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International Conference on information, Communications and Signal processing

作者： Zhao Lv Xiaopei Wu Bangyan Zhou Chao Zhang Key Laboratory of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

ISBN: (纸本)9781467372190

This paper presents a novel permutation algorithm for solving the permutation ambiguity of frequency-domain BSS(Blind Source Separation). The proposed algorithm harnesses the characteristics that signals at adjacent frequency bins have a high similarity and utilizes Dynamic Time Warping (DTW) to compare this similarity in order to obtain an adjusting matrix at adjacent frequency bin. In real experimental environment, ASSNR(Average Segmental SNR) was utilized to evaluate the separation quality and the proposed method is found to improve the separation over the traditional criterions.

关键词： Heuristic algorithms Prediction algorithms Speech Time series analysis Signal processing algorithms Time-frequency analysis

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Whole genome/proteome based phylogeny reconstruction for prokaryotes using higher order Markov model and chaos game representation

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Molecular phylogenetics and evolution 2016年 96卷 102-111页

作者： Wei-Feng Yang Zu-Guo Yu Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China Department of Mathematics and Physics Hunan Institute of Engineering Hunan 411104 PR China. Electronic address: yangweifeng09@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: yuzuguo@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: v.anh@qut.edu.au.

UNLABELLED:Traditional methods for sequence comparison and phylogeny reconstruction rely on pair wise and multiple sequence alignments. But alignment could not be directly applied to whole genome/proteome comparison and phylogenomic studies due to their high computational complexity. Hence alignment-free methods became popular in recent years. Here we propose a fast alignment-free method for whole genome/proteome comparison and phylogeny reconstruction using higher order Markov model and chaos game representation. In the present method, we use the transition matrices of higher order Markov models to characterize amino acid or DNA sequences for their comparison. The order of the Markov model is uniquely identified by maximizing the average Shannon entropy of conditional probability distributions. Using one-dimensional chaos game representation and linked list, this method can reduce large memory and time consumption which is due to the large-scale conditional probability distributions. To illustrate the effectiveness of our method, we employ it for fast phylogeny reconstruction based on genome/proteome sequences of two species data sets used in previous published papers. Our results demonstrate that the present method is useful and efficient. AVAILABILITY AND IMPLEMENTATION:The source codes for our algorithm to get the distance matrix and genome/proteome sequences can be downloaded from ftp://121.199.20.25/. The software Phylip and EvolView we used to construct phylogenetic trees can be referred from their websites.

关键词： Alignment-free whole proteome comparison Chaos game representation Higher order Markov model Phylogenetic tree Shannon entropy

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Motivation Learning in Mind Model CAM

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International Journal of Intelligence Science 2015年第2期5卷 63-71页

作者： Zhongzhi Shi Gang Ma Xi Yang Chengxiang Lu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Qufu Normal University Rizhao China

Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.

关键词： Motivation Learning Motivation processing Consciousness and Memory Model Cyborg Rat

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Multi-path SDN route selection subject to multi-constraints

Multi-path SDN route selection subject to multi-constraints

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Third International Conference on Cyberspace Technology (CCT 2015)

作者： Qingfeng Pan Xianghan Zheng Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China College of Mathematics and Computer Science Fuzhou University 350108 China

ISBN: (纸本)9781510821873

Multi-constrained optimal routing problem and disjoint path selection are NP-hard problem. The conventional single-path and single constraint routing can not meet people's needs. The reliability of network data transmission and packet loss rate can be decreased when the route or the transmission fails. To solve the problems, we need to study the routing path selection strategies. This paper optimizes the routing path that meets the QoS constraints under the SDN pre planning routing, and proposes a routing mechanism that can solve problems in polynomial time. Firstly, the paper combined with Clustering and Gauss algorithm to simulate the network simulation, obtain the initial experimental data. It can reflect the network status more accurately. Secondly, simplify the multi-objectives based on the AHP, It reduces the complexity of the problem and meets QoS requirements. Finally, select the disjoint communication paths and backup paths with the heuristic algorithms in order to balance the load of network and improve the transmission reliability. The selection mechanism can improve the efficiency of data transmission, and the survival of network communication.

关键词： Analytic Hierarchy Process Disjoint Path Multi constraints Optimal Path QoS Routing

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Independent component analysis combined with compressed sensing for EEG compression in BCI

Independent component analysis combined with compressed sens...

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International Conference on information, Communications and Signal processing

作者： Bangyan Zhou Xiaopei Wu Zhao Lv Lei Zhang Chao Zhang Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

Considering the limitation of hardware requirement and power dissipation in wearable brain-computer interface (BCI), the electroencephalogram (EEG) data compression implemented by independent component analysis (ICA) combined with compressed sensing (CS) is proposed in this paper. A simple and effective ICA spatial filtering method is used to obtain motor related independent components (MRICs). Furthermore, CS algorithm is introduced to compress MRICs, which have advantage of frequency sparse. So the proposed scheme can make few MRICs compressed transmission instead of the multi-channel EEG data transmission. Based on the measured motor imagery EEG data, the proposed EEG compression scheme is compared with the traditional CS compression scheme. The experimental results show that, the two system schemes have the similar classification accuracy. However, in the proposed compression scheme, the amount of transmission data can be reduced by 75%.

关键词： Electroencephalography Biomedical monitoring Image reconstruction Image coding Signal processing algorithms Electrodes Dictionaries

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A fast classification method using SVM based on affinity propagation clustering algorithm

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Journal of Computational information Systems 2015年第16期11卷 6073-6080页

作者： Xu, Li Ding, Shifei Huang, Huajuan School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The classification speed of SVM is inversely proportional to the number of Support Vectors (SVs). Therefore, the less SVs mean the more sparseness and the higher classification speed. In order to reduce the number of SVs but without lossing of generalization performance, a new algorithm called Fast Classification Method Using SVM Based on Affinity Propagation clustering algorithm (FCSVM-AP) is proposed, which employs Affinity Propagation (AP) clustering algorithm to cluster the original SVs and the cluster centers are used as the new SVs, then aiming to minimize the classification gap between SVM and FCSVM-AP, a quadratic programming model is built for obtaining the optimal coefficients of the new SVs. Meanwhile, it is proven that when clustering the original SVs, the minimal upper bound of the error between the original decision function and the fast decision function can be achieved by AP. Finally, experiments show that compared with original SVs, the number of SVs decreases and the speed of classification increases using FCSVM-AP, while the loss of accuracy is in the acceptable level. © 2015 by Binary information Press

关键词： Quadratic programming

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A hybrid deep learning CNN-ELM model and its application in handwritten numeral recognition

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Journal of Computational information Systems 2015年第7期11卷 2673-2680页

作者： Guo, Lili Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Convolutional Neural Network (CNN) is a typical algorithm structure of deep learning and it has applied in image recognition field widely. Based on CNN, this paper puts forward a novel hybrid deep learning model CNN-ELM. In this model, CNN is in charge of feature extraction and Extreme Learning Machine (ELM) performs as a classifier to complete the final classification so as to integrate the synergy of two classifiers. We have took experiments on MNIST digit database. Comparisons with CNN and ELM on the same database, CNN-ELM achieved an error rate of 0.67%, which indicate that CNN-ELM has achieved better generalization performance. ©, 2015, Binary information Press. All right reserved.

关键词： Character recognition

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Topic discovery of clusters from documents with geographical location

Topic discovery of clusters from documents with geographical...

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作者： Zhang, Li Sun, Xiaoping Zhuge, Hai Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China University of Chinese Academy of Sciences Beijing China

As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. Keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. © 2015 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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Prediction of negative conversion days of childhood nephrotic syndrome based on the improved backpropagation neural network with momentum

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International Journal Bioautomation 2015年第4期19卷 543-554页

作者： Liu, Yijun Wang, Beihong Tang, Jiali Zhu, Mingfang Chen, Dan Jiang, Hongfen Chen, Xiangjun Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Zhongwu Road 1801 Changzhou213001 China Department of Pediatrics Changzhou No.2 People's Hospital Xinglong Road 29 Changzhou213003 China Guangxi Higher Education Key Laboratory of Science Computing and Intelligent Information Processing Guangxi Teachers Education University Yanziling Road 4 Nanning530023 China

Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

关键词： Momentum

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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