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第27届中国控制与决策会议

作者： Hongpeng Wang Pu Zhang Jingtai Liu Institute of Robotics and Automation Information System Nankai University State Key Laboratory of Robotics Tianjin Key Laboratory of Intelligent Robotics

ISBN: (纸本)9781479970186

In this paper,a salient region detection algorithm based on priors which estimate the likely position of background which we make it more accurate,is *** of considering the contrast between each element（pixel or region） and their surrounding regions,we consider background cues in different ways such as PCA and sparse *** the saliency value of each element（superpixel） is obtained by computing the similarity with background *** such a problem which the background template based on prior probably includes the interference of foreground,so we use graph-based manifold ranking method to eliminate the interference of foreground in background *** a detailed experimental evaluation on large benchmark database,the results shows the proposed algorithm perform well when compare with other state-of-the-art methods and can effectively eliminate the interference of foreground in initial background templates.

关键词： salient region detection background template superpixel PCA sparse representation graph-based manifold ranking

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A novel naive bayes classifier model based on differential evolution 11th

A novel naive bayes classifier model based on differential e...

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11th International Conference on intelligent Computing, ICIC 2015

作者： Li, Jun Fang, Guokang Li, Bo Wang, Chong College of Computer Science and Technology Wuhan University of Science and Technology WUST Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System WUST Wuhan430065 China State Key Laboratory of Software Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9783319221793

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called Differential Evolution-Naive Bayes (DE-NB) which takes advantage of combining differential evolution with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies DE firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Support Vector Machine (SVM) algorithm, Decision Tree (C4.5) algorithm, K-neighbor (KNN) algorithm, the proposed algorithm has higher classification accuracy. © Springer International Publishing Switzerland 2015.

关键词： Feature Selection

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Extended multi-structure local binary pattern for high-resolution image scene classification

Extended multi-structure local binary pattern for high-resol...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaoyong Bian Chen Chen Qian Du Yuxia Sheng Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Department of Electrical Engineering University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering Mississippi State University Mississippi State USA School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The anisotropic features derived from elliptical sampling are also rotation invariant by averaging the histograms over rotational angles and combined with the isotropic features extracted from circular sampling. Experimental results show that the proposed method can effectively capture local spatial pattern and local contrast, consistently outperforming several state-of-the-art classification algorithms.

关键词： Feature extraction Histograms Nickel Image classification Support vector machines Training Image resolution

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Stackelberg Game Model of Wind Farm and Electric Vehicle Battery Switch Station

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IOP Conference Series: Materials Science and Engineering 2017年第1期199卷

作者： Zhe Jiang Zhimin Li Wenbo Li Mingqiang Wang Mengxia Wang Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 China. Electric Power Research Institute State Grid Shandong Electric Power Company Jinan 250003 China. Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan 250061 China. Collaborative Innovation Center for Global Energy Interconnection (Shandong) Jinan 250061 China.

In this paper, a cooperation method between wind farm and Electric vehicle battery switch station (EVBSS) was proposed. In the pursuit of maximizing their own benefits, the cooperation between wind farm and EVBSS was formulated as a Stackelberg game model by treating them as decision makers in different status. As the leader, wind farm will determine the charging/discharging price to induce the charging and discharging behavior of EVBSS reasonably. Through peak load shifting, wind farm could increase its profits by selling more wind power to the power grid during time interval with a higher purchase price. As the follower, EVBSS will charge or discharge according to the price determined by wind farm. Through optimizing the charging /discharging strategy, EVBSS will try to charge with a lower price and discharge with a higher price in order to increase its profits. Since the possible charging /discharging strategy of EVBSS is known, the wind farm will take the strategy into consideration while deciding the charging /discharging price, and will adjust the price accordingly to increase its profits. The case study proved that the proposed cooperation method and model were feasible and effective.

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An LLE based heterogeneous metric learning for cross-media retrieval

An LLE based heterogeneous metric learning for cross-media r...

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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Zhou, Peng Du, Liang Fan, Mingyu Shen, Yi-Dong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Institute of Intelligent System and Decision Wenzhou University Zhejiang325035 China

ISBN: (纸本)9781510811522

With unstructured heterogeneous multimedia data such as texts, images being more and more widely used on the web, cross-media retrieval has become an increasingly important task. One of the key techniques in cross-media retrieval is how to compute distances or similarities among different types of media data. In this paper, we propose a novel heterogeneous metric learning method to compute distances between images and texts. We extend Locally Linear Embedding (LLE) to deal with heterogeneous data, so that we can not only preserve homogeneous local information but also capture heterogeneous constraints. In order to handle the out-of-sample problem, we learn two map functions from the embedding, and use them to transform heterogeneous data into a homogeneous space and do the retrieval in the new space. The experimental results on two real-world datasets show the effectiveness of our approach. Copyright © SIAM.

关键词： Data mining

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RS-CPS: A Distributed Architecture of robotic Surveillance Cyber-Physical system in the Nature Environment

RS-CPS: A Distributed Architecture of Robotic Surveillance C...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent systems

作者： Hongpeng Wang Jingtai Liu Jianda Han Institute of Robotic and Automatic Information System. Nankai University State Key Laboratory of Robotics (Shenyang Institute of Automation Chinese Academy of Sciences)

ISBN: (纸本)9781479987313

Modern nature surveillance systems are highly automated and require distributed architectures in order to increase understanding of the environment by integrating data from various monitors and sensors, which are mounted not only on fixed stations, but also on robotic actuators. In this paper, we propose a distributed architecture of robotic surveillance cyber-physical system (RS-CPS) in the nature environment, which is designed as integration of computation and physical processes. The RS-CPS system consists of five levels including smart connection, data-to-information conversion, cyber communication, cognition, and configuration. Based on this architecture, the nature risk such as wildfires, the activities of wild animals and the climate changes in plants can be constantly monitored by the cooperation of multiple agents such as autonomous UAVs, robotic pan-tilt-zoom cameras, forest infrared cameras, songbird acoustic recorders, GPS tracking collars and ecological factor sensors located in field. The experiments demonstrate the feasibility and effectiveness of the new architecture.

关键词： ARCHITECTURE Fixed platforms Environments Building construction new buildings MONITOR Distribute Sensors Wild Animals Tracking collars

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Environmental Sensing: Designing an Efficient Multimode Environmental Sensor Based on Graphene–Silicon Heterojunction (Adv. Mater. Technol. 4/2017)

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Advanced Materials Technologies 2017年第4期2卷

作者： Khurram Shehzad Tianjin Shi Akeel Qadir Xia Wan Hongwei Guo Ayaz Ali Weipeng Xuan Hua Xu Zhongze Gu Xinsheng Peng Jin Xie Litao Sun Qiyuan He Zhen Xu Chao Gao You-Seung Rim Yaping Dan Tawfique Hasan Pingheng Tan Erping Li Wenyan Yin Zhiyuan Cheng Bin Yu Yang Xu Jikui Luo Xiangfeng Duan Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering and State Key Laboratory of Silicon Materials Zhejiang University Hangzhou 310027 China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou 310052 China State Key Laboratory of Bioelectronics Southeast University 210096 Nanjing China College of Material Science Zhejiang University Hangzhou 310027 China State Key Laboratory of Fluid Power & Mechatronic systems Zhejiang University Hangzhou 310027 China SEU-FEI Nano-Pico Center Key Laboratory of MEMS of the Ministry of Education Southeast University Nanjing 210096 China Department of Chemistry and Biochemistry University of California Los Angeles CA 90095 USA MOE Key Laboratory of Macromolecular Synthesis and Functionalization Zhejiang University Hangzhou 310027 China School of Intelligent Mechatronic Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 05006 Republic of Korea Joint Institute of University of Michigan and Shanghai Jiaotong University 200240 Shanghai China Cambridge Graphene Centre University of Cambridge Cambridge CB3 0FA UK State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 China College of Nanoscale Science and Engineering State University of New York NY 12203 USA Institute of Renewable Energy & Environment Technologies University of Bolton Bolton BL3 5AB UK

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Robust multiple kernel K-means using 2,1-Norm 24

Robust multiple kernel K-means using 2,1-Norm

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24th International Joint Conference on Artificial Intelligence, IJCAI 2015

作者： Du, Liang Zhou, Peng Shi, Lei Wang, Hanmo Fan, Mingyu Wang, Wenjian Shen, Yi-Dong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer and Information Technology Shanxi University China University of Chinese Academy of Sciences China Institute of Intelligent System and Decision Wenzhou University China

ISBN: (纸本)9781577357384

The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the non-linear structure and identify arbitrarily shaped clusters. Since both the standard k-means and kernel k-means apply the squared error to measure the distances between data points and cluster centers, a few outliers will cause large errors and dominate the objection function. Besides, the performance of kernel method is largely determined by the choice of kernel. Unfortunately, the most suitable kernel for a particular task is often unknown in advance. In this paper, we first present a robust k-means using 2,1-norm in the feature space and then extend it to the kernel space. To recap the powerfulness of kernel methods, we further propose a novel robust multiple kernel k-means (RMKKM) algorithm that simultaneously finds the best clustering label, the cluster membership and the optimal combination of multiple kernels. An alternating iterative schema is developed to find the optimal value. Extensive experiments well demonstrate the effectiveness of the proposed algorithms.

关键词： Iterative methods

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Cover Feature: Ultrasensitive, Sustainable, and Selective Electrochemical Hydrazine Detection by ALD-Developed Two-Dimensional WO3(ChemElectroChem 2/2018)

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ChemElectroChem 2017年第2期5卷

作者： Zihan Wei Zhenyin Hai Mohammad Karbalaei Akbari Prof. Jie Hu Lachlan Hyde Prof. Stephen Depuydt Prof. Francis Verpoort Prof. Serge Zhuiykov Ghent University Global Campus Department of Applied Analytical & Physical Chemistry 119 Songdomunhwa-ro Yeonsu-gu Incheon South Korea Micro and Nano System Research Center Key Lab of Advanced Transducers and Intelligent Control System (Ministry of Education) & College of Information Engineering Taiyuan University of Technology Taiyuan 030024 Shanxi PR China Factory of the Future Swinburne University of Technology Hawthorn Australia National Research Tomsk Polytechnic University Lenin Avenue 30 634050 Tomsk Russian Federation Laboratory of Organometallics Catalysis and Ordered Materials State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Chemical and Material Engineering Wuhan University of Technology Wuhan P.R. China

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Design, modeling and control of a novel amphibious robot with dual-swing-legs propulsion mechanism

Design, modeling and control of a novel amphibious robot wit...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Yang Yi Zhou Geng Zhang Jianqing Cheng Siyuan Fu Mengyin School of Automation and State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

This paper describes a novel amphibious robot, which adopts a dual-swing-legs propulsion mechanism, proposing a new locomotion mode. The robot is called FroBot, since its structure and locomotion are similar to frogs. Our inspiration comes from the frog scooter and breaststroke. Based on its swing leg mechanism, an unusual universal wheel structure is used to generate propulsion on land, while a pair of flexible caudal fins functions like the foot flippers of a frog to generate similar propulsion underwater. On the basis of the prototype design and the dynamic model of the robot, some locomotion control simulations and experiments were conducted for the purpose of adjusting the parameters that affect the propulsion of the robot. Finally, a series of underwater experiments were performed to verify the design feasibility of FroBot and the rationality of the control algorithm.

关键词： Wheels Legged locomotion Propulsion DC motors Force

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