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Smoothness in noise reduction of chaotic time series

Smoothness in noise reduction of chaotic time series

引用

2011 International Conference on Computers, communications, Control and Automation

作者： Chenxi Shao Jiangong Ji Jingyuan Shao Lipeng Xiao Department of Computer Science and Technology University of Science and Technology of China Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Information center Jianghuai Automobile Co.Ltd.

Noise is an omnipresent phenomenon. It obscures the real behavior of dynamical system. Lots of methods are proposed to remove the noise contaminating time series. However, almost all the methods consider the noise reduction in the phase space and often sharp points are kept. Different with these methods, this paper proposes a method directly on the time series itself, considering the gauss noise feature and the smoothness of the real data, uses curve-fitting way to eliminate the sharp points. The numeral results verify the effectiveness of our method.

关键词： smoothness,noise reduction,curve-fitting,Local-Geometric-Projection

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ALOHA-like neighbor discovery in low-duty-cycle wireless sensor networks

ALOHA-like neighbor discovery in low-duty-cycle wireless sen...

引用

IEEE Conference on Wireless communications and Networking

作者： Lizhao You Zimu Yuan Panlong Yang Guihai Chen State Key Laboratory of Novel Software Technology Nanjing University China Institute of Computing Technology Chinese Academy and Sciences China Institute of Communication Engineering People''s Liberation Army University of Science and Technology China

Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep active during the process of neighbor discovery, which might be difficult for low-duty-cycle wireless sensor networks in many real deployments. In this paper, we investigate the problem of neighbor discovery in low-duty-cycle wireless sensor networks. We give an ALOHA-like algorithm and analyze the expected time to discover all n - 1 neighbors for each node. By reducing the analysis to the classical K Coupon Collector's Problem, we show that the upper bound is ne(log 2 n + (3 log 2 n - 1) log 2 log 2 n + c) with high probability, for some constant c, where e is the base of natural logarithm. Furthermore, not knowing number of neighbors leads to no more than a factor of two slowdown in the algorithm performance. Then, we validate our theoretical results by extensive simulations, and explore the performance of different algorithms in duty-cycle and non-duty-cycle networks. Finally, we apply our approach to analyze the scenario of unreliable links in low-duty-cycle wireless sensor networks.

关键词： Wireless sensor networks Algorithm design and analysis Upper bound Simulation Random variables Equations Synchronization

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Chaos-based estimation method for software reliability

Chaos-based estimation method for software reliability

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2nd International Conference on Networks Security, Wireless communications and Trusted computing, NSWCTC 2010

作者： Shao, Chenxi Huang, Chao MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei Anhui China Computer Science Department University of Science and Technology of China Hefei Anhui China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China

ISBN: (纸本)9780769540115

Reliability is one of the essential attributes of the dependable software, and an important factor for quantitatively characterizing software quality. Conventional methodology is software Reliability Growth Model (SRGM), which specifies the form of a random process that describes the behavior of software failures with respect to time. In this paper, we propose that the behavior of software failures possesses determinacy as well as randomness. So we apply chaos theory to software reliability assessment. Based on chaos theory, we can estimate software reliability according to the objective law hidden in the data, unlike the SRGM which usually makes a number of assumptions. This approach avoids the subjectivity in the SRGM. Also, we analyze the actual software failures data sets with the add-weighted one-rank localregion method (AOLM) based on chaos theory and compare the results with the conventional stochastic SRGM JM (Jelinsky-Moranda) model and NHPP (nonhomogeneous poisson process) mode. Comparison results show that the proposed method fits better than the stochastic ones. © 2010 IEEE.

关键词： software reliability

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A clustering system for gene expression data based upon genetic programming and the HS-model

A clustering system for gene expression data based upon gene...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Liu, Guiquan Jiang, Xiufang Wen, Lingyun Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset. © 2010 IEEE.

关键词： Cluster analysis

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Interactive PBIL with multiple probability vectors for multimodal optimization with implicit performance indices

Interactive PBIL with multiple probability vectors for multi...

引用

2010 2nd International Conference on Computer Engineering and Technology, ICCET 2010

作者： You, Haifeng Wang, Xufa Department of Computer Science and Technology Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9781424463503

Interactive population-based incremental learning (IPBIL) is an effective method to solve optimization problems with implicit performance indices. It can significantly reduce user fatigue compared with interactive evolutionary computation (IEC). However, each run of IPBIL can only find one solution or some similar solutions. Thus it is not suitable for multimodal optimization problems with implicit performance indices. To solve this problem, we propose an IPBIL with multiple probability vectors (IPBIL-MPV) in this work. The key idea is to utilize multiple probability vectors to catch different search directions and thus find more than one solutions. We perform a subjective experiment in which IPBIL-MPV is applied to a fashion design problem. The experimental results show that IPBIL-MPV can find several distinct solutions in a run. Thus it is an effective method to solve multimodal optimization problems with implicit performance indices. © 2010 IEEE.

关键词： Probability

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A fast algorithm for finding community structure based on community closeness

A fast algorithm for finding community structure based on co...

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3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

作者： Jiang, Xiufang Liu, Guiquan Lin, Zhiting Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9780769540306

Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a fast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk¯+mt), where k¯ denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding. © 2010 IEEE.

关键词： Complex networks

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RETRACTED ARTICLE: A hormone-modulated emotional model

RETRACTED ARTICLE: A hormone-modulated emotional model

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作者： Liang, Jianwei You, Haifeng Wang, Xufa Department of Computer Science and Technology Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9781424463503

Emotions are an important aspect of human intelligence and have been shown to play a significant role in human decision-making process. Researchers in areas such as psychology, neuroscience, cognitive science and artificial intelligence have proposed a variety of emotional models. However, most of the previous models have not solved the complexity of the human emotional system owing to the lack of an effective regulatory mechanism for controlling the emotions. To address the limitation, a hormone-modulated emotional model (HMEM) is presented. Since emotions are imprecise and uncertain in nature, fuzzy logic is employed in the computation of new emotion intensity. The reported experiments test HMEM within a simulated autonomous agent situated in a dynamic environment. Experimental results show that both the hormonal system and emotional system contribute positively to the survival of the agent. © 2010 IEEE.

关键词： Fuzzy logic

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A self-organized algorithm based on digital hormone

A self-organized algorithm based on digital hormone

引用

International Conference on Advanced Computer Theory and Engineering

作者： Xia, Li Xufa, Wang Yang, Lei Haifeng, You Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781424465408

In this paper, a self-organized algorithm for task allocation, based on the hormone reaction-diffusion mechanism, is proposed for a multi-robot system. Hormone messages are used to coordinate the movement of robots. By the regulation of hormones, agents could adjust their behaviors in time and improve the ability of self-organization. A simulation of task allocation in the multi-robot system is performed, experimental results demonstrate that our method can accomplish the task effectively and reduce the total cost. © 2010 IEEE.

关键词： Multipurpose robots

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A hormone-based clustering algorithm in wireless sensor networks

A hormone-based clustering algorithm in wireless sensor netw...

引用

2010 2nd International Conference on Computer Engineering and Technology, ICCET 2010

作者： Dong, Dayuan You, Haifeng Zhang, Yipeng Wang, Xufa Key Laboratory of Software in Computing and Communication Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781424463503

Clustering problem is one of the hottest issues in wireless sensor networks (WSNs). The strategy for selection of cluster head has not been sufficiently investigated. In this paper, we propose a hormone-based clustering algorithm (HCA) inspired by the communication mechanism of bio-endocrine system to prolong the lifetime of the sensor nodes. Two kinds of hormones, cluster hormone and member hormone, are used in the algorithm. Hormone message can promote a node to become a cluster head or a member node. Comparative experiment shows that the proposed algorithm has effectively prolong the lifetime of WSN compared with LEACH[1] and HEED[2]. © 2010 IEEE.

关键词： Clustering algorithms

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Quantitative and qualitative forecasting applied to supply-chain inventory

Quantitative and qualitative forecasting applied to supply-c...

引用

2010 2nd International Conference on MultiMedia and Information Technology, MMIT 2010

作者： Shao, Chenxi Wang, Lizhong Computer Science and Technology School of University of Science and Technology of China Hefei China Anhui Province Key Laboratory Software in Computing and Communication Hefei 230027 China MOE-Microsoft Key Laboratory Multimedia Computing and Communication University of Science and Technology of China Hefei China

ISBN: (纸本)9780769540085

In the process of supply chain (SC) management, time inventory management problem is extremely important. In terms of academic methods, quantitative methods are very popular as the researchers could calculate the inventory if they got the historic data of some SC. However, based on quantitative methods and the results cannot too be convincible. This paper considered a supply chain system includes several kinds of products. As to the historic suppliers' demand data of this SC system, firstly we applied a quantitative forecasting analysis method to it;secondly we made a qualitative analysis to improve the complex system's inventory management performance. According to the comparison between quantitative method and the method used in this paper, we find that the accuracy is more perfect. Not only the inventory cost is lower, also the forecasting accuracy is better. © 2010 IEEE.

关键词： Supply chain management

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