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Firefly algorithm guided by general centre particle and its application in node localisation of wireless sensor networks

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International Journal of Wireless and Mobile Computing 2017年第2期13卷 122-130页

作者： Lv, Li Tian, Hongmin Zhao, Jia Xie, Zhifeng Fan, Tanghuai Han, Longzhe National and Provincial Joint Engineering Lab. for the Hydr. Eng. Safety and Efficient Utilization of Water Resources of Poyang Lake Basin Nanchang Institute of Technology Nanchang330099 China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Labor Union Nanchang Institute of Technology Nanchang330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang330099 China

Firefly algorithm (FA) is a kind of swarm intelligence algorithm that was developed by simulating the behaviour of the flashing of fireflies. However, the population uses only the advantage of the better particles' information to complete optimisation without using the comprehensive information of the population effectively. So, this paper proposes an improved FA, namely firefly algorithm guided by general centre particle (GCPFA), in which the General Centre Particle (GCP) was generated by sharing each particle's historically optimal position information, and each particle would learn from GCP after they learned from the other particles with better performances. The simulation results on 12 benchmark test functions also revealed GCPFA's superiority to the other six famous FAs. In order to improve the unreasonable distribution of sensor nodes randomly and improve the network coverage rate, the above algorithm is applied to optimise the coverage of wireless sensor networks and achieve better optimisation effect. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Sensor nodes

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information granularity and granular structure in decision making

Information granularity and granular structure in decision m...

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7th International Conference on Rough Sets and Knowledge technology, RSKT 2012

作者： Wang, Baoli Liang, Jiye Qian, Yuhua Department of Applied Mathematics Yuncheng University Yuncheng 044000 Shanxi China Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China

ISBN: (纸本)9783642318993

Multiple criteria decision making (MCDM) has received increasing attentions in both engineering and economic fields. Weights of the criteria directly affect decision results in MCDM, so it is important for us to acquire the appropriate weights of the criteria. In some decision making problems, experts always express their preference by multiplicative preference relation and fuzzy preference relation. In this paper, an objective method based on information granularity is proposed for acquiring weights of the criteria in MCDM. Moreover, we prove that the essence of a consistence preference relation is a partial relation, and analyze the corresponding partial granular structure of the alternative set according to the given partial relation. © 2012 Springer-Verlag.

关键词： Decision making

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Adaptive resampling in particle filter based on diversity measures

Adaptive resampling in particle filter based on diversity me...

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International Conference on computer Science and Education

作者： Yu, Jin-Xia Liu, Wen-Jing Tang, Yong-Li College of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Provincial Key Lab. of Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)9781424460052

It is very important to find a criterion when to perform a resampling step. Aimed at this problem, an adaptive resampling algorithm in particle filter based on diversity measures is presented. Based on the analysis and comparison of two diversity measures that are effective sample size (ESS) and population diversity factor(PDF), the resampling algorithm based on ESS is introduced. On the basis of that, PDF is used for adaptively tuning the resampling threshold. At last, with the simulation program using matlab.7.0 to track a single target motion from a fixed visual observation points, the performance of the proposed algorithm is evaluated and its validity is verified. ©2010 IEEE.

关键词： MATlab

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Semantic role lab.ling using Chinese dependency parsing tree

Semantic role labeling using Chinese dependency parsing tree

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Hongling Wang Xiaohong Yuan Guodong Zhou Jiangsu Provincial Key Lab for Computer Information Processing Technology Soochow University Suzhou China

Although recent years have seen much progress in semantic role lab.ling in English, only a little research focuses on Chinese dependency relationship. This paper presents an SRL system on Chinese dependency relation by using the similar method in an English SRL system. We also explore dependency-based predicate analysis in Chinese SRL. We perform our experiments on two datasets. Our results show that dependency-based Chinese SRL is not only feasible but also superior to the constituent-based SRL in some respects.

关键词： lab.ling Gold information processing computer science Data mining Feature extraction Educational programs Performance analysis Natural languages

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The Applications of Generalized Computing In Knowledge Management

The Applications of Generalized Computing In Knowledge Manag...

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2005年公共管理国际会议

作者： YAO Min SHEN Bin ZHANG Yan-gu College of Computer Zhejiang University Provincial Key Lab. for Computer Information processing Technology in Jiangsu Suzhou University School of Computer Wenzhou Normal College

Generalized computing is a new kind of intelligent computing systems, which combines symbolic intelligence and computational intelligence (including neural computing, fuzzy computing and evolutional computing) together. By introducing generalized computing into knowledge management, this paper presents a kind of knowledge decision-making model, and a kind of control model of enterprise knowledge flow.

关键词： Generalized computing, Knowledge management, Enterprise management

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DNN and LS Based Channel Estimation in OTFS System 23

DNN and LS Based Channel Estimation in OTFS System

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Hu, Jiacheng Bai, Zhiquan Yang, Jikai Cai, Yueying Zhou, Di Wang, Yingxun Kwak, KyungSup School of Information Science and Engineering Shandong University Shandong Provincial Key Lab. of Wireless Communication Technologies Shandong Qingdao China School of Computer and Information Engineering Qilu Institute of Technology China Graduate School of Information Technology and Telecommunications Inha University Incheon Korea Republic of

ISBN: (纸本)9798350325959

Orthogonal time frequency space (OTFS) system transmits information in the delay-Doppler domain, and it adapts well to high-speed mobile environments and can realize channel estimation with less overhead. Channel estimation in the OTFS system is necessary for subsequent channel equalization and information recovery process, aiming to eliminate the effects, such as inter-symbol interference (ISI) and noise influence. The paper proposes an effective channel estimation for the OTFS system using deep neural network (DNN) and the least square (LS) algorithm, considering the accurate and low latency channel estimation requirement in the Internet of Vehicles, where LS algorithm is first used to perform a rough time-frequency domain channel estimation, and then the DNN model is taken to optimize the rough channel estimation. Simulation results show that the proposed method achieves more accurate channel estimation performance with low complexity and better flexibility, compared with the typical LS and linear minimum mean square error (LMMSE) scheme. © 2023 IEEE.

关键词： channel estimation deep neural network (DNN) least square (LS) orthogonal time frequency space (OTFS)

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Boosting prediction performance of protein-protein interaction hot spots by using structural neighborhood properties (extended abstract)

Boosting prediction performance of protein-protein interacti...

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17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Deng, Lei Guan, Jihong Wei, Xiaoming Yi, Yuan Zhou, Shuigeng Department of Computer Science and Technology Tongji University Shanghai China Shanghai Key Lab. of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Software Central South University Changsha China

ISBN: (纸本)9783642371943

Binding of one protein to another in a highly specific manner to form stable complexes is critical in most biological processes, yet the mechanisms involved in the interaction of proteins are not fully clear. The identification of hot spots, a small subset of binding interfaces that account for the majority of binding free energy, is becoming increasingly important in understanding the principles of protein interactions. Despite experiments like alanine scanning mutagenesis and a variety of computational methods have been applied to this problem, comparative studies suggest that the development of accurate and reliable solutions is still in its infant stage. We developed PredHS (Prediction of Hot Spots), a computational method that can effectively identify hot spots on protein binding interfaces by using 38 optimally chosen properties. The optimal combination of features was selected from a set of 324 novel structural neighborhood properties by a two-step feature selection method consisting of a random forest algorithm and a sequential backward elimination method. We evaluated the performance of PredHS using a benchmark of 265 alanine-mutated interface residues (Dataset I) and a trimmed subset (Dataset II) with 10-fold cross validation. Compared with the state of the art approaches, PredHS achieves a significant improvement on the prediction quality, which stems from the new structural neighborhood properties, the novel way of feature generation as well as the selection power of the proposed two-step method. We further validated the capability of our method by an independent test and obtained promising results. The PredHS web server and supplementary data are availab.e at http://***/predhs. © 2013 Springer-Verlag.

关键词： Forecasting

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Video object matching based on sift algorithm

Video object matching based on sift algorithm

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2008 IEEE International Conference Neural Networks and Signal processing, ICNNSP

作者： Xuelong, Hu Yingcheng, Tang Zhenghua, Zhang School of Information Engineering Yangzhou University Yangzhou Jiangsu 225009 China Jiangsu Prov. Key Lab for Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing Jiangsu 210003 China Jiangsu Prov. Key Lab. for Computer Information Processing Technology Suzhou University Suzhou Jiangsu 215006 China

ISBN: (纸本)9781424423118

SIFT (Scale Invariant Feature Transform) is used to solve visual tracking problem, where the appearances of the tracked object and scene background change during tracking. The implementation of this algorithm has five major stages: scale-space extrema detection;keypoint localization;orientation assignment;keypoint descriptor;keypoint matching. From the beginning frame, object is selected as the template, its SIFT features are computed. Then in the following frames, the SIFT features are computed. Euclidean distance between the object's SIFT features and the frames' SIFT features can be used to compute the accurate position of the matched object. The experimental results on real video sequences demonstrate the effectiveness of this approach and show this algorithm is of higher robustness and real-time performance. It can solve the matching problem with translation, rotation and affine distortion between images. It plays an important role in video object tracking and video object retrieval. © 2008 IEEE.

关键词： Tracking (position)

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A brief analysis about boundary region partition reduction standard

A brief analysis about boundary region partition reduction s...

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2007 Workshop on Intelligent information technology Application, IITA 2007

作者： Du, Weifeng Yue, Guangxue Cheng, Xu School of Information Engineering Jiaxing University Jiaxing 314001 China School of Computer and Communication Hunan University Changsha 410000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Pos and Telecommunications Nanjing 210003 China

ISBN: (纸本)076953063X

In his paper, Prof. Ye Dongyi pointed out that the reduction approach introduced by Hu Xiaohua etc. will give wrong result in some situation. In this paper we come to a conclusion by analysis that the reduction approach is to keep boundary region partition unchangeable virtually. It is just different from the reduction approach given by Ye Dongyi. Then, we analyze the logic characteristic of boundary region partition reduction standard. © 2007 IEEE.

关键词： data reduction formal logic rough set theory boundary region partition reduction standard logic characteristics Rough set theory Formal logic data reduction boundary regions

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Semi-supervised hierarchical co-clustering

Semi-supervised hierarchical co-clustering

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7th International Conference on Rough Sets and Knowledge technology, RSKT 2012

作者： Huang, Feifei Yang, Yan Li, Tao Zhang, Jinyuan Rutayisire, Tonny Mahmood, Amjad School of Information Science and Technology Provincial Key Lab. of Cloud Computing and Intelligent Technology Southwest Jiaotong University Chengdu 610031 China School of Computer Science Florida International University Miami FL 33199 United States

ISBN: (纸本)9783642318993

Hierarchical co-clustering aims at generating dendrograms for the rows and columns of the input data matrix. The limitation of using simple hierarchical co-clustering for document clustering is that it has a lot of feature terms and documents, and it also ignores the semantic relations between feature terms. In this paper a semi-supervised clustering algorithm is proposed for hierarchical co-clustering. In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes. And in the last step, the documents and merged feature terms are clustered using hierarchical co-clustering algorithm. Semantic information is used to measure the similarity during the hierarchical co-clustering process. Experimental results show that the proposed algorithm is effective and efficient. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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