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Electron dynamics in planar radio frequency magnetron plasmas: III. Comparison of experimental investigations of power absorption dynamics to simulation results

arXiv

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arXiv 2022年

作者： Berger, Birk Eremin, Denis Oberberg, Moritz Engel, Dennis Wölfel, Christian Zhang, Quan-Zhi Awakowicz, Peter Lunze, Jan Brinkmann, Ralf Peter Schulze, Julian Applied Electrodynamics and Plasma Technology Ruhr University Bochum Universitaetsstrasse 150 Bochum44801 Germany Institute of Theoretical Electrical Engineering Ruhr University Bochum Universitaetsstrasse 150 Bochum44801 Germany Institute of Automation and Computer Control Ruhr University Bochum Universitaetsstrasse 150 Bochum44801 Germany School of Physics Dalian University of Technology Dalian116024 China

In magnetized capacitively coupled radio-frequency discharges operated at low pressure the influence of the magnetic flux density on discharge properties has been studied recently both by experimental investigations and in simulations. It was found that the Magnetic Asymmetry Effect allows for a control of the DC self-bias and the ion energy distribution by tuning the magnetic field strength. In this study, we focus on experimental investigations of the electron power absorption dynamics in the presence of a magnetron-like magnetic field configuration in a low pressure capacitive RF discharge operated in argon. Phase Resolved Optical Emission Spectroscopy measurements provide insights into the electron dynamics on a nanosecond-timescale. The magnetic flux density and the neutral gas pressure are found to strongly alter these dynamics. For specific conditions energetic electrons are efficiently trapped by the magnetic field in a region close to the powered electrode, serving as the target surface. Depending on the magnetic field strength an electric field reversal is observed that leads to a further acceleration of electrons during the sheath collapse. These findings are supported by 2-dimensional Particle in Cell simulations that yield deeper insights into the discharge dynamics. © 2022, CC BY-NC-ND.

关键词： Dynamics

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Maturity Model for AI in Smart Production Planning and control System

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Procedia CIRP 2022年 107卷 493-498页

作者： Eduardo Colangelo Christian Fries Theresa-Franziska Hinrichsen Ádám Szaller Gábor Nick Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 70569 Stuttgart Germany University of Stuttgart Institute of Industrial Manufacturing and Management IFF Allmandring 35 70569 Stuttgart Germany Institute for Computer Science and Control Kende str. 13-17. Budapest 1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest 1111 Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17. Budapest 1111 Hungary

The utilization of Artificial Intelligence (AI) to improve processes constitutes a main subject for many enterprises. The area of Production Planning and control (PPC) possesses several functions that could profit from such approaches. However, manufacturing companies find themselves often limited in the application of these approaches. This paper concentrates on three elements to assist enterprises: 1) the clarification of what AI is (in the manufacturing context) and its application to the field of PPC; 2) a review performed together with manufacturing enterprises in Germany and Hungary in order to understand the obstacles for the implementation of AI; and 3) the proposal of a maturity model to help enterprises understand where they are in regards to AI, as a way to help them create a roadmap to achieve their objectives.

关键词： Production Planning control Artificial Intelligence Maturity Model Smart Production

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Vehicle control in Highway Traffic by Using Reinforcement Learning and Microscopic Traffic Simulation 18

Vehicle Control in Highway Traffic by Using Reinforcement Le...

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18th IEEE International Symposium on Intelligent Systems and Informatics, SISY 2020

作者： Szoke, Laszlo Aradi, Szilard Becsi, Tamas Gaspar, Peter Budapest University of Technology and Economics Department of Control for Transportation and Vehicle Systems Megyetem rkp. 3 BudapestH-1111 Hungary Computer and Automation Research Institute Hungarian Academy of Sciences Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9781728173528

The paper presents a simple yet powerful and intelligent driver agent, designed to operate in a preset highway situation using Policy Gradient Reinforcement Learning (RL) agent. The goal is to navigate safely in dense highway traffic and proceed through the defined length with the shortest time possible. The algorithm uses a dense neural network as a function approximator for the agent with discrete action space on the control level, e.g., acceleration and steering. The developed simulation environment uses the open-source traffic simulator called Simulation of Urban MObility (SUMO), integrated with an interface, to interact with the agent in real-time. With this tool, numerous driving and highway situations can be created and fed to the agent from which it can learn. The environment opens the opportunity to randomize and customize the other road users' behavior. Thus the experience can be more diverse, and thus the representation becomes more general. The article describes the modeling environment, the details on the learning agent, and the rewarding scheme. After evaluating the experiences gained from the training, some ideas for optimization and further development goals are also proposed. © 2020 IEEE.

关键词： Reinforcement learning

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Interesting Phenomena in the Synchronisation of Disturbed Systems

Interesting Phenomena in the Synchronisation of Disturbed Sy...

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IEEE Conference on Decision and control

作者： Marc Wissing Thimo Oehlschlägel Jan Lunze IAV GmbH Gifhorn Germany Institute of Automation and Computer Control Ruhr-Universitat Bochum Bochum Germany

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

This paper presents three interesting phenomena that occur in the synchronisation of disturbed systems. The first phenomenon describes the necessity that dynamical local controllers have to be used to introduce the model of the disturbances into each of the extended agents. This result extends the Internal-Model Principle from multivariable control to multi-agent systems. A necessary and sufficient synchronisation condition is given. The second phenomenon concerns the fact that the internal disturbance models of all agents modify the synchronous trajectory of the overall system. For specific initial states the synchronous trajectory is completely fixed by the disturbance models and does not reflect the properties of the agents. This fact leads to the third phenomenon that indicates that an undisturbed virtual agent has to be introduced into the overall system to ensure that the synchronous trajectory is generated by the agent dynamics like in undisturbed multi-agent systems. The considered phenomena are illustrated by numerical examples.

关键词： Synchronization Trajectory Generators Multi-agent systems Attenuation Protocols Laplace equations

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INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

arXiv

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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Practical Application of the Dynamic Output-Only Iterative Learning control Design To The Crane System*

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IFAC-PapersOnLine 2022年第12期55卷 476-481页

作者： Lukasz Hladowski Krzysztof Galkowski Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. prof. Z. Szafrana 2 65-516 Zielona Góra Poland Department of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

Iterative learning control applies to systems that repeatedly execute the same finite duration task. The distinguishing feature of this form of control action is that all data generated on a previous execution of the task are available to compute the control action for the subsequent execution. This paper uses the linear repetitive process stability analysis and optimization techniques to design a dynamic controller that, in contrast to previous designs in the repetitive process/2D systems setting, does not require measurement of the state dynamics or observer-based estimation. Supporting experimental validation results are also given.

关键词： Iterative learning control repetitive processes output control

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An IoT Edge Computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Logical-combinatorial approaches in dynamic recognition problems

arXiv

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arXiv 2021年

作者： Aslanyan, Levon H. Krasnoproshin, Viktor V. Ryazanov, Vladimir V. Sahakyan, Hasmik A. Institute for Informatics and Automation Problems NAS RA Armenia Belarusian State University Department of Information Management Systems Belarus Dorodnitsyn Computing Centre Federal Research Center Computer Science and Control RAS Russia

A pattern recognition scenario, where instead of object classification into the classes by the learning set, the algorithm aims to allocate all objects to the same, the so-called "normal" class, is the research objective. Given the learning set L;the class K! is called "normal", and the reminder l classes K", K#,..., K$ from the environment K are "deviated". The classification algorithm is for a recurrent use in a "classification, action" format. Actions A% are defined for each "deviated" class K%. Applied to an object X ∈ K%, the action delivers update A%(x) of the object. The goal is in constructing a classification algorithm that applied repeatedly (small number of times) to the objects of L, moves the objects (correspondingly, the elements of K) to the "normal" class. In this way, the static recognition action is transferred to a dynamic domain. This paper is continuing the discussion on the "normal" class classification problem, its theoretical postulations, possible use cases, and advantages of using logical-combinatorial approaches in solving these dynamic recognition problems. Some light relation to the topics like reinforcement learning, and recurrent neural networks are also provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Torque Characteristic Analyses and Multi-Objective Optimisation of Multi-Layer Flux-Barrier Less-Rare-Earth Permanent Magnet Synchronous Machine

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IET Electric Power Applications 2025年第1期19卷

作者： Wang, Weinan Gong, Weize Zhu, Liangkuan Wei, Jian Wang, Mingqiao Liu, Yong Zheng, Ping College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Synchronous machinery

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