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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Li, Ting Zhang, Wei Chen, Ning Qian, Minhui Xu, Ying School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China State Key Laboratory of Operation and Control of Renewable Energy Storage Systems China EPRI Nanjing China University of Central Florida Department of Electrical Engineering Computer Science Orlando United States

ISBN: (纸本)9781728131375

With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Blockchain

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Smart train operation algorithms based on expert knowledge and reinforcement learning

arXiv

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arXiv 2020年

作者： Zhou, Rui Song, Shiji Xue, Anke You, Keyou Wu, Hu Department of Computer Science University of Oxford OxfordOX1 1LE United Kingdom Department of Automation Tsinghua University Beijing100084 China Key Laboratory for IOT and Information Fusion Technology of Zhejiang Institute of Information and Control Hangzhou Dianzi University Hangzhou310018 China

During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems. On the one hand, it is more intelligent than traditional manual driving;on the other hand, it increases the energy consumption and decreases the riding comfort of the subway system. This paper proposes two smart train operation algorithms based on the combination of expert knowledge and reinforcement learning algorithms. Compared with previous works, smart train operation algorithms can realize the control of continuous action for the subway system and satisfy multiple objectives (the safety, the punctuality, the energy efficiency, and the riding comfort) without using an offline optimized speed profile. Firstly, through analyzing historical data of experienced subway drivers, we summarize the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality and the safety of the subway system. Then we develop two algorithms to realize the control of continuous action and to ensure the energy efficiency of train operation. Among them, one is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and another is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data collected from the Yizhuang Line of the Beijing Subway and their performance will be compared with existing ATO algorithms. The results of numerical simulations show that the developed smart train operation systems are better than manual driving and existing ATO algorithms in respect of energy efficiency. In addition, STOD and STON have the ability to adapt to different trip times and different resistance conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease

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IEEE Access 2019年 7卷 120315-120325页

作者： Ar Junejo Yin Shen Asif Ali Laghari Xiaobo Zhang Hao Luo School of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan School of Automation Guandong University of Technology Guangzhou China

Today, with the development of industry and mechanized life style, the prevalence of the disease is rising steadily as well. Observing at the trend and lifecycle style, its predict that after ten years around 23.6 million people die because of Cardiovascular Disease (CVD). For that reason, aim to use Deep Learning Techniques (DLTs), to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry (RHI). An objective of this paper is first, Molecular diagnosis (MD), and second using Deep Learning Techniques DLTs, to synthesis and characterize to accumulate (raw information) from CVD patients, those who admitted the emergency section between January (2018 to December 2019). We are using Artificial Neural Network (ANN), model characterize to predict CVD patients and configuration, Feature selection (FS), Mean Square Error (MSE), accuracy, sensitivity. The ANN accuracy is 98.4, K-nearest neighbor (KNN) accuracy is 98.01%, Naïve Bayes (NB), accuracy is 96.99%. Decision tree (DT), accuracy is 87.81%. Our robust data driven model explore the efficient accuracy rate to predict CVD patients. The ANN model in term of their efficient in disease analysis, and prognosis of the RHI.

关键词： Heart Blood Diseases Arteries Mathematical model Artificial neural networks

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ATT: Attention-based Timbre Transfer

ATT: Attention-based Timbre Transfer

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International Joint Conference on Neural Networks (IJCNN)

作者： Deepak Kumar Jain Akshi Kumar Linqin Cai Siddharth Singhal Vaibhav Kumar Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science and Engineering Delhi Technological University New Delhi India Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we tackle the issue of timbre transfer on a given monophonic music sample. The objective is to change the timbre of source audio from one instrument to another while preserving features such as loudness, pitch, and rhythm. Existing approaches use image-to-image translation techniques on the entire region of time-frequency representations of the raw audio wave, which may lead to the addition of unwanted elements in the final audio waveform. We propose Attention-based Timbre Transfer (ATT), an attention-based pipeline for transferring timbre. To the best of our knowledge, ATT is the first approach which leverages attention for achieving timbre transfer. Further, ATT uses MelGAN for spectrogram inversion, which provides a fast and parallel alternative to other autoregressive music generation approaches, without compromising on the quality. ATT shows promising results, thus efficaciously transferring timbre with minimal offset to other physical characteristics.

关键词： Timbre Spectrogram Generative adversarial networks Gallium nitride Instruments Time-frequency analysis

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Performance Evaluation of Faults in a Photovoltaic Array Based on V-I and V-P Characteristic Curve

Performance Evaluation of Faults in a Photovoltaic Array Bas...

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International Conference on Measuring Technology and Mechatronics automation, ICMTMA

作者： Ehtisham Lodhi Wang Lina Yang Pu M. Yaqoob Javed Zeeshan Lodhi Jiao Zhijie Usman Javed School of Automation Science and Electrical Engineering Beihang University Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad Pakistan The Guo China-US photonics Lab Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

The prediction and forecasting of faults occurring in photovoltaic (PV) system is one of the important aspect to escalate the reliability, output power generation, proficiency, lifetime and effectiveness of overall system. In this article, the behavior of PV array is studied under various faulty circumstances and new approach has offered for the better performance assessment of PV array. This approach is established on the analysis of peculiarities displayed by voltage power (V-P) and voltage-current (V-I) characteristics curves of PV array under different faulty conditions. The proposed technique can be used to identify and classify the five common faults in PV system. These progressive faults (includes open circuit, line-to-line, partial shading, degradation and bridging faults) have been occurred at the basic components of PV cell and at the connection point between PV modules. The characteristic curves under normal and faulty conditions have been compared by utilizing Matlab/Simulink environment. Hence, the suggested method can be utilized to determine the important fact about the health PV array.

关键词：

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Comparison of the Effectiveness of Various Structures of Regulators for controlling an Object of the Sixth Order

Comparison of the Effectiveness of Various Structures of Reg...

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2019 International Multi-Conference on Engineering, computer and Information sciences, SIBIRCON 2019

作者： Zhmud, Vadim Dimitrov, Lubomir Nosek, Jaroslav Roth, Hubert Novosibirsk State Technical University Dept. of Automation Novosibirsk Russia Technical University of Sofia Sofia Bulgaria Technical University of Liberec Faculty of Mechatronics Computer Science and Interdisciplinary Research Liberec Czech Republic University of Siegen Department of Automatic Control Engineering Siegen Germany Germany

ISBN: (纸本)9781728144016

Object control requires the calculation of a regulator. One of the most effective methods for calculating the regulator is the method of numerical optimization of the regulator. This paper discusses the solution of the problem of controlling a linear object of the sixth order, the parameters of which are such that this high order significantly and negatively affects the possibilities of successfully managing this object. Using the traditional approach to the design problem of the controller (regulator) allows us to formally solve the problem;however, the result of this solution is not satisfactory. The use of additional derivation does not give any noticeable improvement. A solution to this problem is proposed by using pseudo-local feedback, moreover, this connection is supposedly most effective if it covers one of two identical links in the object model. This allows for a smoother attenuation of the frequency response of the object in the region where the action of these two links begins to affect. For pseudo-local communication, an additional PID controller is used, the coefficients of which are also determined by the method of numerical optimization. The optimization result allows us to conclude that the presence of an integral element in this controller is optional. The calculation result of such a composite controller meets the most stringent requirements for the quality of control, since in such a system the speed is increased at least twice, the oscillations, which were about 10% in amplitude, are completely eliminated, the robustness of the system is ensured, which is verified by changing all the controller coefficients by 5% up and down, and also by changing the time constant of the element of the pseudolocal loop by 10% up and down, changes in the transition process of the system in this case remain are acceptable. © 2019 IEEE.

关键词： Feedback

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

引用

arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Opacity of Nondeterministic Transition Systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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Off-Policy Q-Learning for Anti-Interference control of Multi-Player Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9189-9194页

作者： Jinna Li Zhenfei Xiao Tianyou Chai Frank. L. Lewis Sarangapani Jagannathan School of Information and Control Engineering Liaoning Shihua University Fushun 113001 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China UTA Research Institute the University of Texas at Arlington Arlington TX 76118 USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA

This paper develops a novel off-policy game Q-learning algorithm to solve the anti-interference control problem for discrete-time linear multi-player systems using only data without requiring system matrices to be known. The primary contribution of this paper lies in that the Q-learning strategy employed in the proposed algorithm is implemented in an off-policy policy iteration approach other than on-policy learning due to the well-known advantages of off-policy Q-learning over on-policy Q-learning. All of the players work hard together for the goal of minimizing their common performance index meanwhile defeating the disturbance that tries to maximize the specific performance index, and finally they reach the Nash equilibrium of the game resulting in satisfying disturbance attenuation condition. In order to find the solution to the Nash equilibrium, the anti-interference control problem is first transformed into an optimal control problem. Then an off-policy Q-learning algorithm is proposed in the framework of typical adaptive dynamic programming (ADP) and game architecture, such that control policies of all players can be learned using only measured data. Comparative simulation results are provided to verify the effectiveness of the proposed method.

关键词： H ∞ control off-policy Q-learning game theory Nash equilibrium

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How to Teach control System Optimization (A Practical Decompisition Approach for the Optimization of TDOF control Systems)

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IFAC-PapersOnLine 2019年第9期52卷 115-120页

作者： Cs. Bányász L. Keviczky R. Bars Institute for Computer Science and Control of the Hungarian Academy of Sciences Department of Automation and Applied Informatics Budapest University of Technology and Economics H-1111 Budapest Kende u 13-17 Hungary

The optimality in a generic two-degree of freedom control system can be decomposed into three major steps, because the control error has three major parts: design-, realizability- and modeling-loss. The second term can be made zero for inverse stable processes only. This decomposition opens new ways for practical optimization of two-degree-of-freedom ( TDOF ) systems and helps the construction of new algorithms for robust identification and control. It is more reasonable to teach the optimization of control systems using these components.

关键词： two-degree-of-freedom control systems performance robustness optimality

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