检索结果-内蒙古大学图书馆

International Conference on Identification, information and Knowledge in the Internet of Things (IIKI)

作者： Guangyu Zhu Kang Song Peng Zhang Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China MOE Key Laboratory for Transportation Complex Systems- Theory and Technology Beijing Jiaotong University Beijing China Beijing Municipality Key Laboratory of Urban Traffic Operation Simulation and Decision Support Beijing China

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. In this paper, a travel time analysis and prediction model was established for urban road traffic sensors data based on the change point analysis algorithm and ARIMA model. Firstly, time series of travel time parameters were clustered by using change point mining algorithm after traffic sensors data preprocessing. Then, a travel time prediction model was established based on ARIMA model. Finally, the model was verified with high accuracy through simulation by using multiple sets of data and analysis of its practicability was done.

关键词： Predictive models Data models Roads Time series analysis Detectors Analytical models

来源：评论

学校读者我要写书评

暂无评论

A novel GIS platform for UGV application in the unknown environment

A novel GIS platform for UGV application in the unknown envi...

引用

International Conference on Geoinformatics

作者： Qiangrong Yang Meiling Wang Mei-po Kwan Yi Yang Key Laboratory of Intelligent Control and Decision of Complex System Beijing China School of Automation Beijing institute of Technology Beijing China Department of Geography and Geographic Information Science University of Illinois at Urbana Champaign Champaign IL USA

ISBN: (纸本)9781467376648

Recently, the UGV (Unmanned Ground Vehicle) receives more and more attention from the public with the application in deep space exploration, national defense, and riot relief. Moreover, how to describe the driving environment of UGV with the sensor data objectively and share these data with other UGV efficiently becomes the focus of the research of the UGV. To solve the above problems, a novel GIS platform for UGV application in the unknown environment is proposed in this paper based on the features of UGV. First, the GIS database is redesigned and the properties such as road width, lane number, lane info, and if_traffic are proposed. The data structure of the database is redesigned to fit the need of UGV to describe the environment simply and explicitly. Then a two-line road model is proposed to model the feature of the road network clearly. This model can meet the driving need of UGV and represent the traffic rules of the urban environment. Moreover, the turning path in the intersection is designed to be an arc instead of a right angle path considering the dynamic features of UGV. Finally a cost map for navigating the UGV locally is generated with the road model and the environment information from the sensors. The GIS platform designed in this paper proves to be able to offer guidance and help to UGV for driving through the unknown environment wisely and quickly without human intervention. And it shows promising application of sharing data with other UGV efficiently by using cost map instead of the original sensor data.

关键词： Navigation Vehicles Robots

来源：评论

学校读者我要写书评

暂无评论

Distributed Estimation for Radio Environment Map in Cognitive Radio Networks 33

Distributed Estimation for Radio Environment Map in Cognitiv...

引用

第三十三届中国控制会议

作者： LIU Jianzhi HAN Hui CHEN Cailian LI Li'an GUAN Xinping The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9789881563842

Radio Environment Map(REM)is a map that indicates the radio signal strength(RSS)over a finite geographical *** enables cognitive radio(CR)users to opportunistically access to the spectrum unoccupied by the primary users(PUs)who are licensed to use specific *** this paper,a basis expansion based scheme is presented to estimate *** take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares(RLS)problem with an l1norm constraint.A distributed algorithm is presented to solve the RLS *** SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal ***,simulation studies are presented to validate the performance of the proposed method.

关键词： Cognitive radio,Basis expansion,Cooperative spectrum sensing,Distributed estimation

来源：评论

学校读者我要写书评

暂无评论

Self-triggered Based Semi-global Consensus Tracking of Multi-agent systems with Input Saturation

Self-triggered Based Semi-global Consensus Tracking of Multi...

引用

第三十四届中国控制会议

作者： Xiaoling Wang Housheng Su Jiwei Li Xiaofan Wang Department of Automation Shanghai Jiaotong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 P. R. China

ISBN: (纸本)9781467374439

Self-triggered control is a technique which can determine the next sampling instant by virtue of the last sampling information. Combining this technique and the low-gain feedback one, we investigate the consensus tracking of general linear multi-agent systems with input saturation and updating delays in this paper. Specific formulations of heterogenous updating delays and heterogenous sampling periods are acquired to guarantee the semi-global consensus tracking of multi-agent systems. Numerical simulations are provided to verify the main results.

关键词： Multi-agent systems Consensus Tracking Self-triggered Technique Input Saturation Low-gain Feedback Multi-agent systems sampling time tracking

来源：评论

学校读者我要写书评

暂无评论

Improved low energy adaptive clustering hierarchy protocol based on local centroid bat algorithm

引用

Sensor Letters 2014年第9期12卷 1372-1377页

作者： Cao, Yang Cui, Zhihua Li, Feixiang Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Bat algorithm (BA) is a novel intelligent optimization algorithm. In order to improve the poor local search capability of the algorithm, we present a new variant of bat optimization algorithm based on local centroid strategy (LCBA). Meanwhile, LEACH is a low energy adaptive clustering hierarchy algorithm for wireless sensor networks (WSN). And it has many disadvantages such as random selection of cluster head, taking no account of the remaining energy and position of nodes. To solve these problems, this paper proposes an improved LEACH protocol based on local centroid bat algorithm. The improved protocol divides the cluster head selection process into optimization of temporary cluster head and formal cluster head selection. First, we generate temporary cluster heads by traditional LEACH protocol, then optimize these cluster heads based on LCBA and select formal cluster heads according to the remaining energy of nodes. The result of experiment shows that, comparing with LEACH, LEACH-LCBA can balance the network load efficiently, improve the energy utilization, prolong the network lifetime.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Using artificial plant optimization algorithm with dynamic population and cluster methods to optimize the performance of DV-Hop

引用

Sensor Letters 2014年第8期12卷 1313-1317页

作者： Wu, Xuexue Cui, Zhihua Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

引用

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

来源：评论

学校读者我要写书评

暂无评论

Learning to Rank for Blind Image Quality Assessment

引用

IEEE Transactions on Neural Networks and Learning systems 2015年第10期26卷 2275-2290页

作者： Gao, Fei Tao, Dacheng Gao, Xinbo Li, Xuelong Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an710071 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology 235 Jones Street Sydney UltimoNSW2007 Australia State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China State Key Laboratory of Transient Optics and Photonics Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an Shaanxi710119 China

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image Iais better than that of image Ibfor training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity;such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories. © 2012 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Flocking of partially-informed multi-agent systems avoiding obstacles with arbitrary shape

引用

Autonomous Agents and Multi-Agent systems 2014年第5期29卷 943-972页

作者： Li, Jiaojie Zhang, Wei Su, Housheng Yang, Yupu Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Measurement and Control Technology Shanghai Dian Ji University Shanghai China School of Automation Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan China

In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allowed, no matter if their boundary is smooth or non-smooth, and no matter it they are convex or non-convex. A novel geometry representation rule is proposed to transfer obstacles to a dense obstacle-agents lattice structure. Non-convex regions of the obstacles are detected and supplemented using a geometric rule. The uninformed agents can detect a section of the obstacles boundary using only a range position sensor. We prove that with the proposed protocol, uninformed agents which maintain a joint path with any informed agent can avoid obstacles that move uniformly and assemble around a point along with the informed agents. Eventually all the assembled agents reach consensus on their velocity. In the entire flocking process, no distinct pair of agents collide with each other, nor collide with obstacles. The assembled agents are guaranteed not to be lost in any non-convex region of the obstacles within a distance constraint. Numerical simulations demonstrate the flocking algorithm with obstacle avoidance both in 2D and 3D space. The situation when every agent is informed is considered as a special case.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A secure distributed consensus scheme for wireless sensor networks against data falsification

A secure distributed consensus scheme for wireless sensor ne...

引用

World Congress on intelligent control and Automation (WCICA)

作者： Shichao Mi Hui Han Shanying Zhu Cailian Chen Bo Yang Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Shanghai P. R. China The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE) Luoyang P. R. China

This paper focuses on the problem of secure distributed consensus to defend covert misbehavior in wireless sensor networks (WSNs). Distributed consensus is a promising method to improve the efficiency and precision of consensus results in WSNs, but it introduces new security issues that malicious nodes may manipulate false sensing data to degrade the sensing result of the whole network. A data falsification attack, i.e., the attacker injects random values into its neighboring nodes at each time-step of consensus process, is considered. This kind of attack cannot be defended against by most of existing detection mechanisms. We present a distributed detection mechanism with adaptive local threshold to isolate the abnormal nodes. A Weighted Averaging-based Consensus Scheme (WACS) is proposed to decrease the negative impact of the attack and make the network converge to a consensus value. It is proved that convergence property can be guaranteed by the relationship between weighted average of the noise and stochastic approximation. Simulation results are presented to show the effectiveness of the proposed secure scheme.

关键词： Sensors Wireless sensor networks Detection algorithms Distributed databases Convergence Vectors Approximation methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：