Conference Program of WCICA 2012

WCICA 2012
Book of Abstracts: Friday Sessions
Xie, Nan
Tongji Univ.
According to production tasks, making manufacturing resources reconstruction
via machine tool selection is one of the key issues to achieve
manufacturing system reorganization. At the time when machine tool
is selected, qualitative factors and quantitative factors must be considered.
In order to solve the problem well, the evaluation index system
with a hierarchical structure was presented. A multi-objective optimization
model was established, and then the model was analyzed by using
hierarchical weighting methods. Finally, a practical application was provided
to illustrate the scientific validity of the model and the effectiveness
of the methodology.
◁ PFrA-07
A Hybrid Method for Short-term Load Forecasting in Power System,
pp.696–699
Zhu, Xianghe
Qi, Huan
Huang, Xuncheng
Sun, Suqin
Huazhong Univ.of Sci. & Tech.Wuchang Branch
huazhong Univ. of Sci. & Tech.
Electric Power of HeNan
Electric Power of HeNan
In order to improve the accuracy of power load forecasting, this paper
proposes a hybrid model based on Ensemble Empirical Mode Decomposition
(EEMD), least square-support vector machine (SVM) and BP
nature network as a short-term load forecasting model. At first, the actual
power load series is decomposed into different new series based
on EEMD. Then the right parameters and kernel functions are chosen
to build different LS-SVM model respectively, to forecast each intrinsic
mode functions, due to the change regulation of each of all resulted
intrinsic mode functions. Finally, we use the BP network to reconstruct
the forecasted signals of the components and obtain the ultimate forecasting
results. Simulation results show that the proposed forecasting
method possesses accuracy.
◁ PFrA-08
Group Decision-Making Based Case Retrieval and Its Application,
pp.773–778
Zhang, Chun-xiao
YAN, Aijun
Zhao, Hui
Wang, Pu
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech., China
The distribution of the case feature attribute weights directly affects the
case retrieval result. Aim at improving the retrieval precision, a retrieval
method is proposed based on group decision-making for optimizing
the case feature attributes weights. Firstly, multiple groups of initial
weights are obtained by genetic algorithm. Then, the multiple sets of
retrieval results produced by these weights are optimized through group
decision-making method, and the weights can be dynamic adjusted
through the deviations between individual decision results and group
result. The simulation results indicate that the proposed approach can
fully excavate the potential knowledge of attribute weights and thus result
in higher retrieval accuracy in a case-based reasoning system. The
PID adjusting comparison experiment of typical two order delay system
verifies the effectiveness of the new method.
◁ PFrA-09
Attribute Reduction Method Using Water-Filling Principle for Case-
Based Reasoning, pp.779–782
Zhao, Hui
YAN, Aijun
Zhang, Chun-xiao
Wang, Pu
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech.
Beijing Univ. of Tech., China
As the large number of feature attributes in Case-based reasoning system
(CBR) brings a huge information redundancy which reduces the
retrieval efficiency, a novel reduction method based on Water-Filling is
proposed to remove those unnecessary attributes. In the method, the
importance of each attribute could be calculated by utilizing the ratio
of the standard deviation and the mean value of each attribute data as
evaluation parameter, and the impotance result of each attribute is then
used to guide the reduction process. The experiments on glass identification
showed that the new method could get a better retrieval accuracy
as well as a greater efficiency compared with the methods which do not
conduct the reduction process.
◁ PFrA-10
Application of Self-organizing Feature Map Neural Network Based on
Data Clustering, pp.797–802
Hu, Xiang
Yang, Yun
Zhang, Lihong
Xiang, Tao
Hong, Chengqiu
Zheng, Xiaotong
Tsinghua Univ.
Tsinghua Univ.
Tinghua Univ.
Tsinghua Univ.
Tsinghua Univ.
Tsinghua Univ.
Outlier detection is of much importance in preprocessing of data collected
from complex industry system, for the data has strong nonlinearity
and poor stability, involving much noise. Outlier detection based
on clustering, rejects abnormal data points which have significant difference
from others according to the definition of similarity. Self-organizing
Feature Map (SOM) Neural Network algorithm has the self-study and
adaptive functions of neural networks, so as to be a hot research in
clustering analysis recently. This paper first introduces Self-organizing
Feature Map algorithm based on artificial neural network, and then improves
the algorithm by using weighted Euclidean distance, finally uses
the software of MATLAB to analyze some actual data of electrical power.
The result shows that SOM algorithm achieves a very good effect in
clustering, and the MATLAB toolbox shows favorable visual effects.
◁ PFrA-11
Flow Rate Control and Resource Allocation Policy with Security Requirements
in OFDMA Networks, pp.1020–1025
Zhu, Xingzheng
Yue, Jianting
Yang, Bo
Guan, Xinping
SHANGHAI JIAOTONG Univ.
SHANGHAI JIAOTONG Univ.
Shanghai Jiao Tong Univ.
Shanghai Jiao Tong Univ.
OFDMA-based network performs well in maximizing the overall
throughput, and meanwhile, problems with security requirements are
focused to satisfy the increasing needs of confidential data transmissions.
According to information theory, however, secure transmission
undesirably antagonizes larger flow rate, thus a tradeoff is expected to
balance these two matters. In this paper, we consider a downlink situation
in OFDMA network, where all users request secure transmission
from the base station yet the resources including power and subcarriers
are limited such that an appropriate policy on resource allocation can
play an instrumental role. Dynamic queueing and Lyapunov optimization
are two highlights in this process. We utilize dynamic queueing
so that merely instantaneous state of the network is enough, which is
advantageous for easier implementations. On the other hand, by the
means of Lyapunov optimization, we also prove our algorithm can obtain
a performance of flow rate admission extremely close to the optimal.
Applying this policy can reduce computational complexity significantly
but still perform close to optimality.
◁ PFrA-12
Augmented Dimension Algorithm Based on Sequential Detection for
Maneuvering Target Tracking, pp.1323–1327
Pan, Baogui
Peng, Dongliang
Shao, Genfu
Inst. of Information & Control
Hangzhou Dianzi Univ.
Inst. of Information & Control
In order to solve the problem that target tracking algorithm based on
single model has poor tracking performance when the target occurs
high maneuver and that IMM algorithm has low accuracy in tracking a
constant velocity target, an augmented dimension algorithm based on
sequential detection for maneuvering target tracking is proposed. First,
the KF-UKF joint filtering is proposed. The Kalman filter based on the
CV model is used to estimate the state of a constant velocity target.
When the target maneuver is detected, the dimension of the CV model
is augmented, and the unscented Kalman filter is used to estimate
the state. Second, a fading memory sequential detection algorithm is
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