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IAENG International Journal of Computer Science 2021年第3期48卷 1-7页

作者： Velez-Ramirez, Juan S. Giraldo, Eduardo Graduated student The master on Electrical Engineering program Universidad Tecnologica de Pereira Pereira Colombia Professor at the Department of Electrical Engineering Universidad Tecnologica de Pereira Research Group in Automatic Control Pereira Colombia

In this work, a method for hierarchical hybrid linear-nonlinear microgrid control is proposed based on nonlinear primary control and linear secondary control. The nonlinear controller is an Extended Feedback Linearization method, and the linear controller is a PID structure. The proposed method is evaluated in several scenarios to validate its performance to disturbances and island operation mode. To this end, three units of distributed generation are included: Photo-Voltaic Energy Sources, Energy Storage Systems, and Small-Hydro-Power Sources. The results are simulated over a DC microgrid that contains three different distributed generation units with disturbances and island operation mode. As a result, the proposed approach's robustness is validated for several cases, where the advantage of a hierarchical linear-nonlinear structure is shown. © 2021. All Rights Reserved.

关键词： controllers

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Predictive maintenance for electrical motors: Current approach and usage of artificial intelligence algorithms

Predictive maintenance for electrical motors: Current approa...

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International Conference on Methods and Models in automation and Robotics (MMAR)

作者： Tomasz Herzog Krzysztof Bartecki Faculty of Electrical Engineering Automatic Control and Computer Science Opole University of Technology Opole Poland Rockwell Automation Power Control Business Katowice Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The article presents the current approach in artificial intelligence methods used in predictive maintenance for electrical motors. The study focuses on presenting data classification methods for fault detection and isolation using data from condition monitoring systems. We discuss the advantages and disadvantages of using machine learning methods like perceptron and Support Vector Machine (SVM) for fault classification problems. Our conclusion shows great interest in machine learning methods combined with condition monitoring systems. Our future research will focus on comparing SVM, perceptron networks, and other methods, such as K-nearest neighbors, with their possibility for implementation in embedded firmware systems.

关键词： Support vector machines Condition monitoring Accuracy Fault detection Artificial neural networks Machine learning Prediction algorithms

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SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8 8th

SGDR-YOLOv8: Training Method for Rice Diseases Detection Usi...

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8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024

作者： Thanh, Bui Dang Anh, Mac Tuan Khanh, Giap Dang Dong, Trinh Cong Huong, Nguyen Thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

In Vietnam, rice stands as a vital food source for the whole population, and because of that timely identification and management of rice plant diseases is extremely crucial. Among the diseases that appear in Vietnam, blast leaf, leaf folder, and brown spot are among the most prevalent ailments that harm rice plants, directly impacting cultivation. To address this problem, deep learning, a cutting-edge solution can be used to detect plant diseases. In this paper, we present an approach utilizing Deep learning to identify illnesses affecting rice leaves. YOLOv8 is employed and trained with various strategies of Stochastic Gradient Descent with Warm Restart (SGDR) to identify diseases on rice leaves. The result of the proposed method is assessed using 309 images. The experimental findings reveal an accuracy of 88.6%, indicating superior performance compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Plant diseases

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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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ANALYSIS OF SURFACE-MOUNTED PERMANENT MAGNET SYNCHRONOUS ELECTRIC MACHINE PARAMETERS ON THE OUTPUT TORQUE AND EFFICIENCY

ANALYSIS OF SURFACE-MOUNTED PERMANENT MAGNET SYNCHRONOUS ELE...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Su, Hang Yan, Liang Wei, Xiaocheng Wang, Jin Jin, Zijian Dong, Ziwei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China

Surface-mounted permanent magnet synchronous electric machines play an important role in industrial production. Output torque and efficiency are important indicators of electric machines. The key parameters of electric machines have a great influence on output torque and efficiency. First, this paper gives the conceptual design diagram of the electric machine and derives the governing equations of output torque and efficiency. The electric machine structure parameters are optimized by numerical simulation, and the relationships between the core material, permanent magnet grade and output torque and efficiency are studied. Subsequently, the influence of stator and rotor parameters on output torque and efficiency is researched. The research results show that high-performance cores, high-grade permanent magnets, large tooth width, small slot open width, large magnet thickness and electrical angle, and small magnet reduction can help improve the output torque and efficiency of the motor. © The Institution of engineering & Technology 2024.

关键词： Permanent magnets

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ELECTROMAGNETIC THERMAL DESIGN AND ANALYSIS OF AVIATION DUAL THREE-PHASE PERMANENT MAGNET MACHINE

ELECTROMAGNETIC THERMAL DESIGN AND ANALYSIS OF AVIATION DUAL...

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Wang, Jin Yan, Liang Gao, Xiaoshan Su, Hang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China

The multi-phase redundant permanent magnet machine is essentially a permanent magnet synchronous motor (PMSM) with multiple sets of windings arranged on the stator. This configuration facilitates fault-tolerant operation of the PMSM in the event of one or multiple phase windings failing. In response to the practical requirements of electromechanical actuation systems in the aviation industry, this paper proposed a solution for a high-power, high-speed multi-phase redundant permanent magnet machine. The stator and rotor configuration of the multi-phase machine, along with short-circuit current suppression, are analysed, leading to the design of a prototype with specifications of 7.3 kW and 14,000 rpm. The finite element method is employed to simulate the machine's performance under no-load and full-load conditions, as well as its redundancy and thermal characteristics. The simulation results verify the correctness of the design approach well. © The Institution of engineering & Technology 2024.

关键词： Stators

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Online Learning-Based Predictive control for Nonlinear System

arXiv

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arXiv 2025年

作者： Zhang, Yuanqing Zhang, Huanshui School of Control Science and Engineering Shandong University Shandong Jinan250061 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China

In this paper, we propose an online learning-based predictive control (LPC) approach designed for nonlinear systems that lack explicit system dynamics. Unlike traditional model predictive control (MPC) algorithms that rely on known system models to optimize controller outputs, our proposed algorithm integrates a reinforcement learning component to learn optimal policies in real time from the offline dataset and real-time data. Additionally, an optimal control problem (OCP)-based optimization framework is incorporated to enhance real-time computational efficiency while ensuring stability during online operation. Moreover, we rigorously establish the super-linear convergence properties of the algorithm. Finally, extensive simulations are performed to evaluate the feasibility and effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor

A Nonsmooth Observer Based on Friction Model Compensation fo...

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第42届中国控制会议

作者： Zaishan Zhang Wenwu Zhu Ke Yu Xiangyu Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Electrical Engineering and Automation Hefei University of Technology

In this paper,a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor(PMSM) to estimate the rotor position and *** is one of the main factors affecting the tracking accuracy of the motor at low speed,especially in the back and forth motion of the ***,based on the Stribeck friction model,a nonlinear friction model of the motor is ***,a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor,and the lumped disturbance of the system at the same ***,the stability analysis of this nonsmooth observer is ***,the effectiveness of the proposed method is verified by the simulation results.

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Semi-supervised Learning with GAN for Semantic Segmentation of Wood Defect Image

Semi-supervised Learning with GAN for Semantic Segmentation ...

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Chinese automation Congress (CAC)

作者： Kunxin Zheng Song Zheng Zhezhuang Xu Ye Lin Yuhang Zhu Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data.

关键词： Semantic segmentation Semisupervised learning Generative adversarial networks Generators Robustness Product design Data models Quality assessment Data mining Defect detection

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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Chinese automation Congress (CAC)

作者： Haoran Zhang Zhezhuang Xu Yuhang Zhu Ye Lin Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface.

关键词： Accuracy automation Shape Image edge detection Wood industry Feature extraction Encoding Character recognition Faces Defect detection

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