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An improved PSO algorithm for distributed localization in wireless sensor networks

An improved PSO algorithm for distributed localization in wi...

International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014

作者： Li Dan Wen Xian-bin School of Computer Science and Technology Tianjin University China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology China

Accurate and quick localization of randomly deployed nodes is required by many applications in wireless sensor networks and always formulated as a multidimensional optimization problem. Particle swarm optimization (PSO) is feasible for the localization problem because of its quick convergence and moderate demand for computing resources. This paper proposes a distributed two-phase PSO algorithm to solve the flip ambiguity problem, improve the efficiency and precision. In this work, the initial search space is defined by bounding box method and a refinement phase is put forward to correct the error due to flip ambiguity. Moreover, the unknown nodes which only have two references or three near-collinear references are tried to be localized in our research. Simulation results indicate that the proposed distributed localization algorithm is superior to the previous algorithms.

关键词： distributed localization particle swarm optimization wireless sensor network Wireless sensor networks Distribute Particle swarm optimization parametric subharmonic oscillator algorithms Flip

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Performance evaluation and segmentation for synthetic aperture radar image using mixture multiscale autoregressive model and bootstrap technique

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Chinese Optics Letters 2012年第B06期10卷 72-77页

作者：徐海霞温显斌邹永廖郑永春 Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin 300384 China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China

An unsupervised segmentation and its performance evaluation technique are proposed for synthetic aperture radar （SAR） image based on the mixture multiscale autoregressive （MMAR） model and the bootstrap method. The segmentation-evaluation techniques consist of detecting the number of image regains, esti- mating MMAR parameters by using bootstrap stochastic annealing expectation-maximization （BSAEM） algorithm, and classifying pixels into region by using Bayesian classifier. Experimental results demonstrate that the evaluation operation is robust, and the proposed segmentation method is superior to the tradi- tional single resolution techniques, and considerably reduces the computing time over the EM algorithm.

关键词： 100.0100 Image processing 280.6730 Synthetic aperture radar

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Research of deep packet inspection system based on the MapReduce

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Journal of Computational Information systems 2013年第7期9卷 2587-2594页

作者： Wang, Jinsong Wei, Jun Lin, Sheng Huang, Wei Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

In this paper, we propose a deep packet inspection system based on the MapReduce. The MapReduce which is a parallel distributed programming model developed by Google applies the technology of deep packet inspection. Network protocol analysis technique is invoked to realize detect, effectively reducing the packet number to be matched. Furthermore, cache technology is adopted to store attack signature from database in order to reduce the frequency of reading the rules from library file which improved the detection efficiency. Experimental results show that effectiveness of the proposed system with the high-speed network traffic. Copyright © 2013 Binary Information Press.

关键词： MapReduce

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A new approach for 3D motion estimation of a planar object

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Journal of Computational Information systems 2010年第5期6卷 1517-1524页

作者： Wang, Chundong Ning, Shukun Wen, Xianbin Zhang, Hua Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin 300191 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China

This paper proposes a new nonlinear approach for 3D motion estimation of a planar object. Based on the premise that the object is a plane or a kind of similar plane, we use LSM (Least Squares Method) to estimate the 3D motion parameters. Because the derived equations could not be solved deterministically, the steepest descent method and the annealing algorithm are used to find the solutions. Comparing to the 8-point-algorithm, one of the popular existing methods, our method needs much less corresponding feature points (at least 4 pairs are enough);comparing to the linear estimation methods, such as the homography matrix decomposition, our method needs much less information (only the coordinates of the corresponding feature points on 2 pieces of images) and the estimation result has no ambiguity problem. In the experiment, the result shows a high and stable accuracy. Copyright © 2010 Binary Information Press May, 2010.

关键词： Steepest descent method

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Extracting and Clustering the Evaluation Objects of the Chinese Product Recommendation system Based on the Opinion Mining

Extracting and Clustering the Evaluation Objects of the Chin...

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2019 3rd International Conference on Informatization in Education,Management and Business(IEMB 2019)

作者： Yingbin Xue Xiaoye Wang Yingyuan Xiao Yukun Li Wenguang Zheng Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education

The research of product recommendation system mainly focuses on the user s behavior or the commodities. contents, but rarely focuses on the commodies. reviews. This paper extracts useful information hidden in the commodies. reviews by opinion mining technology. It is more targeted that recommending product to users according to the user’s favorite property. The main process of opinion mining is the extraction of topic words and the polarity judgement of polar words. Because the time complexity of the topic extracting algorithm is high, this paper extracts the explicit evaluation object and evaluation words by using the method of matching noun phrase and then setting up a semantic mapping set of evaluation objects and evaluation words to determine the implicit evaluation object. In this paper, k-means and BIRCH are combined to cluster the evaluation objects. K-Means algorithm is used for pre-clustering for the BIRCH algorithm to solve local optimum. And the advantage of BIRCH is it can get the number of clusters by self-learning. And delete the clusters contained few contents to pruning evaluation objects. It can reduce the time complexity and guarantees the clustering effect.

关键词： Product recommendation opinion mining extraction of theme words clustering

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Ant Colony Algorithm for Multilevel Restricted Searching Area Based on Time Dependent Road Network Model

Ant Colony Algorithm for Multilevel Restricted Searching Are...

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Yunyun Du Hongyun Ning Zhixin Yang Yanxia Cui Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of EducationTianjin University of Technology

Aiming at the defect that the current time dependent road network model cannot fully reflect the information of the road attribute, and considering that the road weights of traffic congestion factors should be based on the characterization of travel time and the impedance function model of average speed relations, we put forward the improved road network model which is based on the edge cost analysis;Then we propose the ant colony optimization algorithm of a new hierarchical restricted search area and the corresponding dynamic switching strategy for traffic jams, when the search level can be dynamically adjusted by the road capacity of traffic congestion, we can achieve the purpose of improving the quality of route planning and avoiding the congested road. Our simulation experiment uses the scheme of random allocation speed value by speed fitting function, and provides the multipopulation ant colony algorithm based on layered restricted searching area is significantly better than others.

关键词： Ant colony algorithm Traffic network Time dependent network Vehicle navigation

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Research on Hepatitis Auxiliary Diagnosis Model Based on Fuzzy Integral and GA-BP Neural Network

Research on Hepatitis Auxiliary Diagnosis Model Based on Fuz...

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2017 IEEE 8th International Conference on Software Engineering and Service Science

作者： Chunyan Yang JinJie Song Tianjin Key Laboratory of Intelligent Computing & Novel Software Technology Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology

In this pa per, in order to impro ve the accuracy of hepatitis diagnosis in computer-aided diagnosis system, the medical data from the network public database were optimized by GA-BP neural network algorithm, and then we obtained the important index of judging the hepatitis and the weight of the index through thi s algorithm, we regarded the weight as the single point measure of non-additive, and then using the fuzzy integral calculated the proba bility of a person being diagnosed with hepatitis. From the results of calculation, using fuzzy integral can greatly improve the accuracy of hepatitis auxiliary diagnosis.

关键词： hepatitis auxiliary diagnosis GA-BP neural network fuzzy integral

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A Framework towards Task-based Query in Personal DataSpace

A Framework towards Task-based Query in Personal DataSpace

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The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)

作者： Yukun Li Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology 300384 Tianjin China

With increment of personal data amount, how to allow users to efficiently re-find personal data items becomes an important research issue. According to general experience of persons, task is taken as a popular way to classify personal dataset, and is often taken as a factor to re-access expected items. In this paper, we propose a framework called TaskSpace to help users re-find expected data items based on user task, and present conceptual model of TaskSpace, framework for implementing a task-based system, methods to identify task relationships and interface for users to perform task-based query. TaskSpace framework provides users an alternative way to re-find personal information, and illustrates some interesting research issues.

关键词： Personal DataSpace Task Framework

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Spike Sorting Based on Window-Gradient Feature

Spike Sorting Based on Window-Gradient Feature

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2018计算机科学与工程技术国际研讨会

作者： Min Dai Feng Jia Wen Yang Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

Spike sorting is difficult when there is high waveforms similarity between different spike or when there is a large number of superimposed spikes in the sample. A new sample optimization method is proposed in the paper, called window-gradient feature. Every spike waveform is segmented into successive fragments in terms of the width σ, and each segment is called a window. Then calculate the gradient change of each window and use the ratio as the new alternative feature of the window. Finally, all window-gradient features of the spike waveforms are used to replace the original waveform features for spike sporting. The method is verified on the simulation data at different SNR. The experimental results show that when using SVM for spike sorting, the optimization effect of the proposed method is better than PCA, especially for data sets data sets with high noise or large sampling waveform similarity.

关键词： SNR Spike Sorting Based on Window-Gradient Feature

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A new clustering classification approach based on FCR

A new clustering classification approach based on FCR

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： De-gan Zhang Weiwei Liu Xuejing Kang Ying Chen Wenbo Dai Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology China Tianjin University of Technology Tianjin CN

A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the sample is resemble in sample space, its fuzzy type will be resemble too. In our research, firstly, using triangle arithmetic operator and triangle transference, we extend fuzzy equivalence relationship to fuzzy closeness relationship, and cluster. Secondly, importing fuzzy coverage based on fuzzy closeness relationship, we judge resemble type of resemble sample. Thirdly, we introduce the clustering classification method based on fuzzy closeness relationship and fuzzy coverage. The method can overcome information more loss in fuzzy equivalence. Finally, we test its feasibility. The approach is applied to knowledge discovery for risk prediction of electronic commerce market, good result has been gotten.

关键词： Classification algorithms Clustering algorithms Electronic commerce Indexes Artificial neural networks Prediction algorithms Risk management

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