with a 64-byte threshold

Fig. 9 Characters in actual plagiarized anddetected by eTSM with a 128-byte threshold

However the results for thresholds of 48 bytes and 64 bytes are lower, and the result for a 128
threshold byte is much less than the actual size. In order to setup an optimal threshold at the filter
process, we selected to calculate a mean value between 24 and 32 byte thresholds.
Searching a large document or long text queries in documents containing hundreds or eventhousands of lines is challenged work. In this case, there is usually a trade-off between performanceand accuracy. Utilizing the advantages of dynamic programming, for two sequences of length m andn (m ≥ n), a basic LCS problem can be solved in O(mn) time and space complexity [13]. Our
approach in dynamic programming, LCS for two strings Ts and Tq such that Mi is a subsequence ofthe solution LCS, then since there are O(m2n2ω) time, where m, n, and ω are length of Ts, Tq, and Mi,
respectively. This prototype implementation has a problem to optimize the time.
4.3 Evaluation
Precision states the degree of detection in text reuse passages between a suspicious document
and source documents. Recall states the percentage of the reused text passages in a suspicious
document that is detected correctly by algorithm. In addition, the harmonic mean between the
precision and recall (F-measure) and the overall percentage of correctness of detection result(Accuracy) are also evaluated. Table 2 shows the results in precision, recall, F-measure, andaccuracy for each suspicious document using an optimal threshold at 28-bytes. Our algorithm
achieves an average of 99.32% precision, 94.22% recall, 96.42% F-measure, and 99.64% accuracy.
Table 2 Results in precision, recall, F-measure, and accuracy
for each suspicious document using a 28-byte threshold
Precision
RecallF-measure
Accuracy

Test -1
98.22
95.97
97.07
99.18

Test-2
99.05
96.08
97.52
99.76

Test -3100.00
84.81
91.08
99.63

Test-4100.00100.00100.00100.00

Averages
99.32
94.22
96.42
99.64

Fig. 10 Performance comparison between
Test-3 and Test-3/1

Moreover, in the real word, the most cases of plagiarism occur by copying from a sourcechanging synonyms (substitution of equivalent words) and noun phrase modifiers. After replacing
5% of the content words in sentences from Test-3 with synonyms, we found that the result in recall
was still maintained, but the precision decreased by 30.67%. Figure 10 depicts the comparison ofrecall, precision, F-measure, and accuracy between Test-3 (text reuse only) and Test-3/1 (replacing
synonyms). In addition, we implemented a n-gram baseline matching algorithm from PAN2013 [7]to compare the performance of our algorithm. In this attempt, the baseline algorithm detected a lessnumber of reused texts in each suspicious document while eTSM retrieved the reused text passages

from the source document correctly. Table 3 depicts the precision of retrieval comparison between
eTSM (at 28 byte threshold) and the baseline algorithm.
Table 3 The Precision of Retrieval Comparison
We setup this baseline algorithm with a filter
Test -1
Test-2
Test -3 Test-4
that breaks tokens into 20-grams to identify the
37.91% 19.35% 23.24%
0%reused texts between source and suspicious
Baseline
documents.
98.22% 99.05%
100%
100%
eTSM

5. Conclusion
In this paper, we demonstrate a technique for plagiarism detection called the enhanced text
similarity metric (eTSM) based on approximate string edit distance measures. We combines tree
based indexing and string edit distance measure algorithms, as well as introducing a text chunking
approach to break a given document into segments that provides a way to efficiently identify thelevel of text reuse plagiarism. The initial results (text reuse) were 99.64% accuracy, 94.22% recall,and 99.32% precision, on average. We also found that if 5% of words in sentences were replacedwith synonyms, the precision was adversely affected. Synonym replacement cannot be detectedeasily and this can make plagiarism detection considerably more difficult. Our approach iscomputationally efficient by using text chunking, an building suffix array index, and threshold
filtering. We expect that it would readily scale up to large data collections and potentially to theplagiarism detection for source retrieval and text alignment tasks. In future work, eTSM has room
for improvement in detecting sentences with replaced synonyms and to optimize the time and space
for the source retrieval and text alignment tasks in the next PAN2015 contest.
References[1]

Paul Clough: Plagiarism in natural and programming languages: an overview of current tools andtechnologies, Journal of Finance, July issue, pp.1-31, doi:10.1.1.65.774, (2000).
[2] Jan Grman and Rudolf Ravas: Improved implementation for finding text similarity in large
collections of data, CLEF 2011 Labs and Workshop Proceedings, Notebook Papers, Amsterdam,
The Netherlands, (2011).
[3] Narayanan Kulathuramaiyer, Bilal Zaka, Denis Helic: Integrated Copy-Paste Checking: Design andServices, Proc.Intl.Conf. the Eighth IEEE International Conference on Advanced Learning
Technologies, ICALT '08, pp. 519 - 523, (2008).
[4] Jan Kasprzak, Michal Brandejs, Miroslav Kripac: Finding Plagiarism by Evaluation Document
Similarities, 3rd PAN Workshop & 1st Competition on Plagiarism Detection Proceedings, pp. 24-28,(2009).
[5] Kong Leilei, Qi Haoliang, Du Cuixia, Wang Mingxing, Han Zhongyuan: Approaches for Source
Retrieval and Text Alignment of Plagiarism Detection, Notebook for PAN at CLIF Evaluation Labs
and Workshop Proceedings, (2013).
[6] Osama Haggag and Samhaa EI-Beltagy: Plagiarism Candidate Retrieval Using Selective Query
Formulation and Discriminative Query Scoring, Notebook for PAN at CLIF Evaluation Labs andWorkshop Proceedings, (2013).
[7] Martin Potthast, Benno Stein, Alberto Barrón-Cedeño, Paolo Rosso: An Evaluation Framework for
Plagiarism Detection, Proceedings of the 23rd International Conference on Computational
Linguistics, COLING (2010).
[8] Baker, Paul, Andrew Hardie & Tony McEnery: A Glossary of Corpus Linguistics. Edinburgh:
Edinburgh University Press, (2006).
[9] U. Manber and E. Myers: Suffix Arrays: A New Method for On-Line String Searches, SIAM
Journal on Computing 22, 5 (1993), 935-948. (Also appeared in: Proceedings of the First
ACM-SIAM Symposium on Discrete Algorithms (1990), 319-327.)[10] Robert Sedgewick and Kevin Wayne: Algorithms Fourth Edition, Addison Wesley, USA, (2011).
[11] Brian Martin, Plagiarism: a misplaced emphasis, Journal of Information Ethics, vol. 3(2), 36-47,(1994).
[12] Dan Gusfield: Algorithms on strings, trees, and sequences: Computer Science and Computational
Biology, Cambridge University Press, (2007).
[13] Robert A. Wagner and Michael J. Fischer, The string-to-string correction problem, J. ACM 21(1974), no. 1, 168–173.

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Identifying the Associations between Music and Emotion
in Developing an Emotion Based Music Recommendation System
Sugeeswari Lekamge, Ashu Marasinghe
Department of Management and Information Systems Engineering,
Nagaoka University of Technology,1603-1 Kamitomioka-cho, Nagaoka 940-2188, Japan
E-mail: sugeeswarilekamge@gmail.com, ashu@kjs.nagaokaut.ac.jp
Emotion based music retrieval has a great potential in catering today’s digitalmusic archives rapidly expanding in the growing mobile and ubiquitousenvironments. Existing literature provides evidence for various efforts made inmusic classification based on genre, artist and emotion which utilize Signal
Processing, Machine Learning and Data Mining techniques. This research aims
at identifying the associations between the acoustic features of music and theperceived emotions as an initiating step in developing an emotion based music
recommendation system. The above discoveries will also be assisting in a
number of areas including music therapy and emotion based music composition.
As the source for identifying the above relationships Sri Lankan folk melodies
have been selected considering their potential in emotional expression. An
emotion annotated corpus is being built storing the above melodies in the digitalform allowing them to be analyzed, enabling them to be utilized in a constructive
manner. Annotating the music clips with emotions which are highly subjective innature and heavily affected by human Kansei, is viewed as a critical step. A newmusic emotion model which will allow to better describe the emotional
expression inherent in Sri Lankan folk melodies is being built followed by areview of the existing general and music-specific emotion models. In identifying
the potential of Sri Lankan folk melodies in emotional arousal, a pilot survey was
conducted using folk music excerpts which are demonstrative of the basic
emotions discussed in the Sri Lankan traditional arts, as the stimuli. Orchestralmusic was used considering the possible impact by the lyrical content on theperceived emotion as the research is focused on the audio content of music.

1. Introduction
Emotion based music classification is currently gaining high popularity among theMusic Information Retrieval research. Along with the growth of digital music archives
accelerated by the internet and mobile phone penetration a higher demand isanticipated for emotion based music retrieval. Music retrieval based on searches given a
particular emotion as the query is believed to be highly preferred over the searches
simply based on the title, artist or genre. Though there are various attempts made in
developing music recommendation systems, most of them are trying to make accurate
predictions about what people demand to listen to, or buy next, independently of how
useful to the user could be the provided recommendations. Quite often these algorithms
tend to recommend popular or well known to the user-music decreasing theeffectiveness of the recommendations [1]. In contrast, emotion based music
recommendation focuses on the perceived quality of music. On the other hand this will
help discover the creative musical content having a huge potential in emotional arousal
which are still hidden in the long tail of the popularity distribution. Music is
successfully used for mental healing in a number of specific applications includingmusic therapy, selection of background music played in shopping malls to stimulate
sales and in context-aware homes to match with the emotional states of the inhabitants.
Identifying the associations between musical features and emotions is also assisting in
automatic composition of emotion-based music.

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In discovering the above associations between music and emotion the study utilizes Sri Lankan
folk melodies which can be regarded the transformations of true and innate feelings of the native
community in the form of music. Emotional expression is inherent in these melodies as they havebeen emerged as a means of expressing or overcoming certain emotional states. This emotional
expression is accompanied by unique ensembles of musical features that are within the humanperception. Though Sri Lanka possesses such a folk song repository enriched with a wide variety offolk songs that can be explored in both the mathematical and psychological perspectives, storing theabove melodies in the digital form which will allow them to be analyzed, enabling them to be
utilized in a constructive manner remains as a need yet to be served. Catering the above need, thisstudy will contribute in preserving the Sri Lankan identity of folk music while promoting it globally.
The emotional experience one gains through listening to a music piece is heavily associatedwith human Kansei. Music induced emotions are always affected by a number of factors including
the listener's previous experiences, contextual knowledge and understanding and the cultural or
religious background he has grown up. Due to the subjective nature inherent in emotions and thevagueness in expressing the perceived emotion, music emotion annotation has become a critical task.
At the same time a single music clip may convey different emotions in various degrees while
representing a single predominant emotion. Therefore assigning a music clip with one discreteemotion label is viewed to be unrealistic and for this reason the dimensional approach of emotion
assignment has been utilized in this study.
Many of the research which tries to address the emotional aspect of music have utilized the twodimensional circumplex model comprising the dimensions valence and arousal, proposed by Russel.
(Fig. 1) However, it is still unclear whether models and theories designed for ‘everyday’ emotions
can also be applied to an aesthetic context such as music, and there have been doubts whether a few
primary basic emotions – or the two dimensions of valence and arousal – are adequate to describe
the richness of emotional experiences induced by music (Zentner, Grandjean, & Scherer, 2008).

Fig. 1 Two Dimensional Circumplex Model proposed by Russel
Source: http://www.ieee-tcdl.org/Bulletin/v5n3/Hu/hu.htm

Another emotion scale which is largely used in music emotion research is the Geneva Emotion
Music Scale (GEMS) - the first instrument that has been specifically devised to measure musicallyevoked emotions [3]. The GEMS is composed of nine emotional scales namely wonder,transcendence, power, tenderness, nostalgia, peacefulness, joyful activation, sadness, and tension
each represented by two or three adjectives (Zentner et al., 2008). Though the application of a music-specific scale in measuring the music induced emotion is viewed to be more effective rather thanapplying a general emotion model, the study tries to better capture the emotions expressed by Sri
Lankan folk melodies through introducing a new music-emotion scale based on the nine basic
emotions discussed in the Sri Lankan traditional arts while trying to be compatible with theestablished models of emotion currently employed in related research.

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2.

The Proposed System

Fig. 2 Major phases of the study
The audio content is to be analyzed by reducing the sheer quantity of data down to a manageable
set of features including the Mel-frequency Cepstral Coefficient (MFCC) and other acoustic features
representing the pitch, timbre and rhythm of the music excerpts. In emotion detection previous
researchers have utilized physiological indices as well as psychological indices whereas someresearchers have employed both types collaboratively with the aim of achieving an enhanced
performance. Heart rate variability and the Skin Conductance Response (SCR) can be regarded as
two commonly used physiological indices used in measuring the induced emotion. Discrete anddimensional emotion models have been used in measuring the music induced emotion in a conscious
manner whereas the study focuses on the dimensional approach. It is aimed at developing an
emotion annotated corpus of Sri Lankan folk melodies allowing the future researchers to further
explore the above melodies in the emotional perspective. Once the acoustic features are extracted
and the emotions are detected, the associations between them could be discovered with the use ofdata mining techniques. Based on the discovered associations, the system will be able to recommend
acoustic features that need to be present in a music clip so as to induce the queried emotion. Each
unlabeled music clip in the music database will be indexed with a predicted emotion distribution andonce a preferred emotion is passed as a query, the system will return a ranked list of recommended
music clips which are capable of inducing the queried emotion.
3. Listening Experiment
A listening experiment was conducted as a pilot survey in identifying the potential of Sri Lankan
folk melodies in emotional expression. For the experiment 13 Sri Lankan folk music excerpts
belonging to diverse emotional categories were selected as the stimuli. Since the research aims at
identifying the associations between acoustic features of music and perceived emotions, orchestralmusic was used in the experiment considering the potential effect by the lyrical content on thesubjects’ emotional experience. The clips were in the .wav format and the duration of each clip wason average 40s. 12 subjects were asked to rate the above music clips in a 5 point Semantic
Differential scale. In selecting the bipolar adjective pairs the nine basic emotions associated with Sri
Lankan traditional arts were considered to serve as the base considering their capability in capturing
the emotions associated with Sri Lankan folk music. The scale is to be refined followed by acomprehensive review of the existing music-emotion thesauri. The experiment was conducted fortwo populations representing Sri Lankans having moderate knowledge in Sri Lankan folk music andprofessionals having expertise domain knowledge.

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4. Results and Discussion
For each stimulus, the targeted predominant emotion was determined with the use of the folksong type the excerpt is associated with. The mean values of the ratings given by the subjects foreach stimulus denoting the degree of emotional experience pertaining to each of the bipolar adjective
pairs were obtained. The semantic differential chart (Fig. 3) shows the mean rating for two of thestimuli of which the predominant emotions were determined as Happy and Sad.

Happy-Sad

Peaceful-Frightening

Descriptors

Holy-Unholy

Happy

Comic-Humourless

Sad

Heroic-Unheroic

Pleasant-Unpleasant
SurprisingUnsurprising

Values
1

1.5

2

2.5

3

3.5

4

4.5

5

Fig. 3 Semantic Differential Chart
A Principal Component Analysis was run and Fig. 4 shows how the music excerpts wereclustered together based on the ratings given by the subjects who were having an average
understanding of Sri Lankan folk music whereas Fig. 5 shows the result of the PCA for the musicstimuli rated by domain experts.
Observations (axes F1 and F2: 77.50 %)Observations (axes F1 and F2: 62.91 %)

4

3

Comic

Holy

3

2

F2 (25.03 %)F2 (33.91 %)2

Peaceful
HappyPleasantHeroic1

Pleasant

Sad

0

Pleasant
-1

-5

-4

-3

-2

Holy
Happy

-1

Heroic-3-1

Pleasant
Pleasant
Pleasant

Sad

Holy
-6

Sad
Peaceful
Sad

0

-2

Peaceful

-3

Comic

Sad

Holy

-2

1

Sad
Peaceful

0

1

2

3

4

-6

5

-5

-4

-3

-2

-1

0

1

2

3

4

F1 (37.88 %)F1 (43.59 %)Fig. 5 Excerpts rated by subjects having
expertise knowledge in SL folk music

Fig. 4 Excerpts rated by the subjects having
moderate knowledge in SL folk music

The emotional experience of music arises as a result of the interaction between a given musicpiece and a particular listener. During this interaction the listener will apply his knowledge in music
and the contextual understanding in arriving at various aesthetic judgments. The individual
differences in terms of personality and emotional intelligence are also among the factors that
influence the perception of emotion in music. Accordingly, remarkable differences are anticipatedbetween domain experts and the general community in the way the music is understood andinterpreted. A rating done by an expert is usually followed by an analysis of the music clip taking
into consideration the rules in music associated with emotional expression, present in thecomposition, instrumentation and performance of the musical work. Therefore it is believed that thelevel of emotional perception of music of an expert usually lies beyond that of a general listener. In

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this regard it should be clearly understood that the emotion perception is all instances where alistener perceives or recognizes expressed emotions in music (e.g., a sad expression), without
necessarily feeling an emotion [4].
While serving as a pilot study, the initial experiment conducted among the two groups provides
evidence for the expected difference in perception of emotion in music, between a common listener
and a domain expert. Using the correlation circle, the axes F1 and F2 can be interpreted and based on
the squared cosines of the variables, the linkage of each variable with the axes can be identified. The
above findings of running a PCA on the experimental results obtained from the two populations will
give important insights on differences in emotional perception of music and serve as an importantconsideration in developing an emotion based music recommendation system.
In realizing the complicated task of meeting the listener expectations, various approaches are
currently being utilized by today’s commercial applications for personalized music recommendation.
Content-Based Filtering, Collaborative Filtering, Demographic Filtering and Context-Based
Filtering are among such algorithms which are being used independently as well as in combination
with others. Although the Nearest-neighbour Collaborative Filtering which is the current mainstream
approach used in web-based recommender systems is believed to provide a successful means ofgenerating recommendations for web users, it suffers from several short comings, including data
sparsity and noise, the cold-start problem, and scalability [5]. Addressing the above issue, Amatriain
etal. [5] have attempted to explore how the professional raters in a given domain (i.e. experts) can
predict the behaviour of the general population, by introducing a collaborative filtering approachbased on expert opinions from the web. In this study, the experiment conducted using the domain
experts serves as a controlled experiment in identifying the potential for fusing the wisdom of the few[5] in enhancing the effectiveness of emotion based music recommendation.
5. Future Developments
Through the pilot survey, several limitations could be identified which need to be addressed in
the future in order to achieve an effective performance. Ambiguities in emotional expression were
noticed in several music clips used. The stimuli used in this survey were derived from existing
compositions in which the primary objective was not the emotional expression. The gap between theexpressed emotion and the perceived emotion can be reduced to a greater extent by purposelycomposing emotion based music, appropriately orchestrating and adjusting the mood of theperformer. Therefore such kinds of music excerpts are to be employed in the future research.
Similarly the emotion scale used needs to be revised by introducing more precise adjective pairs
which will eliminate the existing ambiguities.
Though the music analysis is usually performed using audio files, notated scores and can beenhanced by lyrical analysis, the initial study focuses only on the audio content disregarding the
linguistic approaches. But the emotions evoked within a person through listening to a music piece
are always affected by the lyrical content as well as by human Kansei. Therefore the potential of Sri
Lankan folk melodies in emotional arousal in the universal context, overcoming the above
mentioned language and cultural barriers are to be further explored incorporating Kansei
Engineering approaches.
As a means of promoting emotion based music retrieval, interactions with the interface forquerying can be enhanced by enabling the users to pass the emotion query as a point or a trajectoryon the emotion space displayed on a touch sensitive mobile device.

Acknowledgments
Foremost, I would like to express my sincere gratitude to my academic advisor Prof. Ashu
Marasinghe for the continuous guidance provided in this study from the initiation of the concept towriting of this paper. Besides, I would like to thank the rest of the academic staff members of
Nagaoka University of Technology for their valuable advices and guidance. I am very much gratefulto the veteran artists in the field of Sri Lankan folk music for the shared knowledge and views which
immensely helped me in identifying and shaping the way to proceed in this research. I am thankful
to everyone who supported me in all the means in gathering the audio resources required. I am also
thankful to all the lab mates in the Kansei Informatics Laboratory, Nagaoka University ofTechnology for the stimulating discussions, shared views, motivations and the support provided insuccessfully completing this task. Last but not the least I would like to thank my family; my parents

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for making me the person I am.
References[1] C. Herrada, “Music recommendation and discovery in the long tail,” PhD Thesis, Internet:
http://mtg.upf.edu/static/media/PhD_ocelma.pdf[2] J. K. Vuoskoski and T. Eerola, “Measuring music-induced emotion: A comparison ofemotion models, personality biases and intensity of experiences,” Proc. Musicae
Scientae, 2011 15: 159, Internet: http://msx.sagepub.com/content/15/2/159[3] Zentnerlab,“The
GenevaEmotional
Music
Scale
(GEMS),”
Internet:
http://www.zentnerlab.com/psychological-tests/geneva-emotional-music-scales[4] P. N. Juslin and D. Vastfjall, “Emotional responses to music: The need to consider underlying
mechanisms,” Behavioral and Brain Sciences, vol. 31, pp. 559 –621, 2008.
doi:10.1017/S0140525X08005293[5] X. Amatriain, N. Lathia, J. M. Pujol, H. Kwak and N. Oliver, “The wisdom of the few: A
collaborative filtering approach based on expert opinions from the Web,” Internet:
http://www.nuriaoliver.com/RecSys/wisdomFew_sigir09.pdf[6] A. Mattek, “Computational methods for portraying emotion in generative music composition,”Internet: http://alisonmattek.files.wordpress.com/2010/08/mattek_thesisd2.pdf[7] A. Mattek, “Emotional communication in computer-generated music: Experimenting withaffective algorithms,” Proc. 26th Annu. Conf. of the Society for Electro-Acoustic Music in theU.S, 2011[8] A. Manaris, P. Roos, P. Machado, D. Krehbiel, L. Pellicoro and J. Romero, “A corpus-based
hybrid approach to music analysis and composition,” Proc. 22nd Conf. on Artificial Intelligence(AAAI-07), Vancouver, BC, Jul. 2007
[9] H.Y. Lo, S.D. Lin and H.M. Wang, “Audio tag annotation and retrieval using tag countinformation,” Internet: http://www.iis.sinica.edu.tw/papers/whm/11116-F.pdf[10] J. Taminau, R. Hillewaere, S. Meganck, D. Conklin, A. Now´e and B. Manderick, “Descriptivesubgroup
miningoffolkmusic,”
Internet:
http://www.ehu.es/cs-ikerbasque/conklin/papers/mml09taminau.pdf[11] K. Trochidis, D. Sears, D.L. Tran and S. McAdams, “Psychophysiological measures of
emotional response to romantic orchestral music and their musical and acoustic correlates,”
Internet:http://www.cmmr2012.eecs.qmul.ac.uk/sites/cmmr2012.eecs.qmul.ac.uk/files/pdf/papers/cmmr2012_submission_49.pdf[12] M. Lesaffre, “Music Information Retrieval,” 2005-2006[13] M. M. Wanderley, “Report – Kansei the technology of emotion workshop rapport de mission,”
Proc. Int’l Report-Kansei- The Technology of Emotion Workshop – Genoa, Italy, Oct. 1997[14] P. M. Brossier, “Automatic annotation of musical audio for interactive application,” Internet:
https://qmro.qmul.ac.uk/jspui/bitstream/123456789/3809/1/BROSSIERAutomaticAnnotation20
06.pdf[15] S. Brown, “ From mode to emotion in musical communication,”Internet:
http://www.unige.ch/emotionalpowerofmusic/conference/brown.pdf[16] S. Koga and T. Inoue, “A proposal for intervention by user in interactive genetic algorithm forcreationofmusic
melody,”Internet:
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6603488&abstractAccess=no&userTyp
e=inst[17] S.R. Livingstone, R. Muhlberger, A.R. Brown and W. F. Thompson, “Changing musicalemotion: A computational rule system for modifying score and performance,” Internet:
http://www.mcgill.ca/files/spl/LivingstoneCMJ2010.pdf[18] T. Inoue, M. Fukumoto, “Creation of sound contents by extended interactive evolutionary
computation using heart rate variability,” Proc. Int’l Conf. on Biometrics and Kansei
Engineering, 2013[19] W. Chai and B. Vercoe, “Folk music classification using Hidden Markov Models,” Internet:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.206&rep=rep1&type=pdf

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Application of Unscented Kalman Filter with Non-symmetric Sigma Point
Sampling on the Integrated Navigation System
Ye Chan1, Chan Gook Park2, Pho Kaung1
Universities’ Research Centre, Yangon University, Yangon, Myanmar, 11041.
2)
School of Mechanical and Aerospace Engineering & The Institute of Advanced Aerospace Technology,
Seoul National University, Seoul, Korea, 151744.
yechann@gmail.com
1)

The integrations of a global positioning system (GPS) and an inertial navigation system(INS) usually use error state models with linear or non-linear Kalman filters. In highdynamic environments, these systems introduce errors in the system models due tolinearization. To overcome this drawback, the unscented Kalman filter (UKF) is appliedon the non-linear total state model of the GPS/INS integrated system because the UKF is anonlinear estimator that is particularly well suited for complex nonlinear systems. The
sigma points in UKF are usually sampled symmetrically around the mean value and therandom variable to be transformed is assumed to be Gaussian. If the variables depart fromGaussian nature, the performance of the system is degraded. To enhance the UKF
performance the non-symmetric sigma points sampling is addressed in this paper. The
non-symmetric factors for sigma points are carefully chosen to avoid local and global
sampling problems. The simulations are done using real INS and GPS data for symmetric
UKF (SUKF) and non-symmetric UKF (NSUKF) algorithms. The navigation performanceand robustness of the proposed algorithm are also compared with that of the SUKF.
According to the simulation results from the application of NSUKF on a nonlinear total
state model, the performance and robustness of the navigation system is significantly
improved under the environment with a number of satellites less than four. Hence,
NSUKF is better choice for low cost INS/GPS integrated navigation systems and a goodalternative for SUKF.

1. Introduction
In a nonlinear GPS/INS navigation system, the system and the measurement models are simply
linearized around the current state estimate to apply the Extended Kalman Filter (EKF). EKF techniques
suffer from divergence during GPS outages when using low-cost IMUs due to approximations during
linearization process and suboptimal modeling. The main reason is that the low-cost sensors havecomplex error characteristics which are stochastic in nature and difficult to model. To overcome thesedrawbacks and to enhance the performance and robustness of the tightly coupled integration, instead of
an EKF, the UKF is applied on nonlinear model [1].
The UKF belongs to a bigger class of filters called Sigma-Point Kalman Filters or Linear Regression
Kalman Filters, which are using the statistical linearization technique. This technique is used to linearize
a nonlinear function of a random variable through a linear regression between N points drawn from theprior distribution of the random variables. Since we are considering the spread of the random variables
the technique tends to be more accurate than Taylor series linearization. The spreading sigma points
cover the mean and covariance of the variables [2].
The set of sigma points is carefully selected around the mean but the sampling problem arises out of
the incorrect scaling of these points. To solve both global and local sampling problems the nonsymmetric approach was proposed by K. Kim & C.G. Park [3]. The spreading of sigma points from one
side of mean is fixed and that for other side is adjusted using the non-symmetric factor. Their algorithm
is successfully applied on the in-flight alignment of an aerial vehicle with a large heading error.
Unscented filter also assumes conditional Gaussianity throughout the filter recursions and may leadto misleading results in case the density departs too far from the assumed Gaussian density [2][4]. In this1

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paper, we propose the UKF with two unequal non-symmetric distribution factors. Therefore, the set ofsigma points can be scaled independently. The proposed algorithm is applied on the land integrated
navigation system with GPS signal outages and the performance is evaluated in comparison with thesymmetric UKF.
Section 2 introduces the principles of unscented transformation and section 3 describes incorporating
concepts and method of proposed non-symmetric unscented transformation incorporation the state
constraints. Models of tightly coupled GPS/INS navigation system and the computing steps of NSUKF
are explained in section 4. Simulation of navigation algorithm with real navigation data and thecomparison of performance between SUKF and NSUKF are described in section 5. Conclusions arereported in the last section.

2. Unscented Transformation
In unscented transformation, to calculate the mean and covariance of a random vector, a set ofdeterministically selected sigma-points which have the same mean and covariance as the original
random vector is chosen. Each sigma-point is propagated through the nonlinear models, and the mean
and the covariance of the transformed random vector is calculated from the propagated sigma-points.
The weights
associated with sigma points have to satisfy the following condition to provide anunbiased estimate. [3]1The selected sigma points are propagated through the non-linear function f (_ ).
Their mean and covariant are then calculated by∑∑,
In symmetric sampling strategy, 2n+1 sigma points are symmetrically distributed about the mean
anddefined as follows.
,for i= 1 . . . n,for i= n+1 …2 n
where
and it is a composite scaling parameter. The constant, and√determines the spread of the sigma points aroundand is usually set to a small positive
value 01 . The constant is a secondary scaling parameter which is usually set to 0 to 3 – n
and provides an extra degree of freedom for fine tuning of the higher order moments [2].
The weights associated with the sigma points are defined as
,
1
and
1…2

3. Non-symmetric Sigma Point Sampling
The symmetric distribution of sigma points around the mean value is modified into nonsymmetric form by introducing non-symmetric factors in sampling process. The 2n+1
sigma points are chosen as follows,for i= 1 . . . n(1)for i= n+1 …2 n
are non-symmetric factors and.
where andThe weights corresponding to the sigma points have to match exactly the expected mean andcovariance. It must satisfy the following conditions

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∑

1,
∑

and

∑
.
Substituting sigma points (given in Eqn. (1)) into above equations, we have
1.
The weights can be deduced as
,

,

1

(2)

The proposed algorithm uses two degrees of freedom ( and) for scaling sigma points. Thesesigma points are propagated through the nonlinear function and mean and covariant are calculated using
the scaling weights. Fig.1 shows the non-symmetric sigma points associated with the weights in
1 dimensional case. Dash curve is Gaussian probability density function (pdf) for symmetric unscented
transformation and solid curve represents the non-Gaussian pdf for non-symmetric sampling. According
to the nature of pdf, the distribution of sigma points is spread around the mean.
andare needed toadjust to cover the stochastic nature.
W0

Density

W2

W1

̅

State

Fig.1 Non-symmetric sigma point sampling in 1 dimensional case( solid curve for a non-Gaussian pdf and dash curve for a Gaussian pdf)

4. Tightly Coupled GPS/INS Navigation
The proposed NSUKF is applied on the tightly coupled integration scheme. INS data and GPS raw
measurements (pseudorange and pseudorange rate) are processed in the data fusion algorithm, and theestimated errors are fed back to the INS to prevent the growth of navigation errors with time exhibitedby an unaided INS[5]. The system dynamic models and the pseudorange and pseudorange ratemeasurement models are the key to the development of GPS/INS data fusion algorithms.
4.1. System Models
The total state INS mechanization model is given by the following differential equations. In this work,
navigation frame mechanization was chosen [5][6].
,,2The attitude quaternion is propagated by(3)

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where

,
0
is angular rate of a body frame relative to navigation frame andquaternion vector.
The state vector is defined as
⋮
⋮
⋮
⋮
⋮

is the attitude

andare biases in accelerometer and gyro outputs, and
and
where
are white Gaussian noise corrupting the measurements. Latitude, longitude and height above theellipsoid are denoted as , and and ,andare velocities components in navigation frame
respectively. The accelerometer bias and gyro bias are modeled as random constants and the clock bias
cb in meter and clock drift d in meter per seconds of GPS receiver are calculated using random walkmodel [6].
4.2. The Observation Models
The observables in our integration are pseudorange and pseudo-range rate of a GPS receiver. The
pseudorange measurement to the ith satellite can be modeled as follows[6]:(4),The satellite position in n-frame coordinates is denoted withwhile the position of the GPS antenna.
is the lever arm vector pointing from the origin of the body frame defined by the IMU to theisGPS antenna. Additionally, the receiver clock bias
and the measurement noise , are included. The
pseudorange rate can be described by(5),th
is the unit vector pointing from the GPS antenna to the i satellite andthe rate of body frame
relative to the earth frame. Additionally, measurement noise , and the clock error drift d enter thisobservation model.
To apply the proposed NSUKF to the GPS/INS navigation system, the 2n+1 sigma points are
generated according to the Eqn(1). They are propagated through the system models (Eqn(3)) and themean and covariance are calculated using the weights shown in Eqn(2) as follows.
∑∑
A set of predicted measurements is computed by propagating sigma points through the nonlinear
measurement models given in Eqn(4) and Eqn (5).
The predicted observation, innovation covariance and cross covariance are determined by∑∑∑
Finally, the update can be performed by
)
where

4

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5. Simulation Results
The trajectory data is collected using a low grade IMU and a GPS. The sampling rates are 100 Hz for
IMU and 1Hz for GPS respectively. The differential GPS data is used as a reference data in our
calculation. The numerical simulations are done in MATLAB using these data. First both SUKF andNSUKF are applied on the navigation system with fully GPS supported situation. There is no significant
difference between the results and the trajectories obtained are given in Fig. 2. This is due to the lessnon-linearity in land navigations and small variation in vehicle dynamics during trajectory datacollection.
To test the ability of the NSUKF algorithm on the GPS outages the satellite’s data are rejected in dataprocessing and each outage lasts for 60 seconds. The results under complete GPS outages are firstly
analyzed and then followed by the results under partial GPS outages. Fig. 3, Fig. 4, Fig. 5 and Fig. 6
illustrate the 3D position errors over the 5 simulated outages in each case (i.e. for number of satellite
visible equals 0, 1, 2, and 3). The benefit of more satellite availability can also be seen from these results.
The general trend is that having three satellites visible is better than two, which is better than one andzero case. In each case the maximum position errors of NSUKF is less than that of SUKF. SUKF
provides a minimum position error for the case of 3 satellites visible and NSUKF cannot reduce the error
anymore. The main factor for this is the nonlinear capabilities of both UKFs which enabled the use of
nonlinear system model as well as the nonlinear measurement model of the raw measurements without
any need for approximations during linearization. Fig.7 shows that the comparison of the average
maximum position errors between SUKF and NSUKF. It is found that NSUKF has superior performance
than SUKF.
REF & INS-GPS position
0.65390.6539NSUKF

0.6538long

Ref
0.6538SUKF

0.65370.65370.65362.2157 2.2157 2.2157 2.2157 2.2157 2.2158 2.2158 2.2158 2.2158 2.2158
latFig. 2 Trajectories taken by SUKF and NSUKF solutions

6. Conclusion
In this paper, the non-symmetric unscented transformation with two degrees of freedom is proposed.
The spreading of sigma points around the mean is adjusted carefully by two independent non-symmetricfactors. The proposed algorithm is applied to the tightly coupled INS/GPS integration system and to
avoid the linearization error, total state model of INS is used instead of error state model. The
performance of the NSUKF is compared with the SUKF for the situations of GPS outages. It can beconcluded that applying NSUKF on a nonlinear total state model does not degrade the performance ofthe navigation system significantly under the environment with less than four satellites. Non-symmetricsampling improves the navigation error and, the performance and robustness of the system is better than
the symmetric case.

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Fig. 3 Comparison of 3D position errors undercomplete GPS outages

Fig. 4 Comparison of 3D position errors when
1 satellite is visible

Fig. 5 Comparison of 3D position errors when
2 satellites are visible

Fig. 6 Comparison of 3D position errors when
3 satellites are visible

Fig.7 Average maximum position errors over the 5 outages with different numbers of satellites visible.

References[1] X. Niu, S. Nasser, C. Goodalla1, and N. El-Sheimy, A Universal Approach for Processing any
MEMS Inertial Sensor Configuration for Land-Vehicle Navigation. Journal of Navigation, 2007, vol.
60, pp. 233-245.
[2] S. J. Julier, and J. K. Uhlmann, Unscented Filtering and Nonlinear Estimation, Proceedings of theIEEE, 2004, Vol . 92, pp. 401-422.
[3] K.Kim and C.G. Park, Non-Symmetric Unscented Transformation with Application to In-Flight
Aligment, IJCAS, vol. 8-4, pp. 776-781[4] S. Sarkka, On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear
Systems, IEEE Tr. on Automatic Control,2007, vol. 52, pp. 1631-1641.
[5] D. H. Titteron, and J. L. Weston, Strapdown Inertial Navigation Techonology, 2nd Ed., IET and
AIAA, 2005.
[6] Z. Syed, , P. Aggarwal, Y. Yang, and N. El-Sheimy, Improved Vehicle Navigation Using Aiding
with Tightly Coupled Integration. Proceedings of Vehicular Technology Conference, Marina Bay,
Singapore, 2008,pp. 3077-3081.
(Received; 23rd December, 2013, Accepted; 2nd August, 2014)6

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Enhancement of ionic conductivity in CuI-Cu2MoO4 superionic
conducting glass by nano-structure control
Takao Tsurui*
Faculty of Engineering, Nagaoka University of Technology,1603-1 Kamitomioka-cho, Nagaoka 940-2188, Japan*E-mail:tsurui@mst.nagaokaut.ac.jp
Microstructural changes in CuI-Cu2MoO4 superionic conducting glass in thevicinity of glass transition temperature have been studied by thermal analysis
and transmission electron microscopy (TEM). In (CuI)0.52-(Cu2MoO4)0.48,
nano-crystalline CuI of 2-3 nm in size disperse in amorphous matrix after
the heat treatment above the crystallization temperature. Its electricalconductivity significantly increases by about 50 %, due to thenano-crystallization. On the other side, ionic conductivity decreases dueto the nano-crystallization of CuI of 3-5 nm in size in(CuI)0.40-(Cu2MoO4)0.60. We can successfully control microstructures at a
nanoscopic level by thermal treatment. The correlation between
nano-crystalline size of CuI and the ionic conductivity has been
discussed.

1. Introduction
The class of solids usually called by superionic conductors exhibits an extraordinarily high ionic
conductivity [1-3]. For example, CuI is one of the typical superionic conductors. The value of theelectrical conductivity of CuI crystal is 2.5×10-5 S/cm at 500 K [4]. On the other side, some kindsof Cu+-containing glasses are known to show a very high ionic conductivity up to 10-3 S/cm at room
temperature [3]. The values of Cu+-containing glasses are much higher than that of CuI crystal.
Therefore, superionic conducting glasses have attracted much attention because of the technologicalneeds for solid electrolytes in, for example, high-energy batteries and electrochemical devices.
Improvements of devices continue to be made possible by enhanced ionic conductivities. Therefore,
development of fast ionic solids has been major concern for the ionics community. Structuralproperties have been extensively studied by X-ray scattering [5] neutron scattering [6], infrared
spectroscopy [2,3,7] and other experimental techniques. The close relationship has been pointed out
between the glass structure and the anomalous ionic conductivity.
It has been recognized for a long time that identification of the medium range structure isindispensable for understanding the Cu+ ionic conduction mechanism of Cu+-containing superionic
conducting glasses. Actually some structural models are proposed based on the existence of"clusters" or "microdomains" of CuI with tetrahedral coordination, which is dispersed in the hostmatrix constructed with oxyanions in CuI based oxide glasses [2,3,5-7]. Numerous structural studies
were performed so far to clarify the medium range structure by various experimental techniques.
Among them, it has been reported that small angle scattering appears in a Q range < 1 Å-1 by X-ray
and/or neutron scattering [8-11]. The characteristic peak is called first sharp diffraction peak (FSDP).
The presence of FSDP indicates that some structural ordering exists on a length scale 〜7 Å.
[12,13].
For the development of future applications, it is indispensable to advance a fundamental
understanding of microstructures and overall results of them on the macroscopic properties.
Transmission electron microscopy (TEM) is one of the most powerful tools for studying
microstructures of materials at a nanoscopic level [14,15]. Figure 1 shows the interactions ofelectrons with matter. Incident electrons are scattered by specimens, and transmitted electron forms
TEM images and electron diffraction patterns. In addition, we can analyze chemical composition andelectronic structure by detecting characteristic X-ray and inelastically scattered electrons at a local
area. In our previous report [16-18], we have analyzed microstructures of (CuI)x-(Cu2MoO4)1-x

1

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glasses. In the report, the electrical conductivity increases with finely dispersion of nano-crystalline
CuI of 2-3 nm in diameter in the (CuI)0.52-(Cu2MoO4)0.48 anneal at 463 K [16]. In the report, we havealso reported that CuI clusters such as [CuI4]-tetrahedra are finely dispersed and connected eachother among Cu2MoO4 glassy matrix most probably constructed of [MoO4]-tetrahedra and[MoO6]-octahedra [16]. In addition, we have reported that nanoscale phase separation of 5-10 nm insize has been clarified by high-angle annular detector dark field (HAADF) images and energy
dispersive X-ray spectroscopy (EDS) experiments, and the size is correspond to the correlation
estimated by FSDP wave number [18]. We need further information to clarify the correlation
between microstructures and ionic conductivities. In this paper, we have studied structural changesin (CuI)x-(Cu2MoO4)1-x in the vicinity of glass transition temperature (Tg) by thermal treatment andTEM, and discussed the relationship between nano-crystalline size of CuI and the ionic conductivity.

Fig. 1 Interactions of electrons with matter.

2. Experiment
CuI, Cu2O and MoO3 powders were capsuled in quartz tubes in purified Ar atmosphere andquenched in ice water from the melt at 973 K. The phase identification and macroscopic
homogeneity were examined by conventional powder X-ray diffraction using an X-ray
diffractometer (Rigaku) with a CuKα incident beam. Thermal analysis was performed by adifferential scanning calorimeter, DSC (Seiko Instrument; DSC220) at a heating rate of 0.333K/sec
under an argon gas atmosphere. The electrical conductivities were measured by complex impedance
analysis, using a Solartron SI1260 impedance gain/phase analyzer. In the frequency range from 1 Hz
to 1 MHz over the temperature range from 300 K to 350 K in N2 atmosphere, using a furnace.
Observation of TEM was performed with JEOL JEM-3000F and JEM-2100F microscopes operating
at 300 kV and 200 kV, respectively. In this study, we observed specimens by ion-milling techniquesand crushing method. To observe the morphology of specimens at a low magnification, we had toensure a wide observation area. Thus, specimens were prepared by the standard ion milling
techniques using a Fischione Model 1010 at an accelerating voltage of 3.0 kV. In addition, we
observed specimens by the crushing method. Because we had to confirm that the specimens are notaffected during the ion-milling processes at a local area. Bulk specimens were crushed into finepieces and put onto microgrids covered with carbon films. In determining the lattice spacings, Si
powder (5N) was used as an internal and external standard.
3. Results
In the compositional range of 0.40≦x≦0.52, X-ray diffraction patterns show that the specimens
are fully amorphous phase. However, further increasing in CuI content to 57 at.% results in theformation of crystalline phase, which is identified as cubic CuI [17]. Figure 2 shows DSC curves of(CuI)x-(Cu2MoO4)1-x for x=0.40. 0.52 and 0.57 prepared by quenching into water. A DSC curve for2Transactions on GIGAKU 2(2)(2014)S01008/1-6(CuI)0.52-(Cu2MoO4)0.48 annealed at 463 K is also shown for comparison. Tg and Tc denote glasstransition and crystallization temperatures, respectively. In the compositional range of 0.40≦x≦
0.52, the DSC traces show an endthermal shift due to the glass transition at about 400 K and two
significant exothermic peaks. In (CuI)0.57-(Cu2MoO4)0.40, the DSC trace also shows an endthermal
shift due to the glass transition in spite of the existence of CuI crystals. On the other side,(CuI)0.52-(Cu2MoO4)0.48 annealed at 463 K doesn’t show an endthermal shift due to the glasstransition.
Figure 3 shows the electrical conductivities of (CuI)x-(Cu2MoO4)1-x for x=0.40. 0.52 and 0.57
prepared by quenching into water and annealed at various temperatures. Total electrical conductivity
values were determined by complex impedance analysis. The bulk impedance is attributed to thesemicircles, which are observed at each temperature, and thus the bulk resistance is identified as theintersecting point of the semicircle with the real axis.
In (CuI)0.52-(Cu2MoO4)0.48, the conductivity of the sample annealed at 393 K is slightly higher
than that of as-quenched one. Furthermore, the conductivity significantly increases by about 50 %after the heat treatment at 463 K.
In the compositional range of 0.40≦x≦0.52, electrical conductivity increases with increasing
CuI content. However, the electrical conductivity value of (CuI)0.57-(Cu2MoO4)0.43 sample is much
lower than that of (CuI)0.52-(Cu2MoO4)0.48 one, in spite of the increase of the CuI content associatedwith the ionic conduction.
In (CuI)0.40-(Cu2MoO4)0.60, the conductivity of the sample annealed at 393K is slightly higher
about 10% than that of as-quenched one. Annealing below Tg causes a slight increase of the
conductivity. This result is the same as that of (CuI)0.52-(Cu2MoO4)0.48 one. Further annealing at
443K, the conductivity decreases in the magnitude of about one order compared with that foras-quenched one.

Fig. 2 DSC curves of (CuI)x-(Cu2MoO4)1-x for
x=0.40. 0.52 and 0.57 prepared by quenching
into water and (CuI)0.52-(Cu2MoO4)0.48 annealed
at 463 K.

Fig. 3
Electrical conductivities of(CuI)x-(Cu2MoO4)1-x for x=0.40. 0.52 and 0.57
prepared by quenching into water and annealed
at various temperatures.

Next, we analyze microstructures by TEM in order to clarify the relationship between structuralchanges and ionic conductivity. Figure 4 shows a bright field (BF) image, a high-resolution electron
micrograph (HREM) image and corresponding selected area electron diffraction (SAED) pattern forthe sample of (CuI)0.52-(Cu2MoO4)0.48 annealed at 393K. As shown in Fig. 2, this temperature is3

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below Tg. In Fig. 4(a), the BF image is featureless at relatively low magnification. Furthermore, theHREM image shows a maze pattern that is typical for an amorphous structure. The SAED shows aclear halo-ring contrast that is also typical for an amorphous phase. These results indicate that the
sample is a structurally homogeneous glass below Tg.

Fig. 4 (a) A bright field TEM image and a corresponding SAED pattern and (b) an HREM image for(CuI)0.52-(Cu2MoO4)0.48 annealed at 393 K.
Figure 5 shows a BF image and corresponding SAED pattern for the as-quenched sample of(CuI)0.57-(Cu2MoO4)0.43. The HREM image shows that nano-scale particles of 3-5 nm in diameter are
dispersed uniformly. The SAED shows diffuse rings with a small number of spots and the latticespacing (d-spacing) corresponding to each ring provides a good match to the known d-spacing for
cubic CuI. These results are consistent with those for X-ray diffraction patterns [17]. The spacing of
the crossed fringes as indicated in Fig. 5(b) is 0.35 nm, which corresponds to the spacing of the (111)plane of cubic CuI. Structural inhomogeneities of the glass/crystal composites with a length scale of
3-5 nm have been clearly observed from the HREM images.

Fig. 5 (a) A bright field TEM image and a corresponding SAED pattern and (b) an HREM image forthe as-quenched sample of (CuI)0.57-(Cu2MoO4)0.43.
Figure 6 and Figure 7 show HREM images and corresponding SAED patterns for(CuI)0.40-(Cu2MoO4)0.60 annealed at 393 K and 443 K, respectively. In Fig. 6, the HREM image
shows a maze pattern that is typical for an amorphous structure for the annealed sample below Tg.
The SAED pattern shows a clear halo-ring contrast that is also typical for an amorphous phase.
These result indicate that (CuI)0.40-(Cu2MoO4)0.60 sample is a structurally homogeneous glass below
Tg. On the other side, a lattice fringe is observed for the sample annealed at 443 K. The spacing is0.35 nm, which corresponds to the spacing of the (111) plane of cubic CuI. In addition, a
Debye-Scherrer ring pattern of other unidentified phase has been observed in the SAED pattern as4

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shown by the arrow. At this stage, we cannot identify that phase. This result indicates thatcrystallization into CuI and other unidentified phase occurs at the first step crystallization. The
nano-crystallization process of (CuI)0.40-(Cu2MoO4)0.60 sample is quite different from that of(CuI)0.52-(Cu2MoO4)0.48 one.

Fig. 6 An HREM image and a corresponding
SAED pattern for (CuI)0.40-(Cu2MoO4)0.60
annealed at 393 K.

Fig. 7 An HREM image and a corresponding
SAED pattern for (CuI)0.40-(Cu2MoO4)0.60
annealed at 443 K.

Table 1 List of formation of phase, crystalline size of CuI, electrical conductivities at 323 K andactivation energy for conduction of (CuI)x-(Cu2MoO4)1-x prepared quenching into water andannealed sample at various temperatures.
SampleFormation of phase
CuI size
Ea (eV)
σ323K-2(10 S/cm)
x=0.57 as-quenched
CuI + glass
3-5 nm0.443
0.323
x=0.52 annealed at 393 K
glass single phase
not observed
1.21
0.292
x=0.52 annealed at 463 K [16]CuI + amorphous
2-3 nm0.812
0.281
x=0.40 annealed at 393 K
glass single phase
not observed
0.397
0.285
x=0.40 annealed at 443 K
CuI + unidentified phase3-5 nm
0.01590.407
4. Discussion
Here, we discuss the contribution of microstructures to the Cu+ ionic conductivity. It has beenrecognized that size control is important factors for the enhancement of the ionic conductivity [19].
Therefore, we now focus on nano-crystalline size of CuI and discuss the relationship between
nano-crystalline size of CuI and the ionic conductivity. Formation of phase, crystalline size of CuI,
electrical conductivities at 323 K, σ 323K, and activation energies for conduction of(CuI)x-(Cu2MoO4)1-x as-quenched and annealed sample at various temperatures are summarized inTable 1. Referenced data is also shown for comparison [16]. Nano-crystalline size of CuI is
estimated from TEM images in Figs. 4-7. Activation energies for conduction, Ea, were estimated by
the Arrhenius-type equation
σT=A exp(—Ea/kT)
where σ is the electrical conductivity, A the pre-exponential factor, k the Boltzmann constant andEa the activation energy for conduction. In x=0.52 sample, after the first step crystallization of
precipitating nano-crystalline cubic CuI of 2~3 nm in diameter, its electrical conductivity increases
by about 50%. This result indicates that increase in the conductivity of the annealed sample is causedby the nano-crystallization of cubic CuI. On the other side, the electrical conductivity of x=0.57
sample is much lower than that of x=0.52 one, in spite of the existence of nano-crystalline CuI of 3-5
nm in size. In addition, conductivity decrease in x=0.40 sample annealed at 443K, in spite of theexistence of nano-crystalline CuI of 3-5 nm in size. In Fig.7, a Debye-Scherrer ring pattern of otherunidentified phase has been observed in the SAED pattern. This result shows that the crystallization
into CuI and other phase with increasing temperature accelerates a decrease of the conductivity.

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These results imply that nano-crystallization is one of the important factors for the enhancement of
the ionic conductivity. However, there must be other factors for the enhancement of the ionic
conductivity. The first point to be considered is the increase of the volume fraction of
nano-crystalline CuI and percolative connection among the nano-crystalline CuI clusters. The second
point is interfacial structures between nano-crystalline CuI clusters and matrix phase constructedwith oxyanions as proposed by Adams et al. [20]. In this study, we could successfully control
microstructures of CuI-Cu2MoO4 superionic conducting glass at a nanoscopic level by thermal
treatment. However, it is difficult to estimate the amount of nano-crystalline CuI by HREM.
Therefore, the contributions have not been quantitatively clarified yet at this stage. Precise analyses
are necessary to reveal the detailed local structures on the interface between nano-crystalline CuIclusters and residual oxyanion matrix.
5. Conclusion
Microstructural changes in CuI-Cu2MoO4 superionic conducting glass in the vicinity of Tg havebeen studied by thermal analysis and TEM. In (CuI)0.52-(Cu2MoO4)0.48, nano-crystalline CuI of 2-3
nm in size disperse in amorphous matrix after the heat treatment above Tc. Its electrical conductivity
significantly increases by about 50 % due to then nano-crystallization. On the other side, ionic
conductivity decreases due to the crystallization of CuI of 3-5 nm in size in (CuI)0.40-(Cu2MoO4)0.60.
We can successfully control microstructures at a nanoscopic level by thermal treatment.
Nano-crystallization is one of the important factors for the enhancement of the ionic conductivity.
However, there must be other factors for the enhancement of the ionic conductivity.
Acknowledgments
This work partly supported by Grants-in Aid for Scientific Research (C) Ministry of Education,
Culture, Sports, Science and Technology, Japan (No. 23560827) and by Program for High Reliable
Materials Design and Manufacturing in Nagaoka University of Technology.
References[1] C.A. Angell, Annu. Rev. Phys. Chem 43 (1992) 693.
[2] T. Minami, T. Katsuda and M. Tanaka, J. Non-Cryst. Solids 29 (1978) 389.
[3] N. Machida, M. Chusho and T. Minami, J. Non-Cryst. Solids 101 (1988) 70.
[4] T. Matsui and J.B. Wagner, Jr., J. Electrochem. Soc. 124 (1977) 300.
[5] M. Saito, K. Sugiyama, E. Matsubara, K.T. Jacob and Y. Waseda, Mater. Trans. JIM 36 (1995)
1434.
[6] K. Suzuki, K. Shibata, T. Tsurui and J. Kawamura, J. Non-Cryst. Solids 232-234 (1998) 278.
[7] C.P. Varsamis, E.I. Kamitsos, N. Machida and T. Minami, J. Phys. Chem. B101 (1997) 3734.
[8] M. Tachez, R. Mercier, J.P. Malugani and P. Chieux, Solid State Ionics 25 (1987) 263.
[9] L. Borjesson, L.M. Torell, U. Dahlborg and W.S. Howells, Phys. Rev. B39 (1989) 3404.
[10] A.P. Solokov, A. Kisliuk, M. Soltwisch and D. Quitmann, Phys. Rev. Lett. 69 (1992) 1540.
[11] L. Borjesson, A.K. Hassan, J. Swenson and L.M. Torell, A, Fontana, Phys. Rev. Lett. 70 (1993)1275.
[12] M. Aniya, J. Non-Crystalline. Solids 354 (2008) 365.
[13] M. Aniya and J. Kawamura, Solid State Ionics 154-155 (2002) 343.
[14] T. Tsurui, T. Katsumata and Y. Inaguma, Philosophical Magazine 89 (2009) 843.
[15] T. Tsurui, M. Wataru, M. Watanabe, T. Katsumata and Y. Inaguma, J. Electron Microscopy 58(2009) 349.
[16] T. Tsurui, J. Kawamura and K. Suzuki, J. Non-Crystalline. Solids 353 (2007) 302.
[17] T. Tsurui, J. Kawamura and K. Suzuki, Solid State Ionics 177 (2006) 2605.
[18] T. Tsurui and J. Kawamura, J. Non-Crystalline. Solids 357 (2011) 132.
[19] N. Sata, K.Eberman, K. Eberl and J. Maier, Nature 408 (2000) 946.
[20] S. Adams, K. Hariharan and J. Maier, Solid State Ionics 75 (1995) 193.
(Received; 23rd December, 2013, Accepted; 5th May, 2014)6

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Feasibility Study on a Stand-Alone Photovoltaic Hybrid
Mini-Grid Power Generation System to Promote the
Rural Electrification-Rate in Mandalay Region of Myanmar
1)

Aung Ze Ya1,*
Associate Professor, Department of Electrical Power Engineering,
Mandalay Technological University, Mandalay, Myanmar*E-mail: dr.aungzeya010@gmail.com

It is evident that the major thrust of this comprehensive research is developed
to boost the forward prospect of the rural electrification-rate in Mandalay
Region of Myanmar. Furthermore, it is intended to implement the Global
Strategic reduction of the fossil fuel utilization as well as Green House Gas
Emission. There are 102 households in the proposed Ma Gyi Pin (West)village of Towntha township. The total daily primary demand is 95
kWh/day and 14 kW peak. As a consequence, the total daily deferrable
demand is estimated as 108 kWh/day and 50 kW peak. Different PV Hybrid
Models are simulated by applying reliable and effective software tool,
HOMER. Model A is investigated with 14080 simulations on 300
sensitivities. Then, Model B is modified based on the outcomes from Model
A and its feasibility is analyzed with 3168 Simulations on 300 Sensitivities.
From its result-list of feasible configurations, the most appropriate scenario isselected. Its simulated results are Levelized Cost of Energy (COE) 0.175 $/kWh (172 Myanmar Kyats/kWh), Net Present Cost 165818 $ (162833276
Myanmar Kyats), Operating Cost 6004 $/yr (5895928 Myanmar Kyats/yr). In
the selected scenario, 20 kW PV System and 30 kW Biomass Generator are
involved as the Green Generators. Thus, the PV Hybrid Mini-Grid Power
Generation Model provided with mixed-coupled AC-DC bus architecture andthe maximum RE fraction is addressed to uplift the quality of life of villagers
and promotion of Rural Electrification-Rate in Mandalay.

1. Introduction
A. Motivation
About 70 % of total population lives in the rural areas of Myanmar. Then, there are more than
65000 villages and about 66 % of these villages have no access to electricity. Thus, demonstrative
research generation project with strategic planning, technology selection and practical effective
solution is urgently needed to fulfill the required energy. The stand-alone PV-Biomass Hybrid
System is emphazied to harness its rich Solar and Biomass Energy potentials for generation ofelectrical energy in central dry zone area.
B. Objectives
The objectives of this research are to promote rural electrification-rate by utilizing RE resources
and to support National Development Programmes of Clean, Green and Sustainable Society in rural
areas. It is also intended to support the implementation of Millennium Development Goals (MDG).
C. Methodology
Demand side analysis covers not only for present loads (Pagoda, 3 Monasteries, 102 householdswith population of 510 peoples) but also for future loads (primary school, preschool, library, clinic,
street lighting system).
The methodology of conceptual bottom-up modeling and analyzing stand-alone PV hybrid
micro-grid system is performed by changing the input parameters link with the sensitivity values.
After simulation, the most appropriate generation-mix is selected from the sensitivity results as well
as from the optimization results.
The supportive tool, HOMER, is developed by the U.S. National Renewable Energy Laboratory(NREL) to assist in the design of micro-power systems and to facilitate the comparison of power
generation technologies across a wide range of applications [1].

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2. Development of Sustainable Paradigm for Target Site
In this feasibility study, PV hybrid micro-grid system was architecturally modelled and analyzed
comprehensively in HOMER based on the mutual elaboration between the experience on site visit,load profiles, availability of RE resources, socio-economic background, technological selections,environmental conservation, financial evaluations and sensitivity analysis of different scenarios.
A. Target Site Location
The target site, Ma Gyi Pin (West) village is situated in the Taungtha Township of Myingyan
District in the Mandalay Region, Central Dry Zone Area of Myanmar as mentioned in Fig. 1. It is
located at 11.27 km South to Administrative Seat of Taungtha Township, Taungtha Town and 13.27
km South of Myingyan Town. The interest village has 102 households and estimated population isabout 510.

Fig. 2 Demands for Pagoda’s Precinct
Fig. 1 Location of the Taungtha
B. Selection of Suitable RE
Either strong potential or availability of RE is the dominant issue considered before selecting theappropriate RE in order to accomplish the objectives of this research. As mentioned earlier, thisvillage is located at solar plentiful area. Moreover, the large area of its topography is characterizedwith paddy fields, farmlands, toddy-palms and cowsheds, blessing with enormous biomass energy
resources. So, PV and biomass energies are selected as the main energy components to be utilized.
C. Demand Side Analysis
Demand side analysis was performed based on the outcomes from site visit at this village during
the April in the Year 2012.
(1) Daily Load Demands for the Pagoda’s Precinct
The treasure of religious heritage of this village is the “Thaung Gyi” Pagoda which founded by
Bagan King Anawrahta. It is situated on the small eastern mountain, the strategic site and visible fromall sides. Images of the Lord Buddha, one Buddhist ordination building, the stairways, the religious
buildings on the Pagoda’s platform and nine satellite Pagodas are included in this precinct. Fig. 2
mentions the primary loads and the estimated daily demands of it. The mentioned LEDs in Fig.2 isconsidered to use for 12 hours period (from 6 p.m. to 6 a.m.) and other lamps are used for 4 hours
period (from 6 p.m. to 10 p.m.). Then, the total power rating is 1.41 kW and demand is 10.44 kWh.
(2) Daily Load Demands for Three Monasteries
There are three Monasteries in this village, known as Eastern Monastery, Southern Monasteryand Northern Monastery due to their respective location. Among them, the first one with religious
meditation centre is located near Pagoda. Due to field study, the required energy for the Eastern
Monastery is larger than others. After systematic evaluations of primary loads, the combinedmonthly average for hourly demand scenario of Monasteries is obtained as shown in Fig 3. Similarly,
the combined demands of deferrable loads for Monasteries during March to June, July to Octoberand November to February are calculated as 32.80 kWh/day, 33.76 kWh/day and 32.39 kWh/day.

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Transactions on GIGAKU 2(2)(2014)S01009/1-7(3) Daily Demands for Ma Gyi Pin (West) Village’s Schools, Library and Clinic
There are no schools, library and clinic in this village. But, the daily demands of the Preschool,
Primary school, self-reliance library and clinic are evaluated in order to promote rural education careand health care sectors regarding future load growths. The daily time period for schools is from 9:00
(a.m.) to 3:00 (p.m.). Then, the library is opened between 6:00 (p.m.) to 10:00 (p.m.) and clinic is
generally considered as 24 hour service. Loads are determined with assuming constant trends andresults of hourly demands are expressed as in Fig. 5. Then, the combined demands of deferrable
loads for these places are predicted as11.3 kWh/day for the whole year.
(4) Daily Load Demands for Ma Gyi Pin (West) Village’s Street Lighting System
Although there is no street lighting system in this village, efficient and reliable street lighting is
considered with 25 W high power LED lamps and 25 ft wooden poles. Number of required poles is
30 and daily operating hours are imposed as 12 hours (from 6 p.m. to 6a.m.). Total power rating anddaily demand for street lighting system are calculated as 0.75 kW and 9 kWh /day.
(5) Daily Load Demands for Ma Gyi Pin (West) Village’s Households
In Ma Gyi Pin (West) village, most of the villagers work the agricultural business. They take raw
materials from toddy-palms and agricultural farms. Then, they produce Myanmar traditional snacks
and sell at Taungtha market. Figures 4 and 6 are evident photographs that reflected the daily works.
Collecting income data of households (HHs) was difficult due to the seasonal variations and generallack of consistency. But, incomes were generally observed that 2500, 3000 and 7000 Myanmar
Kyats (MMK). Based on these ranges, it was known that there are 75 Low-Income HHs, 25
Medium-Income HHs and 5 High-Income HHs. Their deferrable loads are predicted as 41.5 kWh/
day; 14.25 kWh/day and 14.25 kWh/day for Low, Medium and High-Income HHs.

Fig. 3 Demand Scenario for Monasteries

Fig. 4 Climbs on the Toddy-Palm

Fig. 5 Demands for Schools, Library, Clinic

Fig. 6 Preparing Toddy-Palm Sap Pots

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D. Stand-Alone PV Hybrid Power Generation Models in HOMER
Two PV Hybrid Mini-Grid Models of this paper are shown in Fig. 7 and Fig. 8. The primarydemands and deferrable loads are combined and the total demand scenarios are obtained. These
scenarios of primary and deferrable load inputs are depicted in Fig. 9 and Fig. 10. Solar resource
data is taken from [2] assuming that this village and Taungtha Township have same solar radiations.
Monthly solar radiations and scaled annual average solar radiation (5.54 kWh/m2/day) are put with
15 sensitivity values as mentioned in Fig.11. Besides, 8 tonnes/day of Biomass is inserted and 4
sensitivity values of Biomass feedstock prices (0, 10, 20, 30, 40) $/t are also inserted. Table 1
obviously expressed the input-matrix that inputted into HOMER.
Table 1 Input-Matrix of PV Hybrid Mini-Grid Models for Ma Gyi Pin (West) Village
Equipment

Capi-

Replace-

O&

Sizes

Sensitivity Values

tal

ment

M

to

(Cost Multiplier of Capital,

Cost

Cost

Cost

ConsiderReplace, O&M)

($)

($)

Model A

Model B

[10,15,(1,0.99, 0.98,(1,0.99,0.98,

30, …,

20, …,

0.97) Link

0.97) Link

90,100]40,50]

with Diesel

with Maximum

kW

kW

Prices ($/L)

Shortage10[10,15,2[10,15,(1,0.99, 0.98,(1,0.99, 0.98,($/yr)

0, 25,

20, 25,0.97) Link

0.97) Link

4KS25P30, 35,30, 35,

with PV Cost

with PV Cost

4V,1900 Ah)

40, 45]40,45]

Multipliers

Multipliers

60 kW[30,…,--(1,0.99, 0.98,-

PV Module

2106

1755

(1 kW)

Battery

1221

1163

(Surrette

Bio Gen:

1000

833

(1 kW)
Diesel

Model

Model

A

B

5

[10,20,($/yr)

0.02($/hr)

9552

6552

Gen:

0.8

[10, 20,($/hr)

50, 70,0.97) Link

80,90,

with Diesel

100] kW

Prices ($/L)(10 kW)
Converter

2608

2608

60]kW
-

40

[4.4, 5,

[4.4, 5,

(1,0.99, 0.98,(1,0.99, 0.98,($/yr)

6, 8, 10,

6, 8, 10,0.97) Link

0.97) Link…, 18]…, 18]

with PV Cost

with PV Cost

kW

kW

Multipliers

MultipliersFig. 7 PV Hybrid Mini-Grid Model A

Fig. 8 PV Hybrid Mini-Grid Model B

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Fig. 9 Scenario of Primary Load Inputs

Fig. 10 Scenario of Deferrable Load Inputs

Fig. 11 Radiation Inputs with Sensitivities

Fig. 12 Money Rates and Fuel Prices

9th -ranked

Fig. 13 Completed Model A Simulation

Fig. 14 Simulated Results of Model A

Consequently, the diesel fuel prices are needed to input. Then, dynamics of American currency’s
exchange rate and diesel oil prices during November and December of the year 2013 from daily
newspapers of Myanmar are collected and illustrated as shown in Fig.11. By referring these values,
the average value on recent diesel prices and maximum exchange rate are predicted as 0.93 $/L and
982 Myanmar Kyats (MMK)/$. To perform sensitivity analysis on diesel fuel prices for pricegrowth, 0.94 S/L, 0.95 $/L and 1 $/L are also inputted into HOMER software.

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3. Simulated Results and Discussion
Based on input-matrix mentioned in Table 1, mixed-coupled PV Hybrid micro-grid systems are
simulated in HOMER. Firstly, the scenario of Model A is analyzed with 14080 simulations on 300
sensitivities. Its simulation process took 8 hours 2 minutes and 36 seconds as depicted with the oval
ring in Fig. 13. As can be seen from Fig. 14, small circle highlights that Diesel Generator is started toconsider at the 9th-ranked scenario and not at the top of overall tabular list. This issue points out thatthe participating of Diesel Generator in this Model cannot create the high ranked scenario.
Furthermore, large circle mentions that even if the first-ranked scenario is chosen in this Model A, its
energy cost 0.282 $/kWh (277 Myanmar Kyats/kWh), is expensive tariff range for rural dwellers. So,
Diesel Generator is decided to remove and observed to know the impact of this change.
Therefore, the new upgraded version, Model B, is provided with necessary modifications.
Sensitivity Variables can be easily changed and analyzed in HOMER software. The significant
change is provided for PV sensitivity values that linked with Maximum Annual Capacity Shortage (0,5, 10 and 15 %). Then, 3168 Simulations on 300 Sensitivities of the Model B is performed in 5 hours
4 minutes and 36 seconds. Thus, Model B takes shorter time with better scenario than Model A.
According to the sensitivity results of Model B expressed in Fig. 20, there are also two scenarios
to be analyzed. Comparing the overall electrical production results and others outcomes from two
scenarios (the first-ranked and fourth-ranked scenarios), Fig. 17 to Fig. 20, Scenario 1 is better than
Scenario 2 due to more excess electricity and more economical at the project life of 25 yr. Thus,
Scenario 1 is selected. It is mixed-coupled AC-DC system of 20 kW PV, 30 kW Biomass Generator,
10 Surrette 4KS25P Battery and 8 kW Converter. Its optimization results are Levelized Cost of
Energy (COE) 0.175 $/kWh (172 Myanmar Kyats/kWh), Net Present Cost 165818 $ (162833276
Myanmar Kyats), Operating Cost 6004 $/yr (5895928 Myanmar Kyats/yr) and 5410 kWh/yr.
Scenario 1
Scenario 2

Fig. 15 During Simulation of Model B

Fig. 16 Sensitivity Results of Model B

Total NPC: $165818
Levelized COE: 0.175 $/kWh

Total NPC: $161479
Levelized COE: 0.170 $/kWh

Excess Electricity:
5410 kWh/yr(6.33%)

Excess Electricity:
3920 kWh/yr(4.64%)

Fig. 17 Electrical Analysis of Scenario 1

Fig. 18 Electrical Analysis of Scenario 2

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Fig. 20 Cash Flows of Scenario 2

Fig. 19 Cash Flows of Scenario 1

4. Conclusion
The proposed model only relies on Renewable Energies to implement the Environmental
Conservation Purpose and Green Energy Evolution. Although there are other Models studied at the
beginning stage of this research, only Model A and B are mentioned. Some Models took longsimulation times. However, the outcomes from these Models were not optimum from the practicaland economical point of views. Therefore, these are not expressed in this paper.
Utilization of electrical loads with high efficiency and saving energy can drive the forward
dynamics of Sustainable Paradigm in rural villages. Thus, demand side analysis is systematicallyemphasized in this comprehensive research. There is no school, library and clinic existing in thisvillage. However, Preschool, Primary school, Self-reliance library and clinic are considered as futureload growth. In the rural areas, it can be regarded as rural library can be the University ofKnowledge and Clinic may be the Hospital of life-saver. To utilize the benefits of HOMER, thedeferrable loads are considered with battery charging, cooking appliances and water pumping
systems that are essential loads. Actually, the power ratings of the cooking appliances are large. Thus,
these are assumed as deferrable loads considering with the cooking time is not fixed.
Levelized cost of energy (COE) is evaluated as 172 Myanmar Kyats/kWh, which is higher thanelectricity tariff from conventional grid connected system at present. However, COE from remotesupply systems in the country may be higher than that value. Thus, COE form the developed
PV-Biomass Hybrid Mini-grid system is cheaper than these systems.
To summarize, the results obtained from 3168 Simulations with 300 Sensitivity Variables
highlight that the developed Model B of PV-Biomass hybrid system is obviously feasible. Generally,
most of the villages in Mandalay Region have similar culture, profile and life styles. Therefore, thisresearch can enhance the access of electricity and promote the rural electrification rate in this area.
Acknowledgments
First of all, the author wishes to express the deepest gratitude to his parents and uncles for theirkind supports and encouragement. Especially, the author is deeply grateful to Dr. Myint Thein, ProRector, Mandalay Technological University, for his noble guidance and support. In addition, theauthor sincerely appreciates to Dr. Yoshiharu MUTOH, Executive Director, Vice President,
Nagaoka University of Technology, for opening and successfully celebrating of ICEEHE 2013 inYangon. Moreover, the author offers respectful gratitude to Dr. Yuichi Otsuka, Associate Professor,
Department of System Safety, Nagaoka University of Technology, for his kind guidance. The author
wishes to express his special thanks to New Energy and Industrial Technology Development
Organization (NEDO, Japan), ARI, IEEJ, AIST (Japan) and SERT (Naresuan University, Thailand)
for their valuable supports during NEDO’s capacity building training program in 2011 and 2012.
References[1] T. Lambert, P. Gilman and P. Lilienthal, Micro-Power System Modelling with HOMER, John
Wiley & Sons, Inc. (2006).
[2] Dr. Serm Janijai, Assessment of Solar Energy Potentials for the Union of Myanmar, Solar
Energy Research Laboratory, Department of Physics, Silpakorn University, Thailand, (2009).

7

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Hardware In The Loop Integration Design Of Micro
Avionics System For AEROMAS UAV
Hline Htet Win1*, Moe Kyaw Naing2
UAV Research Section, MyanmarAerospace Engineering University,
2)
UAV Research Section, Myanmar Aerospace Engineering University,
Meikhtila Myo, Mandalay Division,Myanmar*E-mail: mghlinehtet@gmail.com
1)

This paper discusses the integration and testing of Hardware-In- the-Loopsimulation so called HIL System LAB using a commercial Flight Simulation
software package and a custom design Flight Control System. The Flight
Control System for AeroMas UAV is developed and validated using HILSystem LAB before flight test. The ability and potential of simulation system
are increased significantly when the simulation system is linked directly tothe actual flight hardware. The aerodynamic model for AeroMas UAV is builtwith the help of Plane-Maker program bundled with X-Plane Flight Simulator.
X-Plane uses actual flow calculations many times per second to figure out
how the given aircraft flies in the simulated environment. By accessing
X-Plane state variables through plug-in, the real time avionics sensors’ data
are emulated like an actual way. The X-Plane’s plug-in communicate with the
Flight Control Computer through RS-232 serial communication which is the
GPS port of onboard avionics system. The avionics sensor input data of
Flight Control Computer that should be produced by X-Plane are position
data of Latitude, Longitude, Altitude, Speed and attitude data of Roll, Pitch,
Yaw. The internal design parameters of Flight Control Algorithm can beanalyzed and optimized to reduce time and cost effectively during the designphase. Although robust and mature control is not possible, traditional PID
control method is used as Flight Control Algorithm to simplify design
process and minimize required hardware resource. Monitoring of hardware
resource can be possible to observe whether it can response to avionics
sensors’ data efficiently. Due to the nature of simplicity and fidelity the HIL
System LAB can be deployed as a test platform to assist in the studying offlight control systems at Myanmar Aerospace Engineering University. Over
all, the results obtain in this paper shown that the effectiveness of HIL
System LAB as a Simulated Flight Testing Environment and Educational
design tool.

1. Introduction
Hardware-in-the-Loop (HIL) simulation is a kind of real-time simulation that the input andoutput signals shows the same time dependent values as the real process. It is usually used in alaboratory environment on the ground to test the prototype controller under different working loads
and conditions conveniently and safely. HIL simulation has the abilities to simulate UAV flightcharacteristic, sensor modeling and actuator modeling while communicating in real time with the
UAV autopilot hardware. In Myanmar Aerospace Engineering University (MAEU) UAV
development, HIL laboratory is an important step in autonomous system design and development.
UAV autopilot system is adapted with the HIL simulator to see how the overall system works as aclosed loop system.
Development of HIL simulator is based on commercially available simulation software. The
chosen simulation software for HIL simulator development is X-Plane because its function iscustomizable using a plug- in. A plug-in is executable code that runs inside X-Plane, extending what
X-Plane does. These are modular, allowing developers to extend the simulator without exciting the1

Transactions on GIGAKU 2(2)(2014)S01010/1-7

source code to the simulator. It allows to constructs the aircraft model with weight and balance,engine parameter and control surfaces data like real airframe model. The HIL simulator’s plug incommunicates with the UAV autopilot hardware through HIL bridge like as RS232 serial
communication. The reference input data of UAV autopilot hardware that should be produced by
HIL simulator are aircraft navigation data (speed, altitude, latitude and longitude) and attitude data(roll, pitch and yaw). Then, telemetry protocol is sent to ground control station (GCS). At the sametime, ground station monitors the real-time in-flight data by means of visualization. The
requirements of HIL simulation are shown in Fig. 1. HIL simulation can be used to test autonomous
hardware reliability, check the closed loop performance of the overall system and tune thecontroller’s parameter. By rigorous testing in the HIL simulator, the risk in the field trail can be
minimized.

Fig.1 Block diagram of Hardware In the Loop Environment
Aircraft Model Construction
The test UAV that used in the present project is named as AeroMas which is the fifth generation
prototype of MAEU UAV research. The AeroMas UAV as shown in Fig. 2 is a high-wing
tricycle-gear aircraft. It is made up of carbon and glass composited fiber. It is powered by O.S engine
with a shaft horsepower of 3.8 H.P. The wing employs the NACA 4415 airfoil. The level flight speed
is about 121 kilo meter per hour. The aircraft was configured with the 3 liters fuel tank, wirelessvideo payload and flight control computer (FCC). The major dimensions of this UAV are describedin Table 1.
X-plane supports plane maker program. That can create more realistic flight model. Flight
dynamic data for the selected UAV must be entered in detail to plane maker program interface. The
Fig. 3 shows the completed wing assemblies in plane maker after all the wing data and controls data
are entered.

Fig. 2 AeroMas UAV developed in MAEU

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Table 1 Specification of selected aircraft model
Wing Span

3.2m

Maximum speed

121 km/hour

Length2.4 m

Service ceiling1000 mHeight0.355 mEndurance

1.5 hours

Payload weight

2 kg

Recovery scheme

Return to launch

Takeoff weight

11.5 kg

PayloadCameraFig. 3 Building the X-plane flight model
3. THE TEST PLATFORM
The main components of the test platform are as follows:
1. Flight Control Computer containing the autopilot control firmware and HIL interface code,
2. Flight Simulator X-Plane containing the aircraft model to be controlled,
3. X-Plane plug-in for HIL interface,
4. Ground Control Station for in-flight data monitoring.
The test platform configuration is based on the block diagram presented in Fig. 1. The basic
principle of the test platform consists of establishing the communication between X-Plane and flightcontrol computer as the following process. The parameters calculated by the autopilot control
system are sent to X-Plane in order to command the aircraft’s flight control surfaces. The X-Plane
calculates the new aircraft attitude according to the inputs that receive from FCC. The
X-Plane sends those new aircraft attitude parameters back to FCC as a close loop system. FCC
restarts the process by providing updated servo’s data to X-Plane aircraft. The input data sending to
the aircraft’s flight control surfaces from the X-Plane are simultaneously processed at FCC actuator
ports which translate these commands into effective servo movement control. The FCC calculates
the deflection angle to be imposed to the flight control surfaces and generates a proportional PWM
signal to command the servo. The communication between FCC and X-Plane is made through
Universal Asynchronous Receiver Transmitter (RS-232) which is GPS port in actual flight. Block
diagram shown by Fig. 1 illustrates the platform configuration.
The flight simulator X-Pane provides very accurate aircraft models and has a very
important feature: the possibility to exchange data with external systems [4]. Giving its realistic
simulations capability, X-Plane is also FAA (Federal Aviation Administration) approved for pilot
training. The aircraft models simulated in X-Plane are built based on their real physical dimensions,power and weight among other characteristics. X-Plane is not considered a game, but can be
categorized as an engineering tool that can be used to predict the flying qualities of fixed androtary wing aircraft [4].
Most other flight simulators use the stability derivatives method to compute how an airplanedoes. This technique involves simply forcing the aircraft nose to return to a centered position
along the flight path with certain acceleration for each degree of offset from straight-ahead flightof the airplane. This is too simplistic to be used across the entire flight envelope of the airplane.

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Stability derivatives will not normally take into proper account the asymmetric effects of engine
failures, the chaotic effects of turbulence, stalls, spins and the myriad of dynamic effects that
airplane flight generates. In other words, the commonly used stability derivatives are gross over
simplifications of how an airplane flies. In summary, those simulators cannot predict how the
airplane will fly. Basically, the airplane designer teaches the simulator how the airplane should fly,and the simulator reproduces that information right back to the user [4].
X-Plane instead, assimilates the geometric shape of any aircraft and then figures out how thataircraft will fly. It does this by an engineering process called "blade element theory", which involves
breaking the aircraft down into many small elements and then finding the forces on each little
element many times per second. These forces are then converted into accelerations which are thenintegrated to velocities and positions. This method of computing the forces on the airplane ismuch more detailed, flexible, and advanced than the flight model that is used by most other flight
simulators. By doing this process, X-Plane accurately predicts what will be the performance andhandling qualities of an airplane of a given geometry [4].
The FCC represented by block (1) on Fig. 1 symbolizes the Flight Control board dsPIC
manufactured by Microchip Technology Inc and shown in Fig. 4.

Fig. 4 Installation of onboard autopilot system
The dsPIC is based on 33FJ256GP710A microprocessor family with clock of 16 MHz, lowpower consumption, featured with a variety of I/O (digital, PWM), besides RAM and flash memory
space. The choice for this device is justified by its high processing capability as well as easy
integration with digital servos through PWM I/O ports. On block (3) of Fig. 1, it is represented theGround Control Station that monitor the flight under test. The screen view of GCS is as shownin Fig. 5. It receives telemetry data from the FCC and displays the flight parameters in real time.

Fig. 5 Ground Control Station
Flight Simulator X-Plane has an important feature that is essential to this test platform
development. It is its capacity of sending and receiving data to and from other devices. One way to4Transactions on GIGAKU 2(2)(2014)S01010/1-7

implement this communication is by writing plug-in for X-Plane. The plug-in read 'data-refs',
which are various variables inside X-Plane and send data over a network or serial port, or anythingelse the code do.
The X-Plane SDK API gives a generic, flexible, high performance way to read and write data to
and from X-Plane and other plug-ins. For example, the API allows reading and setting the navigation
parameters, getting the plane location, determining the current effective graphics frame rate, etc. The
data access APIs are the way that read and write data from the X-Plane as well as other plug-ins. The
API works using opaque data references. A data reference is a source of data; it do not need to know
where it comes from, but once it gets, can read the data quickly and possibly write it. Data references
are identified by verbose string names (like that, sim/cockpit/radios/nav1_freq_hz). The actual
numeric value of the data reference is implementation and defined to change in each time when thesimulator is running. The task of looking up a data reference is relatively expensive; look up data
references once based on verbose strings, and save the opaque data reference value for the durationof plug-in's operation. Reading and writing data references are relatively fast. This allows data
accessing rate to be high performance, while leaving in abstraction; since data references are opaqueand are searched for, the underlying data access system can be rebuilt. APIs are provided for reading
and writing data in a number of ways.
In the case of roll attitude control, FCC generates the roll angle reference and sends control
signal to X-Plane. X-Plane commands the simulated aircraft according to the inputs received. The
new aircraft roll angle position is sent from X-Plane back to FCC, restarting the process. Under the
FCC processing, the autopilot control system and HIL interface code are responsible for serial data
decode and encode process. On the side of simulation computer, X-Plane simulates the aircraftmodel to be controlled via FCC. Both systems communicate to each other through their COM
port using UART stream. The FCC also sends servo PWM output to actuator that commands themodel aircraft’s flight control surfaces.
4. TEST RESULTS
In order to demonstrate how the test platform could be applied in laboratory classes and also
help autopilot systems development, the autopilot system was designed and integrated into theplatform. The gains Kp, KI and KD of that system were tune base on trial and error basic. The
analytical method of PID gains tuning cannot be done, because of the complexity of aircraft
dynamics model, in other words, the airframe transfer function. Thus, it’s considered the first step
to tune the PID gain on trial and error basis since airframe crash is not a problem in HIL
simulation.
To initialize the simulation, X-Plane is loaded with AeroMas UAV model with plug-in enable.
As soon as the test platform model shown on Fig. 3 runs, the designed autopilot system takes theaircraft control. In principle, for any waypoint data applied as reference into the autopilot system, theaircraft at X-Plane shall respond that references.

Fig. 6 Testing the AeroMas UAV model in HIL Lab

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Table 1 Final gain tuning results of AEROMAS UAVPID Gains

Roll

Pitch

Yaw

Proportional

0.8

0.58

1.0

Integral

0.01

0.085

0.0

Derivative

0.0

0.2

0.0

(a)

(b)

(c)

Fig. 7 (a)Pitch reference and response (b)Roll reference and response(c)Yaw reference and response6Transactions on GIGAKU 2(2)(2014)S01010/1-7

The reference signals are applied as 3D waypoint (Latitude, Longitudinal, and Altitude) inputs,
the system response and also the commands sent to the aircraft flight control surfaces actuators canbe monitored through GCS. In the simulated flight, the input is the 3D waypoint, the system
response is the new aircraft control command and the commands are the deflections to be imposed tothe three control surfaces and Throttle. Fig. 7 shows the results for the proposed attitude autopilot
system implemented at the test platform.
5. Conclusion
HIL simulation lab in the UAV development is proved to be valuable without risking theairframe and other devices. It allows finding the problems of control algorithm, refining thefirmware of autonomous system, testing the reliability of aircraft models, fine tuning and analyzing
the parameters of autopilot system. It also allows monitoring the aircraft responses for a designed
autopilot system with high degree of realism.
The test platform also permits the study of longitudinal and lateral directional movement of a
specific aircraft which the autopilot system is being designed. Due to the effectiveness of HIL
System LAB, it can perform as a Simulated Flight Testing Environment and also an Educational
design tool. It is usually used in a laboratory environment on the ground to test the prototype UAV
models and autonomous controller under different working loads and conditions conveniently andsafely.
Acknowledgments
The author wish to thank his members of UAV project for their work and warmly friendship andalso wishes to extend his thanks to Dr. Kyi Thwin, Rector of Myanmar Aerospace Engineering
University for his support and guidance. Especially, the author would like to express special thank to
the partners for their noble support and encouragement.
References[1] B. Kada, and Y. Ghazzawi, Robust PID Controller Design for an UAV Flight Control System,
vol II, World Congress on Engineering and Computer Science, (2011).
[2] Lucio R. Ribeiro, and neusa Maria F, UAV Autopilot Controllers Test Platform Using
Matlab/Simulink and X-Plane, S2H, 40th ASEE/IEEE frontiers in education conference, (2010).
[3] Marco A.A. Sanvido, Vaclav Cechticky and Walter Schaufelberger, Testing Embedded Control
Systems Using Hardware In the Loop Simulation and Temporal Logic, 15th Triennial World
Congress, (2002).
[4] LAMINAR RESEARCH, X-Plane Description, X-Plane Manual, (2009).
(Received; 23rd December, 2013, Accepted; 2ndh August, 2014)7

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The Potential of Coconut Oil Biodiesel Utilization in the Delta Regionsof Myanmar
Thet Myo1, Eiji Kinoshita2
United Nations Industrial Development Organization (UNIDO),
No. 35(B-15), New University Avenue Road, Yangon, Myanmar
2)
Department of Mechanical Engineering, Kagoshima University,
1-21-40 Korimoto, Kagoshima 890-0065, Japan1)

Coconut oil mainly consists of lower carbon number saturated fatty acids
such as lauric acid(12:0) and myristic acid(14:0). Research experiments haveshown these saturated fatty acids contribute better and cleaner combustion of
coconut oil biodiesel. In Myanmar, coconut trees grow naturally andabundantly in the southern part of country where are delta regions with thetropical weather. But there are only very few small scaled traditional farms
exist. By developing commercial farming, coconut oil will become one of the
major sources for biodiesel production in these delta regions. On the other
hand, the development of renewable energy including biodiesel is one of theobjectives of ongoing Myanmar’s reform. Currently, Myanmar Government
is setting up a new department to promote the renewable energy andpreparing National Energy Policy including the development of renewableenergy sector. Considering renewable energy and environment concerns,
biodiesel is one of the best alternative fuels for diesel engines. From ourprevious research we investigated the utility of coconut oil ethyl ester (CEE)by conducting engine experiments and we have found CEE has favorable fuelproperties, better combustion characteristics and less exhaust emissions
compared to conventional diesel fuel. Therefore, by promoting the coconut
farming in the delta regions of Myanmar, the potential of coconut oil
biodiesel utilization is very positive and benefits for these regions especially.

1. Introduction
Agriculture is a backbone of the Myanmar economy and it contributed 36% of GDP in 2010 [1].
Rice is main crop of Myanmar mostly planted in delta regions where are known as rice granary of
Myanmar. Currently agricultural mechanization is being implemented and the use of agricultural
machineries like farm tractors, harvesters, threshers and water pumps are increasing. Thesemachineries are mainly powered by internal combustion engines. Especially diesel engines aremostly used because of less fuel consumption, more durability and easier maintenance. Considering
renewable energy and environmental concern, biodiesel is one of the best alternative fuels for diesel
engines.
Generally, biodiesel can be produced form vegetable oils by the transesterification process.
Depend on the utilization of alcohol in transesterification process, methyl ester type and ethyl ester
type biodiesels can be processed. From our previous research we have investigated the utility of
coconut oil ethyl ester (CEE) by conducting engine experiments. The experimental results show
CEE has favorable fuel properties, better combustion characteristics and less exhaust emissions
compared to conventional diesel fuel. Therefore we recommend ethyl ester type coconut oil
biodiesel for agricultural machineries in the delta regions of Myanmar. Furthermore, the alcohol for
CEE production, ethanol, has advantages because it is agro product and regionally available.
As mentioned above, although coconut trees grow naturally and abundantly in the delta regionsof Myanmar, only very few small scaled traditional farms exist. The production of coconut in
Myanmar and neighboring ASEAN countries (2008-2012) [2] is as shown in Table. 1. Comparing
between neighboring ASEAN countries, Myanmar has only one third of the production of Thailand
and less than 3% of the productions of Indonesia and Philippines. In these countries, coconut oil
biodiesel is commercially available in the biodiesel market and widely use. Therefore, by promoting
coconut tree plantation in Myanmar, coconut oil is one of the resources for biodiesel production.

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Table 1 Production of Coconut in Myanmar and neighboring ASEAN countries (2008-2012) [2]Country

2008

2009

2010

2011

2012

Myanmar505,014420,393428,075

420,000

430,000

Thailand

1,483,927

1,380,980

1,298,147

1,055,318

1,100,000

Indonesia

17,937,000

19,000,000

18,000,000

17,500,000

18,000000

Philippines

15,319,500

15,667,565

15,510,283

15,244,609

15,862,386(Unit- tonnes)

2. Research on Coconut Oil Ethyl Ester (CEE)
Test fuels
In our previous research coconut oil ethyl ester (CEE), palm oil ethyl ester (PEE), rapeseed oil
ethyl ester (REE) and JIS No.2 diesel fuel were used as test fuels. CEE, PEE and REE were preparedby transesterification reaction of coconut oil, palm oil and rapeseed oil respectively. In thetransesterification reaction, 6:1 molar ratio of alcohol (ethanol) to oil and 1 mass% of potassiumhydroxide (KOH) to oil were used for PEE and REE. For CEE, 8:1 molar ratio of ethanol to oil and1 mass% of potassium hydroxide (KOH) to oil were used due to the lower ester conversion rate of
CEE in transesterification reaction. The reactions were carried out at 60 oC for 2 hours. At the end of
the reaction, the mixtures were kept at ambient temperature of 20-25 oC for 8 hours and then theglycerin was drained off. The residual alcohol in the ester was evaporated and the ester was washedwith water to remove the residual glycerin and other impurities. The same processes were used in thetransesterification reactions of CEE, PEE and REE.
The properties of the test fuels are shown in Table 2. The density, kinematic viscosity, pour point,
Carbon, Hydrogen and Oxygen contents, the 50% distillation temperature and the residuals, such as
alcohol, and glyceride of the test fuels were carried out by measurements, and the remaining
properties were by calculations. The net calorific value of CEE is 35.93 MJ/kg and it is slightly
lower than those of PEE and REE, and a gap different with diesel. The density of CEE is 867kg/m3
and that is slightly lower than those of PEE and REE and higher than diesel. The kinematic viscosityof CEE is 4.5mm2/s and it is lower than those of PEE and REE, and closer to diesel. The pour point

Table 2 Properties of test fuels
Properties
Cetane number
Net calorific value (MJ/kg)
Density @ 288K (kg/m3)
Kinematic viscosity @ 293K (mm2/s)
Pour point (oC)C (mass%)
H (mass %)O (mass %)Alcohol (mass %)Free glycerin (mass %)Monoglyceride (mass %)Diglyceride (mass %)Triglyceride (mass %)50% distillation temp. (oC)
Stoichiometric air-fuel ratio* Calculated value

CEE
35.93 *
867
4.5
-7.5
74.0 *
12.4 *
13.6 *
0.010
3.095
0.629
0.032
285
12.15

2

PEE
37.16 *
869
7.5
7.5
76.0 *
13.0 *
11.0 *
0.001
0.028
0.213
0.748
0.122
329
12.70

REE
36.86 *
873
7.0
-10.0
78.0 *
12.0 *
10.0 *
0.001
0.003
0.178
0.407
0.012
338
12.63

Diesel
56
43.12
832
4.7
-12.5
87.3
12.5
0
278
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of CEE is -7.5 oC, which is lower than PEE and higher than REE and diesel. CEE, PEE, and REE
contain oxygen about 13.6, 11.0 and 10.5 mass% respectively. CEE has higher monoglyceride
contents compared to PEE and REE and only a few differences in diglyceride and triglyceride. These
residual glycerides were traced by gas chromatography.
The fatty acid ester compositions of CEE, PEE and REE shown in Table 3 were measured by gas
chromatography. From these compositions, the net calorific values and the Carbon, Hydrogen andOxygen contents of CEE, PEE and REE were calculated. In Table 3, it can be seen that CEE contains
about 90% lower carbon number saturated fatty acid, such as laurate and myristate in its fatty acid
ester compositions. But in PEE and REE, the amounts of saturated fatty acid are about 50% and 5%
respectively.
Table 3 Fatty acid ester composition
C:N *
8:0
10:0
12:0
14:0
16:0
18:0
18:1
18:2
18:3
-

CEE
5.4
4.4
52.0
17.3
8.8
1.8
6.9
1.3
0.5
2.5

PEE
0.3
1.1
49.5
0.9
38.3
9.1
0.6
0.2

350

REE
4.5
64.1
19.5
10.1
1.8

Distillation temperature ℃

Fatty
id
Caprylic
CapricLauricMyristic
Palmtic
Stearic
Oleic
Linoleic
Linolenic
Others

300

250
CEE
PEE
REE
Diesel

200

150

0

* carbon double bond

25

50
Recovery

75
vol％

100

Fig. 1 Distillation temperature of test fuels
The distillation temperatures of the test fuels are plotted in Figure 1. From Figure 1 it can be seenthat CEE has lower distillation temperature than that of PEE and REE and the temperature trend iscloser to diesel.
Experimental apparatus and procedure
A single cylinder DI diesel engine was used for the experiments. It is a naturally aspirated,water-cooled, four stroke diesel engine. The specifications of the test engine are shown in Table 4.
The standard fuel injection system recommended by the engine manufacturer was used for all fuels.
The experiments were started after the engine warmed up. When test engine reached a stablecondition, the load was applied and the measurements were started. The engine speed was fixed at2000 rpm and the loads were applied from 0% to 25, 50, 75, 100% using an electronic dynamometer.

Table 4 Specifications of test engine
EngineDI diesel engine

Engine type

Single cylinder, Four

Bore x Stroke

110mm x106mm

Stroke volume

1007cm3

Compression ratio

16.3

Rated power

11.77kW@2200rpm

Nozzle opening pressure

19.6 MPa

Injection nozzle typehole ( 0.33mm´4)

Injection timing

18 deg. BTDC

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At 100% load condition the brake mean effective pressure (BMEP) of the test engine was 0.67 MPa.
The exhaust gases were taken from the exhaust system of the test engine and measured by standard
procedure. From the exhaust gases, the CO emission was measured by a non-dispersive infrared
detector (NDIR), the HC emission was measured by a flame ionization detector (FID), the NOx
emission was measured by a chemiluminescent detector (CLD), and the smoke emission wasmeasured by a light transmitting type smoke meter (Opacimeter). Also the cylinder pressure and theneedle lift of the fuel injector were recorded with a digital scope recorder at all load conditions.
Results and discussion
The fuel injection timing, the ignition delay and the ignition timing of the test fuels at
BMEP=0.67MPa are as shown in Figure 2. These values were carried out from the needle lift of thefuel injector and the heat release rate of test engine combustion chamber. The fuel injection timing of
CEE is almost the same as that of the diesel fuel and those of PEE and REE are faster. The fuelinjection timing is mainly influenced by its bulk modulus and viscosity, and the higher bulk modulus
and viscosity have the faster injection timings [3]. As it was reported in reference [4], the bulk
modulus of saturated fatty acid is lower than that of unsaturated fatty acid. Therefore, it can beconsidered that CEE with higher saturated fatty acid has later injection timing compared to PEE andREE.
In combustion, the ignitability of biodiesel depends not only on the fatty acid composition, butalso on the residual impurities in the fuel [5]. These impurities, such as residual glycerides, methanol
and water in biodiesel, may cause negative effects on combustion process. From Figure 2, it can beseen that the ignition delay of CEE is shorter than those of REE and diesel fuel and slightly longer
than that of PEE. So CEE has better ignition ability and combustion than those of REE and diesel
fuel.
The heat release rate and the needle lift of the fuel injector of CEE, PEE, REE and diesel fuel at
BMEP=0.67MPa are shown in Figure 3. By comparing the needle lift intervals of the fuel injector,
the fuel injection duration of CEE, PEE and REE are longer than that of the diesel fuel. Due to thelower net calorific values of CEE, PEE and REE, more fuel quantity is necessary to take place
combustion process and thus it caused the longer fuel injection duration. The peak heat release ratesof CEE, PEE and REE are lower than that of the diesel fuel. The higher initial heat release rate,
rapidly emitted after ignition, is probably because of the greater amount of combustible fuel air
mixture which formed in the longer ignition delay. Therefore, from Figure 2 and 3, it can be foundthat the peak of heat release rates is directly proportional to ignition delay time. The combustion end
timings of all ester test fuels are almost the same.

2000 rpm, BMEP=0.67MPa
Injection Ignition delay Ignition
6.6
6.3
7.5
7.8

−10

Crank angle

CEE
PEE
REE
Diesel−5deg. ATDC

0

Fig. 2 Injection, ignition delay and ignition
The brake specific fuel consumption (BSFC) and the brake thermal efficiency of (BTE) of thetest fuels are shown in Figure 4. BSFC of CEE, PEE and REE are higher than diesel fuel. The
increase in BSFC is due to the lower net calorific value of CEE, PEE and REE. BTE of the CEE,
PEE and REE are almost the same as that of diesel fuel. This indicates that CEE, PEE and REE havecomparable energy conversion rates as diesel fuel.

4Transactions on GIGAKU 2(2)(2014)S01011/1-7

80

90

REE

BMEP=0.67MPa
2000 rpm

60

100

PEE

80

Diesel60
20

40

Needle lift

0

−10

0

10
Crank angle

30

20

20
30
deg. ATDC

0.17 0.34 0.50
BMEP MPa0

Fig. 3 Heat release rate and needle lift

800

CEE
PEE
REE
Diesel2000 rpm

70
60

CEE
PEE
REE
Diesel2000 rpm

700
600
CO ppm80

0.67

Fig. 4 BFSC and BTE

90

HC ppm70

2000 rpm

40

BTE ％

Heat release rate

J/deg

100

110

CEE

g/MWs

CEE
PEE
REE
Diesel

BSFC

120

50
40
30

500
400
300

20

200

10
0

0.17

0.34
BMEP

0.50
MPa

100

0.67

Fig. 5 HC emissions

0

0.17

0.34
BMEP

0.50
MPa

0.67

Fig. 6 CO emissions

Figure 5 shows the HC emissions of the test fuels. The HC emissions of CEE, PEE and REE arelower than that of diesel fuel. This is probably due to the oxygen contents in CEE, PEE and REE
support better combustion process and reduces incomplete combustion. Therefore, the higher oxygen
content CEE has lowest HC emissions compared to PEE, REE and diesel fuel. Moreover, the shorter
ignition delay of CEE may also effect on the reduction of HC emission. The CO emissions of the test
fuels are shown in Figure 6. The CO emission of CEE is slightly higher than those of PEE and lower
than those of REE and diesel fuel. The emission of CO is related with the oxygen content of
biodiesel [6].
Figure 7 shows the NOx emissions of the test fuels. CEE and PEE have lower and REE has
higher compared to diesel fuel. Especially, in the higher load level, CEE has the lowest NOx
emissions. One of the causes of higher NOx emission is higher combustion temperature. Figure8

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1400

1000
800

CEE
PEE
REE
Diesel2000 rpm

20
Smoke ％

1200
NOx ppm

25

CEE
PEE
REE
Diesel2000 rpm

600
400

15
10
5

200
0

0.17

0.34
BMEP

0.50
MPa

0

0.67

0

Fig. 7 NOx emissions

0.17 0.34
0.50 0.67
BMEP MPa
Fig. 8 Smoke emissions

shows smoke emissions the test fuels. The smoke emissions of CEE, PEE and REE are lower thanthat of diesel fuel. CEE shows the lowest and the significant reductions can be seen. This is thehigher oxygen content in CEE increases the oxygen present in fuel rich regions of the fuel spray,
which results in a reduction of soot formation and smoke emission compared to PEE and REE.

3. Conclusion
CEE can be processed from coconut oil, ethanol and potassium hydroxide (KOH) by transesterification reaction. The process is not so complicated and even farmers can produce fuel for theirmachineries themselves at home. From the experimental results, we found that CEE has favorable
fuel properties, better combustion characteristics and less exhaust emissions compared to diesel fuel.
So we confirmed CEE can be utilized for diesel engines and can be considered as an alternative andrenewable fuel for agricultural machineries in the delta regions of Myanmar. By implementing so,
we can achieve many advantages in environment, renewable energy sector and integrated farming
system. Therefore, interest, initiative and both financially and technically encouragement from thelocal and foreign government organizations, UN originations and NGOs are needed and very
important to develop the commercial coconut farming and to establish home industries to large
scaled biodiesel industries. Finally, we conclude that the potential of coconut oil biodiesel utilizationin the delta regions of Myanmar is very positive.
Acknowledgments
The authors would like to thank the students from Heat Engine Laboratory, Kagoshima University
and colleagues from UNIDO Regional Office in Thailand and Yangon Office for their cooperation.
References[1] Toshihiro Kudo, Satoru Kumagai and Hikari Ishido “Agriculture Plus Plus: Growth Strategy for
Myanmar Agriculture”, Institute of Developing Economics (IDE) Paper No. 421, June 2013.
[2] Food and Agriculture Organization, United Nations, Statistical Databases, “Crop Production”
available at http://faostat.fao.org.
[3] Heywood. J.B., “Internal Combustion Engine Fundamentals”, McGraw-Hill: New York (1988)[4] M.E. Tat and J.H. Van Garpen, “Measurement of Biodiesel Speed of Sound and Its Impact on
Injection Timing”, NREL/SR-510-31462, 2003.
[5] Thet Myo, Eiji Kinoshita, Hidenori Tsuru, Kazunori Hamasaki, “Combustion Characteristics of a
DI Diesel Engine with Palm Kernel Oil Biodiesel and Its Blend(B20)” SAE Technical Paper No.
2007-32-0068; SAE International: Warrendale, PA, USA October 2007.
[6] Eiji Kinoshita, Thet Myo, Kazunori Hamasaki, Hiroshi Tajima and Zhang Ru Kun, “Diesel
Combustion Characteristics of Coconut Oil and Palm Oil Methyl Ester” , SAE Technical Paper
No. 2006-01-3251; SAE International: Warrendale, PA, USA, October 2006.

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Transactions on GIGAKU 2(2)(2014)S01011/1-7(Received; 23rd December, 2013, Accepted;15th July, 2014)7

Transactions on GIGAKU 2(2)(2014)S01012/1-6

Measuring and Estimating Quality of Server Side Scripts: PHP
Cho Thet Mon1, Khin Mar Myo2

1)University of Computer Studies ,Mandalay, Myanmar
2) University of Computer Studies, Mandalay, Myanmar*E-mail:chothetmonucsm@gmail.com, kmmyo@gmail.com

The importance of software measurement is increasing leading todevelopment of new measurement techniques. As the development ofobject-oriented software is rising, more and more metrics are being definedfor object-oriented languages. Many metrics have been proposed related tovarious object-oriented constructs like class, coupling, cohesion, inheritance,
information hiding and polymorphism. Even though the traditional software
metrics are modified to assess object-oriented software system, they are
inadequate to cover all the new and unique aspects of object-oriented
languages. Some metrics may not be suitable for some programming or
scripting languages. Thus, new metrics which reflect the characteristics ofobject-oriented paradigm must be defined. This research addresses this needand introduces a new set of metrics for object-oriented PHP code. Since PHP
is an object oriented language, the present metrics are capable to evaluate any
object-oriented language. This paper proposes two metrics to measure andevaluate reusable and readable qualities of PHP codes. The metrics areanalytically evaluated against Weyuker’s proposed set of nine axioms. To
assess and evaluate the effectiveness and usefulness of proposed metrics, theresults of these measures are evaluated and compared with the results of the
Self-Organizing Maps (SOM) .

1. Introduction
Software metrics make it possible for software engineers to measure and predict software
processes, and necessary resources for a project and work products relevant for a softwaredevelopment effort. A software measure provides software engineers with a means of quantifying theassessment of a software product. A software engineer collects measures and develops metrics sothat indicators can be obtained. An indicator is a metric or combination of metrics that provides
insight into the software process, a software project or the product itself, and a metric is aquantitative measure of the degree to which a system, component or process possesses a given
attribute. A number of metrics have been proposed for computer software, but not all provide
practical support to the software designers, developers and managers [1] (William Frakes, and C.
Terry, 1996). To be useful in a real world context, software metric must be simple and computable,
persuasive, consistent, and objective. It should be independent of programming language andprovide effective feedback to the software engineer [2].
Readability of software has something to do with software development and also its quality. Asa matter of fact, readability is the judgment of humans with respect to the ease of reading of given
source code. Readability can promote software maintainability and the overall software quality canbe ensured [3]. The software systems with less readable source code are recognized as moredifficult to maintain than those with more readable source code.
This research develops a novel approach called impact of programming features on codereadability, to examine the influence of programming features and the effect of these features oncode readability.
Achieving a high level of reuse is also an important goal to accomplish in developing a softwaresystem. The measurement of reuse will help developers to monitor current levels of reuse and help toprovide insight in developing software that is easily reused. The level of reuse must be measured in a
way that provides information on a wide range of reuse attributes. Current reuse metrics are
generally based on only one attribute namely program size or length. Moreover, reusability decreases
the space, cost and time of a project and this research considers measuring reusability quality of PHP
files. This paper presents a set of measurable attributes and two metrics which quantify these
attributes for PHP object oriented systems.

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2. Experiment
Nowadays, most software codes are written by distant teams. It’s very important for source codes
to be understandable so that it can be easily adapted. Each programmer may want to review code andadapt it according to his/her own need, so readability is needed. Furthermore, the ability of reading
and understanding a program written by others is a critical job. Software programming companies
depend on team work instead of one programmer, so each one will write a fragment as part of a teameffort. In addition, tailoring a ready code would be very difficult to specify tasks than modify them(e.g., some programs may have thousands of lines of code such as network protocols). Moreover,some programmers may use meaningless variable names, or omit comments that can lead to
unreadable and complex codes. Using unreadable program codes may lead to code misunderstanding.
Therefore, source code’s readability quality is the important factors for software quality
development.
On the other hand, software reuse is the process of implementing or updating software systems
using existing software components. Reusability of object oriented programming is the use ofprevious classes or functions or methods in the present class is having no problem. [4].
Among the several software measurement techniques, most of those measurements and metrics
are developed covering programming languages. With the emergence of the new technologies, newmeasurement techniques also evolve. There is lack of metrics in literature which specificallymeasure the quality of PHP script language. In this respect, specific metrics should be developed andwe develop two metrics for PHP which are capable to calculate source code readability quality of
PHP code (SCREAphp) and reusability quality of PHP code (SCREUphp).
The main goal of this paper is to develop a tool that can automatically collect different readability
and reusability attributes. We then want to see the value of those attributes and their ability or levelof impact on the overall software quality.
2.1 Attribute Selection for Two Metrics
The aim of metric is to predict the quality of the software products.Various attributes which
determine the quality of the software, include maintainability, defect density, fault proneness,understandability, usability etc. The main contribution of this paper is to relate the readable andreusable attributes with the metrics and to find how these metrics can determine the readability andreusability of the object oriented PHP code.
2.1.1 Source Code Readability (SCREAphp )For measuring qualities of PHP language more accurately, more related attributes for each of
qualities are needed to select. These more related attributes are selected according to experts’
judgments in many guide lines. To measure the quality of readability, we tried to extract several
readability related features from the PHP source codes based on PHP coding guidelines andstandards.PHP coding standards are important factors for achieving a high code quality. A common
visual style, naming conventions and other technical settings allow us to produce a homogenous
code which is easy to read and maintain. These PHP coding guidelines are based largely on
Sun's Code Conventions for the Java Programming Language. Deviations from the Sun code
conventions are largely a result of the interaction of PHP with other web server applications, primarily
databases.
To enhance readability, variable names should be camel-case started with a lower case letter.
Readable class names should be descriptive and should be avoided using abbreviations where possible
and must be started with upper case letter [6]. Method names should be camel-case started with alowercase letter. Private and protected attributes have an underscore prefix. Constants should be all
uppercase and words are separated by underscore. Moreover, it is needed to detect when a field has avery short name or very long name.
According to the above factors, in this paper, the number of readable variable names and method
names are defined as names that are declared with lowerk camel-case letter and the length ofvariable or method names are greater than 3 letters. The number of readable class names is definedas the class name started with upper camel-case letter and the length of it is greater than 3 letters.
Moreover, it is needed to detect when a field has a very short name or very long name. The rules
and usage of line length in source code and documentation is an important aspect of coding standards
for many years. A good line length is one that allows the reader’s eyes to flow from the end of one

2Transactions on GIGAKU 2(2)(2014)S01012/1-6line to the beginning of the next very easily and naturally. A common line length: 80 characters for
PHP to maximize readability [7]. The number of blank lines before functions, return, comment or traits
can also lead to readable code.
According to these factors, readable code lines are defined as the number of lines length that areless than 80 characters and to obtain readable lines, they are subtracted from total number of lines.
Having blank lines in source code can increase user’s readability quality but they are needed to bein the reasonable places. In this respect, in the proposed system, blank lines before functions, blanklines before comment, return and traits are considered as readable blank lines for program source
code.
Using program codes that are deviated from the above factors may lead to unreadable code andmisunderstanding that can mainly degrade software quality. Therefore, it is important to consider theabove factors and the required attributes for readability metrics are described in 2.1.1.
In our contribution, readability attributes are considered based on the above factors and thefollowing is readability metric to measure quality of PHP code and w1, w2, w3, w4 and w5 arecorresponding weights using RS method that will explain in the next section.

SCREAphp  TVN * w1  TMN * w2  TCN * w3  TL * w4  TBL * w5

(1)

Where,
TVN- Typical variable names = number of readable variable names/total number of variable names
TMN- Typical method names = number of readable method names/total number of method names
TCN- Typical class names = number of readable class names/total number of class names
TL- Typical line = (total number of lines-line length greater than 80)/total number of lines
TBL- Typical Blank lines = (blank lines before functions+ blank lines before comment+ blanklines before return+ blank lines before traits)/total blank lines
2.1.2. Source Code Reusability (SCREUphp)
Various metrics for measuring the reusability of software have conventionally been proposed,
mainly for program source code, including the previous researches [8-9]. We qualitatively identified
multiple metrics considered to be effective for measuring reusability. In this paper, we proposed a
set of metrics for measuring the reusability of PHP language program source code, and evaluated theeffectiveness of our metrics using a certain scale of actual metric values. The objective of our study
is to provide a validated way for evaluating the ease of reuse of program source code accurately,
without any other software materials such as documents and specifications.

SCREUphp   NDC* w1 C * w2   NPF* w3  PF* w4  NT* w5(2)

Where,NDC- Number of Descendent Class: The NDC is total number of descendent class (subclasses)of a class. The greater the number of subclasses, the greater the reuse, since inherence is a form ofreuse.
C- Cohesion: Assesses the relatedness of methods and attributes in a class, strong overlap in themethod parameters and attributes types in an indication of strong cohesion. High cohesion implies
simplicity and high reusability.
NPF-No of public functions: Total number of functions that are declared public. The more thenumber of functions that are declared public, the greater reusability of code that is because thefunction name that is declared with public can be accessed and reused anywhere from other classes
or the surrounding applications.
PF-Number of functions without parameters: The greater the value, the smaller the amount of(dependent) data required for use, indicating high reusability of the file.
NT-Number of Traits: Total number of traits that are mechanism for code reuse in single
inheritance languages such as PHP. A PHP trait is intended to reduce some limitations of single
inheritance by enabling a developer to reuse sets of methods freely in several independent classes living
in different class hierarchies.
The greater these attribute values, the higher reusability of the source code.

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2.1.3 Assigning Weight
Weighting attribute is a necessary step of the measuring process to get more accurate result.
There are many alternative methods: the equal weights (EW) method, rank sum (RS) method, rank
reciprocal (RR) method, rank-order centroid (ROC) method and rank exponent method [10]. To
assign attribute weight, we need to rank attributes for particular quality based on experts’ judgments.
The important of individual attributes are not the same and their weights are not equal. The
following based attributes are ranked according to quality assurance guidelines.
For the SCREAphp metric: Ranks of Attributes: (NDC ≥ C ≥ NPF ≥ NT)
For the SCREUphp metric: Ranks of Attributes: (TVN ≥ TMN ≥ TCN≥ TL≥ TBL)
In this paper, after ranking attributes with their important, the attributes are assigned with RS
method because it can produce more accurate result than other methods. For readable and reusable
qualities, metrics’ weights are defined as:
R

i

( u )



t  1 
t



i





1



t ( t



1 )

2 ( t

i )

(3)

j

l  1

l= 1,2,3..,t ;µ = a metric ;t = the total number of attributes for µ metric ; i=1,2,3…,t
3. Results and Discussion
The proposed two metrics are analyzed by using thirty PHP files. Most of them are extracted
from the web and these files are not shown details in this paper. The result of two metrics that arecalculated from equation 1 and 2 is shown in figure 1.
The usefulness of any new measurement system should be validated by proper validation andtesting process. A new metric should be theoretically evaluated based on strong theoretical base andvalidated empirically to prove its practical usefulness. There are many evaluation criteria fortheoretical validation of a metric and most of them are based on principle of measurement theory.
The SCREAphp and SCREUphp metrics are validated theoretically and evaluated empirically in thenext sections.

Fig. 1 Related Result of Readability and Reusability
3.1. Theoretical Validation of Proposed Metrics
Amongst available validation criteria, the proposed metrics are proved by using nine Weyuker’s
properties [5]. This work has been discussed by many authors and is still a point of reference andcomparison for anyone investigating the topic of software metrics. Several object oriented metrics
are suitable only for the six Weyuker’s properties, and other properties are not very useful. The casestudy, the evaluation of SCREUphp and SCREAphp metrics against nine Weyuker’s properties are as
follows: let P, Q and R be programs.

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Property 1: (∃P) (∃Q) (|P| ≠ |Q|)
This property states that the measures should not rank all the programs as equally. There are 30
different values and these different files have different attributes. Therefore, the two metrics satisfy
this property.
Property2. Let c be a nonnegative number, and then there are only finitely many programs of
reusability and readability for c. This means that there are only a finite number of PHP files of thesame quality if quality metric value is non-negative number. Therefore, the two metrics satisfies thisproperty.
Property 3: There are distinct programs P and Q such that |P| = |Q|. The proposed metrics satisfy
this property because the same features of different PHP files have the same quality values.
Property 4: (∃P) (∃Q) (P≡Q & |P| ≠ |Q|). If P and Q are different files having the samefunctionality, their corresponding metrics values can be different because of different
implementation. As the proposed metrics are based on the internal structures of codes, they satisfy
this property.
Property 5: (∀P) (∀Q) (|P| ≤ |P; Q| and |Q| ≤ |P; Q|). This property states that if the combined
program is constructed from P and Q, the value of the combined program is larger than the value ofP or Q. The two proposed metrics also satisfy this property.
Property 6: (∃P) (∃Q) (∃R) (|P| = |Q|) & (|P; R| ≠|Q; R|). In the proposed metrics, the numbersof attributes are fixed for any program. Mapping program R with P and Q results the same values.
Hence, the proposed metrics cannot satisfy this property.
Property 7: There are program bodies P and Q such that Q is formed by permuting the order ofthe statement of P and (|P| ≠ |Q|). In this testing, the proposed metrics satisfy this property.
Property 8: If P is renaming of Q, then |P| = |Q|. This property is also satisfied by the two
metrics.
Property 9: (∃P)( ∃Q)(|P| + |Q|) ≤ (|P;Q|). This property means that when two programs are
combined, the metric value of combined program is greater than or equal than the sum of P and Q. If
two files have nearly same attribute features, they can satisfy this property. Otherwise, they notsatisfy.
In this point of view, satisfying nearly all properties of Weyuker except some case, it can berealized that the proposed metrics are robust measures.
3.2 Empirically Validation of Proposed Metrics
For empirical validation of the proposed metric, the SOM, one of the unsupervised learning
methods in Neural Network, is used. SOM is suitable for measuring the validity and usefulness ofthe proposed metrics that they are difficult to prove their validity and usefulness because they don’t
have historical data and other comparison metric. One advantage is that they do not require more
understanding with input data and they can cluster and estimate incomplete historical data. The SOM
learns similarity of input feature vectors and responds groups of similar input vectors [11].
In this paper, only the empirical validation result for reusability metric is shown as an example
and the result for readability metrics cannot be presented here due to space limitations but theprocess is the same. To check validity of proposed metric, the processes are as follow. First, thirty
PHP example files are used as input and are calculated metric values for each file. Before clustering,two data files are created for proposed metric, one contains the particular proposed metric’s target
values of 30 PHP files and another contains its base attributes NDC, C, NPF and NT. In the
preprocessing step, base attributes are converted into binary data according to the “hard logic”
approach using a threshold t. For example, if xi ≥ t then we assume it as 1 and 0 otherwise [12]. And
then, dissimilarity matrix using Jaccard Coefficients is used [12]. Then two files of SCREUphp metricare clustered by SOM and the cluster matching result is analyzed. As shown in figures, the firstgroup of figure 1 and the first group of figure 2 are compared and analyzed and the other two second
groups are also analyzed. It is clear that the two results are almost equal. Therefore the metric has
high accuracy and the proposed metric is suitable for measuring reusable quality of PHP files. The
processes of readability metric validation are the same.

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Fig.2 SOM Clustering Result Depending only on Target
Values of SCREUphp Metric for 30 PHP Files (ID1-ID30)

Fig. 3 SOM Clustering Result Depending on Based Attributesof SCREUphp Metric for 30 PHP Files (ID1-ID30)

4. Conclusion
Traditional software metrics may not be inadequate to cover all the new and unique aspects ofsome programming or scripting languages. In this respect, new metrics which reflect thecharacteristics of object-oriented paradigm are contributed and defined. The proposed metricmeasures the quality of PHP programming language with respect to readability and reusability.
The proposed metrics are validated both theoretically and empirically and it will prove
usefulness and effectiveness of this proposed system. The two metrics are robust measures becauseof satisfying measurement properties and the cluster matching results are very high. Therefore, theproposed metrics are suitable for measuring quality of PHP language. In future work, other quality
factors such as complexity, maintainability, efficiency and so on can be considered.
Acknowledgments
I would like to express my gratitude and my sincere thanks to Dr. Mie Mie Thet Thwin, Rectorof U.C.S.M, allowing me to develop this thesis and giving me more general guidance during theperiod of study. I would like to express very special thanks to Dr. Khin Mar Myo, Associate
Professor, U.C.S.M, for her continuous advice. I would like to express my respectful gratitude to Dr.
Khin Thida Lynn, Associate Professor for giving enough consideration to my ideas and views.
References[1] William Frakes, and C. Terry (1996)”Software Reuse: Metrics and Models,” ACM Computing
Surveys, vol.28, no.2, Pp.415-435.
[2] Roger S. Pressman, “Software Engineering, A Practitioner’s Approach”, Pp 438[3] Rajendar Namani1, Kumar, “A New Metric for Code Readability”, IOSR Journal of Compute
Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727Volume 6, Issue 6 (Nov. - Dec.
2012), Pp 44-48[4] Sonia Manhas, Rajeev Vashisht, Reeta Bhardwaj, “Framework for Evaluating Reusability ofProcedure Oriented System using Metrics based Approach”[5] E. J. Weyuker, "Evaluating Software Complexity Measures," IEEE Trans. Software Eng., vol.
14, no. 9, pp. 1357-1365, Sept. 1988.
[6] http://phpmd.org[7] http://net.tutsplus.com/tutorials/html-css-techniques/top-15 best practices for writing superreadable code
[8] Ajay Kumar,” Measuring Software Reusability using SVM based Classifier Approach”,
International Journal of Information Technology and Knowledge Management January-June
2012, Volume 5, No. 1, pp. 205-209 .
[9] Hironori Washizaki, Toshikazu Koike, Rieko Namiki, and Hiroyuki Tanabe, “Reusability
Metrics for Program Source Code Written in C Language and Their Evaluation”[10] B. S. Ahn and K. S. Park, “Comparing Methods for Multiattribute Decision Making with
Ordinal Weights”, Computers & Operations Research 35 (2008) 1660-1670.
[11] Kohonen, T. (2001). “Self-Organizing Maps”. Berlin, Springer-Verlag[12] Fernando Lourenço, Victor Lobo, and Fernando Bação “Binary-based Similarity Measures for
Categorical Data and their Application in Self-Organizing Maps”(Received; 23rd December, 2013, Accepted; 13th September, 2014)6

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Simultaneous Faults on Medium Voltage Distribution of Myanmar
Electric Power System in Mandalay
Thet Tin
Department of Electrical Power Engineering, Mandalay Technological University,
Mandalay, Myanmar,
E-mail: thettin.mtu.mdy@gmail.com
In today’s technological world, electrical energy is one of the most important forms ofenergy and is needed directly or indirectly in almost every field. Increase in the demandand consumption of electrical energy leads to increase in the system fault levels. It is notpossible to change the rating of the equipment and devices in the system or circuits toaccommodate the increasing fault currents. The devices in electronic and electricalcircuits are sensitive to disturbance and any disturbance or fault may damage the device
permanently so that it must be replaced. The cost of equipment like circuit breakers andtransformers in power grids is very expensive. Moreover, replacing damaged equipment
is a time and labour consuming process, which also affects the reliability of powersystems. Simultaneous faults may be produced on separate circuits, physically close
together, by the same lightning disturbance; or a single line to ground fault on one phase
may raise the voltage to such an extent as to cause flashovers, or line to line faults, at asecond or third point. Though relatively infrequent, simultaneous faults are importantbecause relay systems, operating satisfactorily for single faults, may fail to isolate
simultaneous faults. Therefore, it seems worthwhile to have in convenient form a method
for calculating and simulation of short-circuit currents and voltages caused by faults
occurring at the same time at two or three separate and distinct points of a system. The
method for the determination of fault currents and voltages on symmetrical three-phasesystems subjected to two simultaneous faults was presented in this paper. The fault
analysis was affected by the Method of Symmetrical Components. In order to perform an
investigation, the Medium Voltage Myanmar Distribution Network in Mandalay, is usedas the building block of Simultaneous fault simulation. Then this network is examinedusing MATLAB/SIMULINK. The system parameters such as line voltage, line current
and reactive power, Q, and real power, P are observed when the simultaneous fault is
applied to this system.

1.
Introduction
The energy chain contains producing the energy, its transmission, and delivering the energy to
the costumer where the distribution feeders are the last nuts of this chain. The importance of thelatter part is no less than the former parts in the electrical power system if not higher. Thus, many
commercial experts believe that the distribution system should be taken into consideration more
seriously as all the efforts to generate electrical power is with the purpose of delivering it to thecustomer [1].
A fault along that distribution system, that could cause outages and damage, is one of the main
problems in the system. It is important that the fault is located in the system so that it can be isolated.
If the fault is not isolated, electrical equipment and other parts of the system could be damaged forthe long term. An important objective of all the power systems is to maintain a very high level ofcontinuity of service, and when abnormal conditions occur, to minimize the outage times. It is
practically impossible to avoid consequences of natural events, physical accidents, equipment failureor misoperation which results in the loss of power, voltage dips on the power system. Natural events
can cause short circuits i.e. faults which can either be single phase to ground or phase to phase orphase to phase to ground or a three phase fault. Most faults in an electrical system occur with a
network of overhead lines are single-phase to ground faults caused due to lightning induced transient1Transactions on GIGAKU 2(2)(2014)S01013/1-8high voltage and from falling trees. In the overhead distribution lines, tree contact caused by wind isa major cause for faults. The appropriate percentages of occurrences various faults are listed below[2]:
Single line to ground fault–
70~80%
Line-Line to ground fault–
10~17%
Line-Line fault–
8~10%
Three phase–
2~3%
When faults occur in the power system, they usually provide significant changes in the system
quantities like over-current, over or under-power, power factor, impedance, frequency and power orcurrent direction. The most common is the over-current and so over-current protection is widelyused. Although, in distribution system the basic approach to calculate the fault location using voltage
and current measurement is still the same as in the transmission system case, fault locating in the
distribution system is not an easy task because of its high complexity and difficulty caused by thenon-homogeneity of lines, fault resistances, load uncertainty, and phase imbalance [3],[4].
2.
Simultaneous Faults
The Method of Symmetrical Components embodies the replacement of a system of threevectors (voltages or currents), regardless of their degree of unbalance, by three sets of balanced
vectors. These three sets of balanced vectors are known as the positive- sequence, negative-sequence,and zero-sequence systems of vectors. Simultaneous faults occurring at two separate and distinct
points on a three-phase system may involve any combination of six conductors and any combinationof the short-circuit type faults. Since the current and voltage at each fault depend upon the current
and voltage at the other fault, simultaneous faults may not be treated independently. Nevertheless,each fault may be considered separately in determining the relations existing between thesymmetrical components of fault current and voltage at each fault point.

Fig. 1 Simultaneous Faults at Three-phase System. X and Y on a Symmetrical
In Figure 1 let X and Y represent the two short- circuited points with a and A, b and B, and c
and C being conductors on the same phase. Va, Vb, Vc and VA, VB, VC indicate line to groundvoltages on phases a, b, c and A, B, C, respectively, and Ia, Ib, Ic and IA, IB, IC, the corresponding
fault currents. The six symmetrical components of current and voltage at C are Ix1, Ix2, Ix0, Vx1, Vx2,and Vx0 and those at D are Iy1, Iy2, Iy0, Vy1, Vy2 and Vy0. Twelve independent equations are necessary
for the determination of the twelve unknown components. Two equations may be obtained from a
consideration of the positive-sequence network impedances, and the relations between thepositive-sequence components of fault current and voltage at each fault location. Similarly, four
more equations may be obtained, two from each of the negative- and zero-sequence networks. Thesesix equations may be formed only because the system is assumed to be balanced, with the result thatthe three sequence-networks are independent of each other. Three more equations, independent of
the system impedances, may be developed for each fault point. These equations, which relate thecomponents of voltage and current for the different sequences, depend only on the conditions
existing at the fault [5].
Figure 2 shows the equivalent wye replacing the zero-sequence network between the two faults
and the zero-potential bus. The branch impedances are X0, Y0, and S0, and they may be either
inductive or capacitive. Before the simultaneous faults occur, there are no currents nor voltages inthe zero sequence network. The effect of the simultaneous faults is to change the voltages at X and Y

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from zero to and from zero to Vx0, and from zero to Vy0, respectively, and the fault currents fromzero to Ix0 and zero to Iy0. Taking the positive direction of current flow as into the faults, thezero-sequence rise in voltage from the zero-potential bus to the points X and Y may be expressed asfollows:
Vx 0  ( I x 0  I y 0 )S0  I x 0 X 0   I x 0 ( X 0  S0 )  I y 0S0(1)

Vy 0  ( I x 0  I y 0 )S0  I y 0 Y0   I x 0S0  I y 0 ( Y0  S0 )(2)

3EquivalentWyetoreplaceFig. 2 Equivalent Wye to replace Zero Fig.
Sequence Network between Fault Points X and Negative-sequence Network between Fault
Points X and Y and Zero-potential Bus
Y and Zero-potential Bus S
The negative-sequence voltages at the fault points may be obtained in a similar manner from a
consideration of Figure 3.
Vx 2   I x 2 ( X 2  S2 )  I y 2S2(3)

Vy 2   I x 2S2  I y 2 ( Y2  S2 )(4)

1

The positive-sequence system will differ from the negative- and zero-sequence systems since itwill contain positive-sequence generated Voltages. Let it be assumed that operating conditions
existing before the occurrence of the faults are:1

Fig. 4 Equivalent Wye to replace Positive-sequence Network between Fault Points X and Y and
Zero-potential Bus
Therefore, the voltage rise from the zero-potential bus to the fault point X is:
Vx1  Vf  I x1 ( X1  S1 )  I y1S1
(5)
Likewise, the voltage at fault point Y is:

Vy1  VF  I x1S1  I y 2 ( Y1  S1 )(6)

The six equations for the two fault points from symmetrical components and equation (1) and(2) from zero sequence network, and equation (3) and (4) from the negative sequence network, thetwo equations can be put in the form:(7)
Vx1=k Ix1 +m Iy1

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Vy1=n Ix1 +l Iy1(8)where k, l, m, and n depend upon the branch impedances of the equivalent Y's replacing thenegative and zero-sequence networks and the particular combination conductors involved in thesimultaneous faults.
Values of k, l, m, and n in terms of the branch impedance networks are tabulated in Table 1 forvarious combinations of a grounded three-phase system.
Table 1. Values of k, l, m, and n in Terms of the Branch Impedance Networks for Simultaneous
Short Circuits at Two Points [6]Case
Two Faults At(c)Phase cA. Single(b)Phase b and A
and A(a)Phase a and A
Line-tok =Zxs
k =Zxsk =ZxsGroundn =S0+S2
n =a2S0+aS2
n =a S0+
Faultsm =S0+S2
m =a S0+ a2S2
a2S2
m =a2S0+aS2
l =Zysl =Zysl =Zys(c)Phase a, b
B. Line-to(b)Phase a, c and B, C(a)Phase b, c and B, C
and B, CLine
k=X2+S2
k=X2+S2
k=X2+S2n=S2n=a S2
Faultsn=a2S2m=S2
m=a2S2
m=a S2l=Y2+S2
l=Y2+S2
l=Y2+S2(a)Phase b, c and B, C

C. Double
Line-toGround
Faultsk 2 ( X 0  S0 )   0 ( X 2  S2 )n

 2 S 0   0S 2

Z xys

Z xys

m
l

D.
Three-phase
Faults(b)Phase a, c and B, C

 2 S 0   0S 2
Z xys

 2 ( Y0  S 0 )   0 ( Y2  S 2 )k 2 ( X 0  S0 )   0 ( X 2  S2 )n

a 2  2 S 0  a 0 S 2m

a 2S0  a 2  0S 2

l

Z xys

Z xys

Z xys  3S 0S 2(c)Phase a, b
and B, C
Similar toC.(b) with aand a2interchanged
in n and m.

Z xys  3S0S 2

 2 ( Y0  S 0 )   0 ( Y2  S 2 )Z xys  3S 0S 2

k=n=m=l=0(a)Phase a, b
and A
k=X2+S2
k=X2+S2
k=X2+S2n= S2n=  aS2
n= a2 S2
m=  S2
m=  a2 S2
m= aS2
l= Zxsl= Zxsl= Zxswhere, Zxs = X0+S0+X2+S2; Zys = Y0+S0+Y2+S2; ∆0 = X0Y0+X0S0+Y0S0; ∆2 = X2Y2+X2S2+Y2S2;
Zxys =(X0+X2)(Y0+Y2)+ (X0+X2)(S0+S2)+ (Y0+Y2)(S0+S2)
3.
Proposed Medium Voltage Myanmar Distribution Network
To evaluate the performance of the proposed network based simultaneous faults, 11 kV, 4
miles (6.437376km) distribution line from Aung Pin Lae Substation Mandalay, Myanmar as shownin Figure 5 is considered in this study. The 11 kV distribution line is represented by distributed
E. Line-toLine Fault
at X and1LG Fault
at Y(a)Phase a, c and A(a)Phase b, c and A

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parameters of the line parameters is taken into account. The line conductor cross-sectional area of 11kV line is 35 mm2. There are bamboo tree closely to the 11 kV MTU feeder line as shown in Figure
6 and Figure 7. There are so many bamboo trees and the medium voltage power lines are shortcircuited when the heavy rain and wind.

Fig 5. Proposed System for StudyFig 6. Bamboo Trees at X in Fig 5

Fig 7. Tree Branches over 11 kV MTU Line at
Y in Fig 5

4.
Simulation Results and Discussion
In the circuit of SIMULINK model, programmable voltage source of amplitude values (p.u) [11.06 0.94 1] at time [0 0.2 0.3 0.4] on the 11kV MTU feeder and MTU four transformers withvariable Loads are presented as shown in Figure 8.

Fig 8. Circuit SIMULINK Model for Simultaneous Faults on MTU 11 kV Feeder5Transactions on GIGAKU 2(2)(2014)S01013/1-8

Fig 9. Results of Scope 1 for First Case

Fig 10. Results of Scope 2 for First Case

Fig. 11 Results of Scope 3 for First Case

Fig 12. Results of Scope 7 for First Case

Fig 13. Results of Scope 1 for Second Case

Fig 14. Results of Scope 2 for Second Case

Fig 15. Results of Scope 3 Second Case

Fig 16. Results of Scope 7 Second CaseIn the first case, the simultaneous fault on X with Line-to-Line fault at phase a, b and Y with6

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Line-to-Line fault at phase B, C were occurred between 0.2 sec and 0.3 sec duration. The simulation
results are shown in Fig. 9 to Fig. 12. From these results, MTU transformer 1 and 3, and substationtransformer are impacted from the first case simultaneous faults currents. During the fault occur,
active and reactive power of the Aung Pin Lae substation transformer is about 6MW and 3Mvar. The
fault current at Aung Pin Lae Substation and MTU transformer 1are about 2200 A and at the MTU
transformer 3 is about 1000 A.
In the second case, the simultaneous fault on X with single Line-to-Ground fault at phase a andY with Line-to-Line fault at phase B, C were occurred between 0.2 sec and 0.3 sec. The simulation
results are shown in Fig. 13 to Fig. 16. During the fault occur, active and reactive power of the Aung
Pin Lae substation is about 4MW and 2Mvar. The fault current at Aung Pin Lae Substation and MTU
transformer 1are about 2000 A and at the MTU transformer 3 is about 1200 A. From these results,
MTU transformer 3 is more impacted than in the first case from the second case simultaneous fault.
But, the Aung Pin Lae substation transformer and MTU transformer 1 are more impacted than in thefirst case from the second case simultaneous faults. In both case, the simultaneous faults are
happened in transient condition. So, the substation circuit breaker will not open or one phase or twophase will open and simultaneous faults at three point may be happened.
5.

Conclusion

Reliable protection systems for medium voltage distribution in Myanmar are a prerequisite fordevelopment of Myanmar Electric power system reliability. However, due to lack of operational
experience, this is a relatively new area to research this field in Myanmar. In this paper, thesimultaneous faults on medium voltage distribution line are proposed for fault conditions in
Mandalay 11kV distribution feeder. Two types of short-circuit faults (line-to-ground and line-to-line)
and combinations thereof were considered in this paper. Simulation results from this paper show for
the effectiveness of the protection system giving better responses for Mandalay 11kV distribution
feeder. According to this paper results, so many faults are occurred and so many of power distribution
transformers are impacted of fault current and reduce of transformers lifetime in Myanmar. Nowadays,
power system engineers in Myanmar are neglected this fault impact and they are solved with thisproblem as cut out the power when heavy rains and wind, and so many consumers are unsatisfied. Thiscan be solved with cut trees, use good quality circuit breakers and so on. The active and reactive
powers are about four times for active power and six times for reactive power during the fault time.
Therefore so many power losses in medium voltage distribution system of Myanmar and must be
solved with convenient solutions.
Acknowledgments
The author is deeply gratitude to Dr. Myint Thein, Pro-Rector, Mandalay Technological
University, for his permission to present oral presentation this paper in Yangon.
References[1] M.A. Mirzai, A.A. Afzalian, “A Novel Fault-Locator System; Algorithm, Principle andPractical Implementation,” Power Delivery, IEEE Transactions on , vol.25, no.1, pp.35,46, Jan.
(2010).
[2] J. Lewis Blackburn, Protective Relaying Principles and Applications, Second Edition[3] E.C. Senger, G. Manassero, C. Goldemberg, E.L. Pellini, “Automated fault location system forprimary distribution networks,” Power Delivery, IEEE Transactions on , vol.20, no.2,
pp.1332,1340, April (2005).

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Transactions on GIGAKU 2(2)(2014)S01013/1-8[4] R.H. Salim, M. Resener, A.D. Filomena, K. Rezende Caino de Oliveira, A.S. Bretas,“Extended Fault-Location Formulation for Power Distribution Systems,” Power Delivery,
IEEE Transactions on , vol.24, no.2, pp.508,516, April (2009).
[5] Robert M. Montomer, "Analysis of Simultaneous Faults by the Method of Symmetrical
Components," Master Thesis University of Missouri, (1948).
[6] Edith Clarke, “Circuit Analysis of AC Power Systems,” Volume I Symmetrical and Related
Components, John Wiley &Sons, Inc: (1948).
(Received; 23rd December, 2013, Accepted; 2nd August, 2014)8

Transactions on GIGAKU 2(2)(2014)S01014/1-6

Optimizing Feature Selection Techniques for
Network Intrusion Detection System
1)

Phyu Thi Htun1, Kyaw Thet Khaing2
Faculty of Information and Communication Technology,University of Technology(Yatanarpon
Cyber City), Myanmar
2)
Department of Hardware Technology, University of Computer Studies, Yangon, Myanmar*E-mail:ms.phyuthihtun@gmail.com, kyawthetkhaing.ucsy@gmail.com

Intrusion Detection Systems (IDSs) are applied to detect network intrusions
identified from different sources. Anomaly and signature based schemes areused for the intrusion detection process. Signature based intrusion detection
schemes uses the predefined signature collection for the detection process.
The anomaly-base model detects the intrusions by learning the networktransaction patterns. Feature selection schemes are used to reduce the
network transaction features. Performance,
time,
accuracyandreliability are improved. The main approach is to use a data mining
technique for the feature selection process. The Random Forest (RF), themachine learning algorithm which embedded Information gain ratio measure,is used in these feature selection process. The pattern recognition algorithm,
K-Nearest Neighbors (KNN), have been used for detection and classificationof the intrusions. Scalability and high training error rate problems can besolved by using these feature selection techniques. So, the proposed system isdesigned to perform the feature reduction and intrusion detection process on
Network by using RF and KNN respectively. The central idea is to utilize
auditing programs to select an extensive set of features that can get theeffective for training error rate, and apply to be used for misuse detection andanomaly detection. Detection
modelsfornewintrusionsor
specific components of a network system are incorporated into an
existing IDS through a meta-learning (or co-operative learning) process,which produces a Meta detection model that combines evidence frommultiple models. We discuss the strengths of our data mining
programs, namely, classification, meta-learning. We report our results ofapplying these programs to the (extensively gathered) network audit
data from the DARPA Intrusion Detection Evaluation Program.
Experimental results prove that the proposed method can get the highaccuracy in detection those known and unknown attacks by using WEKA
tool.

1. Introduction
Intrusions are the result of flaws in the design and implementation of computer systems,
operating systems, applications, and communication protocols. Statistics [14] show that the numberof identified vulnerabilities is growing. Exploitation of these vulnerabilities is becoming easier
because the knowledge and tools to launch attacks are readily available and usable. It has become
easy for a novice to find attack programs on the Internet that he/she can use without knowing how
they were designed by security specialists.
Indeed, the absence of physical boundaries in the network to monitor, meaning that an attack canbe perpetrated from anywhere, is a major threat that can be exploited to undermine the integrity andsecurity of the network. It is, therefore, essential to take into account these considerations when
designing and deploying an intrusion detection system. To detect intrusions, classifiers are built todistinguish between normal and anomalous traffic. It has been proved that optimizing the feature set
has a major impact on the performance, speed of learning, accuracy, and reliability of the intrusion
detection system

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2. Related Works
Many researchers tried to increase the performance of detection attacks by using many machinelearning algorithms. Reference [3] have presented a comparative study between five data mining
algorithms (ID3, C4.5, Random Forest, Multilayer Perceptron, Naive Bayes, k-Nearest Neighbor)applied to the classification of network intrusions. The Algorithms that have shown the highest
prediction rate are the Random Forest and Naive Bayes Algorithms. ID3 is a supervised algorithm
developed by [4] whose purpose is to build decision trees from a data set. Decision trees are very
efficient as they classify new cases from the training data and test data to properly assess the qualityof the tree constructed. The decision tree is built recursively. The ID3 calculates, among theremaining attributes, the ones which will generate the most information (information gain), which
will classify examples of any level of the decision tree.
The literature suggests that hybrid or assembling multiple classifiers can improve the accuracy ofa detection [5][6]. According to [5], an important advantage for combining redundant andcomplementary classifiers is to increase robustness, accuracy and better overall generalization.
Reference [8] demonstrated the use of ensemble classifiers gave the best accuracy for each categoryof attack patterns. Ensemble methods aim at improving the predictive performance of a given
statistical learning technique.
The most related work is done by [5]. They use Random Forests Algorithm [7] over rule-based
NIDSs. They applied Random Forests Algorithm for only misuse detection. Misuse detection
discovers attacks based on the patterns extracted from known intrusions.
Thus, novel attacks can’t be detected in this network intrusion detection system. Surveys of thevarious data mining techniques have been proposed towards the enhancement of IDSs. Reference [9]shown the ways in which data mining has been known to aid the process of Intrusion Detection andthe ways in which the various techniques have been applied and evaluated by researchers.
3. Datasets Descriptions
Since 1999, KDD’99 [13] has been the most widely used data set for the evaluation of anomaly
detection methods. This data set is built based on the data captured in DARPA’98 IDS evaluation
program. DARPA’98 is about 4 gigabytes of compressed raw (binary) tcpdump data of 7 weeks of
network traffic. The two weeks of test data have around 2 million connection records. KDD training
dataset consists of approximately 4,900,000 single connection vectors each of which contains 41
features and is labeled as either normal or an attack, with exactly one specific attack type. The
simulated attacks fall in one of the following four categories: (1) Denial of Service Attack (DoS), (2)User to Root Attack (U2R), (3) Remote to Local Attack (R2L) and (4) Probing Attack.
Table 1 showed the four categories and their corresponding attacks on each category. It is
important to note that the test data is not from the same probability distribution as the training data,and it includes specific attack types not in the training data which make the task more realistic.
Some intrusion experts believe that most novel attacks are variants of known attacks and thesignature of known attacks can be sufficient to catch novel variants.
This testing datasets contains more examples of attacks than normal connections and the attack
types are not represented equally. In this table described the attacks types in two, known andunknown. In the testing datasets, it will contains the attack which are not included in training
datasets are called unknown attacks and described as the bold letters.( such as . apache2 in DoS
attack type ).
TypeProbing
Denial of Service(DoS)
User to Root (U2R)
Remote to Local(R2L)

Table 1 Classifying attacks on KDD Datasets
Attack Name
Port-sweep, IP-sweep, Nmap, Satan , Saint, Mscan
Neptune, Smurf, Pod, Teardrop, Land, Back,
Apache2, Udpstorm, Processtable, Mailbomb
Buffer-overflow, Load-module, Perl, Rootkit, spy,
Xterm, Ps, Http-tunnel, Sqlattack, Worm, Snmp-guess
Guess_password, Ftp_write, Imap, Phf, Multihop, Warezmaster,Warezclient,
Snmp-getattack, Named,Xlock, Xsnoop, Send-mail2Transactions on GIGAKU 2(2)(2014)S01014/1-6

For the relevant and redundant of KDD datasets, the NSL-KDD datasets modified those data sets
and shared 4 dataset file, Train+, Train+_20Percent,Test+ and Test-21. The first two files represent
for training datasets and contain the general attacks. The rest two files represent for testing datasetsand contain not only general attacks but also the unknown (novel) attacks which are described withbold in Table 1. The connection for each attack type is shown in Table 2.
Table 2 Number of connections in each attack type
Datasets

Normal

DoS

U2R

R2L

Probe

Total

Train+

67343

45927

993

54

11656

125973Train+20 Percent

13449

9234

206

12

2289

25190

Test+

9711

7458

2421

533

2421

22544

Test-21

2152

4342

2421

533

2402

11850

4. Experimental Results
Since 1999, KDD’99 [13] has been the most Complex relationships exist between the features,
which are practically impossible for humans to discover. IDS must therefore reduce the amount ofdata to be processed. This is extremely important if real-time detection is desired. Reduction canoccur in one of several ways.
Finally, some data sources can be eliminated using feature selection.
A. Data Filtering
The purpose of data filtering is to reduce the amount of data directly processed by the IDS. Some
data may not be useful to the IDS and thus can be eliminated before processing. This has theadvantage of decreasing storage requirements, reducing processing time and improving the detection
rate (as data irrelevant to intrusion detection are discarded). However, filtering may throw out useful
data, and so must be done carefully [10].
B. Features Selection
The purpose Feature selection (also known as subset selection or variable selection) is a processcommonly employed in machine learning to solve the high dimensionality problem. It selects a
subset of important features and removes irrelevant, redundant and noisy features for simpler andmore concise data representation.
The benefits of feature selection are multi-fold. First, feature selection greatly saves the running timeof a learning process by removing irrelevant and redundant features. Second, without theinterference of irrelevant, redundant and noisy features, learning algorithms can focus on mostimportant aspects of data and build simpler but more accurate data models. Therefore, theclassification performance is improved. Third, feature selection can help us build a simpler and moregeneral model and get a better insight into the underlying concept of the task [11],[12].
C. Information GainIn this method, the important features are calculated over multiple RDF iterations, the least
important features being removed after each. The objective of using Random Forest is to reduce theimpurity or uncertainty in data as much as possible .A subset of data is pure if all instances belong to
the same class. The heuristic is to choose the attribute with the maximum Information Gain or Gain
Ratio based on information theory.
Entropy is a measure of the uncertainty associated with a random variable. Given a set of examples
D is possible to compute the original entropy of the dataset such as:|C |H [ D]   P (c j ) log 2 P (c j )j 1where C is the set of desired class.
If we make attribute Ai, with v values, the root of the current tree, this will partition D into v subsets
D1,D2….Dj . The expected entropy if Ai is used as the current root.

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Transactions on GIGAKU 2(2)(2014)S01014/1-6v| Dj |j 1|D|

H Ai [ D]  

H [D j ]Information gained by selecting attribute Ai to branch or to partition the data is given by thedifference of prior entropy and the entropy of selected branch.
gain ( D, Ai )  H [ D ]  H Ai [ D ]We can choose the attribute with the highest gain to branch/split the current tree numbered.
D. Enhanced Classification System

Fig. 1 Proposed System
There are two phase in my proposed Model. This system is process of identifying the abnormal andnormal instances in first and then identifying again to classify attacks types of those abnormal
instances. The first phase is the training phase and intends to reduce the irrelevant features. Afterloading the training dataset, features are reduced by Random Forest Algorithm with information
Gain. Using those features, the system will classify the normal and abnormal data in first step.
If normal, system keeps that connection in normal database. And then continuing classifies theabnormal connections in four types of attacks and keeps in attack database.
Next phase is detection phase. It will input the testing data set and mapping the data sets as theremaining features of the training data set. As the training phase, will detect normal and four attacks
type and display an alert if a connection is a one of the attacks. This system is shown in Fig. 1.
E. Using WEKA Tool
WEKA Tool is a popular suite of Machine Learning software witten in Java, developed at the
University of Waikato, New Zealand. The WEKA workbench contains a collection of visualization
tools and algorithms for data analysis and predictive modeling together with graphical user interface
for easy access to this functionality Weka support several standard data mining tasks, more
specifically, data preprocessing, clustering, classification, regression, visutalization, and feature
selection(selected attributes).
We summarize the experimental results to detect attacks for intrusion detection with over the
NSL-KDD datasets by using WEKA tools. Experimental results are presented in terms of the classes
that achieved good level of discrimination from others in the training set.

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Firstly, our proposed system will load the training dataset which contain the connection of thenormal and attacks class in preprocessing. This dataset also contained the 42 features which
important to decide the normal and attacks classes for each connection. We will reduced somefeatures in training dataset by using Random Forest algorithm to each connection at filtering state
after loading a training file was loaded in the preprocessing state. The system will use information
Gain Algorithm and Ranker to rank and eliminate the amount of features. The amount of eliminatedfeatures was getting by using LOOCV(Leave One Out Cross Validation) to eliminate as Fig. 2 .

Fig. 1(a)Filtering with Information Gain

Fig. 3

method of Random Forest for feature selection(b)
Classification step using with KNN inproposed system

in proposed system
So, system will try to detect various anomaly attacks using KDD dataset with KNN classification
algorithm in the classification process as shown in fig (3). The proposed system will reduced intraining time and will increase the accuracy of the system’s classification. It will also solve theimbalance intrusions problem by increasing the accuracy on minority attacks.
For the improvement of detection rate on our proposed system, we examined the number of features
to use by ranking and reducing features and getting the best experimental results as shown in fig (4),getting the best number of features is 9.

Fig. 4 The comparison results for number ofFig. 5 The comparison between proposed

selection to be used in proposed system

method, RF and KNN in overall attacks typeIn the experiments process, the system use 10 trees and reduced 9 features in my proposed
method to classify. The accuracy of the system will be increased other systems in overall and thedetection rate using proposed method on each minority attack types.
The experiment result in fig. 5 is the comparison between Random Forest, KNN and the proposed
method by classifying with training dataset in cross-validation mode and testing with test datasets.
The results will shown that the proposed method can detect in more precisely than others.

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5. Conclusion
The classification algorithm, Random Forest and KNN are the most useful classification tools
and many researchers are presented as a classical tool for Intrusion Detection System as separated
algorithm. But in each algorithm has the weakness and draw backs in timing and performance. So,
we intend to solve this by combining the advantages of feature selection of Random Forest and thepattern classification of KNN. The experiments prove that the proposed method can stand as aclassical tool for IDSs..
Acknowledgments
I would like to express my respectful gratitude to our Rector of University of Technology(Yatanarpon Cyber City),Dr. Aung Win and Department head, Faculty of Information andCommunication Technology, Dr. Soe Soe Khaing, for allowing me to develop my research andgiving me general guidance during period of my study.
References[1] F. Anjum, D. Subhadrabandhu and S. Sarkar, “Signature Intrusion Detectio for Wireless Ad
Hoc Networks: A Comparative study of various routing protocols”, in 2003.
[2] P C Kishore Raja, Dr.Suganthi.M, R.Sunder, “wireless node behavior based intrusion
detection using genetic algorithm”, Ubiquitous Computing and Communication Journal, 2006.
[3] Y. Chihab, A. A. Ouhman, M. Erritali and B. E. Ouahidi, “Detection & Classification ofInternet Intrusion Based on the Combination of Random Forest and Naïve Bayes,” Internal
Journal of Engineering and Technology, IJET, Vol 5 No 3 Jun-Jul 2013. ISSN : 0975-4024 p.
2116-2126[4] J. Ross Quinlan, Machine Leaming, 1986, “Induction of decision trees,” p. 81-106.
[5] J. Zhang and M. Zulkernine,” Network Intrusion Detection using Random Forests,” School ofComputing Queen’s University, Kingston Ontario, Canada.
[6] D. J. Hand, H. Mannila and P. Smyth, Principles of Data Mining, The MIT Press,[7] L. Breiman, “Random Forests”, Machine Learning 45(1):5–32, 2001.
[8] J. Zhang and M. Zulkernine, ”Anomaly Based Network Intrusion Detection with
Unsupervised Outlier Detection”, Symposium on Network Security and Information
Assurance Proc. of the IEEE International Conference on Communications (ICC), 6 pages,
Istanbul,Turkey, June 2006.
[9] T. Lappas and K. Pelechrinis, “Data mining Techniques for(Network)Intrusion Detection
System,”, UC Riverside, Riverside CA92521.
[10] WEKA software, Machine Learning, http://www.cs.waikato.ac.nz/ml/weka/, The Universityof Waikato, Hamilton, New Zealand.
[11] M. Dash and H. Liu, “Feature selection for classification,”International Journal of Intelligent
Data Analysis, 1(3), 1997.
[12] F. Tan, "Improving Feature Selection Techniques for Machine Learning" (2007). Computer
Science Dissertations. Paper 27.
http://digitalarchive.gsu.edu/cs_diss/27[13] M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99
Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security
and Defense Applications (CISDA), 2009.
[14] CERT, http://www.cert.org/stats/, 2010.
[15] Khalil El-Khatib “Impact of Feature Reduction on the Efficiency of Wireless Intrusion
Detection Systems” IEEE Transactions on Parallel and Distributed Systems, Vol. 21, no. 8,
August 2010.
(Received; 23rd December, 2013, Accepted; 5th May, 2014)6

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Robust Interactive Image Segmentation Integration of Region and Boundary
Information
May Thu Win1, Kay Thi Win2

1)University of Computer Studies, Mandalay, Myanmar2)University of Computer Studies, Mandalay, Myanmar*E-mail:maythu.ycc@gmail.com, kthiwin11@gmail.com

Interactive image segmentation has many applications in image processing, computer
vision, and computer graphics. In many editing tasks, the aim is to separate aforeground object from its background. Since the color and texture features in a natural
image are very complex, the fully automatic segmentation of the object from thebackground is a big challenge. Due to the difficulty of fully automatic segmentation ona wide range of different images, semi-automatic segmentation algorithms are
becoming more and more popular. A wide range of applications motivate thedevelopment of efficient interaction methods for image segmentation. Therefore, theproposed system presents a new region merging method based on the color and texture
features for interactive image segmentation. The proposed system solves the problemof the purely region-based method and purely boundary-based method.Firstly, the usermarks the interest of object and background in the input image.An initial segmentationis required to partition the image into homogeneous regions for merging.Mean shiftalgorithm is used for initial segmentation.After finishing the initial segmentation, it isnecessary to represent initial segmented regions by means of some descriptors such ascolor and texture to guide the region merging process. And then, detect possible
erroneous low-contrast object boundaries to solve the minor inaccuracies and edge
misalignments. Finally, refine those boundary regions by using active contour toachieve the accurate object boundary. Therefore, the proposed method is effective andcan quickly and accurately segment a wide variety of natural images with ease.

1. Introduction
Interactive image segmentation involves minimal user interaction to incorporate user intention into thesegmentation process and is an active research area in recent years because it can achieve satisfactory
segmentation results that are unattainable by the state-of-the-art automatic image segmentation algorithms.
Interactive image segmentation, which incorporates small amount of user interaction to define the desired
content to be extracted, has received much attention in the recent years [11]. Many interactive image
segmentation algorithms have been proposed in the literature. In general, interactive image segmentation
algorithms can be classified into two categories: boundary-based approaches and region-based approaches. In
boundary-based approaches, the user is often asked to specify an initial area that is close to the desirable
boundary. This approaches require great care to specify the boundary area or the boundary points, especiallyfor complex shapes, most recent interactive image segmentation algorithms take the regional information as
the input. In particular, in region-based approaches, the user is often asked to draw two types of strokes to label
some pixels as foreground or background, after which the algorithm completes the labeling of all other pixels.
Early interactive image segmentation algorithms utilize either regional properties such as Adobe’s magic wandor boundary properties such as active contour [8] and intelligent scissors [5],[3].
Due to the increasing proliferation of digital photos and photo management/editing tools such as Picasa
and Adobe Photoshop CS5, image editing is now commonly used in desktop application. Interactive
segmentation is a fundamental part of such image editing tools. Extracting the object of interest from the nontrivial background is a crucial step in many interactive multimedia applications. So, these applications
motivate the development of effective method for interactive image segmentation. Moreover, the issue of theregion based approach is that the distribution in color space of foreground and background pixels have aconsiderable overlap, thus a satisfactory segmentation result is not achieved. The issue of the boundary based
approach is that the shortest path may “shortcut” the desired boundary in the cases of low contrast or noisy
boundaries. Another problem is that there exist many alternative “minimal” paths, which require a large

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number of user interactions for highly textured regions in order to obtain a satisfactory result. Therefore, theissues of the region and boundary based approaches motivate the proposed method for interactive image
segmentation.
Recently, an interactive segmentation based on region merging was proposed by Ning [7]. In [7], the initial
image segmentation is firstly used to divide the image into various homogeneous regions and then the color
feature is only used for the region merging. So, the satisfactory result is not achieved with this method whensegmenting the camouflage images, which are overlapped with its foreground and background colors.
The proposed method in this paper is different from the method by Ning [7]. In the proposed system, theinitial segmentation is required to partition the image into the homogeneous regions by Mean shift algorithm.
And then, we use both the color and texture features for region merging to segment the foreground from its
background because the objective of the proposed method is to achieve the segmentation for different naturalscene images, even though the camouflage images. Moreover, if the results obtained by the first segmentation
are not satisfactory, the user can refine the selection in the boundary part of the object. So, the proposed
method considered the integration of region and boundary information in order to get the accurate result.
The contributions of this paper are (i) a method is proposed by using the color and texture features inregion merging, (ii) the integration of the region and boundary information is used in the proposed method;(iii) the hybrid system is designed and implemented using the color, texture and boundary of the interest of
object.
2. Experiment
There are two major parts in the proposed system: the proposed region merging and the boundaryrefinement. For the region merging process, the initial image segmentation is firstly used to divide the image
into various homogeneous regions. Mean shift segmentation algorithm [4] is used for initial segmentation. The
users need to specify the object and background conceptually. Similar to the users can input interactive
information by drawing markers, which could be lines, curves and strokes on the image. The regions that havepixels inside the object markers are thus called object marker regions, while the regions that have pixels inside
the background markers are called background marker regions. The green markers to mark the object while
using blue markers to represent the background are used.
After marking the user input, it is necessary to represent initial segmented regions by means of somedescriptors such as color, texture features to guide the region merging process. Therefore, the color histogram
to represent each region is used. The color histogram is an effective descriptor to represent the object color
feature statistics and it is widely used in pattern recognition and object tracking, etc. The RGB color space isused to compute the color histogram in this work. For the texture descriptor, Gabor filter is used fordiscriminating different texture regions. Oriented filter banks have proven to be an effective method tocharacterize textures [1], [10]. This paper primarily uses local statistics of oriented filter responses todifferentiate textures. A two-dimensional Gabor function consists of a sinusoidal plane wave of somefrequency and orientation, modulated by a two-dimensional Gaussian. The Gabor filter in the spatial domain isgiven by
,

exp

where

cos 2

(1)

,

In [1], representsthe wavelength of the cosine factor, represents the orientation of the normal to theparallel stripes of a Gabor function in degrees, is the phase offset in degrees, and is thespatial aspect ratio
and specifies the elliptically of the support of the Gabor function, and σ is the standard deviation of theGaussian determines the size of the receptive field [9].
The parameter λ¸ is the wavelength and f = 1/λ is the spatial frequency of the cosine factor. The ratio σ/λdetermines the spatial frequency bandwidth of simple cells and thus the number of parallel excitatory andinhibitory stripe zones which can be observed in their receptive fields. The half-response spatial frequency
bandwidth b (in octaves) and the ratio σ/λ are related as follows:
log

,

(2)

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0˙◌ํ and90˙◌ํ return the real part and the imaginary part of Gabor filter respectively.
In this paper, a total of 24 Gabor filters is used at six orientations (0˙, 30˙, 60˙, 90˙, 120˙, 150˙) and fourscales (eg. 4√2, 8√2, 16√2, 32√2 ) at every pixel. The selection of parameters for Gabor filters is very
important because the texture segmentation result from the Gabor filters is very good but computationallyexpensive. Therefore, we choose the restriction of orientations is six because it is made for computation saving.
A 2-D Gabor function is an oriented complex sinusoidal grating modulated by a 2-D Gaussian function.
The parameters of the Gabor function are specified by the frequency, the orientation of the sinusoid (orrepresented by the center frequency), and the scale of the Gaussian function. Therefore, local orientations andspatial frequencies explicit in Gabor filters are used as the key features for texture processing. The input image
is generally filtered by a family of Gabor filters tuned to several resolutions and orientations. The texture
features are extracted by filtering the given image using the family of Gabor filters.
Object and background markers provide some key features of object and background, respectively. The
proposed region merging method also starts from the initial marker regions and all the non-marker regions will
be gradually labeled as either object region or background region. In this work, an adaptive maximal similarity
based merging mechanism to identify all the non-marker regions under the guidance of object and backgroundmarkers is presented. Let Q be an adjacent region of R and denote SQ = {SiQ} i= 1, 2, q the set of Q's adjacentregions. The similarity between R and Q is the maximal one among all the similarities ρ(Q, SiQ), then R and Q
will be merged. The following merging rule is defined: Merge R and Q(3)ρ R, Qmax , ,……… ρ Q, S
The merging rule (3) is very simple but it establishes the basis of the proposed region merging process.
One important advantage of (3) is that it avoids the presetting of similarity threshold for merging control.
The whole maximal similarity based region merging process can be divided into two stages, which arerepeatedly executed until no new merging occurs. Firstly, the marked object regions are merged unmarked
object regions until no similar object region left. Secondly, the left unmarked regions are then merged together,and thus the object can be successfully extracted from the complex background.
Input Image

Pre-segment the input image with Mean Shift
Algorithm
Mark the input image with strokes for foregroundand background

Merge the regions using the color and texture
featuresDetect low contrast object boundary

Refine object boundary with Active Contour

Segmented object from the backgroundFig 1. Flowchartof Proposed System
The segmentation from the aforementioned process may still have minor inaccuracies and edge
misalignments. It is still a challenging problem to extract accurately the object contour from the background.

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Therefore, the boundary refinement stage is necessary to resolve them. Firstly, it is necessary to detect the low
contrast object boundary by using the Weighted Canny Edge detection method. Traditional Canny edge
detection [6] works on gray scale images and produces binary edge detection results. For color images,
weighted canny edge is used in each color channel independently [13].
Finally, this study exploits the active contour for boundary refinement. Snakes, or active contours, are used
extensively in computer vision and image processing applications, particularly to extract object boundaries.
Snakes use the technique of matching a deformable model to an image by means of energy minimization. A
snake initialized near the target gets refined iteratively and is attracted towards the salient contour [5].
Therefore, the satisfying result is achieved using the active contour for boundary refinement. Fig 1 shows the
flowchart of the proposed system.
The proposed method is adaptively region merging method based on the color and texture features forinteractive image segmentation. With the markers input by the user, it will automatically merge regions andlabel the non-marker regions as object or background. Therefore, the marker input by the user is important tosegmentation. The proposed method is evaluated with many examples. Experiments are conducted to evaluate
the effectiveness of the proposed framework. The test images come from the Berkeley segmentation dataset[14].
3. Results and Discussion
The objective of the proposed system is to achieve robust and accurate segmentation in different naturalscene images. Table 1 shows the result of initial segmentation with different images by using mean shift. Fig 2shows the straightforward examples for segmenting the target object from the background with the user input
markers. Since the proposed method is mainly based on color and texture features for region merging to extract
the desired object, it is very convenient for these examples to get the accurate result.
Table 1Initial segmentation result of different images
Image
FlowerFruit
Starfish-2
Starfish-1
Size of image

229*216259*329448*368444*417

Number of regions
after initial
segmentation

236

218

1572

2353

Fig. 2 (a) Original Input Image (b) The source images with user input stokes, where blue strokes are used tomark the background regions, and green strokes are used to mark the foreground regions (c) Result obtained by
the proposed method

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Fig. 3(a) Original Input Image (b) The source images with user input stokes, where blue strokes are used tomark the background regions, and green strokes are used to mark the foreground regions(c) Result obtained by
the proposed method
However, for some natural scene images such as complex background, it may require more user input
markers to achieve the satisfying result. Therefore, in the proposed method, the color and texture features areused for region merging for the complex images. Fig 3 shows the result of segmenting the foreground from thecomplex background with less user inputs because of the advantages of the proposed method.
In this paper, not only region information but also boundary information is used to extract the target object
from the corresponding background. For some images, if the user’s input markers don’t cover to indicate thedesired object and background region, the segmentation result may be poor. If so, the boundary refinement isnecessary for segmenting the target object. In the boundary refinement stage, the user needs to draw the shortboundary strokes on the desired object. Therefore, Fig 4 shows the boundary refinement examples. This resultshows the accurate result with the proposed method for interactive image segmentation.

Fig. 4(a) input image with strokes (green for foreground and blue for background); (b) corresponding result;(c) with an additional stroke for refinement (in red); (d) corresponding new result5Transactions on GIGAKU 2(2)(2014)S01015/1-6

4. Conclusion
Interactive image segmentation is still a challenging problem. Therefore, a robust interactive image
segmentation method is proposed in this paper. The user input strokes are very important for the foregroundand background to get a desired object accurately. This paper consists of two components: proposed region
merging and boundary refinement. The color and texture features are mainly used in the region merging
process. So, it is very convenient for segmenting the object from the complex background in interactive
scheme. Sometimes, if the region merging cannot get the satisfying result because of the user’s inputs orcomplex scene images, the additional stage is included in this paper to get the desired object. Therefore, therefining boundary step is used to achieve the accurate result. Moreover, this paper solves the problem of thepurely region based method and purely boundary based method. By integrating the region and boundary
information for the interactive scheme, the accurate result may be achieved. However, the boundary refinement
process is an ongoing research in our proposed system. Therefore, we will continue the second part of the
proposed system.
References[1] B. Manjunath and W. Ma, “Texture Features for Browsing and Retrieval of Image Data,” IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, Aug. 1996.
[2] Blake, C. Rother, M. Brown, P. Perez, and P. Torr, “Interactive image segmentation using an adaptive
GMMRF model,” Lecture Notes in Computer Science, 2004.
[3] C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: Interactive foreground extraction using iterated
graph cuts,” in ACM SIGGRAPH, 2004.
[4] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2002.
[5] E. N. Mortensen and W. A. Barrett, “Interactive segmentation with intelligent scissors,” GraphicalModels in Image Processing, 1998.
[6] J.F. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and MachineIntelligence, Nov. 1986
[7] J. Ning, L. Zhang, D. Zhang, and C. Wu, “Interactive image segmentation by maximal similarity based
region merging” ,Pattern Recognition, 2009.
[8] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” IJCV, vol. 1, no. 4, pp. 321–
331, 1988.
[9] NaotoshiSeo, “Texture Segmentation using Gabor Filters”,ENEE731 Project, Nov8, 2006.
[10] T. Leung and J. Malik, “Representing and Recognizing the Visual Appearance of Materials Using
Three-Dimensional Textons,” Int’l J. Computer Vision, vol. 43, no. 1, pp. 29-44, 2001.
[11] W. Yang, J. Cai, J. Zheng, and J. Luo, “User-friendly interactive image segmentation through unified
combinatorial user inputs,” IEEE Transactions on Image Processing, 2010.
[12] Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary and region segmentation of
objects in N-D images,” in ICCV, 2001.
[13] Yugang Liu and Yizhou Yu, “Interactive Image Segmentation Based on Level Sets of Probabilities”,
IEEE Trans. on Visualization and Computer Graphics, vol. 18, 2012.
[14] http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/.

(Received; 23rd December, 2013, Accepted; 2nd August, 2014)6

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Edge embedded Marker based Watershed Method for image
segmentation
Khin Lay Mon1, Mie Mie Thaw2
Hardware Department, University of Computer Studies, Mandalay, Myanmar
2)
Hardware Department, University of Computer Studies, Mandalay, Myanmar*E-mail: khinlaymon.tk@gmail.com, miemiethaw@gmail.com
1)

Image segmentation has been a significant and difficult task digital image
processing. Conflict between generality and objectivity becomes animportant issue in image segmentation. Segmentation accuracy determines
the success or failure of computerized analysis procedure. Therefore, we
propose an automatic segmentation method that is useful for computer vision.
The Proposed system are combined the modified Laplacian of Gaussian(LoG) edge detection filter which is used to produce accurate gradientmagnitude and Marker Controlled Watershed Transformation. In this paper,
firstly, watershed algorithm with Laplacian of Gaussian (LoG) edge detector
is used to detect the clear edge map of the input image and produce theimage which is less sensitive to noise. Marker-controlled watershedsegmentation provides to get the beter segmented image with less oversegmentation. Therefore, our proposed method can detect a detailed, accurate
and satisfactory segmentation image without any broken edges while
overcoming over segmentation problem due to conventional watershedsegmentation.

1. Introduction
Image segmentation separates the objects and components of the image [4-6]. Segmentation
algorithms are classified on the basis of the segmentation techniques like edge and contour based
techniques, region based techniques, threshold selection based image segmentation techniques, etc.
All these methods have their own limitations and advantages in terms of suitability, applicability,
computer’s memory space, transmission time of image data, computational cost and overall
performance. The main aim of segmentation is to find certain interested objects which may be
depicted in the image. But it is encountered with noise. That means if image contains noisy signals,
this result makes an unwanted segmentation and cannot detect the detailed image. Noise is therandom variation of brightness or color information which is either due to technology limitation orenvironmental factor. It produces unpredictable results. Therefore, before performing segmentation
on images, it is necessary to remove noise from it. Although various spatial and frequency domain
filtering techniques exist, in this paper, Laplacian of Gaussian (LoG) edge detector are used. LoG
edge detector is combined with the watershed segmentation to yield good results.
A good number of works has already been carried out on watershed segmentation and applied tosolve the problem related to digital image segmentation. These are available in the published or
online literature [7-12]. Pinaki Pratim Acharjya, Santanu Santra, Dibyendu Ghoshal presented an
improved scheme on morphological image segmentation using the gradients. They introduced theconcept of edge detection with gradient and used the system to produce an effective watershed
technique for natural images[1].
Pinaki Pratim Acharjya, Dibyendu Ghoshal [12] discussed an improved scheme onmorphological image segmentation that is suitable for human visual system. They had shown thatwatershed segmented image obtained by extended LoG mask appears to be much more clear withsharp and prominent watershed ridges and the number of segmented have been found to much lessthan conventional filter. Bieniek and Moga [8] present an efficient watershed algorithm based onconnected components.

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Vincent et al. [11] proposed a famous watershed algorithm called ‘immersion algorithm,’ which
provides an effective and efficient implementation for watershed transform. In order to avoid oversegmentation, generally marker controlled watershed technique [7] is followed but the whole process
has been found to be a comparatively lengthy process in terms of computation. Therefore, many
researchers had to try an efficient technique which may yield larger segmented regions and it can be
expected that it would solve for the suppression of over segmentation and it will be also easier forhandling by the machine. Other researchers also proposed different methods to avoid the problem ofwatershed. Therefore, in this paper, this research work is expected and motivated to overcome theover segmentation problem and produce the meaningful result.
In the present study, LoG edge detection operator is used with the watershed algorithm togenerate the better segmentation results with less over segmentation. Although the 5x5 LOG filteringkernal proposed by Marr-Hildreth [10] is a default mask, the mask can be extended dimensionally on
the basis of the need of the image processing situation. A modified scheme of LoG operator with 7x7
mask is proposed to find an initial edge map and contour of a digital image and produces greater
accuracy and lesser over segmentation in edge detection. It has been found from the segmented
image that in this case the over segmentation is appreciably less than those obtained by using otheredge detecting techniques. It has been already found that the watershed segments are very prominentand the watershed boundaries are also very sharp and this lower scale of over segmentation will
enable the computers to process the segmented images more easily and with lower cost.
The structure of this work is the following: Section 2 introduces our proposed scheme of themodified Laplacian of Gaussian detector with 7x7 kernel, the conventional Laplacian of Gaussian
and describes the brief procedure of the proposed system. Section 3 presents a brief description ofexperiment. The results are discussed in section 4 and the detailed steps of the proposed system arealso described in this section together. We finish this paper with some concluding remarks withsection 5.

2. Proposed Scheme
The Laplacian of Gaussian filter (LoG) plays a very important role in image segmentation. Thisdetector finds edges by looking for zero crossings after filtering f(x, y) with a Laplacian of Gaussian
filter. It is a convolution filter that is used to highlight edges of different objects.
In this method, the Gaussian filtering is combined with Laplacian to break down the image
where the intensity varies to detect the edges effectively.
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Fig. 1 A conventional 5x5 mask LoG filter

Fig. 2 A modified 7x7 mask LoG filter
The proposed system can be divided into two parts: preprocessing step and segmentation step. In
the preprocessing part, 7x7LoG mask has been proposed as shown in figure (2). It has been studied
that conventional5x5LoG mask with default standard deviation 0.5 in figure (1) needs to be extended
in any arbitrary size for more accurate detection of objects. Therefore, we have tried a new approachfor modifying conventional LoG mask. To construct the modified 7x7LoG mask, the LoG function
centered zero and approximated with the profile of standard deviation 1.4 in equation (1).
(1)
In this mask, there are two annular regions with positive and negative weights which surroundthe central pixel. The negative weights are assigned for inner one and the positive weights are2

Transactions on GIGAKU 2(2)(2014)S01016/1-6

assigned for outer one. The weights are depended on the distance from the center pixel and theapproximation of Gaussian standard deviation. The size of optimal mask was found in 7x7
dimensions after calculating the larger number of trails. We have also found that there is no loss ofgenerality if the weights of individual pixel in the mask are changed slightly because of keeping in
mind the basic requirement of being isotopic for the construction of mask. The better approximationof gradient magnitudes of gray level image is subsequently computed by using this modified
7x7LoG filter. Therefore, the better approximation of gradient image can avoid extremely oversegmented regions. The flowchart of the proposed algorithm is depicted in figure (3).
Input Real Life Image

Convert Input Image to Grayscale
Pre-processing

Image

step
Compute the Gradient Magnitude
using the Proposed 7x7 LoG edge

Apply Watershed Transform

Enumerate Internal Markers
Segmentation
Enumerate External Markers

step

Apply Watershed Transform

Output Segmented Image

3. Fig. 3 The flowchart of the Proposed Scheme

4. Experiment
The present study performs mainly two processes: preprocessing step and segmentation step. In
preprocessing step, the images which obtained from the real life have been tested to find the gradientimage with the proposed LoG filter and subsequently applied watershed as a segmentation function.
As the initial stage, a color image from the real life is chosen and read with MATLAB editor using‘imread’ function. The next step is converting the color image into grayscale or black and white
image by using ‘rgb2gray’. Then, the gradient image is resulted from the gray level image with thehelp of proposed modified mask of LoG. The gradient image can directly apply to watershedsegmentation. In segmentation step, watershed algorithm is applied for finding initial segmentation
map and the image is analyzed the result (over-segmentation). We can easily see many
over-segmented regions that are not suitable in subsequent tasks. To reduce over segmented region, asolution can be achieved by using Marker controlled watershed segmentation. External markers arecomputed by using opening and reconstruction procedure. As the final step, the internal markers arecomputed and applied the watershed transform as the final segmentation function and visual the3Transactions on GIGAKU 2(2)(2014)S01016/1-6

result. The segmented output is more pleasing without over-segmentation and is also less timecomplex than other conventional algorithms.

5. Results and Discussion
The focus on this experiment is both for getting better image segmentation result and solving
over segmentation problem by using marker controlled watershed segmentation and modified LoG
edge detector. The experiments are carried out color images from real life. Figure 5(a) and 6(a)display the original colorful images.
The image is converted to gray level image for processing the subsequent algorithms. In
watershed segmentation, a gray level image can be interpreted as the topographic image of landscape.
Then, the gray level image has been processed to get the corresponding gradient image by using
modified 7x7 LoG mask. The gradient image can be directly applied to watershed segmentation.
Figure 5(b) and 6(b) displays the corresponding gradient image that has been observed with theproposed system. After that, the gradient image which obtained by using 5x5 LoG filter mask issegmented with conventional watershed transform .There has found over segmented regions asshown in figure 5(c) and 6(c) respectively. It results an unsuitable final segmented image as shownin figure 5(e) and 6(e). In order to get better result, these over segmented regions are processed withmarker-controlled watershed transform. The gradient image by applying 7x7 LoG filter is shown infigure 5(d) and 6(d). The image is passed with marker controlled watershed to get the final
segmented image. Figure 5(f) and Figure 6(f) display the acceptable segmented image by using the
proposed approach.

Fig. 5(a) Original color
Image

Fig. 5(b) Gradient image
using 7x7 LoG filter

Fig. 5(c) Over segmented
image
byapplying
watershed and 5x5 LoG
filterFig. 5(d) Over segmented
image by applying watershed
and 7x7 LoG filter

Fig. 5(e) Segmented image
byapplying
MarkerControlled watershed and 5x5

Fig. 5(f) Segmented image byapplying Marker Controlledwatershed and 7x7 LoG filter

We can easily seen by comparing with the segmented images of modified 7x7 LoG mask that4

Transactions on GIGAKU 2(2)(2014)S01016/1-6conventional 5x5 Log edge detector have produced pale edges (i.e, the edges in image are not very
sharp). Therefore, in practice, these gradient images can affect the segmented images in the next step.
On the other hand, we have been found that modified LoG filter can give more enhance gradientimages than conventional LoG filter. Thus, it results less over segmented image and gets better final
segmented image.
In this case, it has been observed that the edges and boundaries of the images are also seen to be
more prominent and clear with better contrast and less over segmented regions. Over segmentation isa very difficult problem in watershed segmentation. However, the proposed filter can be optimized
the segmented image by following marker-controlled watershed segmentation.

Fig. 6(a) Original Color
Image

Fig.
6(d)
Over
Segmented Image byapplying watershed and7x7 LoG filter

Fig. 6(c) Over Segmented
Imagebyapplying
watershed and 5x5 LoG
filterFig. 6(b) Gradient Imageby applying 7x7 LoG filter

Fig. 6(e) Segmented

Fig. 6(f) Segmented image

image by applying Marker

by applying Marker

Controlled watershed andControlled watershed and

5x5

7x7 LoG filter

LoG filter

Table1. Entropy of Final Segmented Image
FigureEntropy of the final

Entropy of the final

segmented

segmented

LoG

Image(5x5andMarkerControlled Watershed)

LoG

and

Image(7x7)MarkerControlled Watershed)

Figure 5(f)1.38131.3567

Figure 6(f)0.9762

0.9737The entropies of the final segmented images using marker controlled watershed segmentation
through 5x5 LoG filter and the proposed 7x7 LoG filter have been calculated and the values havebeen shown in the table 1. Experimental results presented in this paper are obtained by using5

Transactions on GIGAKU 2(2)(2014)S01016/1-6

MATLAB R2010a.

6. Conclusion
Image Segmentation is one of the important fields of image processing and computer vision. The
role of image segmentation is to decompose an image into parts that are meaningful with respect to aparticular application such as image analysis, image understanding activities, image description andrecognition, image visualization and object-based image compression etc. These subsequence steps
are highly depend on the segmentation results obtained from previous image segmentation stage.
The goal of image segmentation process is to identify the segments of the image according to thecharacteristics of objects e.g. object shape, image color etc. We have proposed a very simple
methodology to control over segmentation for the natural images with marker controlled watershedalgorithm. In order to solve the over segmentation problem of traditional watershed technique an
improved technique is proposed that uses pre processing methods to reduce the noise of image andadjust the image intensity. The proposed method with LOG filter can make to be better localization
when the edges are not very sharp. The presented method has been found to yield better output interm of image quality, clarity and sharpness. The proposed system can perform the input image withthe less number of segments and can give the meaningful and acceptable results of image
segmentation. The proposed method with marker controlled watershed overcomes over segmentation
problem more than conventional watershed transform. This system can be very helpful for image
segmentation to detect an accurate image clearer. Experimental results presented in this paper are
obtained by using MATLAB R2010a.
References[1] Pinaki Pratim Acharjya, Santanu Santra, Dibyendu Ghoshal “Improved Scheme onMorphological Image Segmentation Using the Gradients” IJACSA International Journal ofAdvanced Computer Science and Applications, vol 4, No.2, 2013.
[2] Usha Mittal, Sanyam Anand, “Effect of Morphological Filters on Medical Image Segmentation
using Improved Watershed Segmentation” ISSN.Vol 4, No.6 Jun,2013.
[3] Ms.K.Kavi Niranjana, Ms.M.Kalpana Devi, Ms.L.Mary, International Journal of Engineering
Research & Technology (IJERT),Vol. 1 Issue 7, September,2012” Image Segmentation Using
MCWS Algorithm”.
[4] A.K.Jain, Fundamentals of digital image processing, Second Edition, Prentice Hall, 2002.
[5] S. Beucher, “Watersheds of functions and picture segmentation,” in Proc.IEEE Int.Conf.
Acoustic, Speech, Signal processing pp.182-931, 1982.
[6] Gonzalez & Woods, Digital Image Processing 3rd edition, Prentice Hall India, 2008.
[7] K. Parvati, B. S. Prakasa Rao and M. Mariya Das, “Image Segmentation Using Gray-Scale
Morphology and Marker-ControlledWatershed Transformation”, Discrete Dynamics in Natureand Society,vol. 2008, pp. 1-8, 2008.
[8] Bieniek, A., & Moga, A., “An efficient watershed algorithm based on connected components”,
Pattern Recognition, 33, 907-916, 2000
[9] Jie Chen, Meng Lei, Yao Fan, Yi gao, “Research on an improved Watershed algorithm toImage segmentation”, The 7th International Conference on Computer Science & Education(ICCSE 2012), 2012[10] D Marr and E Hildreth, “Theory of edge detection”, in proceedings of the Royal Society, vol.
207, pp. 187-217, London, 1980.
[11] Vincent, P.Soille,”Watersheds in Digital Space: “an Efficient Algorithms Based on Immersion
Simulation,” [J]-IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, No.6,
pp.583-598, 1991.
[12] Pinaki Pratim Acharjya ,Dibyendu Goshal, “A Gradient Based Morphological Watershed
Segmentation Approach Suitable For Human Visual System” International Journal ofComputer Science and Engineering(IJCSE) ISSN 2278-99960, vol.2, Issue 1, Feb 2013, 59-68.
(Received; 23rd December, 2013, Accepted; 11th August, 2014)6

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Quantifying and Evaluating of Evolvability Software Quality for
Aspect-Oriented Software
Khine Zar Ne Win
Ministry of Science and Technology, Myanmar,
University of Computer Studies, Mandalay,*E-mail: khinezarnewinn1@gmail.com
Today, real-world software needs evolution after a certain period of time to meet thecurrent trends, technology, changes in user requirement and operational environment.
During system evolution, software change is an essential operation. After the evolutionof system, we must find out that how much new evolved system
is extensible when the software functionalities are added or removed
during maintenance, or when the existing programs are modified to reuse.
Moreover, we also find that how much design pattern is stable for new environment andhow much new evolved system is sustainable. Therefore, we need to have measurement
mechanisms for evaluating software quality. In the process of developing software andfulfilling to the present time, every modularized concern needs Stability, Sustainability,
Changeability and Extensibility. These qualities are focused as the sub-characteristics
of Evolvability in this research. So, we propose metrics to measure these qualities ofaspect-oriented software. In this study, we aim to measure the sub characteristics ofaspect-oriented (AO) software implemented in AspectJ. The analysis of these sub
characteristics is based on MobileMedia, AspectJ project, by using Self-Organizing
Map (SOM).

1. Introduction
Aspect-oriented programming (AOP) defines a new program construct, called an aspect,which is used to capture cross-cutting aspects of a software application in separate program entities.
It comes into picture because of some limitations of Object-Oriented Programming. In
Object-Oriented Programming, there are parts of a system that cannot be viewed as being the
responsibility of only one class, they cross-cut the complete system and affect parts of many classes.
This problem can be solved by using Aspect-Oriented Approach of Software Development [1].
Separation of concern is vital to quantitatively assess the quality of software produced
using Aspect-Oriented Software Development (AOSD). In this view, software metrics are needed
to do such assessment. The external quality attributes of aspect-oriented software are usually
measured, using modeling techniques, as a function of metrics that quantify the internal structuralproperties of the aspect-oriented software. However, there are few empirical studies [2,3,4,5]
that have been conducted to relate software metrics with external quality attributes of aspect-oriented
software in general, and metrics have not been evaluated as indicators of aspect extensible, changeable,
design stable and sustainable qualities by using Self-Organizing Map (SOM) in particular.
Motivated by afore mentioned issues, we attempt to prove the presented metrics empirically
MobileMedia, AspectJ projects. MobileMedia is a Software Product Line (SPL) that manipulates
photo, music, and video on mobile devices. This is a joint project coordinated by Lancaster Universityand developed on top of MobilePhoto. The availability of adequate metrics for these assessments
provides software managers early insight into trends in software evolution, and thus assists them inmanaging and controlling long-lived software systems. To measure the internal attributes of the
proposed metrics, we use the AspectJ Assessment Tool (AJATO) [6] and WinMerge Tool [7].
2.

Experiment

2.1 Proposed Quality Measurement Process
To demonstrate the four metrics, the aspect-oriented software is analyzed and measured. The

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quality measuring process is presented in Fig.1 and the quality model is illustrated in Fig.2.

Fig.2 The Quality Model

Fig.1 Proposed Quality Measuring System
2.2 Attribute Selections

To evaluate the qualities of the aspect-oriented software more accurately, the related attributes foreach of qualities are needed to select. These attributes are selected according to many guide lines andaspect-oriented features. The proposed metrics are formulated based on the rank weighting method tomeasure the quality of extensibility, changeability, design stability and sustainability. According to
guidelines [8], [9], [10] and [11] the selected attributes are identified as having more positively impact
on the sub characteristics to measure the evolvable AspectJ projects. In this study, the following
attributes are selected for proposed metrics.
Table 1 Selected Attributes for Proposed Metrics
No of Classes Deleted (DJ)No of Aspects added (Aaj)
No of Classes added (AJ)

No of Advices Deleted (Dadv)

No of Advices added (Aadv)

No of Declares Deleted (Ddc)

No of Declares added (Adc)

No of Statements Deleted (Dst)

No of Statements Added (Ast)No of Operations Deleted (Dop)

No of Operations Added (Aop)

No of Attributes Deleted (Datt)

No of Attributes Added (Aatt)

No of Members Deleted (Dm)

No of Members Added (Am)No of LOCs Deleted (Dloc)

No of LOCs Added (Aloc)

No of Aspects Reused (Raj)

No of Packages Added (Apkg)

No of Classes Reused (RJ)

No of Aspects Changed (Caj)

No of Advices Reused (Radv)

No of Classes Changed (CJ)

No of Declares Reused (Rdc)

No of Aspects Deleted (Daj)

No of Operations Reused (Rop)

2.3 Weight Assignments
The based attributes in MobileMedia project are counted and used by the proposed metrics formeasuring the quality of this project as the input based metrics. To quantify the proposed metrics,
the weights of based metrics is needed to assign. Because the important of individual attributes are
not the same, their weights are not equal. There are a variety of situations where it is reasonable to2

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use ranked weights, and there have been various techniques developed to deal with ranked weights
and arrive at a choice or rank alternatives under consideration. Therefore, rank-order weighting
methods are used to assign weights of attributes.Among these methods,Rank-Order Centroid (ROC)
method is suggested the best method in many literatures [12] and is defined as:∑, i 1,2, … t
(1)
ROC μ
where µ is a metric, pl is the position of ith attribute for µ metric and t is the total number of attributes.
After calculating the weight value by using equation 1, it is assigned to each selected attribute and thenwe can measure the quality factors by using the proposed metrics.
2.4 Changeability Quality Measurement (CQM)
As changeability grows in importance as a consideration in the engineering of systems, there
is a critical need to have a more rigorous and quantified definition. Change can be
defined as the transition over time of a system to an altered state. A change applies toa class, to a variable or to a method [13]. In this study, the formula for CQM metric is:
CQM

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2.5 Extensibility Quality Measurement (EQM)
Aspect-oriented software development (AOSD) is likely to have positive effect on performance,
modularity and evolution. So, it becomes important to reuse components. We focus on a specifickind of reusing of component called extensibility, i.e. the extension ofsoftware without
accessing existing code to edit or copy it. Extensibility is a systemic measure of the ability
to extend a software design principle where the implementation takes into
consideration future growth [13]. So, the formula for EQM is:
EQM

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2.6 Design Stability Quality Measurement (DSQM)
Design Stability covers the sustenance of system modularity characteristics and the absence ofripple-effects in the presence of change [14]. Designs cannot be completely static. Some volatility isnecessary if the design is to be maintained. Modules that are more difficult to change are going to beless volatile. For design stability, the harder the module is to change, i.e. the more stable it is, the lessvolatile it will be. Therefore, we propose that the removing of changeable elements are themeasurements of design stability metrics. In this study, the formula for DSQM metric is:
DSQM

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2.7 Sustainability Quality Measurement (SQM)
The Software Sustainability Institute define sustainability as “software we use today will beavailable and continue to be improved and supported in the future” which implicitly suggests thatsustainability is concerned with concepts of availability, extensibility, and the maintainability of thesoftware[5]. As a basis for proposed metric, we consider that software architectures and architectural
evaluation methods provide a mechanism for reasoning about software sustainability at an3

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architectural level [15]. So, we propose that sustainability will be considered in a similar manner tothe concept of reusability [16] by using architectural metrics. So, the formula for SQM is:
1

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2.8 Evolvability Quality Measurement
Software evolvability is a multifaceted quality attribute. It is characterized by inevitable changesof software and increasing software complexities, which in turn may lead to huge costs unlessrigorously taking into account change accommodations. This is in particular true for long-lived
systems in which changes go beyond maintainability[17]. In this paper, we have identified thesubcharacteristics that are primary importance for an evolvable software system. These
subcharacteristics serve as the proposed metrics in above mentions and each metric is validated byusing the self-organizing map (SOM) in result and discussion session. Finally, we can measure theevolvability of software system by using the following formula:(6)
Evolvability = ( EQM * 0.25 ) + ( CQM * 0.25 ) + ( DSQM *0.25 ) + ( SQM * 0.25 )3.
Results and Discussion
In this paper, we use the MobileMedia (version 1 to 7) as a case study project [18]. All of theattributes used in quantifying the result are normalizing by total number of each attribute of eachversion. Fig.3 shows the progress of extensibility quality, changeable quality, design stability andsustainable quality of MobileMedia projects’ versions paired from version 1 to 7. These figure
shows that the results of how much new evolved system is extensible and sustainable, how muchdesign model is stable and then how many changes are there.

Fig. 3 Comparison of Target Values for Proposed Quality Metrics
Empirical Validation Using Self-Organizing Map (SOM)
The unsupervised learning method, SOM is suitable for measuring the validity and utility of the
proposed metrics that they are difficult to prove their robustness and usefulness because they don’t
have historical data and other comparison metrics. One advantage is that they do not require more
understanding with input data and they can cluster and estimate incomplete historical data. The SOM
learns similarity of input feature vectors and responds groups of similar input vectors [12].
Before clustering MobileMedia projects, two data files are created for each of proposed metrics,
one contains the particular proposed metric values of the combination of number of versions nCr =7
C2 = 21 pairs and another contains its based attributes of these versions’ comparisons. In
preprocessing step, file attributes are converted into binary data according to equation 7:

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f x

1,

(7)
0, herwise
where f(x) is the binary conversion function that converts the x based metric value into binary data andf(y) is the total number of each attributes of the latest version of MobileMedia, AspectJ project.
After converting binary data, the contingency tables are created for each of versions’ pairs. To
create the contingency table, dissimilarities are calculated by Jaccard coefficient [12]:p(8)d x, yp q rp=number of times xi=1 and yi=1
q=number of times xi=1 and yi=0
r=number of times xi=0 and yi=1
These dissimilarity matrixes of versions are trained with the SOM and each version are clusteredin terms of dissimilarity matrixes. For instance, the internal attributes of EQM, CQM, DSQM andSQM metrics are clustered by SOM and the cluster matching result is analyzed. According to ourexperiment, most of the clustering results are matched. Therefore, it is obvious that the proposed
metrics are validated and useful. The clustering results for all proposed metrics are shown in thefollowing table.
Table 2 Analytical Results for Proposed Metrics Using SOM

In Fig. 4, SOM clusters MobileMedia project’ versions into two groups depending on only EQM
values. The first group contains version: V[1,7] and the second group contains other versions. The
version contained in first group are the highest value of EQM. So, the other group contains theversions that have the average value of extensibility. In Fig. 5, SOM also clusters two groups
depending on its based attributes. The first group contains version: V[1,7], V[2,7] and V[3,7] and theother group contains other versions’ pairs. The first groups’ members of two types are thencompared and analyzed. It is clear that 1 are matched. Moreover, the second groups’ members of
two types are also compared and analyzed. The result shows that 18 are matched. Therefore the totalcluster matching result is 19( 91%) for proposed metric EQM.

Fig.4 Proposed Metric clustering for
Fig.5 Based Metrics clustering for Extensibility
Extensibility Metrics
Metrics
After validating the proposed metrics, we can measure the evolvability of MobileMedia, AspectJ
projects by using equation 6.We have also tested the AspectJ projects such as HealthWatcher and
AJHOTDRAW. According to the accuracy of experiment by using the Self-Organizing Map

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Algorithm, we can see that the proposed metrics are validated and useful for software development
processes.
4.
Conclusion
For improving aspect-oriented software applications, the extensible, changeable, design stable
and sustainable quality metrics help software developers to measure these qualities ofaspect-oriented software as a self-assessment tool and to save time and money of softwaredevelopment process. To check the validity of the proposed metrics empirically, the self-organizing
feature map (SOM) and well-known AspectJ projects are used. Further extension for this research is
to create configurability metric as a sub characteristic to measure the evolvability of aspect-oriented
software. Therefore, aspect-oriented applications will be robust and widely used for businessapplications and other web-based information systems.
Acknowledgments
I would like to express my deep gratitude to my supervisor Dr. Khin Mar Myo, Associate
Professor, of University of Computer Studies, Mandalay. I greatly appreciate the help I havereceived from her. Her excellent help and creative ideas have assisted me in broadening my researchskills. I am extremely fortunate to work under her supervision of my research.
References[1] “Aspect oriented Programming in Java”,http://www.voelter.de/data/articles/aop/aop.html[2] C .S. Anna, A. Garcia, C. Chavez, C. Lucena, and A. Staa, "On the Reuse and Maintenance ofAspect-Oriented Software: An Assessment Framework," in Brazilian Symposium on Software
Engineering, Agosto, 2003
[3] R. Burrows, F. Ferrari, A. Garcia, and F. Taïani, "An Empirical Evaluation of Coupling Metrics
on Aspect Oriented Programs," ICSE, pp. 53-58, 2010[4] H.Shen, S. Zhang, and J.Zhao, "An Empirical Study of Maintainability in Aspect-Oriented
System Evolution Using Coupling Metrics," in 2nd IFIP/IEEE, pp. 233-236, 2008.
[5] P.Tonella and M. Ceccato, "Refactoring the aspectizable interfaces: an empirical assessment,"
IEEE Transactions on Software Engineering, vol. 31, pp. 819-832, 2005
[6] “AJATO : An AspectJ Assessment Tool”, http://homepages.dcc.ufmg.br/~figueiredo/ajato/[7] “WinMerge Tool”, http://tour.winmerge.org/[8] K. Mguni, Y.Ayalew, “An Assessment of Maintainability of an Aspect-Oriented System”, ISRN
Software Engineering Volume 2013, http://dx.doi.org/10.1155/2013/121692[9] A. Przybylek, “Analysis of the impact of aspect-oriented programming on source code
quality”, GDANSK UNIVERSITY OF TECHNOLOGY, Faculty of Electronics,
Telecommunications and Informatics,2011[10] H.Koziolek, “Sustainability Evaluation of Software Architectures: A Systematic
Review”, from ACM Digital library[11] Z. Durdik, B. Klatt, H.Koziolek, K. Krogmann, J. Stammel, R. Weiss, “Sustainability
Guidelines for Long-Living Software Systems”[12] T.Z.Thaw, “Measuring and Evaluation of Reusable Quality and Extensible Quality for XML
Schema Documents”, IEEE,2011, Cyberjaya, Selangor, Malaysia.
[13] K.Z.N.Winn, “Quantifying and Validation of Changeability and Extensibility for
Aspect-Oriented Software”, ICAET'2014, Singapore[14] P.Kumar, “Aspect-oriented Software Quality Model: The AOSQ Model”, Advanced
Computing: An International Journal (ACIJ), Vol.3, No.2, March 2012[15] F. Albertao, J. Xiao, C. Tian, Y. Lu, K. Q. Zhang, C. Liu, “Measuring the Sustainability
Performance of Software Projects”, IEEE, 2010[16] F. Amin, A. K. Mahmood, A. Oxley, “Reusability Assessment of Open Source Components for
Software Product Lines”, IJNCAA, 2011 (ISSN: 2220-9085)[17] H. P. Breivold, I. Crnkovic, P. J. Eriksson, “Analyzing Software Evolvability”, Computer
Software and Applications, 2008. COMPSAC '08. 32nd Annual IEEE International Conference[18] “MobileMedia”,http://mobilemedia.cvs.sourceforge.net/viewvc/mobilemedia/(Received; 23rd December, 2013, Accepted; 13th September, 2014)6

Transactions on GIGAKU 2(2)(2014)S01018/1-9Mission Control over Multi UAVs in
the Real-time Distributed Hardware-In-the-Loop Environment
Soe Linn Htet1,*, Tin Naing Lat2, Ye Chan3, Soe Mya Mya Aye1 and Pho Kaung3
1
Department of Computer Studies, University of Yangon
2
Department of Physics, Loikaw University
3
Universities’ Research Centre, University of Yangon*E-mail: fulcrum.slh@gmail.com

Mission control has been designed for Multiple Unmanned Aerial
Vehicles (Multi-UAVs) and the overall control performance verified by areal-time distributed Hardware-In-the-Loop (HIL) environment. Four
modules - onboard hardware, flight control, ground station and mission
control software, have been integrated to form a framework under which
the HIL test is realized. This design is successfully utilized for several
flight tests including basic flight motions, full-envelope flight andmultiple UAV formation flight. Results show that the constructed HIL
verification system is highly effective and useful tool for UAV mission
operations such as disaster management, civil security applications andmilitary operations.

1. Introduction
Unmanned Aerial Vehicles (UAVs) have been widely used not only in military applications, butalso in civilian and commercial applications. These include (but not limited to) environmental
monitoring, natural resource assessment, wildlife monitoring, bushfire monitoring, search andrescue,telecommunication, precision agriculture, power line inspection, and pipeline patrol.
UAVs require human guidance to varying degrees and an Unmanned Aerial System (UAS) is
essentially defined often through several operators. The challenge in achieving effective management ofmultiple UAV (multi-UAV) -systems in the future is to determine not only whether automation can beused to improve human and system performance but also how and to what degree across hierarchicalcontrol loops, the types of decision support the needs of operators in the high-workload environment.
This research addresses when and how increasing levels of automation should be incorporated in multiUAV systems and discusses the impact on human performance and more importantly system
performance. It has developed hierarchical control algorithms and a control architecture for multi-UAV
systems and extensively tested in simulations with various fidelities, from low fidelity proof of concept
simulations, to high fidelity Hardware-In-the-Loop (HIL) simulations.

2. Architecture of Single UAV Control
The architecture of single UAV control operation is hierarchical as represented in Figure 1. The
innermost loop represents the basic guidance and motion control, which is the most critical loop thatmust obey physical laws of nature such as aerodynamic constraints for UAVs. In this loop, operator
actions are focused only on the short term and local control (keeping the aircraft in stable flight), andgenerally human control in this loop requires skill-based behaviors that rely on automaticity.

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Status monitoring

MissionManagement

Navigation

Obstacle
avoidanceandroute headings(Auto)Pilot

FlightControls

Pitch, yaw , air speed and altitudelFig.1 Architecture of UAV Control Operation
The second loop or the navigation loop represents the actions that some agent, whether humanor computer-driven, must execute to meet mission constraints such as routes to waypoints, time on
targets, and avoidance of threat areas and no-fly zones. The outermost loop represents the highest levelsof control, mission and payload management. In this loop, sensors must be monitored and decisions
made based on the incoming information to meet overall mission requirements. In this loop, decisions
require knowledge-based reasoning that includes judgment, experience, and abstract reasoning thatcannot be generally performed by automation.
2.1
Flight Control System
The hardware module consists of the following components: (1) an onboard computer processing
system; (2) flight sensors (3) actuators and (3) a wireless modem as shown in Figure 2. Sensors are used
to determine the state of the aircraft. Microprocessor analyzes the data attained from the sensors alongwith commands from ground control radio signals to achieve suitable flight control according toequations of motion. Controls determined by the processor are then used to adjust the flight control
surfaces, through the actuators. The control surfaces include ailerons, elevators and a rudder to control
the roll, pitch and yaw of the aircraft. The microcontroller does most of the calculations and decision
making. In our proposed framework, flight control execution is done by the onboard computer system.

Fig.2 Basic Components of Flight Control System

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2.1.1

Controller Design
There are three basic controllers for the UAV flight control which are (a) Attitude controller
shown in Figure 3 and Figure 4, (b) Navigation controller in Figure 3 and(c) Altitude and Speed
controller in Figure 4 and Figure 5. Attitude controller maintains the aircraft at desired position andaltitude and speed controller holds the UAV to fly at a desired altitude and stabilizes the speed in level
flight in landing and in take-off. The guidance and navigation controller makes the UAV fly through thedesired waypoints.

Fig.3 Navigation Control (Outer Loop) and Roll/Yaw Control (Inner Loop)

Fig.4 Altitude Control (Outer Loop) and Pitch Control (Inner Loop)

Fig.5 Speed Controller (Outer Loop)
2.1.2

Embedded Firmware
The common methods for embedded application developing environment are super-loop design
and thread-oriented design. In this work, the thread-oriented design is used because it has advantages
over the super loop design and suitable for more complex applications. With a real time operating
system (RTOS), the developer gets not only the tool to create threads, but also tools to communicate
between the threads within their real-time constraints.
2.1.3

Thread-Oriented Design of Flight Control System
ChibiOS/RT RTOS is used to develop a flight control system (FCS) on STM32F405RGT6
microcontroller supporting multiple architectures and released under the GPL3 license. It is designed forembedded applications on 8, 16 and 32 bit microcontrollers where size and execution efficiency are their3

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main requirements. Thread-oriented design of FCS is depicted in Figure 6. It is represented for both
typical FCS and simulated FCS for HIL testing. There are five groups of thread in its firmware.

Fig.6 Thread-Oriented Design of Flight Control System
2.2

Ground Control System
Ground control can be divided into three sub systems. They are (1) Flight Instruments, (2) Map
and (3) Communication Status. All flight data from FCS via communication system are displayed on the
fly on the flight instruments while real time aircraft trajectory and mission status are updated on thelocalized Map. Task planning and mission control can also be performed on the map system.
Communication system, the heart of the entire system, takes responsibility for data transferring between
FCS and ground control system (GCS).
In both flight time and pre-flight time, mission controller can plan the missions for all UAVs on
the Graphical User Interface (GUI) of the GCS and hence, the communication backbone must be robust.
Moreover, weather condition of operation area can also be obtained from the weather server via this link.
2.2.1

Flight Instruments
Flight instruments include Artificial Horizon, Speed Indicator, Altimeter, and Direction Indicator.
These instruments can connect to communication system and monitor the flight data of entire flight.

2.2.2

Map System

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In GCS, implementation of map system is based on the Google tile map engine. Figure 7 shows
the structure of Google tile image.

Fig.7 Google Tile Image Structure
Google holds the world in a number of 256*256 pre-rendered images (“tiles”) for about 18
zoom stages. The lowest zoom is 0 and the highest 17. At zoom 0, the entire world is kept in one single
tile. At zoom 17, the world spreads over giant 17,179,869,184 tiles. The number of tiles needed to cover
the entire world for a zoom stage can be calculated with the relation, Number of Tiles = 2 ^ (2*Zoom).
Google uses Mercator projection. There are a couple of algorithms out in order to determine
bitmap pixel coordinates for a given geo-coordinate and zoom-stage. The following relations are not
only simple but easy to understand the solution of this problem.
X = λ – λ0
Y = lnwhere,
λ = Longitude
λ0 = Longitude in center of map= Latitude
There is a simple HTTP request that can be sent to Google Maps servers to obtain these tiles.
This is an example of the HTTP request sent to Google:
http://mts0.googleapis.com/vt?lyrs=m&x=48&y=96&z=8

Fig.8 Ground Control System with Customized Map
3. Multi-UAV System Architecture5Transactions on GIGAKU 2(2)(2014)S01018/1-9

The hardware architecture of a typical multi-UAV system is illustrated in Figure 9. The main
components in the ground station are the Ground Control Stations (GCSs), Mission Controller (MC),weather station and uplink controllers (GBB). During the taxi, takeoff and landing, human pilots control
the UAVs with modified hand held Radio Control (R/C) units.It can send the servo control signals to theuplink controllers instead of transmitting them directly to the UAVs. An uplink controller is a unit built
around an embedded microcontroller and a spread spectrum radio modem.The GCS for each UAV
displays and logs the real-time telemetry data and monitors the data related to mission and cooperative
control.
3.1 Mission Controller
Mission controller (MC) is a Ground Control System integrating some authority
featuresMission controller has some authority to interact the entire flight and ground system such as taskplanning, dynamically changing the mission objectives and all other high level decisions. Whereas the
low level ground control system can control only its predefined UAV, the mission controller can control
all the UAVs and the low level ground control system.

Fig.9 Typical Multiple UAVs Control and Coordination System
3.2 Hardware in the Loop Simulation (HIL)
Figure 10 illustrates the architecture of the distributed real-time HIL test for multi UAVs system.
It consists of a number of computers networked with the hardware and software components of the
UAVs. In order to reflect the real operational system configuration, the distributed HIL system isdesigned in such a way that it has almost identical architecture with the system hardware of the real
flight missions. Ethernet network (Wi-Fi 801.11b) is used as the communication backbone of HIL
system. To perform the HIL test, FlightGear flight simulator is run on each GCS computer of UAVs andit generates the flight data and weather condition of the operation area. These computers also6Transactions on GIGAKU 2(2)(2014)S01018/1-9

concurrently run flight vehicle dynamics models to simulate the UAV motions.

Fig.10 HIL Simulation Network

4. Results and Discussion
In this HIL simulation test, three UAVs are flown in a same flight path at different height levels
as shown in Figure 11. They can coordinate each other at specified height levels and be able to avoid thecollisions.The results have clearly indicated that the proposed multi-UAV control and coordination
system is capable of accurately and efficiently realizing the HIL trial in actual field. Figure 12 shows
how accurate each UAV maintained the specific altitude at single flight level.

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Fig.12 Coordination Flight of Three UAVs on Single Flight Path

Fig.13 Comparison of Three UAVs’ Altitude and Flight Path Coordination
5. Conclusion
The multiple UAV control and coordination system and the complete design of an HIL
simulation system have been addressed. The basic motion flight test and the multiple UAV formation test
have been carried out at the specific operation area with interactive weather conditions. The results haveclearly indicated that the designed HIL system is capable of accurately and efficiently predicting the real
flight situations including potential dangers and accidents. The system is certainly an effective and useful
tool for UAV mission operations such as disaster management, civil security applications and military
operations.
Our system is especially designed for rescue mission in disaster like Cyclone. UAVs are used toassess the situation at first because in rescue situations time is an important factor. Thus, the mission
control on the ground station computes initial flight routes for every UAV which can be adopted during
mission execution either by sending updated plans from the ground station to the UAVs or by the UAV's
on-board drone control. High-resolution images of the affected area will be taken by UAVs withinminutes. After the primary danger has been averted, the UAVs will have been used to monitor the
progress of securing the operation area by updating the overview image periodically.

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References[1] A HGöktoğan, SSukkarieh, “Simulation of Multi-UAV Missions in a Real-Time Distributed
Hardware-In-The-Loop Simulator”,Proceeding of the 4thInternational Symposium on
Mechatronics and its Applications (ISMA07), Sharjah, U.A.E. March 26-29, 2007
[2] Caccia Claudio, “ChibiOS-EmbrIO, Embryo Virtual Machines running on Chibi RTOS”, September,
16 2012[3] Darkoman, “GoogleMapsNet - GoogleMaps Control for .NET”, http://www.codeproject.com/Articles/463434/GoogleMapsNet-GoogleMaps-Control-for-NET, 21 Sep 2012.
[4] GuoweiCai, Ben M. Chen, Tong H. Lee, Miaobo Dong, “Design and implementation of a hardwarein-the-loop simulation system for small-scale UAV helicopters”, Department of Electrical andComputer Engineering, National University of Singapore, Singapore 117576, Singapore, 2009.
[5] Jerry Farmer, “Real-Time Operating System for PICmicro™ Microcontrollers”, Microchip
Technology Inc, 2002.
[6] Markus Loecher, “Overlays on Google map tiles in R”, Berlin School of Economics and Law(BSEL), 2013-08-27.
[7] Mats Pettersson, “Basic Concepts for Real Time Operating Systems”, IAR System[8] Richard E. Kowalski, Frank Huy, “Real-Time Operating Systems”, NASA.
[9] Robert L BorettiJr, Breakdown - HTTP Request Headers, WebSphere Application Server L2
Support, IBM Software Group, 2002.
[10] Wikipedia: Chibios/rt. http://en.wikipedia.org/wiki/ChibiOS/RT(Received; 23rd December, 2013, Accepted; 19th April, 2014)9

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Web Document Clustering Using Cuckoo Search Clustering Algorithm
Based On Gauss Distribution
Moe Moe Zaw1, Ei Ei Mon2
Faculty of Information and Communication Technology
University of Technology (Yatanarpon Cyber City)*E-mail: moemoezaw@gmail.com
The World Wide Web continues to grow rapidly a vast resource of
information and services. The number of web documents on the internet is
growing rapidly day by day. So, the process of finding the relevent
information on the web is a major challenge in Information Retrieval. The
increasing size and the dramatic content of World Wide Web has created aneed for automatic categorization of web pages. One of the techniques thatcan play an important role towards the achievement of this objective is web
document clustering. This paper aims to develop a clustering algorithm andapply in web document clustering area. The novel Cuckoo Search
optimization algorithm base on gauss distribution is a recently developed
optimization algorithm to increase the convergence rate of the cuckoo searchalgorithm base on levy flight.In this paper, Cuckoo Search Clustering
Algorithm based on gauss distribution is proposed to increase theperformance of the Cuckoo Search Algorithm based on levy flight. For
testing the performance of the proposed method, this paper will show theexperience result by using the benchmark dataset. It can be seen that the
Cuckoo Search Clustering algorithm based on gauss distribution outperforms
the Cuckoo Search Clustering Algorithm based on levy flight in web
document clustering.

1. Introduction
The increasing size and dynamic content of the World Wide Web has created a need forautomated organization of web-pages. Document clusters can provide a structure for organizing
large bodies of text for efficient browsing and searching. For this purpose, a web-page is typically
represented as a vector consisting of the suitably normalized frequency counts of words or terms.
Each document contains only a small percentage of all the words ever used in the web. If weconsider each document as a multi-dimensional vector and then try to cluster documents based on
their word contents, the problem differs from classic clustering scenarios in several ways. Document
clustering data is high dimensional, characterized by a highly sparse word-document matrix withpositive ordinal attribute values and a significant amount of outliers [1].
Nowadays, the internet has become the largest data repository, facing the problem of
information overload. In the same time, more and more people use the World Wide Web as theirmain source of information. The existence of an abundance of information, in combination with the
dynamic and heterogeneous nature of the Web, makes information retrieval a tedious process for the
average user. Search engines, meta-search engines and Web Directories have been developed inorder to help the users quickly and easily satisfy their information need [3].
Clustering is a technique, in which the data objects are given into a set of disjoint groups
called clusters so that objects in each cluster are more analogous to each other than the objects fromdifferent clusters. Clustering techniques are used in several application areas such as patternrecognition, data mining, machine learning, and so on.
Text clustering, which has been extensively studied in many scientic disciplines, plays an

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important role in organizing large amounts of heterogenous data into a small number of semanticallymeaningful clusters .In particular, web collection clustering is useful for summarization, organizationand navigation of semi-structured Web pages.
One of the best known and most popular clustering algorithms is the k-means algorithm.
The algorithm is efficient at clustering large data sets because its computational complexity only
grows linearly with the number of data points. However, the algorithm may converge to solutions
that are not optimal [6].
PSO is algorithm is presented as document clustering algorithm in [7]. It outperforms
K-means clustering algorithm.
In [8], to cluster the web pages, they use the dictionary (standardized) to obtain the context
with which a keyword is used and in turn cluster the results based on this context.
A combine approach is proposed to cluster the web pages which first finds the frequent
sets and then clusters the documents in [7].
In [10], the Cuckoo Search Clustering Algorithm (CSCA) is proposed. The algorithm isvalidated on two real time remote sensing satellite- image datasets. In their algorithm, new Cuckoo
is calculated by their new equation.In their new Cuckoo solution, the current best solution isconsidered.
In [4], the Cuckoo Search Clustering Algorithm based on levy flight is proposed. In thisalgorithm, the new cuckoo solution is generated by levy distribution. The algorithm is applied in
web document clustering area and tested with benchmark dataset.The result shows that the algorithm
is effective.
The other new Cuckoo equation for CSCA algorithm is proposed in [11].The new Cuckoo
solution is calculated by using both current solution and current best solution.
In [2], the KEA-Means algorithm is proposed and it is used for web page clustering. Thisalgorithm combines key phrase extraction algorithm and k-means algorithm.
This paper describes the application of a novel Cuckoo Search optimization algorithm baseon Gaussion distribution [1] to find global solutions to the clustering problem in web document
clustering area. The optimization algorithm was developed according to the low convergence rate of
Cuckoo Search optimization algorithm via levy flight [13] . In this paper, a cuckoo search clustering
algorithm based on gauss distribution is proposed and applied in web dosument clustering.
The rest of this paper will present the following. Web document Clustering is
explained and Cuckoo Search Clustering Algorithm based on Levy Flight will be discussed. Then,
Cuckoo Search Clustering Algorithm based on Gauss Distribution will be proposed. Experimental
Setup will be presented and results and discussion will also be presented. Then, we will conclude
this paper and suggest future work.

2. Web Document Clustering
Web document clustering is widely applicable in areas such as search engines, web mining,
information retrieval and topological analysis. Most document clustering methods perform several
pre-processing steps including stop words removal and stemming on the document set. Eachdocument is represented by a vector of frequencies of remaining terms within the document. Some
document clustering algorithms employ an extra pre-processing step that divides the actual term
frequency by the overall frequency of the term in the entire document set.
The text content of a web document provides a lot of information aiding in the clustering ofa page. There are many document clustering approaches proposed in the literature. They differ inmany parts, such as the types of attributes they use to characterize the documents, the similarity
measure used, the representation of the clusters etc. The different approaches can be categorized into
i.texbased, ii.linkbased and iii.hybrid one. The text-based web document clustering approaches
characterize each document according its content, i.e the words (or sometimes phrases) contained init. The basic idea is that if two documents contain many common words then it is likely that two
documents are very similar.
In most clustering algorithms, the dataset to be clustered is represented as a set of vectors
X={x1, x2, …., xn}, where the vector xi corresponds to a single object and is called the feature vector.
The feature vector should include proper features to represent the object. The web document objects

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can be represented using the Vector Space Model (VSM) . In this model, the content of a document
is formalized as a dot in the multidimensional space and represented by a vector d, such as d= { w1 ,w2, wn..... } , where wi (i = 1,2,…,n) is the term weight of the term ti in one document. The term
weight value represents the significance of this term in a document. To calculate the term weight, theoccurrence frequency of the term within a document and in the entire set of documents must be
considered. The most widely used weighting scheme combines the Term Frequency with Inverse
Document Frequency (TF-IDF) . The weight of term i in document j is given in Eq (1):(1)
wj i= tfji*idfji = tfji*log2(n/dfji)
where tfji is the number of occurrences of term i in the document j; dfji indicates the term frequency
in the collections of documents; and n is the total number of documents in the collection. Thisweighting scheme discounts the frequent words with little discriminating power.
The similarity between two documents is measured in clustering analysis. This algorithm
uses the normalized Euclidean distance as the similarity metric of two documents mp and mj in thevector space. Eq-2 is the distance measurement formula.
(2)
where mp and mj are two document vectors; dm is the dimension number of the vector space; mpk andmjk denote the documents mp and mj 's weight values in dimension k. This similarity metric is widelyused in the text document clustering.
3. Cuckoo Search Clustering Algprithm based on Levy Flight
Cuckoo Search Clustering Algorithm based on levy flight is designed as a clustering
algorithm from Cuckoo Search Optimization algorithm to locate the optimal centroids of the cluster.
In web document clustering area, it is possible to view the clustering problem as an optimization
problem that locates the optimal centroids of the clusters rather than an optimal partition finding
problem. This algorithm aims to group a set of input samples (data points) into clusters withsimilar features. It will work without the knowledge of the class of the input data during the process.
In this algorithm, new cuckoo solutions will be moved by using levy flight.
In this clustering algorithm, the new cuckoo solution is generated by levy distribution as
follows:(3)
x i (t+1) = xi(t) +α * levy (λ )x i (t+1) = xi(t) + α * S (xi(t) – xbest(t)).r
In Mantegna's algorithm, the step Length can be calsulated by [11].
S=µ/|v|1/β

(4)
(5)

where β is a parameter between [1,2] and considered to be 1.5. µ and v are drawn from normal
distribution asµ~N, v~ N (0,)(6)

4. Cuckoo Search Clusering Algorithm based on Gauss Distribution
Levy flight is a random walk whose random steps are drawn from a Levy distribution forlarge steps. Cuckoo search with levy flight can find the optimal solution but the search relies entirelyon random walks. So, a fast convergence and precision cannot be guaranted. So, in this paper, a3Transactions on GIGAKU 2(2)(2014)S01019/1-7

cuckoo search clustering algorithm based on gauss distribution is proposed to increase theconvergence rate and precision. New cuckoo solutions are generated by gaussian distribution.The
algorithm is as shown in fig 1.

1. Begin(Parameter Initialization- no of clusters, no of host nests)
2. Consider NH host nests containing 1 egg (solution) each3.
For each solution of host i(corresponding to k
4.
Initialize xi to contain k randomly selected cluster centroids
clusters), as xi = (mi,1,….,mi,j,…mi,k)where mi,k represents the kth cluster centroid
vector of ith cluster centroid vector of ith host.
End for loop5.
For t iterations
6.
For each solution of host i of the population
7.
For each data document zp
8.
Calculate distance d(zp,mj,k) from all cluster centroids Ci,kby using Euclidean Distance eq-2
9.
Assign zp to Ci,k byd(zp,mj,k) = mink= 1…k { d(zp,mj,k) }End for loop in step 7
10.
Calculate fitness function f(xi) for each host nest i by Eq-(9)11.
End for loop in step 6
12.
Replace all worse nests by new Cuckoo eggs produced with gaussian
distribution from their positions
13.
A fraction pa of worse nests are abandoned and new ones are built randomly
14.
Keep the best solutions (or nests with quality solutions)15.
Find the current best solution
End for loop in step 5
16. Consider the clustering solution represented by the best solution
17. EndFig. 1 Cuckoo Search Clustering Algorithm based on Gauss Distribution(7)
where,(8)and µ are constants. k is the current generation.

f

=

(9)

mi,j = jth document vector which belong to cluster i;oi= the centroid vector of ith clusterd(oi,mi,j)= distance between document mi,j and the cluster centroid oi
pi = the number of documents which belongs to cluster Ci
Nc= number of clusters

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Fig. 2 Proposed Method in Web Document Clustering
In Fig.2, the documents to be clustered must be collected first. The proposed method
includes two phases: preprocessing phase and clustering phase. In preprocessing phase, eachdocument will be tokenized and the stop words such as a, an, the etc., will be removed. The
remaining words will be represented in Vector Space Model with their TFIDF weight values. In
clustering phase, the distance from the center documents to the other documents will be measured by
Cosine Similarity measure. The documents to the nearest center will go to this cluster. For next
center document selection, the old center will be moved to the new center by Cuckoo Solutions
based on gaussian distribution.This clustering process will be performed for a defined number ofcriteria. The algoithm will finally produce the user-defined number of document clusters.

5. Experimental Setup
Cuckoo Search Clustering Algorithm based on levy flight and the cuckoo search clustering
algorithm based on gauss distribution are tested on 7 sector benchmark data set. For our testingprocess, 600 web pages are randomly selected from the dataset and clustered into 3 classes. The
algorithm is tested by using Euclidean distance as similarity measure of the two documents. The
algorithm executes for 200 iterations and uses 10 cuckoos.The parameter pa is set as 0.25. µ is
is 0.5 in gaussian clustering algorithm. α is 1 in both algorithms.
0.0001 and5Transactions on GIGAKU 2(2)(2014)S01019/1-7

6. Results and Discussion
The fitness equation is also used for the evaluation of the cluster quality. The smaller thecluster quality value, the more compact the clustering solution. The average fitness comparison of
the two algorithms are as shown in Fig 3. The cuckoo search clustering algorithm with gauss
distribution achieves its convergence over 120 generations while the other achieves its convergenceover 160 generations.
A famous method for evaluating measure in information retrieval (IR) is F-measure. The
cluster results of the system are also evaluated using F-measure. It considers the precision (P), recall(R) and is shown in Eq (10). Eq (11) shows F-measure formula. The F-measure values are theaverage of 100 runs.

(10)

(11)

Fig.3 Comparison of Average fitness values of the two algorithms over generation
Table 1 Precision, Recall and F-measure
Methods

Precision

RecallF-measure

CSCA with

0.635

0.619

0.626

0.623

0.615

0.619

gauss
distribution
CSCA
based

on

levy flight6

Transactions on GIGAKU 2(2)(2014)S01019/1-7

Table.1 illustrates the F-measure of the proposed method. High F-measure shows the highaccuracy. The proposed method achieves 0.626 of F-measure in clustering 300 web documents into 3
clusters.

7. Conclusion
Cuckoo Search Clustering Algorithm based on gauss distribution is proposed and applied in
web document clustering area. The result shows that the cluster quality and the evaluation measureobtained are good. Its performance is compared to the performance of cuckoo search clustering
algorithm based on levy flight. The gauss distribution can produce more accurate precision than levy
flight in the clustering algorithm. As our future work, the clustering accuracy the Cuckoo Search
Clustering Algorithm based on gauss distribution can also be applied to other datasets. And it canalso be compared to other swarm intelligence clustering algorithms.
References[1] Hongqing ZHENG and Yongquan ZHOU, "A Novel Cuckoo Search Optimization Algorithm
base on Gauss Distribution",Journal of Computional imfprmation Systems 8: 10(2012),
4193-4200[2] Swapnali Ware and N.A.Dhawas, "Web Document Clustering Using KEA-Means Algorithm",
International Journal of Technologies and Applications, Vol-3(5),1720-1725[3] N. Oikonomakou and M. Vazirgiannis,"A Review of Web Document Clustering Approaches"[4] Moe Moe Zaw and Ei Ei Mon, "Web Document Clustering Using Cuckoo Search Clustering
Algorithm based on Levy Flight", International Journal of Innovation And Applied Studies, vol.
4, no 1, pp. 182-188 Sep 2013[5] Jain, A.K. and Dubes, R.C, Algorithms for Clustering Data, 1988 .Prentice Hall, Englewood
Cliffs, New Jersey, USA[6] Bottou, L. and Bengio, Y. ,"Convergence properties of the k-means algorithm", Advances in
Neural Information Processing Systems, 1995, 7, 585-592[7] Xiaohui Cui, Thomas E. Potok, Paul Palathingal, "Document Clustering Using Particle Swarm
Optimization", Swarm Intelligence Symposium, IEEE publication, 8-10 June 2005[8] Jeevan H E, Prashanth P P , Punith Kumar S N , Vinay Hegde , "Web Pages Clustering: A New
Approach", International Journal Of Innovative Technology & Creative Engineering(ISSN:2045-8711) vol.1 no.4 April 2011[9] Rajendra Kumar Roul1, Dr.S.K.Sahay, "An Effictive Web Document Clustering For
Information Retrieval", International Journal of Computer Science and Management Research,
vol. 1, no. 3, p. 481, 2012[10] Samiksha Goel, Arpita Sharma, Punam Bedi, "Cuckoo Search Clustering Algorithm: A novel
strategy of biomimicry", World Congress on Information and Communication Technologies,
IEEE publication, 2011[11] Moe Moe Zaw, Ei Ei Mon, "Improved Cuckoo Search Clustering Algorithm(ICSCA)" ,Proceedings of the 11th International Conference on Computer Applications, pp.22-26, 2013[12] Swapnali Ware, N.A.Dhawas, "Web Document Clustering Using KEA-Means Algorithm",
International Journal Of Computer Technology & Applications, vol 3 (5), pp.1720 -1725, 2012[13] Xin-She Yang, Suash Deb, "Cuckoo Search via L´evy Flights", World Congress on Nature andBiologically Inspired Algorithms, IEEE publication, pp.210-214, 2009[14] X.-S. Yang and S. Deb, "Engineering Optimisation by Cuckoo Search", International Journalof Mathematical Modelling and Numerical Optimisation", vol. 1, no. 4, pp.330-343 ,2010[15] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, 2010
(Received; 23rd December, 2013, Accepted; 11th August, 2014)7