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The nonlinear system identification of rolling force model in the production of hot strip rolling

Journal of Information and Computational science 2015年第16期12卷 5977-5985页

作者： Xie, Yonghong Lu, Na Fan, Linlin Hu, Xiaojing Wang, Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Materials Science Knowledge Engineering Beijing China

Rolling force is one of the most important parameters during the process of strip steel hot continuous rolling, since rolling force model is a factor that directly affects the controlling accuracy of strip steel's thickness. The more accurate the rolling force model is, the better the need for production development gets. In this paper, firstly, a hybrid algorithm of the Least Squares Support Vector Regression machine (LS-SVR) and Particle Swarm Optimization (PSO) algorithm is proposed. Then the parameters that influencing the rolling force model can be identified by utilizing measured data, which, got during finishing rolling process. Finally, these parameters have to be optimized and the optimal solution is obtained. By this means, we successfully solve the problem of the identification of rolling force model. Then we also solve the problems in actual production. © 2015 by Binary Information Press

关键词： Nonlinear systems

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GENOUD-BP: A novel training algorithm for artificial neural networks

GENOUD-BP: A novel training algorithm for artificial neural ...

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2015 5th International Conference on Information engineering for Mechanics and Materials(ICIMM 2015)

作者： Zhiyong YANG Shiyuan ZHANG Taohong ZHANG School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Automation & Electrical Engineering Beijing University of Posts &Tel

BP Algorithm, as a well-known training method for artificial neural network, has been widely used in all the main fields of science and engineering. However, owing to the overwhelming dependency on gradient of loss function, BP-ANN still suffers several drawbacks, for example, the training process is prone to stuck at local optima, cause early convergence while the whole process is lack of generalization performance. Aimed at improving the current BP training Algorithm, a new algorithm called GENOUD-BP is proposed in this paper by introducing the GENOUD algorithm which combines the global searching power of genetic algorithms and convergence speed of traditional gradient based optimization algorithms. Two UCI datasets are employed to carry out benchmark experiments, the result of which shows that the GENOUD-BP significantly outperforms traditional BP algorithms.

关键词： neural networks optimization evolutionary algorithms GENOUD BP

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Formal Modeling of Testing Software for Cyber-Physical automation Systems

Formal Modeling of Testing Software for Cyber-Physical Autom...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Igor Buzhinsky Cheng Pang Valeriy Vyatkin Computer Technologies Laboratory ITMO University Russia Department of Electrical Engineering and Automation Aalto University Finland Department of Computer Science Luleå University of Technology Sweden

ISBN: (纸本)9781467379533

The paper presents a framework which uses formal models for testing control software for industrial automation systems. The formalism called Net Condition/Event Systems (NCES) is applied to model the program under test, along with the system under control (plant) and the testing environment. The benefits of using the framework include the opportunities to test systems with time delays without the need to wait, to test parameterized sets of systems with a single execution of a test suite, and to check test suites for correctness. The use of the framework is illustrated on a simple system consisting of a lab-scale plant and a control application for it.

关键词： Testing Software IEC Standards automation Delays Computational modeling Formal languages

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Integration of Electric Vehicles Charging in Unit Commitment

Integration of Electric Vehicles Charging in Unit Commitment

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3rd International Multi-Conference on HUmanities, Law, Literature and Management (IMHLLM’15);3rd International Multi-Conference on Agriculture, Ecological and Medical sciences (IMAEMS’15);3rd International Multi-Conference on Research and Innovations in engineering & Technology (IMRIET’15);2015 International Conference on Innovative Computational Technologies and Industrial engineering (ICTIE-2015)

作者： Letian Zhang Qun Niu Zhile Yang Kang Li School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai University the School of Electrical Electronics and Computer Science Queen’s university Belfast

Integration of electric vehicles(EVs) to grid may encounter several challenges such as high power consumption and uncertain charging time which impose significant impact on the operation of power systems. In this paper, two charging modes of EVs are incorporated into the power load demand of the power system. Then, a model of unit commitment(UC) with charging EVs(UCEV) is established, and two different genetic algorithms(GAs) are employed for solving this problem. The first GA method uses hybrid coding methods with the binary(0/1) variables and continuous real variables(GAH). The second GA only applies the decimals from 0 to 1 for representing the UCEV problem solutions(GAD). The results are analyzed in detail to assess the impact of charging EVs on UC and demonstrate that GAD is practicable for UCEV problem when compared with GAH.

关键词： Electric vehicle,Unit commitment,Genetic algorithm

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SHREC'15 track: 3D object retrieval with multimodal views 8

SHREC'15 track: 3D object retrieval with multimodal views

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8th Eurographics Workshop on 3D Object Retrieval, 3DOR 2015

作者： Gao, Yue Liu, Anan Nie, Weizhi Su, Yuting Dai, Qionghai Chen, Fuhai Chen, Yingying Cheng, Yanhua Dong, Shuilong Duan, Xingyue Fu, Jianlong Gao, Zan Guo, Haiyun Guo, Xin Huang, Kaiqi Ji, Rongrong Jiang, Yingfeng Li, Haisheng Lu, Hanqing Song, Jianming Sun, Jing Tan, Tieniu Wang, Jinqiao Yin, Huanpu Zhang, Chaoli Zhang, Guotai Zhang, Yan Zhao, Chaoyang Zhao, Xin Zhu, Guibo School of Medicine University of North Carolina Chapel Hill United States School of Electronic Information Engineering Tianjin University China Department of Automation Tsinghua University China School of Information Science and Engineering Xiamen University China Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China College of Computing and Digital Media DePaul University United States School of Computing Tianjin University of Technology China School of Computer Science and Information Engineering Beijing Technology and Business University China

ISBN: (纸本)9783905674781

This paper reports the results of the SHREC'15 track: 3D Object Retrieval with Multimodal Views, which goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 505 objects is generated and both the color images and the depth images are provided for each object. 311 objects are selected as the queries and average retrieval performance is measured. The track attracted six participants and the submission of 26 runs, to two tasks. The evaluation results show a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data. © The Eurographics Association 2015.

关键词： Information retrieval

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3D garment segmentation based on semi-supervised learning method

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 657-665页

作者： Huang, Mian Liu, Li Liu, Ruomei Fu, Xiaodong Liu, Lijun Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

In this paper, we propose a semi-supervised learning method to simultaneous segmentation and labeling of parts in 3D garments. The key idea in this work is to analyze 3D garments using semi-supervised learning method which can label parts in various 3D garments. We first develop an objective function based on Conditional Random Field (CRF) model to learn the prior knowledge of garment components from a set of training examples. Then, we exploit an effective training method that utilizes JointBoost classifiers based on the co-analysis for garments. And we modify the JointBoost to automatically cluster the segmented components without requiring manual parameter tuning. The purpose of our method is to relieve the manual segmentation and labeling of components in 3D garment collections. Finally, the experimental results show the performance of our proposed method is effective. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Random processes

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A survey of major techniques of clothing in vision

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 725-732页

作者： Huang, Dongyan Liu, Li Fu, Xiaodong Liu, Lijun Huang, Qingsong Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

Clothing in vision plays an important role in many applications of digital clothing, virtual fitting, fashion design and manufacturing. The research of clothing in vision is full of technical challenges, such as background detection and features extraction. In this paper, we introduce the latest remarkable works of clothing in vision area, including clothing segmentation, clothing recognition and clothing retrieval. There are many remarkable advances on the basis of the predecessors' achievements in the three fields. Firstly, we provide an overview of the techniques for clothing segmentation and clothing recognition. Then, we introduce and analyze the representative works on clothing retrieval. Finally, we forecast broader application prospect and future research work of clothing in vision. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Textiles

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Distance-based formation shape stabilisation via event-triggered control

Distance-based formation shape stabilisation via event-trigg...

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第三十四届中国控制会议

作者： Qingchen Liu Zhiyong Sun Jiahu Qin Changbin Yu Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Research School of Engineering The Australian National University Department of Automation University of Science and Technology of China

ISBN: (纸本)9781467374439

Existing results on distance-based rigid formation stabilization commonly require continuous measurements and control update. In this paper, we consider an event-triggered scheme for distance-based formation control problem. We prove the local exponential stability of this event-driven system for stabilizing formations which are infinitesimally and minimally rigid. Furthermore, we prove that Zeno-behavior does not exist and obtain a strict positive lower bound in the inter-event *** are also provided to verify the proposed controller.

关键词： Shape stabilization,event-triggered,graph rigidity

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Predicate-argument relevance model for chinese-to-naxi SMT

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Journal of Computational Information Systems 2015年第13期11卷 4857-4861页

作者： Cheng, Li Yu, Zhengtao Guo, Jianyi Gao, Shengxiang Yang, Xiuzhen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Intelligent Information Processing Computer Technology Application Key Laboratory of Yunnan Province Kunming China

Aiming at the Naxi characteristics of verb post and attributive post, this paper presents a method based on predicate-argument relevance model for Chinese-to-Naxi statistical machine translation (SMT) tasks. We first parsed all source sentences using the Berkeley Chinese parser for Chinese-to-Naxi word alignment sentences and ran the Chinese semantic role labeler on all source parse trees to annotate semantic roles and obtain relevance between predicates and arguments. Then, we project semantic roles from the source side to the target side through word alignments and train the predicate-argument relevance model according to predicate-argument structure both sides. Finally, the proposed model is integrated into loglinear model to generate candidate sentences which are appropriate for Naxi grammar. Experimental results demonstrate that the proposed model significantly improves the translation accuracy. ©, 2015, Journal of Computational Information Systems. All right reserved.

关键词： Semantics

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Impact of Electric Vehicles on a Carbon Constrained Power System——A Post 2020 Case Study

Impact of Electric Vehicles on a Carbon Constrained Power Sy...

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The 7th Asia-Pacific Power and Energy engineering Conference (APPEEC 2015)

作者： Zhebin Sun Kang Li Zhile Yang Qun Niu Aoife Foley School of Electronics Electrical Engineering and Computer ScienceQueen’s University Belfast School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation TechnologyShanghai University School of Mechanical and Aerospace Engineering Queen’s University Belfast

Electric vehicles(EVs) offer great potential to move from fossil fuel dependency in transport once some of the technical barriers related to battery reliability and grid integration are resolved. The European Union has set a target to achieve a 10% reduction in greenhouse gas emissions by 2020 relative to 2005 levels. This target is binding in all the European Union member states. If electric vehicle issues are overcome then the challenge is to use as much renewable energy as possible to achieve this target. In this paper, the impacts of electric vehicle charged in the all-Ireland single wholesale electricity market after the 2020 deadline passes is investigated using a power system dispatch model. For the purpose of this work it is assumed that a 10% electric vehicle target in the Republic of Ireland is not achieved, but instead 8% is reached by 2025 considering the slow mar-ket uptake of electric vehicles. Our experimental study shows that the increasing penetration of EVs could contribute to approach the target of the EU and Ireland government on emissions re-duction, regardless of different charging scenarios. Furthermore, among various charging scena-rios, the off-peak charging is the best approach, contributing 2.07% to the target of 10% reduction of Greenhouse gas emissions by 2025.

关键词： Carbon Emissions Electric Vehicles Power System PLEXOS Energy Forecasting

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