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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Jia, Mingwei Liu, Yi Xu, Danya Yang, Tao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The development of an accurate data-driven fault diagnosis model faces several obstacles. One significant challenge is how to combine the model with the process mechanism rationally and improve the model interpretability. In this work, a fault diagnosis method based on a topology-informed graph convolutional network (TIGCN) is proposed. This model first uses the empirical knowledge that analyzes the operating state of the process and combines the operating data to construct a signed directed graph. Then the operating data is divided into three modalities in the time dimension. The model utilizes a well-designed spatial-temporal convolutional layer and signed directed graph to construct different modalities separately to effectively detect fault types. The superior fault diagnosis performance of the proposed TIGCN model is demonstrated on the Tennessee-Eastman process as compared to traditional approaches. © 2022 IEEE.

关键词： Failure analysis

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Time-Attention Graph Convolutional Network Soft Sensor in Biochemical processes 4

Time-Attention Graph Convolutional Network Soft Sensor in Bi...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

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Reinforcement Fuzzy Q-Learning Incorporated with Genetic Kinematics Analysis for Self-organizing Holonomic Motion control of Six-Link Stewart Platforms

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International Journal of Fuzzy Systems 2023年第3期25卷 1239-1255页

作者： Huang, Hsu-Chih Xu, Sendren Sheng-Dong Chen, Yu-Xiang Chen, Chien-Ming Department of Electrical Engineering National Ilan University Yilan260007 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

This paper contributes to the development of reinforcement fuzzy Q-learning incorporated with genetic kinematics analysis for self-organizing holonomic motion control of six-link Stewart platforms. The fuzzy theory is integrated with the Q-learning algorithm to develop a hybrid reinforcement learning strategy. Both the actions and the Q-functions in the classical Q-learning algorithm are inferred by means of fuzzy rules. The proposed hybrid reinforcement learning is then applied to self-organizing holonomic motion control of parallel Stewart platforms for industrial applications. After the kinematics analysis using a geometrical approach and genetic algorithm (GA)-based Newton–Raphson formulation, this study develops a practical control scheme that combines fuzzy Q-learning and proportional–integral–differential (PID) control scheme, called FQPID. In the proposed FQPID holonomic motion control method, the PID control gains are online-tuned via the fuzzy Q-learning computation process. An experimental Stewart platform consisting of six servo motors, adjustable links, and two platforms is constructed to illustrate the effectiveness of the presented methods. The experimental results are compared with some existing control methods using the same evaluation criterion. Through the experimental results and comparative works, the superiority of the proposed FQPID control scheme is presented and discussed. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Reinforcement learning

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Boundary Gap Based Reactive Navigation in Unknown Environments

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 468-477页

作者： Zhao Gao Jiahu Qin Shuai Wang Yaonan Wang the Department of Automation University of Science and Technology of ChinaHefei 230027China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082China

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing ***,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the *** paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation ***,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper ***,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local ***,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

关键词： Boundary gap local minima reactive navigation unknown environments

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Design Optimization of High Frequency Transformer for Resonant DC/DC Converter 6

Design Optimization of High Frequency Transformer for Resona...

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6th IEEE International electrical and Energy Conference, CIEEC 2023

作者： Yu, Xiangdong Zhang, Zheng Wang, Xiaozhou Xu, Jian Zhao, Shuang Ding, Lijian Shen, Min Yin, Chenxu School of Electrical Engineering and Automation Hefei University of Technology Hefei China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China State Grid Anhui Electric Power CO. Ltd Corporate Management Department Hefei China

ISBN: (纸本)9798350346671

Resonant DC/DC converter is extensively applied in the industry due to their superiorities such as zero voltage switching which enables it to operate under higher frequency. As the core component of resonant DC/DC converter, the high-frequency transformer (HFT) is critical to the performance of the converter in regard of power density, efficiency, and reliability. However, as the frequency and power rating increase, the power loss on the wire and core increase as well. It is desired to propose the optimal design process for integrated HFT which considers the tradeoff between volume and power. This paper comprehensively investigates the impact of air gap length, location and winding structure on the transformer. Based on the analytical model, the design flow of a magnetics integrated transformer which incorporates intrinsic magnetizing inductance and leakage inductance for the resonant tank is presented. Finally, the analytical model and the optimization design of HFT are validated with experimental results. © 2023 IEEE.

关键词： High frequency transformers

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Model predictive control of microgrid energy management system with optimal power flow constraints using second order cone programming 18

Model predictive control of microgrid energy management syst...

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18th International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Boonpeng, Jirapong Maneeratpongsuk, Jitpanu Banjerdpongchai, David Chulalonkom University Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9780738111278

Energy management system (EMS) is important for the planning and operation of power system as the power generation must supply enough energy for load with the minimum operating cost. This paper presents nonlinear MPC to dispatch power generation within the limitation of the power system. The objectives are to improve EMS by considering AC optimal power flow and provide a dispatch strategy to reduce total operating cost (TOC). To realize the proposed nonlinear MPC, we consider the power plant model of Mae Hong Son in the northern part of Thailand. Load data and power generation profile are utilized as the inputs to MPC of EMS to minimize TOC for one day ahead. In this work, the optimal power flow is incorporated as the second order cone constraints. It is shown that EMS with optimal power flow can operate within the rated voltage for all buses. When comparing the results in normal mode with one-shot optimization, the proposed MPC can reduce the TOC by 1.72% in the rainy season. © 2021 IEEE.

关键词： Second-order cone programming

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Empowering Lab Education: Integrating a Vision-Based Monitoring System with Small-Scale Self-Driving Experiment Platforms

Empowering Lab Education: Integrating a Vision-Based Monitor...

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IEEE/SICE International Symposium on System Integration

作者： Junru Ren Nis Fisker-Bødker Ronja Güldenring Jin Hyun Chang Tejs Vegge Ole Ravn Lazaros Nalpantidis Department of Electrical and Photonics Engineering Automation and Control Group Technical University of Denmark Department of Energy Conversion and Storage Technical University of Denmark

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The rise of self-driving laboratories has seen significant growth across various research domains, particularly in chemistry, materials science and life science. However, a major challenge persists—the majority of self-driving systems are costly due to the use of highly precise lab equipment, robotic platforms, and case-specific algorithms, rendering these systems less accessible for educational purposes. This paper takes a multidisciplinary approach; we first introduce a small-scale self-driving experiment platform tailored for educational use, focusing on liquid materials mixing tasks commonly seen in chemistry and life sciences. To understand the operational status in real-time while maintaining self-driving capability and efficiency, we propose a novel system concept: employing a mobile robot as the lab supervisor to monitor the experiment process across multiple identical self-driving platforms. Specifically, this paper focuses on implementing a vision-based monitoring system. A deep learning architecture with a new training strategy is presented to jointly address two tasks: (a) vessel and content material segmentation and (b) volume estimation. The two tasks can be trained independently but can be inferred end-to-end by integrating them into the Mask R-CNN framework. Through evaluating the monitoring module on a real dataset, the results showcase promising detection capabilities, good real-time performance, and compatibility with the self-driving platform, indicating the feasibility of our proposed system.

关键词： Training Materials science and technology Chemistry Volume measurement System integration Rendering (computer graphics) Real-time systems Life sciences Mobile robots Monitoring

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China 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

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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RCDue - experimental identification of continuous- and discrete-time models

RCDue - experimental identification of continuous- and discr...

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International Conference on process control (PC)

作者： František Dušek Daniel Honc Aleš Novotný Department of Process Control Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic

The paper is devoted to the education and teaching of process control and automation. Various laboratory equipment is used to explain and better understand the theory and to gain practical experience. The authors have designed and developed a simple electrical dynamical system RCDue (dynamic model with passive RC components and Arduino Due as measurement and communication unit) that allows students to perform various laboratory experiments – e.g. static and dynamic characteristics measurements, modeling, experimental identification, control design and application of from the simplest strategies to advanced methods. Specifically, in this paper, the authors focus on experimental identification.

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