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International Joint Conference on Neural Networks

作者： Honglei Shi Shiliang Sun Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 500 Dongchuan Road Shanghai 200241 China

ISBN: (纸本)9781479919611

In this paper, we present a novel dimensionality reduction method, called sparse uncorrelated cross-domain feature extraction (SUFE), for signal classification in brain-computer interfaces (BCIs). Considering the differences between the source and target distributions of signals from different subjects, we construct an optimization objective which aims to find a projection matrix to transform the original data in a high-dimensional space into a low-dimensional latent space. In the low-dimensional space, both the discrimination of different classes and transferability between the source and target domains are preserved. To make sure the minimum information redundancy, the extracted features are designed to be statistically uncorrelated. Then, by adding the l_1 -norm penalty, we incorporate sparsity into the uncorrelated transformation. In the experiments, we evaluate the method with multiple datasets, and compare with the state-of-the-art methods. The results show that the proposed approach has better performance and is suitable for cross-domain signal classification.

关键词： Signal Classification Brain-computer Interfaces SUFE

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International Joint Conference on Neural Networks

作者： Honglei Shi Jinhua Xu Shiliang Sun Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 500 Dongchuan Road Shanghai 200241 China

ISBN: (纸本)9781479919611

This paper presents a novel dimensionality reduction method, called uncorrelated transferable feature extraction (UTFE), for signal classification in brain-computer interfaces (BCIs). Considering the difference between the source and target distributions of signals from different subjects, we construct an optimization objective that finds a projection matrix to transform the original data in a high-dimensional space into a low-dimensional latent space and that guarantees both the discrimination of different classes and transferability between the source and target domains. In the low-dimensional latent space, the model constructed in the source domain can generalize well to the target domain. Additionally, the extracted features are statistically uncorrelated, which ensure the minimum informative redundancy in the latent space. In the experiments, we evaluate the method with data from nine BCI subjects, and compare with the state-of-the-art methods. The results demonstrate that our method has better performance and is suitable for signal classification in BCIs.

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A density peak clustering approach to unsupervised acoustic subword units discovery

A density peak clustering approach to unsupervised acoustic ...

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Asia-Pacific Signal and information processing Association Annual Summit and Conference (APSIPA)

作者： Jia Yu Lei Xie Xiong Xiao Eng Siong Chng Haizhou Li Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science. Northwestern Polytechnical University Xi'an China School of Computer Engineering Nanyang Technological University Singapore A *STAR Institute for Infocomm Research Singapore

This paper studies unsupervised acoustic units discovery from unlabelled speech data. This task is usually approached by two steps, i.e., partitioning speech utterances into segments and clustering these segments into subword categories. In previous approaches, the clustering step usually assumes the number of subword units are known beforehand, which is unreasonable for zero-resource languages. Moreover, the previously-used clustering methods are not able to detect non-spherical clusters that are often present in real-world speech data. We address the two problems by a brand new clustering method, called density peak clustering (DPC), which is motivated by the observation that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from other points of a higher density in the space. Experiments on unsupervised acoustic units discovery demonstrate that our DPC approach can easily discover the number of subword units and it outperforms the recently proposed normalized cuts (NC) clustering approaches [1].

关键词： Acoustics Speech Hidden Markov models Clustering algorithms Clustering methods Laplace equations Training

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An improved group search optimizer for multi-objective problems

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ICIC Express Letters, Part B: Applications 2015年第9期6卷 2623-2628页

作者： Zheng, Xiangwei Li, Yazhou Lang, Xianting Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University No. 88 East Wenhua Road Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

In order to enhance the convergence ability of multi-objective group search optimizer (MGSO) and solution distribution of non-dominated Pareto set, a novel multiobjective group search optimizer based on multiple producers and crossover operator of genetic algorithm (MCGSO) is proposed in this paper. The producer of MCGSO is extended from one to multiple ones;the metropolis rule of simulation annealing algorithm is introduced into the search pattern of producers and rangers’ search strategies and crossover operator are combined. Experimental results demonstrate that MCGSO can effectively and efficiently solve multi-objective optimization problems compared with similar algorithms such as NSGA-II and other MGSOs. © 2015 ISSN 2185-2766.

关键词： Multiobjective optimization

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Rapid display optimization based on on-chip programmable memory for mobile intelligent devices

Rapid display optimization based on on-chip programmable mem...

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Chinese Automation Congress (CAC)

作者： Ruo Jia Wei Hu Ziheng Li Zewei Ma Jiapei Wang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China

Mobile intelligent devices are popular nowadays. As one type of embedded systems, such devices have strict response requirements. When the users click on the screen, the display should give the response as soon as possible. The on-chip programmable memory can be used to optimize the process. The main on-chip programmable memory is on-chip SRAM, which is called scratchpad memory. In this paper, a novel approach is proposed to speed the display for mobile intelligent devices based on scratchpad memory. The programs are analyzed and the most used parts are selected and assigned to on-chip programmable memory. Such memory has faster access speed and lower power consumption compared with the traditional off-chip memory and cache. The experimental results show that this approach has reduced the response time and improved the performance of the system.

关键词： System-on-chip Random access memory Optimization Program processors Memory management Mobile communication Frequency conversion

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Communication energy oriented scheduling optimization for network on chip

Communication energy oriented scheduling optimization for ne...

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Chinese Automation Congress (CAC)

作者： Dongsheng Ye Wei Hu Benben Liu Xinwei Cao Zhanyan Zhang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9781467371902

It is difficult to improve the performance for the traditional processor architecture. The power consumption and the temperature of the processors are the main bottlenecks for CPU with single core. Chip multiprocessor is proposed to solve such problems. In order to solve the communication problem between the on chip components, network on chip is taken as the promising diagram. The on-chip cores are connected by lines. However, the communication is now the core of the on-chip network and the power consumed is more than before. This paper aims at the energy consumption problem in communication. The tasks will be mapped to the cores according to the communication density to reduce the power consumption and improve the performance. The experimental results show that our algorithm can achieve its target.

关键词： System-on-chip Power demand Optimization Silicon Multicore processing Dynamic scheduling

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Effective handwritten digit recognition based on multi-feature extraction and deep analysis

Effective handwritten digit recognition based on multi-featu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Caiyun Ma Hong Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781467376839

Handwritten digit recognition is an important research topic in computer vision and pattern recognition. This paper proposes an effective handwritten digit recognition approach based on specific multi-feature extraction and deep analysis. First, we normalize images of various sizes and stroke thickness in preprocessing to eliminate negative information and keep relevant features. Secondly, considering that handwritten digit image recognition is different from traditional image semantics recognition, we propose specific feature definitions, including structure features, distribution features and projection features. Moreover, we fuse multiple features into the deep neural networks for semantics recognition. Experiments results on benchmark database of MNIST handwritten digit images show that the performance of our algorithm is remarkable and demonstrate its superiority over several existing algorithms.

关键词： Feature extraction Handwriting recognition Image recognition Databases Neural networks Error analysis Training

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A feature selection algorithm towards efficient intrusion detection

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2015年第11期10卷 253-264页

作者： Yin, Chunyong Ma, Luyu Feng, Lu Yin, Zhichao Wang, Jin School of Computer and Software Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing No.1 Middle School Nanjing China

Feature selection algorithm plays a crucial role in intrusion detection, data mining and pattern recognition. According to some evaluation criteria, it gets optimal feature subset by deleting unrelated and redundant features of the original data set. Aiming at solving the problems about the low accuracy, the high false positive rate and the long detection time of the existing feature selection algorithm. In this paper, we come up with a feature selection algorithm towards efficient intrusion detection, this algorithm combines the correlation algorithm and redundancy algorithm to chooses the optimal feature subset. Experimental results show that the algorithm shows almost and even better than the traditional feature selection algorithm on the different classifiers. © 2015 SERSC.

关键词： Redundancy

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Feature subset selection using naive Bayes for text classification

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Pattern Recognition Letters 2015年 65卷 109-115页

作者： Guozhong Feng Jianhua Guo Bing-Yi Jing Tieli Sun Key Laboratory for Applied Statistics of MOE School of Mathematics and Statistics Northeast Normal University Changchun 130024 China Key Laboratory of Intelligent Information Processing of Jilin Universities School of Computer Science and Information Technology Northeast Normal University Changchun 130117 China Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Feature subset selection is known to improve text classification performance of various classifiers . The model using the selected features is often regarded as if it had generated the data. By taking its uncertainty into account, the discrimination capabilities can be measured by a global selection index (GSI), which can be used in the prediction function. In this paper, we propose a latent selection augmented naive (LSAN) Bayes classifier. By introducing a latent feature selection indicator, the GSI can be factorized into each local selection index (LSI). Using conjugate priors , the LSI for feature evaluation can be explicitly calculated. Then the feature subset selection models can be pruned by thresholding the LSIs, and the LSAN classifier can be achieved by the product of a small percentage of single feature model averages. The numerical results on some real datasets show that the proposed method outperforms the contrast feature weighting methods, and is very competitive if compared with some other commonly used classifiers such as SVM.

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Ontology-based knowledge modeling for using physical effects

Ontology-based knowledge modeling for using physical effects

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World Conference: TRIZ FUTURE 2011-2014

作者： Yan, Wei Zanni-Merk, Cecilia Rousselot, François Cavallucci, Denis Collet, Pierre School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China LGECO INSA Strasbourg 24 Boulevard de la Victoire Strasbourg Cedex67084 France Pôle API BP 10413 Illkirch Cedex67412 France

Su-Field analysis, as one of the inventive problem solving tools, can be used to analyse and improve the efficacy of the technical system. Generally, the process of using Su-Field model to solve a specific inventive problem includes: building a Problem Model, mapping to a Generic Problem Model, finding a Generic Solution Model based on the corresponding inventive standard, and finally establishing and instantiating a Solution Model. As one of the most important phases of Su-Field analysis, the last step is normally implemented manually with the help of physical effects, which link generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose, that is, given a certain function, too many physical effects are chosen while with the detailed context of the problem, no physical effect is returned. This paper firstly proposes a new way of representing physical effects using the change of two states, that is, the couple of two states before and after applying physical effects. Then, the knowledge about using physical effects is formalized in OWL (Ontology Web Language) - an ontology language for semantic web, and the constraint knowledge, such as the condition to use each kind of physical effect, is formalized in SWRL (Semantic Web Rule Language) - a rule language. Finally, the reasoning process of using physical effects is performed with the support of JESS (Java Expert System Shell) rule engine. © 2015 The Authors. Published by Elsevier Ltd.

关键词： Ontology

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