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Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of intelligent perception and image understanding of the ministry of education of china, Institute of intelligent Information Processing, Xidian University, Xi’an 710071, china key laboratory of intelligent perception and image understanding of the ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng key laboratory of intelligent perception and image understanding of ministry of education of china,Institute of intelligent Information Processing,Xidian University,Xi’an 710071,china key laboratory of intelligent perception and image understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

intelligent multi-user detection using an artificial immune system

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Science in china(Series F) 2009年第12期52卷 2342-2353页

作者： GONG MaoGuo, JIAO LiCheng, MA WenPing & MA JingJing key laboratory of intelligent perception and image understanding of ministry of education of china Institute of intelligent Information Processing, Xidian University, Xi’an 710071, china key laboratory of intelligent perception and image understanding of ministry of education of china Xidian University Xi’an China Institute of intelligent Information Processing Xidian University Xi’an China

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

关键词： artificial immune systems clonal selection multi-user detection code-division multiple-access genetic algorithm

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Memetic computation based on regulation between neural and immune systems: the framework and a case study

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Science china(Information Sciences) 2010年第8期53卷 1519-1527页

作者： GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie key Lab of intelligent perception and image understanding of ministry of education of china, Institute of intelligent Information Processing, Xidian University, Xi’an 710071, china key Lab of intelligent perception and image understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an China

Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.

关键词： memetic computation artificial immune system Lamarckian learning genetic algorithm numerical optimization

Immune secondary response and clonal selection inspired optimizers

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Progress in Natural Science:Materials International 2009年第2期19卷 237-253页

作者： Maoguo Gong a,Licheng Jiao a,Lining zhang a,Haifeng Du b a key laboratory of intelligent perception and image understanding of ministry of education of china,Institute of intelligent Information Processing,Xidian University,Xi’an 710071,china b School of Public Policy and Administration,Xi’an Jiaotong University,Xi’an 710049,china key laboratory of intelligent perception and image understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China School of Public Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms

Adaptive spatial neighborhood analysis and Rayleigh-Gauss distribution fitting for change detection in multi-temporal remote sensing images

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遥感学报 2009年第4期13卷 631-638页

作者： WANG Gui-ting, WANG You-liang, JIAO Li-cheng key laboratory of intelligent perception and image understanding of ministry of education of china, Institute of intelligent Information Processing, Xidian University, Shaanxi Xi’an 710071, china 西安电子科技大学智能信息处理研究所智能感知与图像理解教育部重点实验室陕西西安 710071

This paper proposes a novel automatic change detection approach for single band multi-temporal remote sensing images (MTRSI). First, the difference image is constructed by combining the spatial neighborhood information with the improved multiplying transform fusion (MTF) technique, which can well weaken noises and eliminate the effects caused by the registration error of multi-temporal images. In the segmentation processing of the difference image, the distributions of changed and unchanged classes are fitted by Rayleigh-Gauss models (RGM) and the probability densities of changed and unchanged pixels are estimated. Then the optimal change detection threshold is calculated automatically and effectively by the improved Kittler-Illingworth (KI) threshold selection algorithm. Finally, the changed regions are extracted. The experimental results obtained on the simulated MTRSI and the real MTRSI confirmed the effectiveness of the proposed approach. In particular, the results in terms of overall error and overall detected accuracy proved that the proposed generation approach of the difference image could have better performance than the MTF technique. In addition, as expected, the RGM was proved to be more suitable than the Gauss models (GM) and the Generalized-Gauss models (GGM) to fit the distributions of changed and unchanged classes. And the change detection experiments also confirmed that the proposed automatic threshold selection method based on RGM fitting technique could achieve the very similar performance to the optimal results exhibited by the supervised manual trial and error procedure (MTEP).

关键词： change detection spatial neighborhood analysis Rayleigh-Gauss models threshold selection

Neuronal population dynamic model: An analytic approach

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Progress in Natural Science:Materials International 2009年第9期19卷 1159-1163页

作者： Wentao Huang a, Licheng Jiao a, Yuelei Xub, Shiping Ma b, Jianhua Jia a a Institute of intelligent Information Processing, key laboratory of intelligent perception and image understanding of ministry of education, Xidian University, P.O. Box 224, Xi’an 710071, china b Department of Avionics Engineering, Engineering College of Air Force Engineering University, Xi’an 710038, china Institute of intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University P.O. Box 224 Xi’an 710071 China Department of Avionics Engineering Engineering College of Air Force Engineering University Xi’an 710038 China

A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.

关键词： Analytic approach Neuronal population Dynamic model Fourier transform

Variable structure multiple model for articulated human motion tracking from monocular video sequences

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Science china(Information Sciences) 2012年第5期55卷 1138-1150页

作者： HAN Hong 1,TONG MingLei 2,CHEN ZhiChao 1 & FAN YouJian 1 1 key laboratory of intelligent perception and image understanding of ministry of education of china,Institute of intelligent Information Processing,Xidian University,Xi'an 710071,china 2 School of Computer and Information Engineering,Shanghai University of Electric Power,Shanghai 200090,china key laboratory of intelligent perception and image understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an China School of Computer and Information Engineering Shanghai University of Electric Power Shanghai China

A new model-based human body tracking framework with learning-based theory is introduced in this *** propose a variable structure multiple model (VSMM) framework to address challenging problems such as uncertainty of motion styles,imprecise detection of feature points,and ambiguity of joint *** human joint points are detected automatically and the undetected points are estimated with Kalman *** motion models are learned from motion capture data using a ridge regression *** model set that covers the total motion set is designed on the basis of topological and compatibility relationships,while the VSMM algorithm is used to estimate quaternion vectors of joint *** using real image sequences and simulation videos demonstrate the high efficiency of our proposed human tracking framework.

关键词： human motion tracking ridge regression model groups design variable structure multiple model (VSMM) motion model group adaptation

GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS image DENOISING

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Journal of Electronics(china) 2011年第Z1期 539-547页

作者： Li Xiangping Wang Xiaotian Shi Guangming(key laboratory of intelligent perception and image understanding of the ministry of education of china,Xidian University,Xi’an 710071,china)

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise ***,high computational load limits its wide *** on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of ***,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of *** this paper,an improved scheme for image denoising is *** scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of *** is then used to project those filtered image neighborhood vectors onto a lower-dimensional *** the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual *** experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

关键词： image denoising NonLocal Means(NLM) Gaussian filter Principle Component Analysis(PCA)