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A novel anti-swing positioning controller for two dimensional bridge crane via dynamic sliding mode variable structure 8

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A novel anti-swing positioning controller for two dimensiona...

8th International Congress of Information and Communication Technology, ICICT 2018

作者： Wang, Tianlei Tan, Nanlin Zhou, Chang Zhang, Chi Zhi, Yihang School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China School of Information Engineering Wuyi University Jiangmen529020 China School of Mechanical and Electrical Engineering Wuyi University Jiangmen529020 China School of Computer Wuyi University Jiangmen529020 China

To improve the control performance of anti-swinging and the orientation of two-dimensional bridge crane, a novel dynamic sliding mode variable structure control algorithm is presented here. The proposed controller can ensure global stability through improved design of sliding surface for the drive control force, and obtain the continuous driving control force in time domain, by using the Sigmoid function for switching function to suppress chattering. Experimental results demonstrated that it is effective on chattering suppression, swing reduction, and robust with various load qualities and friction resistances. © 2018 The Authors. Published by Elsevier Ltd.

关键词： controllers

Mobility in MANET Using Robot: A Review 1st

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Mobility in MANET Using Robot: A Review

1st International Conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2018

作者： Muchtar, Farkhana Abdullah, Abdul Hanan Ahmmad, Siti Nor Zawani Kumar, Yugal Faculty of Computing Universiti Teknologi Malaysia SkudaiJohor81310 Malaysia School of Computer Sciences Universiti Sains Malaysia GelugorPenang11800 Malaysia Instrumentation and Control Engineering University Kuala Lumpur MITEC Persiaran Sinaran Ilmu Bandar Seri Alam MasaiJohor81750 Malaysia Department of Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat SolanHimachal Pradesh173234 India

ISBN: (纸本)9789811338038

Recently, MANET (Mobile Ad Hoc Network) researchers have shown increased interest towards using mobile robot technology for their testbed platforms. Despite the existence of articles that discuss the usage of mobile robot technology pertaining to MANET testbed from the perspective of MANET researcher, the discussion provided in the papers are rather lacklustre as it is not the sole purpose of those articles. Hence, this review aims to discuss MANET testbeds that were facilitated with mobile robot technology from previous undertaken researches. With the wealth of information provided in this paper, it is hoped that this paper will be the ultimate source of reference for other MANET researchers who need to choose the most suitable mobile robots with real mobility in their MANET testbeds. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

Exploiting contextual information via dynamic memory network for event detection

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arXiv 2018年

作者： Liu, Shaobo Cheng, Rui Yu, Xiaoming Cheng, Xueqi School of Computer and Control Engineering University of CAS

The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Wind Turbine Performance Assessment and Condition Monitoring with Performance Index

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Wind Turbine Performance Assessment and Condition Monitoring...

Sustainable Power and Energy Conference (iSPEC)

作者： Xiangyu Zhou Xin Huang Zhulin Dong Weizhi Li Minghao Jiang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA U.S.A. School of Control and Computer Engineering North China Electric Power University Beijing China Department of Computer Science Purdue University West Lafayette IN U.S.A. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Champaign IL U.S.A.

ISBN: (纸本)9781728149318

The major wind turbine performance assessment methodology established in the field of wind energy is observing and comparing wind turbine power curves (WTPC) of multiple wind turbines. In this paper, adjustments to this method are proposed to improve overall accuracy of the assessment. While preprocessing the supervisory control and data acquisition (SCADA) data points, density peak clustering (DPC) filters out invalid points such as maintenance and peak-power limitation for a more accurate WTPC. This paper introduces a new variable, performance index "S", to evaluate both overall and segmented wind turbine performance without the influence of wind distribution. Further researches are also discussed in this paper.

关键词： wind turbine diagnosis performance assessment power curve density peak clustering (DPC) supervisory control and data acquisition (SCADA) power curve performance indicators Wind Energy Conversion Systems Performance assessment Supervisory control and data acquisition Condition monitoring Supervisory control Wind energy

The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

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arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

Multi-objecive LPV control of wind energy conversion system under wide-scale operating conditions 8

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Multi-objecive LPV control of wind energy conversion system ...

8th Renewable Power Generation Conference, RPG 2019

作者： Qu, Chenzhi Lin, Zhongwei Xie, Zhen Li, Yilin Zhang, Shangang Li, Xiongwei Duan, Zhenqing State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China New Energy Co. Ltd Jinan250099 China Guodian New Energy Technology Research Institute Co. Ltd Beijing102209 China

ISBN: (纸本)9781839531255

Wind energy conversion system (WECS) is essentially a time-varying system with high nonlinear characteristics, which leads to the difficulty of guaranteeing the control performance in a wide-scale operating conditions for the conventional linear control strategies. In this paper, a linear-parameter-varying (LPV) gain-scheduled controller is proposed for variable-speed-variable-pitch wind turbines above rated wind speed, which improves both steady- and dynamic-response performances under wide-scale operating conditions. Firstly, the H2/H∞ technique is adopted to reduce the rated power tracking errors, while enhancing the robustness against high-frequency turbulence. Secondly, the regional pole placement technique is applied on WECS, which builds the connections to the dynamic response performance. To verify the effectiveness of the proposed method, the simulation is implemented on the NREL 5 MW wind turbine, which is compared with the conventional local linear control strategy with a full analysis on the related performances. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Wind power

Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

Predictive generalized graph fourier transform for attribute compression of dynamic point clouds

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arXiv 2019年

作者： Xu, Yiqun Wang, Shanshe Zhang, Xinfeng Wang, Shiqi Ma, Siwei Guo, Zongming Gao, Wen Institute of Computing Technology University of Chinese Academy of Sciences National Engineering Laboratory for Video Technology Peking University Institute of Computer Science and Technology Peking University National Engineering Laboratory for Video Technology Peking University School of Computer and Control Engineering University of the Chinese Academy of Sciences Department of Computer Science City University of Hong Kong

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as tele-presence, navigation for autonomous driving and heritage reconstruction. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. We thus propose a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal inter-prediction and predictive transform coding assuming the Gaussian Markov Random Field model for attributes of dynamic point clouds, where the optimal predictive transform proves to be the Generalized Graph Fourier Transform (GGFT). Secondly, we propose refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of point clouds. Finally, we construct a complete framework based on the optimal inter-coding and our previously proposed intra-coding, where we determine the optimal coding mode from rate-distortion optimization with the proposed offline-trained λ-Q model. Experimental results show that we achieve 13:4% bitrate reduction on average and up to 25:8% over the state-of-the-art Region-adaptive Hierarchical Transform method. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion estimation

An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： Complex network prisoner’s dilemma reinforcement learning temporal differences learning