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7th Annual International Conference on Network and Information Systems for computers, ICNISC 2021

作者： Su, Xinghua Zhan, Sheng Lv, Zhe Gao, Xiang Su, Hang Information Center of CNPC Chuanqing Drilling Engineering Co Ltd Xi'an710018 China School of Computer Science Northwest University of Technology Xi'an710072 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China Metall. Automation Research and Design Institute of China Steel Research Technology Group Co Ltd Beijing100081 China

ISBN: (纸本)9781665402323

For most steel materials, the conventional corrosion method can only observe the martensite structure after transformation. There are some problems in measuring austenite grain size, such as complex operation, difficult to ensure the corrosion quality and so on. Therefore, we use machine learning to identify the original austenite grain boundary according to the martensite structure of conventional corrosion. In this paper, image style transfer is realized by iterative method based on generating model, and austenite grain boundary recognition during martensitic transformation is realized by means of pre training network model vgg19. Firstly, the pre trained deep network vgg19 is used to extract the style and content features of martensite metallographic images. Then, the loss function of style and content is defined, and the gradient descent method is used to iterate step by step to optimize the total loss. Finally, the austenite image with clear grain boundary is obtained by texture segmentation. © 2021 IEEE.

关键词： Grain boundaries

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization

Towards Integrated Traffic Control with Operating Decentrali...

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International Conference on Intelligent Transportation

作者： Shengyue Yao Jingru Yu Yi Yu Jia Xu Xingyuan Dai Honghai Li Fei-Yue Wang Yilun Lin Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization

arXiv

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arXiv 2023年

作者： Yao, Shengyue Yu, Jingru Yu, Yi Xu, Jia Dai, Xingyuan Li, Honghai Wang, Fei-Yue Lin, Yilun Urban Computing Shanghai AI Lab Shanghai China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group The Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

关键词： Integrated control

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Szilá rd Hunor Tó th Zsolt Já nos Viharos Á dá m Bá rdos Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering &#x0026 Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Budapest Hungary Faculty of Economics John von Neumann University Kecskem&#x00E9 t Hungary

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions.

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Impact of Generation Companies' Heterogeneous Investment Behaviors on the Effects of Non-fossil Energy Incentive Policies

Impact of Generation Companies' Heterogeneous Investment Beh...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Cai, Bin Li, Tianran Wang, Kui Li, Zhiyuan Zhu, Lei Wen, Jun Xue, Yusheng Nari Group Corporation State Grid Electric Power Research Institute State Key Laboratory of Smart Grid Protection and Control Nanjing China Integrated Energy of Jiangsu Province Nanjing Normal University Nari School of Electrical and Automation Engineering Engineering Laboratory of Gas-Electricity Nanjing China China Huaneng Group Co. Ltd Beijing China

ISBN: (纸本)9781665414395

The incentive policies for non-fossil energy are important factors affecting the investment behavior of generation companies (GENCOs). However, the investment behavior of different types of GENCOs in the system could be quite different, so the implementation of the policy must consider how to achieve incentive compatibility with the investment behavior of GENCOs. This article constructs a power transition simulation model that considers the investment behavior of GENCOs. Taking the wind power feed-in tariff policy as an example, it analyzes the impact of policy on the whole system's low carbon transition pathway of the power structure and the evolution trajectory of system carbon emissions under different behavior scenarios. © 2021 IEEE.

关键词： Investments

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A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

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Healthcare Analytics 2024年 5卷 100286-100286页

作者： Dadhich, Ajay Patel, Jaideep Tiwari, Rovin Verma, Richa Mishra, Pratha Jain, Jay Kumar Department of Electronics Instrumentation and Control Engineering Engineering College Ajmer Rajasthan Ajmer India Department of Computer Science and Engineering Sagar Institute of Research & Technology MP Bhopal India Department of Electronics & Communication Engineering Amity University MP Gwalior India Department of Computer Science and Application Makhanlal Chaturvedi University of Journalism and Mass Communication MP Bhopal India Department of Electronics & Communication Engineering Technocrats TIT Group Madhya Pradesh Bhopal India Department of Computer Science & Engineering Indian Institute of Information Technology (IIIT) MP Bhopal India

Mind-wandering (MW) is when an individual's concentration drifts away from the task or activity. researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society. © 2023 The Author(s)

关键词： Brain–computer interface Classification Electroencephalogram signals Flexible analytic wavelet transform Machine learning Mind-wandering

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Data-driven real-time control method for process equipment in flow shop towards product quality improvement

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Procedia CIRP 2022年 107卷 908-913页

作者： Chaofan Ke Meng Zhang Ying Zuo Feng Xiang Dashun Zhang Fei Tao School of Automation Science and Electrical Engineering Beihang University Beijing 100191 PR China Department of Automation Tsinghua University Beijing 100084 PR China Research Institute for Frontier Science Beihang University Beijing 100191 PR China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan 430081 PR China No. 55 Research Institute China North Industries Group CorporationChangChun 130012 PR China

Real-time control of complex process equipment is a crucial aspect for product quality assurance in the flow shop. However, there are two bottleneck problems, including the inaccurate quality prediction and the unstable process control, which would lead to poor product quality. To solve these problems, a data-driven real-time product quality prediction and control method for the process equipment is elaborated in this paper. First, to improve the prediction accuracy, a delay-mean rule is designed to eliminate the negative effects of time lag focused on the modeling data. Second, a variable optimization method based on the improved simulated annealing algorithm and a time window is proposed to enhance the stability of control. Finally, a case study regarding the real-time control for a dryer equipment is used to verify the proposed method.

关键词： Process equipment Quality prediction Quality control

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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2022 Chinese automation Congress, CAC 2022

作者： Zhao, Xiaoxuan Wang, Haoyu Wang, Xiujuan Lewlomphaisarl, Udom Li, Dong Hua, Jing Kang, Mengzhen The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

ISBN: (纸本)9781665465335

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration. © 2022 IEEE.

关键词： Greenhouses

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Rethinking development and major research plans of Industrial Internet in China

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Fundamental research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural Science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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