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Congress on Evolutionary Computation

作者： Ruochen Liu Zhengchun Shen Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.

关键词： Clustering algorithms Variable speed drives Immune system Partitioning algorithms Algorithm design and analysis Accuracy Immunity testing

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Clonal Selection Algorithm for image Compression

Clonal Selection Algorithm for Image Compression

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Congress on Evolutionary Computation

作者： Ruochen Liu Licheng Jiao Wei Zhang Jingjing Ma Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, Clonal Selection Algorithm for image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

关键词： image coding Clustering algorithms Algorithm design and analysis Training Encoding Vector quantization

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A hybrid multiobjective immune algorithm with region preference for decision makers

A hybrid multiobjective immune algorithm with region prefere...

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Congress on Evolutionary Computation

作者： Licheng Jiao Wei Zhang Ruochen Liu Fang Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Recently, one of the main tools of decision maker (DM) preference incorporation in the multiobjective optimization (MOO) has been using reference points and achievement scalarizing functions (ASF). The core idea of these methods is converting the original multiobjective problem (MOP) into single objective problem by using ASF to find a single preferred point. However, many DMs not only interest in a single point but also a set of efficient points in their preferred region. In this paper, we introduce a hybrid multiobjective immune algorithm (HMIA) for DM. It combines the immune inspired algorithm and region preference based on a novel dominance concept called region-dominance without ASF. The new algorithm can let DMs flexibly decide the number of reference points and accurately determine the preferred region with its simple and effective interactive methods. To exemplify its advantages, simulated results of HMIA are shown with some well-known problems.

关键词： Delta modulation Neodymium Immune system Cloning Encoding Computational complexity

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Quantum genetic algorithm for mobile robot path planning

Quantum genetic algorithm for mobile robot path planning

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International Conference on Genetic and Evolutionary Computing

作者： Sun, Yangguang Ding, Mingyue College of Computer Science South-Central University for Nationalities Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769542812

Based on the theory of quantum mechanics and quantum computing, a path planning method for mobile robot based on quantum genetic algorithm was presented in this paper. By using the quantum-bit with the superposition state to encode a route and introducing quantum genetic algorithm to optimize the cost function of path planning, the proposed method guides and realizes path optimization by introducing genetic operators including quantum crossover operator and quantum gate mutation operator with the essential characteristics of quantum. Primary experiments demonstrated that our method not only has the diversity of population, but also improves effectively the capabilities of searching and convergence. © 2010 IEEE.

关键词： Mobile robots

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Small universal asynchronous spiking neural P systems

Small universal asynchronous spiking neural P systems

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Linqiang Pan Xiangxiang Zeng Xinyi Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic started to be considered also in the framework of (synchronized) spiking neural P systems. In this work, it is focused on small universal spiking neural P systems working in a non-synchronized manner. Specifically, it is proved that there is an asynchronous spiking neural P system with 76 neurons that is equivalent to a universal register machine for computing functions. As generator of sets of numbers, a universal asynchronous spiking neural P system with 75 neurons is constructed.

关键词： Variable speed drives Neurons

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Upper limb rehabilitation robot integrated with motion intention recognition and virtual reality environment

Upper limb rehabilitation robot integrated with motion inten...

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Chinese control Conference (CCC)

作者： Jun Wu Jian Huan Yongji Wang Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan 430074 China

Robotic-assisted therapy is of benefit to the recovery of upper limb motor function for the patients survived stoke. Whereas, there are few emphases on the patients' motion intention during the rehabilitation process. The goal of this study is to combine the control strategies based on patients' motion intention with an upper limb rehabilitation robot to improve the recovery for the patients. In this paper, we propose an integrated robot-assist rehabilitation system, in which a 3 degree-of-freedom (DOF) exoskeletal rehabilitation robot, an EMG-based intention recognition module and a VR game environment are seamlessly combined. According toharacteristics of EMG signals, the wavelet package analysis approach is applied to extract the features of EMG. The node energy is used to construct the feature vector instead of the original coefficients of wavelet package decomposition to resolve the time-invariance problem. Then feature projection results in the singularity problem of with-in scatter matrix during the feature dimension reduction. To overcome the disadvantage of the with-in scatter matrix, this paper uses a recursive algorithm which is proposed in our previous work. The reduced feature vector is recognized by a neural network classifier and the output of the classifier is used for the control inputs. Preliminary experiments are also performed to implement the control of the rehabilitation robotic system by using the proposed EMG reorganization method, together with a dart game realized in the virtual reality environment. Experimental results show that the performance of motion intention recognition is satisfactory and the entire integrated system is feasible.

关键词： Robots Feature extraction Electromyography Classification algorithms Wavelet packets Games

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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Study of high-resolution range profile for monostatic MIMO radar based on SIAR

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2010年第4期32卷 734-739+763页

作者： Zhao, Guang-Hui Chen, Bai-Xiao Gao, Zhao-Zhao Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China Xidian Univ. Xi'an 710071 China National Key Lab for Radar Signal Processing Xidian Univ. Xi'an 710071 China National Information Control Laboratory Chengdu 610036 China

Conventional pulse compression use a periodical echo of single receive antenna, which is modulated by a certain carrier-frequency, in other words, single spectrum is exploited. But for MIMO radar, as the multi-carrier-frequency signals are transmitted simultaneously, if the spectrum of the target echo after channel separation can be combined to form the whole band spectrum echo, the corresponding range resolution can improve several times as compared with the conventional method, and it will be more convenient for follow-up detection and tracking. Considering the difference between the frequency modulation band and the interval between the adjacent frequencies, the spectrum joint after channel separation will be overlapped or spaced. The methods of spectrum moving of each echo and the spectrum extrapolation with Root-MUSIC algorithm are proposed, by which high-resolution range profile of the target is obtained. Simulation results verify the validity of these methods.

关键词： Multiple signal classification

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Algorithm of Partition based Network Boosting for imbalanced data classification

Algorithm of Partition based Network Boosting for imbalanced...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gou Shuiping Yang Hui Jiao Licheng Zhuang Xiong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine.

关键词： Laboratories Impedance matching Breast Boosting Liver

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Matrix representation of spiking neural P systems

Matrix representation of spiking neural P systems

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11th International Conference on Membrane Computing, CMC 2010

作者： Zeng, Xiangxiang Adorna, Henry Martínez-Del-Amor, Miguel Ángel Pan, Linqiang Pérez-Jiménez, Mario J. Image Processing and Intelligent Control Key Laboratory of Education Ministry Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China University of the Philippines Diliman 1101 Quezon City Philippines Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Avda. Reina Mercedes s/n 41012 Sevilla Spain

ISBN: (纸本)3642181228

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation of SN P systems with extended rules and without delay is proposed. Specifically, matrices are used to represent SN P systems. In order to represent the computations of SN P systems by matrices, configuration vectors are defined to monitor the number of spikes in each neuron at any given configuration;transition net gain vectors are also introduced to quantify the total amount of spikes consumed and produced after the chosen rules are applied. Nondeterminism of the systems is assured by a set of spiking transition vectors that could be used at any given time during the computation. With such matrix representation, it is quite convenient to determine the next configuration from a given configuration, since it involves only multiplication and addition of matrices after deciding the spiking transition vector. © 2010 Springer-Verlag.

关键词： Vectors

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