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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

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Multimedia Tools and Applications 2024年 1-25页

作者： Polo-Rodríguez, A. Valera, J.C. Peral, J. Gil, D. Medina-Quero, J. Department of Computer Engineering Automation and Robotics University of Granada Granada18071 Spain Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings. © The Author(s) 2024.

关键词： Ultra-wideband (UWB)

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Robust Stability and Stabilizability Conditions for Time-Delay Systems Under Stochastic Uncertainties

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IEEE Transactions on Automatic control 2024年第6期70卷 4037-4044页

作者： Chen, Jianqi Mao, Qi Zhao, Di Chen, Chao Nanjing University Center for Advanced Control and Smart Operations Suzhou China Nanjing Normal University School of Electrical and Automation Engineering Nanjing China Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Shanghai China Leuven3001 Belgium

This study first explores the mean-square robust stability problem of stable continuous-time linear time-invariant systems subject to stochastic multiplicative uncertainties with prescribed variance bounds. The internal structures of uncertainties, however, are not presumed to cope with diverse random noises and errors arising from networked channels. A necessary and sufficient mean-square stability condition is obtained involving a novel small-gain type characterization. Next, we consider the output feedback controller synthesis problem of networked control systems facing stochastic multiplicative uncertainties and intrinsic channel-wise time delays simultaneously. Based on the obtained mean-square stability condition, further, we develop a fundamental necessary and sufficient condition of mean-square stabilizability explicitly. Such a condition equivalently determines that an open-loop unstable system can be stabilized by output feedback in the mean-square sense. Finally, an analysis of delay robustness is elaborated, in which the mean-square stabilizability condition is extended to the case tolerating uncertain but upper-bounded time delays. Overall, this study provides a comprehensive analysis of the robust mean-square stability and mean-square stabilizability of time-delay systems under stochastic uncertainties, which can have practical implications for networked control systems. © 1963-2012 IEEE.

关键词： Continuous time systems

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LLM-Powered Multi-Actor System for Intelligent Analysis and Visualization of IEC 61499 control Systems 50

LLM-Powered Multi-Actor System for Intelligent Analysis and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Xavier, Midhun Laikh, Tatiana Patil, Sandeep Vyatkin, Valeriy Department of Computer Science Electrical and Space Engineering Lulea Tekniska Universitet Sweden Department of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (纸本)9781665464543

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems. © 2024 IEEE.

关键词： control system analysis

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PV Grid Forecasting Analysis using Artificial Neural Network 25

PV Grid Forecasting Analysis using Artificial Neural Network

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25th International Middle East Power System Conference, MEPCON 2024

作者： Aboelmagd, Mohamed R. Selim, Ali Abdel-Akher, Mamdouh Aswan University Faculty of Engineering Department of Electrical Engineering Aswan Egypt Almaaqal University Department of Control and Computer Engineering Dep. Basra Iraq

ISBN: (纸本)9798350379648

In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison depends on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather;unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation. © 2024 IEEE.

关键词： Solar irradiance

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Assessing the Suitability of Software Tools for System-Theoretic Process Analysis of Nuclear Instrumentation and control Systems 29

Assessing the Suitability of Software Tools for System-Theor...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： King, Akira Ovsiannikova, Polina Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350361230

Modernization of currently operational nuclear power plants is becoming increasingly important to maintain their performance and safety. Ensuring the safety of newer Instrumentation and control (I&C) systems used in modernization efforts requires hazard analysis techniques suitable for the analysis of complex and software-heavy systems. System-Theoretic Process Analysis (STPA) has proven to be a suitable hazard analysis method for these complex I&C systems, however, its practical use is still often limited by its labor-intensive and time-consuming nature, partially due to the limitations of the tools used to perform the analysis: common Office tools such as Microsoft Excel or Visio. Conducting an STPA analysis could be simpler and more attractive with software tools specific to the method. This work introduces the requirements for these software tools and lays the foundation for further work, in which software tools will be evaluated against these requirements. © 2024 IEEE.

关键词： Nuclear power plants

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Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 control Applications 29

Generative AI Co-Pilot for Rapid Prototyping of IEC 61499 Co...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Ovsiannikova, Polina Liakh, Tatiana Jhunjhunwala, Pranay Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350361230

This work develops a concept of a natural language co-pilot for the creation of IEC 61499 applications to accelerate initial testing and development. The co-pilot addresses the challenge of generating the correct function blocks that can be opened in popular IEC 61499 IDEs. In our case study, we focus on the generation of the centralised controller that operates particular equipment to perform a defined process. In addition, we have developed a scheme for how the co-pilot will be integrated into the FBME IDE as an AI assistant in the next steps. In the discussion, we provide valuable information for future co-pilot developers on the possible problems they might face in this area and how to overcome them. © 2024 IEEE.

关键词： Rapid prototyping

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Image-Based Fast Finite-Time Target Tracking control of Onboard Inertially Stabilized Camera Platform

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IEEE Transactions on Industrial Electronics 2024年第12期71卷 16409-16418页

作者： Sun, Xunhong Zhu, Wenwu Du, Haibo Chen, Weile Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

In this article, we investigate the problem of fast finite-time target tracking control of the inertially stabilized platform (ISP) with a camera that mounted on a mobile robot. At first, the kinematics of the onboard ISP target tracking system is established by the geographic coordinate and pinhole camera model, which includes the state-dependent uncertainties and external disturbances. Then, based on the image information captured by camera, a continuous image-based fast finite-time control algorithm is designed for target tracking. Rigorous analysis proves that the target can be tracked at the center of camera's view in finite time in the absence of disturbances. In the presence of disturbances, the tracking error can converge to a region around the origin in finite time. Simulation and experimental comparisons are presented to confirm the efficiency of the proposed method. © 1982-2012 IEEE.

关键词： Mobile robots

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Induction motor speed control performance enhancement using fractional order filters

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International Journal of engineering Systems Modelling and Simulation 2025年第3期16卷 161-173页

作者： Hassainia, Saida Ladaci, Samir Kechida, Sihem Khelil, Khaled Department of Electrical Engineering and Automation University of 08 May Guelma1945 Algeria Mohamed Cherif Messaadia University Souk-Ahras Algeria Department of Automatic Control Engineering Ecole Nationale Polytechnique Algiers16200 Algeria University of 8 May Guelma1945 Algeria

This paper proposes a novel methodology for performance enhancement of simple feedback control by means of fractional order filters applied to the speed drive of an induction machine. Three performance indexes are used for this aim namely the integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE). A first order filter in different positions of the control loop is considered in presence of a noise source in order to design the filter configuration. Then, an optimisation of the fractional order filter is realised. Comparative simulation results are given to illustrate the superiority and robustness of the proposed control scheme with regard to integer order filters and unfiltered controllers. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Proportional control systems

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