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IEEE/SICE International Symposium on System Integration

作者： Yao Qian Fuji Ren Changqin Quan Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China P. R University of Tokushima Tokushima Japan Hefei University of Technology Hefei Anhui CN

This paper presents a new preprocessing algorithm with local binary pattern for facial expression recognition. Firstly, we establish the skin color model to extract the face region, and then we use the cumulative projection to obtain the completely face region. Secondly, we rotate the inclined faces, normalize all the images, and remove the effect of light to get the required experimental samples. Finally, we use rotation invariance uniform local binary pattern operator to get the facial expression features, and then the support vector machine classifier is used for expression classification. The algorithm is implemented with Matlab and experimented on Indian male facial expression database. The proposed method obtained an accuracy of 72.75% which shows the effectiveness of the proposed algorithm.

关键词： Skin Image color analysis Face Histograms Color Feature extraction Databases

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Attribute reduction based on approximation dependency degree

Journal of Computers (Finland)

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Journal of Computers (Finland) 2013年第4期8卷 920-928页

作者： Li, Min Deng, ShaoBo Feng, Shengzhong Fan, Jianping NanChang Institute of Technology Nanchang Jiangxi 330099 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong 518055 China Key laboratory of intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Attribute reduction is one of the core research content of Rough sets theory. Many existing algorithms mainly are aimed at the reduction of consistency decision table, and very little work has been done for attribute reduction aimed at inconsistency decision table. In fact, the methods finding Pawlak reduction from consistent decision table are not suitable for inconsistency decision table. In this paper, we introduce the approximation dependency reduction modal and present the Quick Attribution Reduction based on Approximation Dependency Degree (Quick-ARADD), which can retain the original boundary region and the original positive region unchanged, and keep the approximation accuracy unchanged for all decision equivalence classes (the partition of universe on decision attributes) of a decision table. Theoretical analysis and experimental results show that the Quick-ARADD algorithm is effective and feasible. © 2013 ACADEMY PUBLISHER.

关键词： Decision tables

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Feature fusion of HOG and WLD for facial expression recognition

Feature fusion of HOG and WLD for facial expression recognit...

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IEEE/SICE International Symposium on System Integration

作者： Xiaohua Wang Chao Jin Wei Liu Min Hu Liangfeng Xu Fuji Ren AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology China School of Computer and Information Hefei University of Technology China The Japan Federation of Engineering Societies University of Tokushima Shinkura-cho Tokushima Japan

A considerable amount of research work has been done for facial expression recognition using local or global feature extraction methods. Weber Local Descriptor (WLD), a simple and robust local image descriptor, is recently developed for local feature extraction. In facial expression recognition, the information contained in the local is important for the recognition result. The Histograms of Oriented Gradients (HOG) can well describe the local area information using gradient and orientation density distribution of the edge. In order to solve the lack of contour and shape information only by WLD features and to extract facial local features more efficiently, we propose a hybrid approach that combines the WLD with HOG features. We divide the images into blocks and weight each of them, then extract the two features and fuse them. At last, the weighted fused histograms are used to classify facial expressions by chi-square distance and the nearest neighbor method. The proposed method is applied on popular JAFFE and Cohn-Kanade facial expression databases and recognition rate is up to 93.97% and 95.86%. Compared with the Gabor Wavelet, LBP, and AAM and experimental results show that the proposed method achieves better performance for facial expression recognition.

关键词： Feature extraction Histograms Databases Face recognition Equations Active appearance model Data mining

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Research of unconventional emergency evolution state transfer rule

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ICIC Express Letters, Part B: Applications 2013年第4期4卷 1023-1030页

作者： Li, Tong Xia, Zhangli Zhou, Qing Yang, Wei Management College Hangzhou Dianzi University No. 1 2nd Ave Xiasha Higher Education Zone Hangzhou 310018 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University No. 10 Xuefu Ave Jinzhou Xinqu Dalian 116622 China

Unconventional emergency is essentially a developing process that event state variable changes from steady to transient, then to a new steady state. This paper uses plant growth dynamics theory to study microcosmic dynamical mechanism of unconventional emergency state space transformation. Under the effect of microcosmic dynamical mechanism, the state space transformation path may be countless. Herein we do not study each possible developing path of unconventional emergency individually, but focus on the analysis of the probability distribution of different paths, then determine the impact of controlling variable on probability distribution and confirm new steady points of unconventional emergency. This paper reveals the scenario elements and coping strategies that the unconventional emergency scenario evolution process faced, and finally provides scientific basis to cope with unconventional emergency. © 2013 ISSN 2185-2766.

关键词： Probability distributions

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Laplacian coordinates-based motion transition for data-driven motion synthesis

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IET Image Processing 2012年第9期6卷 1331-1337页

作者： Yi, P. Zhang, Q. Wei, X. School of Mechanical and Engineering Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

As a fundamental technique in data-driven (or example-based) methods, motion blending has been employed to produce new motion clip from two or more clips or introduced as a medium to improve the final result of other method, for example, motion graphs. As methods of motion blending always require the system or users to classify the motion samples, it is difficult to accomplish motion blending automatically and to preserve local details, such as angular accelerations of joints of original motions. In this study, the authors developed a framework based on Laplacian coordinates to produce transitions between two motion clips, emphasising on preserving local details, without any need of motion classification. At first, multidimensional Laplacian coordinates are introduced to present local details of joints' angle. Then an error function is deduced to measure how much result motions differ from original ones in local details. By minimising such error function, new motions can then finally generated, which can preserve local details of input motion clips as much as possible. The following experiments show the effectiveness of the authors' framework. © The Institution of Engineering and Technology 2012.

关键词： Laplace transforms

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EMOTIONAL SPEECH RECOGNITION BASED ON SVM WITH GMM SUPERVECTOR

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Journal of Electronics(China) 2012年第3期29卷 339-344页

作者： Chen Yanxiang Xie Jian (Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science&InformationHefei University of TechnologyHefei 230000China

Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).

关键词： Emotional speech recognition Support Vector Machines (SVM) Gaussian Mixture Model (GMM) supervector Universal Background Model (USB)

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Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

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Journal of molecular modeling 2013年第9期19卷 3883-91页

作者： Changjun Zhou Caixia Hou Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian China. zhou-chang231@***

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.

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CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

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Protein & Cell 2012年第2期3卷 148-152页

作者： Guoguang Zhao Dechao Bu Changning Liu Jing Li Jian Yang Zhiyong Liu Yi Zhao Runsheng Chen Bioinformatics Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China State Key Laboratory for Molecular Virology and Genetic Engineering National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100176China Graduate School of the Chinese Academy of Sciences Beijing 100190China

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.

关键词： CloudLCA metagenome analysis cloud computing

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microRNA的功能(英文)

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生物化学与生物物理进展 2012年第10期39卷 979-980页

作者：罗海涛赵屹 Bioinformatics Research Group Advanced Computing Research LaboratoryKey Laboratory of Intelligent Information ProcessingInstitute of Computing TechnologyChinese Academy of Sciences Research Center of Bioinformatics Kunshan RNAi Institute

MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as

关键词：肾癌 IL-8 上皮细胞-间质细胞转化蛋白激酶C

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A RGB image encryption algorithm based on DNA encoding and chaos map

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Computers and Electrical Engineering 2012年第5期38卷 1240-1248页

作者： Lili Liu Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

In this paper, a RGB image encryption algorithm based on DNA encoding combined with chaotic map is proposed aiming at characteristics of RGB image. The algorithm firstly carries out DNA encoding for R, G, B components of RGB image; then realizes the addition of R, G, B by DNA addition and carries out complement operation by using the DNA sequence matrix controlled by Logistic; three gray images are got after decoding; finally gets the encrypted RGB images by reconstructing R, G, B components which use image pixels disturbed by Logistic chaotic sequence. Simulation result shows that the proposed algorithm has a large secret key space and strong secret key sensitivity. Meanwhile, it can resist exhaustive attack, statistical attack, and thus it is suitable for RGB image encryption.

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