Advancing Computing, Communication, Control and Management

296 Pages·2010·8.569 MB·English

by  Liang Jun, Xianyi Cheng, Xiaobo Chen (auth.), Qi Luo (eds.)

Lecture Notes in Electrical Engineering
Volume 56
Qi Luo (Ed.)
Advancing Computing,
Communication, Control
and Management
ABC
QiLuo
SchoolofElectricalEngineering
WuhanInstituteofTechnology
Wuhan430070
China
E-mail:[email protected]
ISBN978-3-642-05172-2 e-ISBN978-3-642-05173-9
DOI10.1007/978-3-642-05173-9
LectureNotesinElectricalEngineering ISSN1876-1100
LibraryofCongressControlNumber:2009940122
(cid:2)c 2010Springer-VerlagBerlinHeidelberg
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Preface
A large 2008 ISECS International Colloquium on Computing, Communication, Control,
and Management (CCCM 2008), was held in Guangzhou, August 2008, China. Just like
the name of the Colloquium, the theme for this conference is Advancing Computing,
Communication, Control, and Management Technologies. 2008 ISECS International
Colloquium on Computing, Communication, Control, and Management is co-sponsored
by Guangdong University of Business Studies, China, Peoples’ Friendship University of
Russia, Russia, Central South University, China, Southwestern University of Finance &
Economics, China, and University of Amsterdam, Netherlands. It is also co-sponsored
IEEE Technology Management Council, IEEE Computer Society, and Intelligent
Information Technology Application Research Institute. Much work went into preparing
a program of high quality. We received about 972 submissions. Every paper was
reviewed by 3 program committee members, about 382 were selected as regular papers,
representing a 39% acceptance rate for regular papers. The CCCM conferences serve as
good platforms for the engineering community to meet with each other and to exchange
ideas. The conference has also stroke a balance between theoretical and application
development. The conference committees have been formed with over two hundred
committee members who are mainly research center heads, faculty deans, department
heads, professors, and research scientists from over 30 countries. The conferences are
truly international meetings with a high level of participation from many countries. The
response that we have received for the congress is excellent.
This volume contains revised and extended research articles written by prominent
researchers participating in the conference. Topics covered include intelligent computing,
network management, wireless networks, telecommunication, power engineering, control
engineering, Signal and Image Processing, Machine Learning, Control Systems and
Applications, The book will offer the states of arts of tremendous advances in
Computing, Communication, Control, and Management and also serve as an excellent
reference work for researchers and graduate students working on Computing,
Communication, Control, and Management Research.

Qi Luo
Table of Contents
Study on MRF and REF to Semi-supervised Classification ............ 1
Liang Jun, Xianyi Cheng, and Xiaobo Chen
An Extension Method of Space Syntax and Application............... 7
Xinqi Zheng, Lu Zhao, Yanjun Su, Guang Yan, and Shuqing Wang
A Feasible Registration Method for Underwater SLAM ............... 15
Feng Sun, Wenjing Wang, Fuqiang Liu, and Wenfeng Wang
A Promoted Global Convergence Particle Swarm Optimization
Algorithm....................................................... 23
Du Ronghua and Cai Yue
A Web-Based Integrated System for Construction Project Cost
Prediction ...................................................... 31
Huawang Shi and Wanqing Li
Research of Corporate Credit for Anhui Province’s Listed Companies
Based on Computer Technology.................................... 39
Li Yang and Malin Song
Evaluation of Industrial Parks’ Industrial Transformations and
Environmental Reform Actualized by AHP Based on MatLab
Software ........................................................ 47
Malin Song
A Model Integrated Development of Embedded Software for
Manufacturing Equipment Control ................................. 55
Zhumei Song and Di Li
Two Stage Estimation Method in Data Processing and Simulation
Analysis ........................................................ 62
Pan Xiong, Chunru Zhao, and Lihong Jin
Research and Implementation of a Reconfigurable Parallel Low Power
E0 Algorithm.................................................... 71
Li Wei, Dai Zibin, Nan Longmei, and Zhang Xueying
A Model of Car Rear-End Warning by Means of MAS and Behavior.... 79
Liang Jun, Xianyi Cheng, Xiaobo Chen, and Yao Ming
Novel Hue Preserving Algorithm of Color Image Enhancement......... 88
Caixia Liu
VIII Table of Contents
Artificial Immune System Clustering Algorithm and Electricity
Customer Credit Analysis......................................... 97
Shu-xia Yang
Virtual Space Sound Technique and Its Multimedia Education
Application ..................................................... 103
Xinyu Duan
Survey on Association Rules Mining Algorithms ..................... 111
Mingzhu Zhang and Changzheng He
Application of Rough Set Theory and Fuzzy LS-SVM in Building
Cooling Load.................................................... 119
Xuemei Li, Ming Shao, and Lixing Ding
An Efficient Feature Selection Algorithm Based on Hybrid Clonal
Selection Genetic Strategy for Text Categorization ................... 127
Jiansheng Jiang, Wanneng Shu, and Huixia Jin
Power Demand Forecasting Based on BP Neural Network Optimized
by Clone Selection Particle Swarm ................................. 135
Xiang Li and Shi-jun Lu
Research on Simplifying the Motion Equations and Coefficients
Identification for Submarine Training Simulator Based on Sensitivity
Index........................................................... 142
Zhao Lin and Zhu Yi
Evaluating Quality of Networked Education via Learning Action
Analysis ........................................................ 150
Bin Xu
Research on Face Recognition Technology Based on Average Gray
Scale ........................................................... 158
Weihua Wang
The Active Leveled Interest Management in Distributed Virtual
Environment .................................................... 166
Jia Bei and Yang Zhao
The Research of the Intelligent Fault Diagnosis Optimized by ACA for
Marine Diesel Engine............................................. 174
Peng Li, Lei Liu, and Haixia Gong
Extraction and Parameterizationof Eye Contour from Monkey Face in
Monocular Image ................................................ 182
Dengyi Zhang, Chengzhang Qu, Jianhui Zhao, Zhong Zhang,
Youwang Ke, Shizhong Han, Mingqi Qiao, and Huiyun Zhang
Table of Contents IX
KPCA and LS-SVM Prediction Model for Hydrogen Gas
Concentration ................................................... 190
Minqiang Pan, Dehuai Zeng, Gang Xu, and Tao Wang
Random Number Generator Based on Hopfield Neural Network and
SHA-2 (512) .................................................... 198
Yuhua Wang, Guoyin Wang, and Huanguo Zhang
A Hybrid Inspection Method for Surface Defect Classification ......... 206
Mengxin Li, Chengdong Wu, and Yang Cao
Study on Row Scan Line Based Edge Tracing Technology for Vehicle
Recognition System .............................................. 214
Weihua Wang
Study on Modeling and Simulation of Container Terminal Logistics
System ......................................................... 222
Li Li and Wang Xiaodong
Digitalized Contour Line Scanning for Laser Rapid Prototyping........ 231
Zeng Feng, Yao Shan, and Ye Changke
3D Surface Texture Synthesis Using Wavelet Coefficient Fitting........ 239
Muwei Jian, Ningbo Hao, Junyu Dong, and Rong Jiang
BuildingServiceOrientedSharingPlatformforEmergencyManagement
– An Earthquake Damage Assessment Example...................... 247
Ying Su, Zhanming Jin, and Jie Peng
An Interactive Intelligent Analysis System in Criminal Investigation.... 256
Ping He
Research on Functional Modules of Gene Regulatory Network ......... 264
Honglin Xu and Shitong Wang
Block-BasedNormalized-Cut Algorithm for Image Segmentation ....... 272
Haiyu Song, Xiongfei Li, Pengjie Wang, and Jingrun Chen
Semantics Web Service Characteristic Composition ApproachBased on
Particle Swarm Optimization...................................... 279
Zhou Xiangbing
Author Index.................................................. 289
Study on MRF and REF to Semi-supervised
Classification
Liang Jun, Xianyi Cheng, and Xiaobo Chen
School of Computer Science and Telecommunication Engineering,
Jiangsu University, China, 212013
[email protected]
Abstract. We study the semi-supervised classifier with a decision rule learning
from labeled and unlabeled data. A model to semi-supervised classification is
proposed to overcome the problem induced by mislabeled samples. A new
energy function based on REF (robust error function) is used in MRF (Markov
Random Field). Also two algorithms based on iterative condition mode and
Markov chain Monte Carlo respectively are designed to infer the label of both
labeled and unlabeled samples. Our experiments demonstrate that the proposed
method is efficient for real-world dataset.
Keywords: semi-supervised learning, classifier, Markov Random Field,
Simulating.
1 Introduction
Semi-supervised learning has received considerable attention in the machine learning
literature due to its potential in reducing the need for expensive labeled data [1]. Given a
sample setY ={y ,y ,...,y ,y ,...,y }⊂ Rmand a label setL={1,2,......,C}, the
1 2 l l+1 N
first l samples y(1≤i≤l) are labeled as f ∈L and the remaining
i i
samplesy(l+2≤i≤N) are unlabeled. The goal is to classify the unlabeled samples to its
i
latent class.
Most formulations of semi-supervised learning approach the problem from one of
the two ends of the unsupervised-supervised spectrum: either supervised learning in
the presence of unlabelled data [2,3] or unsupervised learning with additional
information [4,5].
Almost all current research has not considered the situation when there exist
mislabeled samples in semi-supervised classification. Because of the little number of
labeled samples in semi-supervised classification, the mislabeled samples will have
severe influence on the final classification result. An example is show in figure 1
which contains 70 samples from two classes. One class is show in blue and the other
in red. There are eight labeled samples in the data set which are show in green. Three
of them are from class 1, four of them are from class 2 and especially one sample
from class 1 is mislabeled to class 2 (show by the green diamond on top most).
Q. Luo (Ed.): Advancing Computing, Communi., Control and Management, LNEE 56, pp. 1–6.
springerlink.com © Springer-Verlag Berlin Heidelberg 2010
2 L. Jun, X. Cheng, and X. Chen
According to the algorithm in literature [6], we can see that many samples in class 1
are misclassified to class 2 because of the existence of mislabeled sample.
In order to tackle the problem, we proposed a new model based on MRF and REF.
The paper is organized as follows. In section 2, we proposed a MRF with REF. In
Sect 3, we describe two different inference algorithm for the model and Section 4
reports the experiments on the above examples and its application in semi-supervised
classification. Some conclusions are given in section 5.
2 MRF with REF
2.1 MRF for Semi-supervised Classification
We first recall MRF (MRF) models [7] which originated in statistical physics and
have been extensively used in image processing. MRF constraints the spatial
smoothness though puts high probability to the class labels which are consistent
locally. In the context of machine learning, what we can do is create a graph with a
node for each sample, and with undirected edges between them that are similar to
each other. A typical MRF model is show in figure 1. The nodes in the upper level are
that we can observe while the nodes in the lower level are unobserved. Because of its
flexibility, MRF have been introduced into semi-supervised learning [8].In the
context of semi-supervised classification, some hidden labels are known as prior
which differentiate it from the conventional MRF as illustrated in figure 2. A natural
energy function takes the form:
E (x)=−∑ωx x
g ij i j (1)
{i,j}
wherex ∈{−1,+1} are binary labels and ωis the weight on edge{i, j}, which is a
i ij
measure of the similarity between the samples.
2.2 REF
Recently, many researchers have found that the use of REF can lead to improved
results on applications such as super-resolution, CCD demosaicing, image in-painting,
and image denoising. Examples of REFs include the truncated quadratic function and
the Lorentzian error function [9] which is
1
E (x)=ln(1+ x2) (2)
l 2
The most important feature of REF is that unlike the quadratic error function, the
magnitude of the derivative of the error function does not increase as the error rises.
This feature is show in figure 3 which we can see the rate of increase in the
Lorentzian error function actually decreases as the error rises. It is this property that
makes this function robust to large outlier errors.
Study on MRF and REF to Semi-supervised Classification 3
2 2
1.5 1.5
1 1
0.5 0.5
0 0
-0.5 -0.5
-1 -1
-1.5 -1.5
-20 0.5 1 1.5 -20 0.5 1 1.5

Fig. 1. Classification results according to the semi-supervised method of [6]
7
6
5
4
3
2
1
-030 -20 -10 0 10 20 30
Fig. 2. MRF Fig. 3. Lorentzian error function
2.3 MRF with REF
Due to the analysis above, we incorporate the global energy function of MRF which
means a good classifying function should not change too much between nearby
samples with the local Lorentzian error function which means a good classifying
function should not change too much from the initial label assignment into the follow
energy function:
1
E(x)=λE(x)+(1−λ)E(x)=λ∑ω(x−x)2+(1−λ)∑log(1+ (x−f)2) (3)
g l ij i j 2 i i
{i,j} 1≤i≤l
whereω =exp(−||y −y ||2 /2σ2), which is a measures of the similarity between
ij i j
λ
samples. is a parameter which balance the global consistency and local
consistency. To assign a probability distribution to the labels, we form a random field:
1
p(x)= exp{−βE(x)} (4)
Z
where the partition functionZ normalizes over all labels.
So our goal is to maximizep(x)which is equivalent to minimizeE(x)as below:
x=argminE(x)
(5)
x