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Immunodominance and clonal selection inspired multiobjective clustering

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 [a]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

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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 [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

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

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Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

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Nano-Micro Letters 2018年第1期10卷 12-19页

作者： Xi Yin Jing Yang Feng Xiao Yang Yang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Computer Science Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.

关键词： Transmembrane a-helices Structure prediction Machine learning Contact map prediction Relative accessible surface area

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Abnormal EEG Microstates in Post-stroke Patients: A Pilot Study

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IEEE Sensors Journal 2025年

作者： Chen, Xiaoling Zhai, Jiashun Cai, Yuhao Zhang, Tengyu Wang, Juan Mao, Gengsheng Cheng, Shengcui Zhang, Yuanyuan Xie, Ping Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Hebei Province Qinhuangdao 066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao Hebei China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing China PLA General Hospital Senior Department of Neurosurgery Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin 300072 China Capital Medical University Xuanwu Hospital Beijing China

Exploring brain function is crucial for unraveling the pathological mechanism underlying stroke. while most studies focus on brain function emphasize dynamic connections and interactions within or between brain regions, they often ignore the global properties of large-scale network topology. In this study, we analyzed resting-state EEG microstates in stroke patients, calculating key parameters such as mean duration (MD), occurrence (OC), time coverage (TC), and transition probability (TP) across different microstate classes. As a result, we identified four microstate classes (A, B, C, and D) in both healthy subjects and stroke patients. Notably, stroke patients showed significant changes in Class B, C, and D, with increased MD, OC, and TC in Class B and C and decreased MD, OC, and TC in Class D. additionally, stroke patients displayed higher TP between Class B and C than healthy controls. Moreover, we observed a positive correlation between the OC of Class D and clinical Fugl-Meyer scores, suggestion a link between microstate dynamic and motor recovery. This study highlights the connection between specific microstates and brain regions, providing evidence that stroke patients demonstrate increased activity in visual and salience networks, but decreased activity in the dorsal attention network. We speculate these results from changes in the occipital lobe, frontal lobe, parietal lobe, and dorsal anterior cingulate cortex. These findings deepen our understanding of stroke pathophysiology and support further research. © 2001-2012 IEEE.

关键词： EEG Microstate Resting-state Stroke

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Tissue P Systems with Look-ahead Mode

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Chinese Journal of Electronics 2014年第1期23卷 81-86页

作者： JIANG Yun SONG Tao ZHANG Zheng School of Computer Science and Information Engineering Chongqing Technology and Business University Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a spacetime tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： Tissue P system Look-ahead mode Recursively enumerable language Cell separation 3-Coloring

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Bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO Hong-Bin SHEN Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition

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Textural fabric defect detection using adaptive quantized Gray-level Co-occurrence Matrix and Support Vector Description Data

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Information Technology Journal 2012年第6期11卷 673-685页

作者： Bi, Mingde Sun, Zhigang Li, Yesong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology 1037 Luoyu Road Hubei 430074 Wuhan China

A new defect detection algorithm base on Support Vector Data Description (SVDD) is proposed. A fabric texture model is built on the gray-level histogram of textural fabric image. Two Gray-level Co-occurrence Matrix (GLCM) features are used to characterize the fabric texture. And an adaptive quantization scheme base on the texture mode is proposed to reduce the size of GLCM and reduce the computational complexity of feature extraction. Besides, twonew features are proposed to characterize the continuous property of the fabric defects. The SVDD classifier is used as a detector for defect detection. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the proposed detection algorithm. And a prototyped detection system is built to evaluate the real-time performance of the detection algorithm. © 2012 Asian Network for Scientific Information.

关键词： Data description

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Fabric defect detection using undecimated wavelet transform

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Information Technology Journal 2011年第9期10卷 1701-1708页

作者： Bi, Mingde Sun, Zhigang Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology 1037 Luoyu Road 430074 Hubei Wuhan China

In this study, a new fabric defect detection algorithm base on undecimated wavelet transform is proposed. The selection scheme of wavelet decomposition scales is investigated to set the decomposition scales adaptively to the fabric texture. The objective of the scheme is to enhance the energy of defective region and attenuate the energy of non-defective region. And the performance of detection results with different number of decomposition scales is also discussed. A simple and computationally effective data fusion scheme combined with amplitudes division of wavelet coefficients is used to fuse data from multiple scales together. And several features based on defective energy estimation are extracted from fused image. By examining the extracted features the proposed algorithm can provide not only the location of defects but also some detailed information about them which can be used for defect recognition and classification. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the defect detection algorithm. © 2011 Asian Network for Scientific Information.

关键词： Wavelet decomposition

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Neutrosophic combination rules based on dempster-shafer theory and Dezert-Smarandache theory

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Information Technology Journal 2008年第7期7卷 1023-1029页

作者： Gao, Jian Huang, Xinhan Wang, Min Li, Xinde Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of CSE HUST Wuhan 430074 China School of Automation Southeast University Nanjing 210096 China

Several neutrosophic combination rules based on the Dempster-Shafer theory (DST) and Dezert-Smarandache theory (DSmT) are presented in this study. The new information fusing approaches proposed the neutrosophic belief assignment to represent the evidences and connect the DST and DSmT with neutrosophic theory. Neutrosophic theory used the components truth, indeterminacy and falsity to represent any idea, which provides a better approach to express the real world. The DST-based and DSmT-based combination rules can fuse information more effectively. The new combination rules utilized the advantages of DST, DSmT and neutrosophic theory. They are more adapted to describing the human mind and helping to make correct decisions. To fuse the information expressed in natural language, the qualitative neutrosophic combination rules of information have been proposed in this study too. Some application examples have been given to support the proposed combination rules. Compared with the original DST-based and DSmT-based combination rules, the neutrosophic combination rules can provide more useful information and have wider application field. © 2008 Asian Network for Scientific Information.

关键词： Information fusion

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Motion cue based pedestrian detection with two-frame-filtering

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High Technology Letters 2015年第3期21卷 328-332页

作者：吕敬钦 Zhang Miaohui Yang Jie Institute of Image Processing and Pattern Recognition Shanghai Jiaotong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient （HTffOG） feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.

关键词： pedestrian detection two-frame-filtering (TFF) Tff magnitude vector (TffMV) Histogram of Tff oriented gradient ( HTffOG) SVM video surveillance

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