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Hybrid multiobjective estimation of distribution algorithm by local linear embedding and an immune inspired algorithm

Hybrid multiobjective estimation of distribution algorithm b...

引用

Congress on Evolutionary Computation

作者： Dongdong Yang Licheng Jiao Maoguo Gong Hongxiao Feng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A novel hybrid multiobjective estimation of distribution algorithm is proposed in this study. It combines an estimation of distribution algorithm based on local linear embedding and an immune inspired algorithm. Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise continuous (m-1)-dimensional manifold, where m is the number of objectives. By this regularity, a local linear embedding based manifold algorithm is introduced to build the distribution model of promising solutions. Besides, for enhancing local search ability of the EDA, an immune inspired sparse individual clone algorithm (SICA) is introduced and combined with the EDA. The novel hybrid multiobjective algorithm, named HMEDA, is proposed accordingly. Compared with three other state-of-the-art multiobjective algorithms, this hybrid algorithm achieves comparable results in terms of convergence and diversity. Besides, the tradeoff proportions of EDA to SICA in HMEDA are studied. Finally, the scalability to the number of decision variables of HMEDA is investigated too.

关键词： Electronic design automation and methodology Machine learning algorithms Probability distribution Evolutionary computation Cloning Hybrid power systems Genetic mutations Genetic algorithms Sampling methods Principal component analysis

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3D medical image compression based on multiplierless low-complexity RKLT and shape-adaptive wavelet transform

3D medical image compression based on multiplierless low-com...

引用

IEEE International Conference on image processing

作者： Lei Wang Jiaji Wu Licheng Jiao Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A multiplierless low complexity reversible integer Karhunen-Loe¿ve transform (Low-RKLT) is proposed based on matrix factorization. Conventional methods based on KLT suffer from high computational complexity and unability of applying in lossless medical image compression. To solve the two problems, multiplierless Low-RKLT is investigated using multi-lifting in this paper. Combined with ROI coding method, we have proposed a progressive lossy-to-lossless ROI compression method for three dimensional (3D) medical images with high performance. In our proposed method Low-RKLT is used for the inter-frame decorrelation after SA-DWT in the spatial domain. Simulation results show that, the proposed method performs much better in both lossless and lossy compression than 3D-DWT-based method.

关键词： Biomedical imaging image coding Wavelet transforms Medical diagnostic imaging Karhunen-Loeve transforms Decorrelation Computational intelligence Computational complexity Wavelet analysis Wavelet domain

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Feature Extraction Combining PCA and Immune Clonal Selection for Hyperspectral Remote Sensing image Classification

Feature Extraction Combining PCA and Immune Clonal Selection...

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International Conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Xiangrong Zhang Runxin Li Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A new feature extraction method based on immune clonal selection (ICSA) and PCA is proposed for classification of hyperspectral remote sensing image. As the hyperspectral remote sensing image is acquired in very narrow spectral channels, the resulting high-dimensional feature sets may contain redundant information. Therefore, feature extraction is necessary to classify a data with large dimension such as the hyperspectral remote sensing image. PCA is popularly used for feature extraction. However, in traditional PCA, selecting the larger eigenvectors as principal components implies information loss. There is no systematic way to determine which principal components (PCs) should be used. A new feature-extraction model to select the optimal principal components using ICSA is developed. The data acquired by the NASA airborne AVIRIS instrument over the Kennedy Space Center, Florida is used for evaluation. Experimental results show that our method can get better results.

关键词： Feature extraction Diversity reception Principal component analysis Hyperspectral sensors Hyperspectral imaging Remote sensing image classification Personal communication networks NASA Instruments

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Medical image segmentation based on immune clonal optimization

Medical image segmentation based on immune clonal optimizati...

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IEEE International Conference on intelligent Computing and intelligent Systems (ICIS)

作者： Wenping Ma Licheng Jiao Ronghua Shang Fujia Zhao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781424447374;9781424447541

Based on the clonal selection theory of artificial immune system, a novel optimal entropy threshold medical image segmentation method is proposed, in which, the affinity function is the optimal entropy threshold, and the medical image segmentation is considered as a optimization problem, clonal operator effectively enlarges searching range, supplies the diversity of solutions and can find the optimal threshold. This paper applies new algorithm to the challenging application: gray matter/white matter segmentation in MRI images, the algorithm is depicted in detail and the convergence is proven, the performance and computational complexity of the algorithm are described by quantitative analysis. Experiment results demonstrate the potential of the algorithm for medical image segmentation.

关键词： Biomedical imaging image segmentation Immune system Entropy Artificial immune systems Optimization methods Magnetic resonance imaging Computational complexity image analysis Performance analysis

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Design RF diplexer by directional immune clonal selection algorithm

Design RF diplexer by directional immune clonal selection al...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiuyi Wu Licheng Jiao Yangyang Li 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 the clonal selection theory of the immune system, a novel optimal algorithm, called a directional immune clonal selection algorithm (DICSA), is proposed to design RF diplexer. In DICSA, antibody is proliferated and divided into a set of subpopulation groups. In the antibody's updating, the clonal mutation is applied to avoid premature convergences. The clonal selection realizes the information communication between the subpopulation groups so as to improve the search efficiency. DICSA is applied to a practical problem, designing RF diplexer. The results show that DICSA has stable performance during the optimization process with a satisfactory result.

关键词： Algorithm design and analysis Radio frequency Filters Laboratories Information processing Design optimization Circuits Artificial intelligence Artificial immune systems Least squares methods

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Human Detection Based on Second Generation Bandelet Transform

Human Detection Based on Second Generation Bandelet Transfor...

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International Conference on Information Science and Engineering (ICISE)

作者： Hong Han Xing Wu Licheng Jiao Lichuan Yue Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781424449095

Base on geometric flow of images and the second generation bandelet transform, a new feature extraction method was proposed, and it was used to detect human in still images. In the paper, bandelet coefficients and their statistical values were extracted as the feature of human images, combined with Adaboost classifier to classify and detect human in images. In the proposed algorithm, some general statistical values of the bandelet coefficients were computed to enhance the classification capacity. The results of human detection experiments indicate that the proposed method can describe human characteristics and improve the performance of classifier effectively. It is very suitable for human detection in still images.

关键词： Humans Feature extraction Discrete wavelet transforms Neural networks Support vector machines Support vector machine classification image analysis Information science Laboratories Information processing

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Hybrid Immune Algorithm with intelligent recombination

Hybrid Immune Algorithm with intelligent recombination

引用

Congress on Evolutionary Computation

作者： Maoguo Gong Licheng Jiao Wenping Ma Ronghua Shang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this study, we introduce a hybrid immune algorithm based on the intelligent recombination operator and clonal selection algorithm. The intelligent recombination operator uses orthogonal experimental design for factor analysis which identifies the potential gene segments from two individuals to improve their antigenic affinities. The new algorithm, termed as Hybrid Immune Algorithm with Recombination (HIAR), can avoid the decrease of gene diversity in evolutionary process. It evaluates the hamming distance before recombination and uses the two individuals which have the largest hamming distance between each other to implement intelligent recombination operator. It is shown empirically that HIAR has better performance in solving benchmark functions as compared with intelligent Evolutionary Algorithm and Clonal Selection Algorithm.

关键词： Immune system Competitive intelligence Design for experiments Hamming distance Computational intelligence Approximation algorithms Design optimization Algorithm design and analysis Evolutionary computation Protection

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Quantum-inspired evolutionary multicast algorithm

Quantum-inspired evolutionary multicast algorithm

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yangyang Li Licheng Jiao Jingjing Zhao Qiuyi Wu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of ¿prematurity¿ makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the ¿prematurity¿ problem in genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.

关键词： Multicast algorithms Competitive intelligence Laboratories Information processing Evolutionary computation Routing Genetic algorithms Quality of service Quantum computing Heuristic algorithms

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Decentralized adaptive stabilization of stochastic nonlinear interconnected systems by state feedback

Decentralized adaptive stabilization of stochastic nonlinear...

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2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009

作者： Fan, Huijin Xie, Wenxiang Key Laboratory of Image Processing and Intelligent Control Dept. Control Science and Engineering Huazhong University of Science and Technology 430074 China Seagate Technology International Singapore Science Park 63 The Fleming Science Park Drive Singapore 118249 Singapore

ISBN: (纸本)9781424428007

This paper studies a class of stochastic nonlinear interconnected systemswith unknown parameters involved. By employing the stochastic Lyapunov-like theorem and the back-stepping design technique, a decentralized controller and parameter adaptive law are proposed by state-feedback, which can globally stabilize the overall system asymptotically in probability. © 2009 IEEE.

关键词： State feedback

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Reconstruction of images from compressive sensing based on the stagewise fast LASSO

Reconstruction of images from compressive sensing based on t...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Wu, Jiao Liu, Fang Jiao, Licheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China College of Sciences China Jiliang University Hangzhou 310018 China

ISBN: (纸本)9780819478092

Compressive sensing (CS) is a theory about that one may achieve a nearly exact signal reconstruction from the fewer samples, if the signal is sparse or compressible under some basis. The reconstruction of signal can be obtained by solving a convex program, which is equivalent to a LASSO problem with l1-formulation. In this paper, we propose a stage-wise fast LASSO (StF-LASSO) algorithm for the image reconstruction from CS. It uses an insensitive Huber loss function to the objective function of LASSO, and iteratively builds the decision function and updates the parameters by introducing a stagewise fast learning strategy. Simulation studies in the CS reconstruction of the natural images and SAR images widely applied in practice demonstrate that the good reconstruction performance both in evaluation indexes and visual effect can be achieved by StF-LASSO with the fast recovered speed among the algorithms which have been implemented in our simulations in most of the cases. Theoretical analysis and experiments show that StF-LASSO is a CS reconstruction algorithm with the low complexity and stability. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Compressed sensing

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