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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Huan Yang Dongsheng Cai Olusola Bamisile Jian Li Zhenyuan Zhang Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

In the context of carbon neutrality, the power industry is becoming the main battlefield of carbon emission reduction. However, the low-carbon transformation of energy structure is a complex long-term task, it needs to be analyzed in combination with the actual situation. This paper is based on the microgrid in the grid connection mode, considering microgrid power output and carbon emission constraints. An optimal model is proposed to reduce carbon emission cost, the optimal economic operation, and build a low-carbon scheduling model of a microgrid system. In this paper, the improved multi-objective particle swarm optimization algorithm is used to solve the problem. Finally, the simulation results verify the effectiveness of the model and the superiority of the improved algorithm.

关键词： Job shop scheduling Costs Wind energy Heuristic algorithms Optimal scheduling Microgrids system integration

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Local Topology Inference of Mobile Robotic Networks under Formation control

arXiv

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arXiv 2022年

作者： Li, Yushan He, Jianping Cai, Lin Guan, Xinping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Dept. of Electrical and Computer Engineering University of Victoria BC Canada

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Platoon-Based Distributed control for Automated Material Handling systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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Study of residual stress distribution in car body welds based on X-ray diffraction method

Study of residual stress distribution in car body welds base...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering (QR2MSE 2022)

作者： R. Zhang J. He Z. Ju H. Tang Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education Northeastern University Shenyang People's Republic of China School of Mechanical Engineering and Automation Northeastern University Shenyang People's Republic of China

Welding residual stress has a significant influence on the initiation and propagation of fatigue cracks in welded structures. It is important to determine the distribution of residual stress in welding parts when the fatigue life is evaluated. The specimen of aluminum butt welding seam was taken from the car body of high speed train, and surface residual stress was measured by using X-ray diffraction method. The results show that there are obvious differences in the uniformity and diffraction peak intensity of the Debye ring of different parts of specimen. Along the line perpendicular to the weld, the distributions of residual stress appear bimodal shape. The peak values appear in the weld near to base metal and the valley value appears in the center of the weld. The maximum value of longitudinal residual stress is far higher than that of the transverse residual stress. The welding boundary has few effect on the residual stress by measuring the residual stress at different distances from the welding free boundary.

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Enhanced Industrial Process Modeling with Transfer-Incremental-Learning: A Parallel Sae Approach and its Application to a Sulfur Recovery Unit

SSRN

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SSRN 2023年

作者： Mou, Tianhao Liu, Jinfeng Zou, Yuanyuan Li, Shaoyuan Xibilia, Maria Gabriella Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Engineering University of Messina Messina98166 Italy

In industrial processes, quality variable prediction is important for process control and monitoring. The emergence of deep learning (DL) methods and their excellent prediction performance present a potential paradigm revolution in the modeling of quality variable prediction. However, in real-world production, the lack of offline labeled data and the presence of time-varying data distributions commonly exist, which seriously prohibits practical applications of DL-based predictive models. This paper introduces an enhanced quality variable prediction framework, Transfer-Incremental-Learning Parallel Stacked Autoencoders (TIL-PSAE), to address this issue. TIL-PSAE establishes an effective predictive model through an integration of three key components: a parallel model structure, a transfer- learning (TL)-based offline training strategy that accumulates knowledge from other similar processes, and an incremental-learning (IL)-based online adaptation strategy that preserves offline knowledge. The model structure contains two parallel SAEs, which separately extract process-invariant features and target-process-specific features. The offline training involves sequential training using data from different domains, facilitating the accumulation of knowledge from different domains into different parts of model. During online adaptation, the accumulated knowledge remains unchanged while a new combination is learned, thus improving online prediction accuracy and avoiding knowledge forgetting. The proposed model is applied to a sulfur recovery unit of four parallel sub-units. Experimental results demonstrate the effectiveness of the proposed model, in terms of both offline prediction performance and online prediction performance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

arXiv

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arXiv 2022年

作者： Liu, Qi Yang, Bo Wang, Zhaojian Zhu, Dafeng Wang, Xinyi Ma, Kai Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect sufficient fault samples, but their data is not allowed to be shared directly due to potential conflicts of interest. Therefore, federated learning can be exploited to train a collaborative fault diagnosis model. However, the modeling efficiency is seriously affected by the model update mechanism since each PV station has a different computing capability and amount of data. Moreover, for the safe and stable operation of the PV system, the robustness of collaborative modeling must be guaranteed rather than simply being processed on a central server. To address these challenges, a novel asynchronous decentralized federated learning (ADFL) framework is proposed. Each PV station not only trains its local model but also participates in collaborative fault diagnosis by exchanging model parameters to improve the generalization without losing accuracy. The global model is aggregated distributedly to avoid central node failure. By designing the asynchronous update scheme, the communication overhead and training time are greatly reduced. Both the experiments and numerical simulations are carried out to verify the effectiveness of the proposed method. © 2022, CC BY.

关键词： Failure analysis

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Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

arXiv

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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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Research on Intrusion Detection Based on BP Neural Network

Research on Intrusion Detection Based on BP Neural Network

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Haonan Chen Yiyang Liu Jianming Zhao Xianda Liu College of Information Science and Engineering Northeastern University Shenyang China Chinese Academy of Sciences Industrial Control Network and System Laboratory Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Industrial Control Network and System Laboratory Shenyang Institute of Automation Shenyang China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

The purpose of network security is to prevent the data transmitted over the Internet from being stolen and tampered with, and to ensure the security of the data. It is not only necessary to ensure that the information entering and exiting the network is not stolen or tampered with, but also to ensure the integrity and confidentiality of the information in the information system. The network environment is becoming more and more complex, and the attack methods are becoming more and more diverse. Therefore, intrusion detection systems have some common problems, such as low detection rate and high false alarm rate, and it is difficult to meet the real-time requirements of intrusion detection systems. Currently, deep learning is increasingly used in intrusion detection. In order to solve the problems existing in the current intrusion detection system, this paper studies the application of deep learning in intrusion detection. First, it analyzes the BP neural network (BP-NN) technology, and proposes an improvement method for the shortcomings of the current BP-NN, and finally conducts an empirical analysis. Experimental results show that intrusion detection based on BP-NN has a high accuracy rate, and the false alarm rate and false alarm rate are both at a low level.

关键词： Deep learning Neural networks Intrusion detection Feature extraction Real-time systems Internet Information systems

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Research on the seamless positioning method of an unmanned system based on UWB/IMU/GNSS fusion for indoor and outdoor

Research on the seamless positioning method of an unmanned s...

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International Conference on Advanced Mechatronic systems (ICAMechS)

作者： Tao Jiang Qian Wang Linshuai Zhang Shuoxin Gu Lin Xu Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu Sichuan School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

This paper studies the multi-sensor fusion positioning methods of Ultra-wideband (UWB), Global Navigation Satellite system (GNSS) and Inertial measurement unit (IMU), and constructs a seamless positioning system based on UWB/IMU/GNSS multi-sensor fusion. In this paper, the indoor location information provided by UWB is fused with IMU location data through extended Kalman filter, which solves the problems of signal loss, track interruption and jump in positioning information when unmanned systems enter indoor environment, and also improves the positioning accuracy of the overall system. In the experiment of the indoor and outdoor scenes of the unmanned vehicle system, the trajectory diagram of each sensor combination positioning mode is counted, and the error of positioning information is analyzed and studied. The results show that the overall positioning error of this method is small, it can provide stable and reliable whole-time positioning information, and achieve the high-precision seamless positioning required by unmanned systems.

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Development of a UAV-based system for Technical Diagnostics of Overhead Power Lines

Development of a UAV-based System for Technical Diagnostics ...

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International Conference on Computational Problems of Electrical engineering (CPEE)

作者： Oleksandr Kozlovskyi Dmitro Trushakov Serhiy Rendzinyak Vasyl Korud Department of Electrical Systems and Energy Management Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Automation and Control in Technical Systems Central Ukrainian National Technical University Kropyvnytskyi Ukraine Faculty of Electronics and Computer Technologies Ivan Franko National University of Lviv Lviv Ukraine Institute of Power Engineering and Control System Lviv Polytechnic National University Lviv Ukraine

The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics of overhead lines based on UAVs was developed, for which the necessary diagnostic parameters were selected according to informative criteria. An analysis of types of UAVs was carried out in order to determine their suitability and efficiency of use for diagnosing the condition of overhead lines.

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