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AN EFFICIENT MULTI-PURPOSE OPTIMIZATION METHOD FOR QOS ROUTING USING GENETIC ALGORITHM

Journal of Interconnection Networks 2004年第4期5卷 409-428页

作者： AKIO KOYAMA LEONARD BAROLLI GENCI CAPI BERNADY O. APDUHAN JUNPEI ARAI ARJAN DURRESI Department of Informatics Yamagata University 4-3-16 Jonan Yonezawa Yamagata 992-8510 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology (FIT) 3-30-1 Wajiro-Higashi Higashi-ku Fukuoka 811- 0295 Japan Department of System Management Fukuoka Institute of Technology (FIT) 3-30-1 Wajiro-Higashi Higashi-ku Fukuoka 811-0295 Japan Department of Intelligent Informatics Kyushu Sangyo University 2-3-1 Matsukadai Higashi-ku Fukuoka 813-8503 Japan Department of Information and Control Engineering Yamagata College of Industry and Technology 1-2-2 Matsuei Yamagata 990-2473 Japan Department of Computer Science Louisiana State University Baton Rouge LA 70803 USA

In order to support multimedia communication, it is necessary to develop routing algorithms that make decisions based on multiple Quality of Service (QoS) parameters. This is because new services such as video on demand and remote meeting systems have multiple QoS requirements. However, the problem of QoS routing is difficult because finding a feasible route with two independent path constraints is NP-complete problem. Also, QoS routing algorithms for broadband networks must be adaptive, flexible, and intelligent for efficient network management. In this paper, we propose a multi-purpose optimization method for QoS routing based on Genetic Algorithm (GA). The simulation results show that the proposed method has a good performance and therefore is a promising method for QoS routing.

关键词： Broadband Networks Genetic Algorithm QoS Routing Multi-purpose Optimization

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Relative entropy multilevel thresholding method based on genetic optimization

Relative entropy multilevel thresholding method based on gen...

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2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03

作者： Yang, Zhao-Hua Pu, Zhao-Bang Qi, Zhen-Qiang Department of Automation Measurement and Control Harbin Institute of Technology Harbin 150001 China Department of Control Science and Engineering Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)0780377028

Traditional optimal thresholding methods are very computationally expensive when extended to multilevel thresholding for their exhaustively search mode. So their applications are limited. In this paper, a relative entropy multilevel thresholding method based on genetic algorithm (RE-GA) is developed. The proposed method makes use of GA's properties such as high efficiency, rapid convergence and global optimization. The relative entropy is treated as the fitness function. Applying the proposed method to process image, the computation speed is accelerated and the quality is improved. Simulation results verify the performance of the proposed method by comparison with the traditional optimal thresholding methods. © 2003 IEEE.

关键词： Global optimization

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Multispectral thermometry based on neural network

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Journal of Harbin institute of technology(New Series) 2003年第1期10卷 108-112页

作者：孙晓刚戴景民 Post doctor Station of Electrical Engineering Harbin Science and Technology University Dept. of Automation Measurement & Control Harbin Institute of Technology Harbin 150001 China Dept. of Automation Measurement & Control Harbin Institute of Technology

In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.

关键词： multispectal thermometry emissivity neural networK

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Flight guidance control using genetic algorithm combined with singular perturbation technique

Flight guidance control using genetic algorithm combined wit...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhen-Qiang Qi Guang-Da Hu Zhao-Hua Yang Fu-En Zhang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Automatic Measurement and Control Harbin Institute of Technology Harbin China

ISBN: (纸本)0780378652

An advanced midcourse guidance law based on genetic algorithm (GA) and singular perturbation technique (SPT) is proposed in this paper. The proposed midcourse guidance formulation minimizes the flight time and maximizes terminal energy subject to a terminal intercept condition. GA is used to search the optimal attack angle for the flight trajectory, and SPT is applied to approximate the terminal flight time. By combining GA and SPT, the optimal flight guidance law is obtained consequently. Meanwhile, the paper completely eliminates the need for solving two-point boundary-value problems (TBPVP), which is too complex for derivation and implementation. The simulation results show that the resulting guidance law is near-optimal and the proposed method is valid.

关键词： Genetic algorithms Perturbation methods Automatic control Nonlinear equations Missiles Sliding mode control Trajectory Aerospace engineering Genetic engineering Proportional control

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Novel edge preserving multiscale filtering method based on mathematical morphology

Novel edge preserving multiscale filtering method based on m...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhao-Hua Yang Zhao-Bang Pu Zhen-Qiang Qi Department of Automation Measurement and Control Harbin Institute of Technology Harbin China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)0780378652

During the course of conventional multiscale morphological filtering, when the noise is filtered, the signals which are smaller than the structuring elements (SE) may be also removed. In this paper, a novel edge preserving multiscale filtering (EPMF) method based on mathematical morphology is proposed. The EPMF method adds the multiscale top-hat transformation and bottom-hat transformation to the conventional multiscale morphological opening and closing filtering. The two added transformations are used to extract and smooth the features which are smaller than the current scale. It is also found that the smaller features have greater possibilities to contain noise particles. Accordingly, the coefficients of top-hat transformation and bottom-hat transformation are modified. Simulation results on the standard gray-level images show that the proposed EPMF method can effectively remove noise and completely preserve the edge of images. It demonstrates better performance than the conventional filtering methods.

关键词： Morphology Filtering theory Automatic control Image recognition Image analysis Inspection Image processing Morphological operations Automation Noise measurement

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Relative entropy multilevel thresholding method based on genetic optimization

Relative entropy multilevel thresholding method based on gen...

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International Conference on Neural Networks and Signal Processing

作者： Zhao-hua Yang Zhao-bang Pu Zhen-qiang Qi Department of Automation Measurement and Control Harbin Institute of Technology Harbin China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)0780377028

关键词： Entropy Optimization methods Image segmentation Histograms Optimal control Automatic control Genetic algorithms Acceleration Automation Computational modeling

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Machine learning for multi-class protein fold classification based on neural networks with feature gating

Machine learning for multi-class protein fold classification...

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Joint International Conferences on Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003

作者： Huang, Chuen-Der Chung, I.-Fang Pal, Nikhil Ranjan Lin, Chin-Teng Electronics and Communication Sciences Unit Indian Statistical Institute Calcutta India Department of Electrical and Control Engineering National Chiao-Tung University Hsinchu Taiwan Department of Electrical Engineering HsiuPing Institute of Technology Taichung Taiwan

ISBN: (纸本)3540404082

The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications the role of appropriate features has not been paid adequate importance. In this investigation we use two novel ideas. First, we use neural networks where each input node is associated with a gate. At the beginning of the training all gates are almost closed, i.e., no feature is allowed to enter the network. During the training, depending on the requirements, gates are either opened or closed. At the end of the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly. And of course, some gates may be partially open. So the network can not only select features in an online manner when the learning goes on, it also does some feature extraction. The second novel idea is to use a hierarchical machine learning architecture. Where at the first level the network classifies the data into four major folds: all alpha, all beta, alpha + beta and alpha/beta. And in the next level we have another set of networks, which further classifies the data into twenty seven folds. This approach helps us to achieve the following. The gating network is found to reduce the number of features drastically. It is interesting to observe that for the first level using just 50 features selected by the gating network we can get a comparable test accuracy as that using 125 features using neural classifiers. The process also helps us to get a better insight into the folding process. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it reduces the computation time. The use of the hierarchical architecture helps us to get a better performance also. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Feature extraction

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A fast method to evaluate the performances of image fusion techniques and its error analysis

A fast method to evaluate the performances of image fusion t...

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IEEE Instrumentation and measurement technology Conference

作者： Qiang Wang Yi Shen Ye Zhang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electronics and Communication Engineering Harbin Institute of Technology Harbin China

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The Design and Realization of Intelligent Loop-coil Detector in Traffic

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微计算机信息 2003年第9期 56-57页

作者： Jiang,Yonglin(Dept.of control Science and engineering,Harbin institute of technology,150001)Sun,Guodong(Harbin Synjones Electric Company,150086)Liang,Qi(Dept.of Electric and communication,Harbin institute of technology,150001) Dept. of Control Science and Engineering Harbin Institute of Technology 150001 Harbin Synjones Electric Company 150086 Dept. of Electric and Communication Harbin Institute of Technology 150001

In this paper,we introduce the principle of intelligent loop-coil detector in traffic,present some chief interface circuit,and describe the highlights of designing this kind of detector by the microprocessor AT89C52.

关键词： AT89C52singlechip loop-coil detector fuzzy classification

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Identification and robust control of flexible space structures

Identification and robust control of flexible space structur...

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American control Conference (ACC)

作者： M.P. Schoen S. Chinvorarat G.M. Schoen Measurement and Control Engineering Research Center Idaho State University USA King Mongkut''s Institute of Technology Bangkok Thailand Steinmaur Zurich Switzerland

This paper is concerned with the problem of identifying and controlling large flexible space structures (LFSS). These types of structures have a particular characteristic, which makes the task of identifying accurate system models more problematic. The proposed algorithm utilizes modal contribution coefficients to monitor the data collection and a simple genetic algorithm to define the parameters of the robust controller. Several objectives are included in the optimization process, such as rise time, overshoot, control energy, and a robustness measure among others. The proposed method is tested numerically and promises to work well for LFSS.

关键词： Robust control Vibration control Frequency System identification Sliding mode control Space technology Extraterrestrial measurements Damping control systems Velocity control

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