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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Kharitonov, Andrey S. Sarakhanova, Regina Yu. Bodin, Oleg N. Zolotukhin, Yuri N. Kharitonov, Sergey A. Novosibirsk State Technical University Department of Design of Technological Machines Novosibirsk Russia Novosibirsk State Technical University Department of Electronics and Electrical Engineering Novosibirsk Russia Penza State University Department of Information and Measuring Technology and Metrology Penza Russia Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences Laboratory of Fuzzy Technologies Novosibirsk Russia

ISBN: (纸本)9781665434089

The article presents a DC power supply system based on a contactless synchronous generator with combined excitation and an uncontrolled rectifier. One of the possible options for constructing a DC power supply system for autonomous objects is described. In particular, such a power supply system can be used as an on-board power supply system for aircraft. A synchronous generator with combined excitation replaces three-stage synchronous generators, as an electric generator on board aircraft. The working magnetic flux in such a generator is created from two sources of magnetomotive force-a permanent magnet and an excitation winding. The main, as a rule, most of the working magnetic flux is created by permanent magnets, the excitation winding creates an additional, as a rule, smaller part of the flux than provides a change in the total flux and regulation in a certain range of the output voltage of the generator. Scenarios for selecting parameters and controlling a synchronous generator that ensure the stability and controllability of the system are proposed and investigated. The results obtained can be useful in the development of power supply systems for aircraft and autonomous objects. © 2021 IEEE.

关键词： Synchronous generators

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Implementation of a low-cost current perturbation-based improved PO MPPT approach using Arduino board for photovoltaic systems

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 10卷

作者： Chellakhi, Abdelkhalek Beid, Said El Marghichi, Mouncef El Bouabdalli, El Mahdi Harrison, Ambe Abouobaida, Hassan Laboratory of Engineering Sciences for Energy (LabSIPE) National School of Applied Sciences of El Jadida Chouaib Doukkali University El Jadida 24000 Morocco CISIEV Team Cadi Ayyad University Marrakech 40160 Morocco Intelligent Systems Design Laboratory (ISDL) Faculty of Science Abdelmalek Essaadi University Tetouan 93000 Morocco Department of Electrical and Electronics Engineering College of Technology (COT) University of Buea PO Box Buea 63 Cameroon

In photovoltaic (PV) systems, the conversion of solar energy into electrical energy by the PV module is influenced by various factors, including sunlight intensity and temperature. To achieve optimal performance, it is crucial to accurately track the maximum power point (MPP) of the PV module. Among the numerous MPP tracking (MPPT) techniques that have been developed, the perturbation and observation (PO) method has gained significant attention due to its simplicity and reliability. However, the fixed perturbation step size used in the traditional PO algorithm can result in poor tracking capability, excessive ripple, and drift, leading to high power loss and low tracking efficiency. To address these limitations, this paper proposes an improved version of the PO strategy that introduces an adaptable step-magnitude mechanism, utilizing current perturbation instead of the voltage perturbation typically employed in the classical PO method. This enhanced indirect PO approach maintains low complexity and can be easily implemented on a cost-effective Arduino Uno board. A streamlined C++ code integrates the improved PO MPPT strategy with a Proportional Integral Derivative (PID) controller, eliminating the need for separate PID blocks and further reducing system complexity. To evaluate the effectiveness of the proposed technique, comparative analyses are conducted against the traditional PO algorithm, particle swarm optimization (PSO), fuzzy logic control (FLC), and a recently introduced approach, the zone voltage (ZV) method. Simulation results using Proteus software demonstrate that the improved PO approach outperforms the other techniques in various aspects, including achieving the highest static and dynamic tracking efficiencies of 99.38% and 99.88%, respectively, negligible power loss and fluctuations, the fastest convergence speed, and the shortest tracking time of 0.19 seconds. © 2024 The Author(s)

关键词： Adaptable step magnitude Arduino Uno board C++ code Maximum power point tracking (MPPT) Perturbation and observation (PO) Photovoltaic system Proteus software

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Mixture of Experts-Enabled Parallel Scheduling and Processing for Vehicular Generative AI Services

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Xie, Gaochang Xiong, Zehui Xie, Renchao Deng, Xiumei Guo, Song Guizani, Mohsen Han, Zhu Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing 100876 China Purple Mountain Laboratories Nanjing 211111 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates University of Houston Department of Electrical and Computer Engineering Houston 77004 TX United States Kyung Hee University Department of Computer Science and Engineering Seoul 446-701 South Korea

Foundation models (FMs) have revolutionized generative AI (GAI) lifecycle with their pre-trained intelligence capabilities. While the recent success of web-based models like GPT-4 has spurred interest in extending FMs to edge scenarios like the Internet of Vehicles (IoV), challenges such as data privacy, network congestion, and limited edge resource awareness hinder the direct application of cloud-based FMs. To address these challenges, this paper reconfigures the mixture of experts (MoE) architecture to deploy Transformer-based FM experts across vehicles for distributed GAI inference. Specifically, we propose an MoE-empowered vehicular FM system with two key innovations: a physical gating network that dynamically adapts to wireless environments, and a vehicle-to-vehicle (V2V) communication-based expert parallelism mechanism to enhance efficiency and resource utilization. Then, we formulate the communication, computation, and memory models for analyzing inference latency. Furthermore, to optimize the computational resource allocation for enhancing inference performance, we establish a Stackelberg game and propose the Gradient Ascent and Evolutionary Optimization-based Competitive Pricing and Allocation (GECPA) algorithm, which balances resource allocation and usage costs by combining the rapid convergence of gradient ascent with the broader exploration capability of evolutionary optimization. Simulation results demonstrate the superior parallel processing efficiency and reduced latency of the proposed MoE-based FM inference scheme. Compared with the best-performing benchmark algorithm, GECPA improves the average utility of infrastructure vehicles by up to 2.01 times, reduces inference latency by 16.58%, and increases the successful execution rate of GAI tasks by 10.66%, thereby achieving a better balance between efficiency and incentive compatibility in dynamic IoV environments. © 2015 IEEE.

关键词： expert parallelism foundation models (FMs) Generative AI (GAI) Internet of Vehicles (IoV) mixture of experts (MoE) resource allocation

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Adaptive Control with Global Exponential Stability for Parameter-Varying Nonlinear systems under Unknown Control Gains

arXiv

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arXiv 2022年

作者： Ye, Hefu Wu, Haijia Zhao, Kai Song, Yongduan Chongqing Key Laboratory of Autonomous Systems Institute of Artificial Intelligence School of Automation Chongqing University 400044 China Army Logistics Academy Chongqing401331 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this paper, without the need for PE condition, we address the problem of global exponential stabilization of strict-feedback systems with mismatched uncertainties and unknown yet time-varying control gains. The resultant control, embedded with time-varying feedback gains, is capable of ensuring global exponential stability of parametric-strict-feedback systems in the absence of persistence of excitation. By using the enhanced Nussbaum function, the previous results are extended to more general nonlinear systems where the sign and magnitude of the time-varying control gain are unknown. In particular, the argument of the Nussbaum function is guaranteed to be always positive with the aid of nonlinear damping design, which is critical to perform a straightforward technical analysis of the boundedness of the Nussbaum function. Finally, the global exponential stability of parameter-varying strict-feedback systems, the boundedness of the control input and the update rate, and the asymptotic constancy of the parameter estimate are established. Numerical simulations are carried out to verify the effectiveness and benefits of the proposed methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Modeling, Analysis and Simulation of the Effect of Temperature on Magnetostrictive Actuators

Modeling, Analysis and Simulation of the Effect of Temperatu...

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International Conference on Fluid Power and Mechatronics (FPM)

作者： Xinyuan Zhang Shaoping Wang Yu Niu Xiao Wu Xingjian Wang Alex I. Gavrilov School of Automation Science and Electrical Engineering Beihang University Beijing China Tianmushan Laboratory Hangzhou China Ningbo Institute of Technology Beihang University Ningbo China Research Institute of Aero-Engine Beihang University Beijing China Department of Automatic Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798350304947

ISBN: (纸本)9798350304954

The increased demand for active control of engines has made the study of high-frequency response actuators increasingly important, and actuators based on magnetostrictive materials are promising for a wide range of applications. In this paper, the magnetic field distribution and various thermal losses including Joule heat loss, eddy current loss and hysteresis loss inside the giant magnetostrictive actuator (GMA) are analyzed, and the mathematical model of the current-driven GMA operation is derived, where the new J-A model considering the temperature effect is used to describe the hysteresis nonlinearity of the material. This paper simulates the actuator model under the action of multiple physical fields in COMSOL, focusing on the variation of the output displacement of the actuator when the thermal field and thermal expansion are integrated, and provides the theoretical basis for the design of the GMA control algorithm in the future.

关键词： Actuators Analytical models Thermal expansion Temperature distribution Mechatronics Magnetostriction Magnetic hysteresis

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Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

arXiv

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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Fatigue Life Analysis of Piezoelectric Actuating Mechanism with Micro-displacement Amplifier

Fatigue Life Analysis of Piezoelectric Actuating Mechanism w...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xinyuan Zhang Shaoping Wang Xiao Wu Siyuan He Xingjian Wang Yu Niu Alex I. Gavrilov School of Automation Science and Electrical Engineering Beihang University Beijing China Tianmushan Laboratory Xixi Octagon City China Ningbo Institute of Technology Beihang University Ningbo China Research Institute of Aero-Engine Beihang University Beijing China Department of Automatic Control Systems Bauman Moscow State Technical University Moscow Russia

Since piezoelectric actuating mechanism generally operate in high frequency response state, fatigue life has become an important factor influencing the performance and reliability of the entire drive mechanism. In order to investigate whether the designed piezoelectric ceramic actuator meets the active control requirements of the aero-engine, the paper analyzes the fatigue characteristics and fatigue life of the piezoelectric ceramic materials and the micro displacement amplification mechanism with finite element analysis softwareˈ and the optimal spacing value is obtained by studying the effect of the flexible hinge spacing on the fatigue characteristics of the displacement amplifier. And then, the fatigue life of the displacement amplifier is analyzed based on the drive load spectrum of the aircraft during simulated flight, whose result is 340.93 hours. Finally, the fatigue life of the piezoelectric actuating mechanism is 32 minutes when loaded with full power, which meets the design target initially.

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Research and practice of lightweight digital twin speeding up the implementation of flexible manufacturing systems 1

Research and practice of lightweight digital twin speeding u...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhang, Xiaodong Hu, Bin Xiong, Gang Liu, Xinjie Dong, Xisong Li, Dongjia School of Information Engineering Yantai Institute of Technology Yantai China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijing China Yantai University Department of Mechanical and Electrical Engineering Yantai China School of Artificial Intelligence Yantai Institute of Technology Yantai China

ISBN: (纸本)9781665433372

Parallel manufacturing in Industry 5.0 requires digital twin to digitize physical systems, building virtual models to open up channels connecting physical systems, information systems, and social systems, and transforming the physical models of the existing production environment to achieve two-way feedback of virtual and real is the current research direction. This paper proposes the modeling idea of lightweight digital twin, extracts core dimensions and performs digital virtual simulation, so as to quickly realize the complete process of two-way feedback, and realize a set of chess flexible parallel manufacturing production lines as a practice for the design of complete lightweight digital twin. © 2021 IEEE.

关键词： Industrial research

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Urban trip generation forecasting based on gradient boosting algorithm 1

Urban trip generation forecasting based on gradient boosting...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Zhishuai Xiong, Gang Zhang, Yu Zheng, Meng Dong, Xisong Lv, Yisheng Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Traffic Planning Department Beijing China

ISBN: (纸本)9781665433372

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately. © 2021 IEEE.

关键词： Decision trees

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