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Research on DNA sequence homology based on second order Markov model

Journal of Fiber Bioengineering and Informatics 2015年第3期8卷 539-546页

作者： Zhang, Junyan Yang, Chenhui College of Computer Science Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu China

DNA sequence homology is a critical and fundamental problem in bioinformatics. In this paper, we solve this problem by use of the second order Markov modal instead of traditional sequence alignment because DNA character sequence meets the Markov properties. Hence, the characteristics of DNA sequences are represented by using their two-step transition probabilities matrices. The similarity degree measurement between two different DNA sequences is defined. Our DSHM algorithm is put forward which is implemented by MyEclipse. The contrast experiments are done between DSHM and other two methods. The experimental results show that DSHM algorithm can determine DNA sequence homology correctly in the more effective way. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society.

关键词： DNA sequences

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An improved K-OPT local search algorithm for the vertex separator problem

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Journal of Computational and Theoretical Nanoscience 2015年第11期12卷 4942-4958页

作者： Zhang, Zhiqiang Shao, Zehui Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province China School of Computer Science Chengdu University Chengdu610106 China

A vertex separator in an undirected graph is a subset of the vertices, whose removal disconnects the graph in at least two nonempty connected components. Given a connected undirected graph G = (V ,E) with |V| = n, an integer 1 ≤ b ≤ n, the vertex separator problem (VSP) asks to find a partition of V into disjoint sets A, B, C, with A and B nonempty, such that (1) E contains no edge (i, j) with i A, j ? B, (2) max{|A|} |B|} = b, (3) |C| is minimized. The vertex separator problem is a classic NP complete problem. It is widely applied to circuit design, computer network communication, protein combination in bioinformatics, etc. At present there has already been exact algorithm to search vertex separator of undirected graph, while there are only few heuristic algorithms. Aiming at the VSP and a solution design for heuristic algorithm, this paper designs an improved K-OPT local search algorithm. In order to compare the searching efficiency of a variety of heuristic algorithms for VSP, this paper also designs simulated annealing algorithm and variable neighborhood search algorithm, and performs comparative experiments on these algorithms using DIMACS graph, MATRIXMARKET graph, MIPLIB graph. The experimental results show that the improved K-OPT local search algorithm goes the best. In the experiment, using improved K-OPT local search algorithm, existing Relax-and-Cut algorithm is further compared, whose experimental results show that in some graph tests, the improved K-OPT local search algorithm goes better than Relax-and-Cut algorithm. © 2015 American Scientific Publishers.

关键词： Undirected graphs

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Theory of ϕ-Jensen variance and its applications in higher education

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Journal of Inequalities and Applications 2015年第1期2015卷 1-40页

作者： Wen, JiaJin Huang, Yi Cheng, Sui Sun Institute of Mathematical Inequalities and Applications College of Mathematics and Computer Science Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Higher Education Institutions of Sichuan Province College of Mathematics and Computer Science Chengdu University Chengdu 610106 China Department of Mathematics Tsing Hua University Hsinchu 30043 Taiwan

This paper introduces the theory of ϕ-Jensen variance. Our main motivation is to extend the connotation of the analysis of variance and facilitate its applications in probability, statistics and higher education. To this end, we first introduce the relevant concepts and properties of the interval function. Next, we study several characteristics of the log-concave function and prove an interesting quasi-log concavity conjecture. Next, we introduce the theory of ϕ-Jensen variance and study the monotonicity of the interval function JVar_ϕ φ(X_[a,b]) by means of the log concavity. Finally, we demonstrate the applications of our results in higher education, show that the hierarchical teaching model is ‘normally’ better than the traditional teaching model under the appropriate hypotheses, and study the monotonicity of the interval function Var A (X_[a,b]). © 2015, Wen et al.

关键词： hierarchical teaching model interval function k-normal distribution truncated random variable ϕ-Jensen variance

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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A New Method for recognition Partially Occluded Curved Objects under Affine Transformation

A New Method for Recognition Partially Occluded Curved Objec...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： GuiMei Zhang JiYuan Xu JianXin Liu Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Province China School of Mechanical Engineering and Automation Xihua University Chengdu Sichuan China

A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.

关键词： Transforms Shape Robustness pattern recognition Image recognition Mathematical model Distortion

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Detection Level of Apple Based on BP Neural Network

Detection Level of Apple Based on BP Neural Network

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International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Xiaoling LI Jimin YUAN School of Computer Science and Technology Chengdu University University Key Laboratory of Pattern Recognition and Intelligent information Processing The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College

In view of the draw backs of apple grade identification in China,which still relies on photoelectric sorting and manual separation,this paper presents a processing method on the basis of the technology of computer vision and digital *** image processing technology,the researcher calculated the length of the long-short-axis,marked the location of it and calculated the 4 parameters,color,mean square,shape,size,as the key characteristics of the BP input of network to build a network and identify the level of apple through analysis of the external characteristics of *** optimum structure parameters of the BP neural network which had 9 hidden layer neurons were determined by RP training *** showed that average accuracy for fruit classification can reach 92.5% by using this model,and the executing time of microcomputer for grading of one apple is 9.3 *** method has the characteristics of high accuracy and good real-time performance.

关键词： characteristic parameter BP neural network apple detect level

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Improved global optical flow estimation with mean shift algorithm for target detection

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International Journal of Wireless and Mobile Computing 2015年第4期8卷 323-328页

作者： Wang, Zhonghua Li, Chunyong Liu, Qingping Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China

In view of the problem that the global optical flow algorithm cannot acquire accurate motion parameter estimation at a low-gradient value, an improved method has been presented in order to enhance the self-adaptive ability. The proposed method is divided into three steps: firstly, the Gaussian filter is used to restrain the background noise;secondly, the optical basic constraint weighted function is modified to acquire targets area;finally, the morphological filtering and mean shift segmentation algorithm is adopted to reduce the false alarm rate. Compared with other algorithms, the experiment results show that the proposed method can improve the self-adaptive ability and the detection rate. © 2015 Inderscience Enterprises Ltd.

关键词： Optical flows

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Corrigendum to ‘Modeling the packing coloring problem of graphs’ [Appl. Math. Model. 39 (2015) 3588–3595]

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Applied Mathematical Modelling 2016年第2期40卷 1683-1683页

作者： Zehui Shao Aleksander Vesel School of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province China Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia

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Motion estimation and spatial-temporal filter-based infrared small target detection algorithm

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International Journal of Wireless and Mobile Computing 2015年第3期8卷 256-261页

作者： Wang, Zhonghua Liao, Yuan Liu, Qingping Li, Chunyong School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China Department of Information Engineering Nanchang Hangkong University Nanchang330063 China

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: (1) researching the image transformation technology from greyscale pattern to entropy pattern;(2) studying the anisotropic smoothing and local constraint criterion of entropy flow;and (3) introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Entropy

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Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Shape driven kernel adaptation in Convolutional Neural Netwo...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Shaoxin Li Junliang Xing Zhiheng Niu Shiguang Shan Shuicheng Yan Department of Electrical and Computer Engineering National University of Singapore Singapore Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

ISBN: (纸本)9781467369657

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

关键词： Kernel Shape Face Face recognition Robustness Neural networks Computer architecture

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