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Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network System

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Defence technology（防务技术） 2009年第2期5卷 152-157页

作者：杨晨光涂序彦陈杰 Systems Engineering Research Institute China State Shipbuilding Corporation Department of Automation School of Information Science and Technology Beijing Institute of Technology Ministry of Education of Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are *** calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly *** such method,we can avoid the inconformity of the description obtained from the traditional index *** new indexes are also proposed,*** index,overlap index and cover index,which help manage the relationship among several *** normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well ***,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement *** is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

关键词：网络系统优化问题破坏概率算法改进核武器蜜蜂婚姻 Sigmoid函数

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Sentiment Classification for Chinese Reviews Based on key Substring Features

Sentiment Classification for Chinese Reviews Based on Key Su...

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International Conference on Natural Language Processing and Knowledge Engineering(IEEE自然语言处理与知识工程国际会议 IEEE NLP-KE 2009)

作者： Zhongwu ZHAI Hua XU Jun LI Peifa JIA State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology CS&T Department Tsinghua University Beijing 100084 China P.R.

One of the most widely-studied sub-problems of opinion mining is sentiment classification, which classifies evaluative texts as positive or negative to help people automatically identify the viewpoints underlying the online user-generated information. Most of the existing methods for sentiment classification ignore word sequence and unlabeled test documents’ structural information. This paper proposes a transductive learning based algorithm considering both of these two types of information. The proposed algorithm is implemented by firstly selecting key substrings in the suffix tree constructed from the strings in training and unlabeled test documents and then converting each original text document to a bag of numbers of the key substrings. Finally, SVM is employed to classify the converted documents. Experiments on the open dataset (16,000 Chinese reviews) demonstrate promising performance of the proposed algorithm, the accuracy being over 93.15%, which is much better than the performance ofthe existing sentiment classification methods, such as n-gram features based classification algorithms. Experimental results also show that “tfidf-c” performs much better than other term weighting approaches in sentiment classification for large text corpus. In particular, the reasons behind the proposed algorithm’s outstanding performance are further studied and analyzed in this paper. Moreover, the proposed algorithm can avoid the messy and rather artificial problem of defining word boundaries in Chinese language.

关键词： Sentiment Classification Substring Transductive Learning Opinion Mining

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StatSnowball: A statistical approach to extracting entity relationships 09

StatSnowball: A statistical approach to extracting entity re...

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18th International World Wide Web Conference, WWW 2009

作者： Zhu, Jun Nie, Zaiqing Liu, Xiaojing Zhang, Bo Wen, Ji-Rong Dept. of Comp. Sci. and Tech. Tsinghua University Beijing 100084 China Microsoft Research Asia No. 49 Zhichun Road Beijing 100080 China Dept. of EEIS University of Sci. and Tech. of China Hefei 230027 China State Key Laboratory of Intelligent Technology and Systems China Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781605584874

Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Further-more, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic networks (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an 1-norm penalized maximum likelihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

关键词： Search engines

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A Structural Health Monitoring System Using Wireless Sensor Network

A Structural Health Monitoring System Using Wireless Sensor ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Jianjun Niu Zhidong Deng Fengguang Zhou Zongsheng Cao Zhanqing Liu Fangzhou Zhu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University China Beijing Special Engineering Design and Research Institute China

Wireless sensor network (WSN) based structural health monitoring (SHM) system attracts increasing interest in recent years. However, the restricted bandwidth, memory and energy pose great challenges to the application of SHM in large scale building. Supported by a real engineering project, a WSN based prototype of SHM system is designed in this paper. Acceleration data, synchronously sampled in each sensor node, are transported to a data processing computer through a base station. In order to achieve a high network throughput, a time division multiple access (TDMA) approach is proposed to reduce the packet collision and energy consumption. 17 sensor nodes, one base station and a processing computer are distributed to validate the prototype system. Test results present that the approach, we proposed, can reduce the signal collision and increase data throughput. The prototype is feasible to the SHM project of a large scale building.

关键词： Monitoring Wireless sensor networks Prototypes Large-scale systems Base stations Throughput Time division multiple access Bandwidth Application software Buildings

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Pedestrian Detection Fusion Method Based on Mean Shift

Pedestrian Detection Fusion Method Based on Mean Shift

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International Conference on Machine Vision (ICMV)

作者： Liping Yu Wentao Yao College of Computer Science and Technology Shandong Institute of Business and Technology Yantai China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science & Technology Tsinghua University Beijing China

Detecting pedestrians is a challenging task, which requires precise localization of pedestrians that appear in images and videos. Window-scanning based detection methods have demonstrated their promise by scanning the image densely with multi-scale detection window. However, an essential and critical issue, i.e., how to fuse these dense detections obtained through pedestrian detector and yield the final target detection, is not well addressed in the literature. This paper proposes and implements a general method for fusing pedestrian detections. In this method, detection fusion is regarded as a kernel density estimate and implemented through mean shift iterative procedure. Moreover, the notion of nearest neighbor consistency is adopted, which significantly accelerates the fusion procedure. Experimental results demonstrate the efficiency of the mean shift-based fusion method.

关键词： Phase detection Fuses Nearest neighbor searches Support vector machines Support vector machine classification Computer science Detectors Object detection Shape Humans

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Efficient multi-label classification with hypergraph regularization

Efficient multi-label classification with hypergraph regular...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gang Chen Jianwen Zhang Fei Wang Changshui Zhang Yuli Gao State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology(TNList)Department of Automation Tsinghua University Beijing China Hewlett Packard Laboratories Palo Alto CA USA

Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regularization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and MediaMill demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Image classification Laboratories Indexing Optimization methods Computer vision Application software Support vector machines Humans Classification algorithms intelligent systems

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Unsupervised Maximum Margin Feature Selection with manifold regularization

Unsupervised Maximum Margin Feature Selection with manifold ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Bin Zhao James Kwok Fei Wang Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering HKUST Hong Kong China

Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature selection remains as a rarely touched research topic. In this paper, we propose manifold-based maximum margin feature selection (M3FS) to select the most discriminative features for clustering. M3FS targets to find those features that would result in the maximal separation of different clusters and incorporates manifold information by enforcing smoothness constraint on the clustering function. Specifically, we define scale factor for each feature to measure its relevance to clustering, and irrelevant features are identified by assigning zero weights. Feature selection is then achieved by the sparsity constraints on scale factors. Computationally, M3FS is formulated as an integer programming problem and we propose a cutting plane algorithm to efficiently solve it. Experimental results on both toy and real-world data sets demonstrate its effectiveness.

关键词： Filters Pattern recognition Clustering algorithms Face recognition Laplace equations Laboratories Linear programming Feature extraction Unsupervised learning Supervised learning

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A New Data Streaming Method for Locating Hosts with Large Connection Degree

A New Data Streaming Method for Locating Hosts with Large Co...

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GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

作者： Xiaohong Guan Pinghui Wang Tao Qin MOE Key Laboratory of Intelligent Networks and Network Security State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Xi'an China Department of Automation Tsinghua National Laboratory of Information Science and Technology Tsinghua University Beijing China

Locating hosts with large connection degree is very important for monitoring anomalous network traffics. The in-degree (out-degree), defined as the number of distinct sources (destinations) that a network host is connected with (connects) during a given time interval. Due to massive amount of data in high speed network traffics and limit on processing capability, it is difficult to accurately locate hosts with large connection degree over high speed links on line. In this paper we present a new data streaming method for locating hosts with large connection degree based on the reversible connection degree sketch to monitor anomalous network traffics. The required memory space is small and constant, and more importantly the update/query complexity would not depend on the amount of data. The hash functions for data sketch are designed based on the remainder characteristics of the number theory so that in-degree/out-degree associated with a given host can be accurately estimated. Although the connection degree sketch does not preserve any host address information, we can analytically reconstruct the host addresses associated with large in-degree/out-degree by a simply equation purely based on the characteristics of the hash functions without using any host address information. This procedure is highly efficient since the computational time is constant and ignorable. Furthermore, this reversible connection degree sketch based method can be easily implemented in distributed systems. The experimental and testing results based on the actual network traffics show that the new method is truly accurate and efficient.

关键词： Telecommunication traffic Computer crime Intrusion detection Statistical distributions Velocity measurement Sampling methods Condition monitoring Failure analysis Scalability Statistics

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Equivalence Knowledge Cumularspharolith and Multiple Approximate Reasoning in R - Logic CR

Equivalence Knowledge Cumularspharolith and Multiple Approxi...

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Computer Science and Information Engineering, CSIE, WRI World Congress on FLAGGED

作者： Yalin Zheng Guang Yang State Key Laboratory of Intelligent Technology and Systems Department of Automation Faculty of Information Science and Technology Tsinghua University Beijing China Research Center of Intelligent Information Technology Department of Computer Science and Technology Faculty of Software Dongguan University of Science and Technology Dongguan China

By casting off the direct restriction of topological structure, this paper presents another matching scheme between the input A * and the knowledge A rarr B based on the equivalence relation R on formulae set F(S) and the corresponding equivalence classification F(S)/R = { [A] R | A isin F(S)} therefore, obtains another algorithm of approximate reasoning - the IV-type R-algorithm. The paper presents the IV-type R-algorithm for the simple approximate reasoning model and the multiple approximate reasoning model in the frame of R-logic C R .

关键词： Logic Chromium Computer science intelligent structures Knowledge engineering Information technology Laboratories intelligent systems Automation Information science

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RESEARCH ON AUTONOMOUS CONTROL SYSTEM STRUCTURE OF UCAV BASED ON COGNITIVE SCIENCE

RESEARCH ON AUTONOMOUS CONTROL SYSTEM STRUCTURE OF UCAV BASE...

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2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

作者： Jihong Zhu Xianyu Meng Guanglin Li Xuyan Tu State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing College Electronics & Informatics Engineering Liaoning University Of Technology Jinzhou Information Engineering School University of Science and Technology Beijing Beijing

ISBN: (纸本)9781424448999

The unmanned combat aerial vehicle(UCAV) is a intelligent weapon. Based on artificial brain theory, its autonomous control system was divided into three layers which are perception fusion layer, cognition and decision layer, and behavior control layer. In perception fusion layer, information was transmitted from multi-sensors system to relative system and was fused ultimately. In cognition and decision layer, a cognitive model was designed based on three-phase memory mechanisms which were ultra short-time memory and long-time memory. In behavior control layer, planning function was added that increased behavior intelligence. The flight control was simulated using dynamic inversion method. And the result showed the design of execution sub-system was reasonable through tracking the mobile trajectory.

关键词： unmanned combat aerial vehicle(UCAV) autonomous control artificial brain cognitive science

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