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Field Robotics 2025年 2卷 222-240页

作者： Antonella Barišić Frano Petric Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb Croatia

This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the position, orientation, and size of a figure-eight shaped trajectory, which we approximate using a Bernoulli lemniscate. Once the approximation is deemed sufficiently precise, as measured by the distance between observations and estimate, we calculate an interception point to position the interceptor UAV directly on the intruder's path. Our method, which we have significantly improved based on the experience gathered during the MBZIRC competition, has been validated in simulation and through field experiments. Our results confirm that we have developed an efficient, visual-perception module that can extract information describing the intruder UAV's motion with precision sufficient to support interception planning. In a majority of our simulated encounters, we can track and intercept a target that moves 30% faster than the interceptor. Corresponding tests in an unstructured environment yielded 9 out of 12 successful results.

关键词： Autonomous aerial vehicles Trajectory Target tracking Cameras Three-dimensional displays Robots Radar tracking Image reconstruction Estimation Detectors

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PMSM Sensorless control Based on Extended State Model Improved Sliding Mode Observer

PMSM Sensorless Control Based on Extended State Model Improv...

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International Conference of electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Wu, Qinghui Zhang, Ruilong Tang, Guoshang Xu, Tongxi Hou, Yuanxiang Faculty of Electrical and Control Engineering Liaoning Technical University Liaoning Province Huludao City125000 China Yantai Dongfang Wisdom Electric Co Ltd Shandong Province Yantai City264003 China

ISBN: (纸本)9789819618514

The sensorless control strategy is a commonly used motor control strategy. To solve the problem of the traditional sliding mode observer’s oscillation during the operation of permanent magnet synchronous motors (PMSM), a piecewise elliptic function is proposed in the paper. Combined with an improved integral type non-singular terminal sliding mode observer based on the extended state model, further suppresses the oscillation phenomenon of sliding mode and the phase delay generated by filters. A normalized phase-locked loop is proposed to address the issue of bandwidth changes in traditional phase-locked loops during motor speed regulation. Experiments show that the proposed scheme has the advantages of high observation accuracy, good dynamic performance, and low chattering in the static stage. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Permanent magnets

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Autonomous, Mobile Manipulation in a Wall-Building Scenario: Team LARICS at MBZIRC 2020

Field Robotics

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Field Robotics 2025年 2卷 201-221页

作者： Ivo Vatavuk Marsela Polic Ivan Hrabar Frano Petric Matko Orsag Stjepan Bogdan Laboratory for Robotics and Intelligent Control systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, autonomously, to detect, manipulate, and transport bricks in an unstructured, outdoor environment. Our control approach is based on a state machine that dictates which controllers are active at each stage of the Challenge. In the first stage our UGV uses visual servoing and local controllers to approach the target object without considering its orientation. The second stage consists of detecting the object's global pose using OpenCV-based processing of RGB-D image and point-cloud data, and calculating an alignment goal within a global map. The map is built with Google Cartographer and is based on onboard LIDAR, IMU, and GPS data. Motion control in the second stage is realized using the ROS Move Base package with Time-Elastic Band trajectory optimization. Visual servo algorithms guide the vehicle in local object-approach movement and the arm in manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we developed a passively-compliant, electromagnetic gripper with tactile feedback. Our fully-autonomous UGV performed well in Challenge 2 and in post-competition evaluations of its brick pick-and-place algorithms.

关键词： Robots Robot kinematics Navigation Manipulators Cameras Buildings Grippers Automation Service robots Laser radar

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Overview on Contact Bounce Phenomena of Electromagnetic Type Switch electrical Apparatus 11th

Overview on Contact Bounce Phenomena of Electromagnetic Type...

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11th Frontier Academic Forum of electrical engineering, FAFEE 2024

作者： Wang, Zhiyong Zhu, Zhongheng Wang, Xili Zhao, Qi Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125105 China Huludao Jili Industrial Control Automation Technology Co. Ltd. Huludao125001 China Guoneng Nortel Shengli Energy Co. Ltd. Xilingol026000 China

ISBN: (纸本)9789819788156

This article initially presents the working principle of electromagnetic switching devices and the significance of the contact system, distinguishing contact vibration into contact closure bounce and contact release rebound. It emphasizes that these phenomena are crucial factors influencing the lifespan and reliability of electrical devices. Subsequently, through the analysis of domestic and international literature, the paper discusses the academic community's progress in understanding and improving these vibration phenomena. The study shows that closure bounce and release rebound are affected by factors such as structural parameters and the contact engagement method. Notably, scholars have proposed innovative solutions, such as employing the principle of eccentric impact with inclined stationary contacts to suppress closure bounce, and adding limit stop plates between moving and stationary contact leaves to significantly mitigate release rebound, thereby markedly improving the appearance of contact vibration. Finally, the paper summarizes the current state of research on contact vibration in electromagnetic switches, pointing out the deficiencies in existing studies. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Vibration analysis

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Multi-objective optimization model at park level considering economy and PV uptake rate 24

Multi-objective optimization model at park level considering...

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8th International Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Jin, Fuguo Chen, Xinyu Lei, Xiangyu Li, Kun State Grid Liaoning Province Electric Power Co. Ltd. Fuxin Power Supply Company Liaoning Fuxin China Faculty of Electrical and Control Engineering Liaoning Technical University Liaoning Huludao China

ISBN: (纸本)9798400710094

To address the issues of low absorption rate and high operating cost in the distributed photovoltaic power generation system within the park, a multi-objective optimization model built upon the foundation of the Internet of Things technology is proposed within the context of this study. Firstly, collect the relevant data of distributed photovoltaic new energy in the park, design a reasonable park-level power scheduling strategy, Secondly, the multi-objective optimization model is established to minimize the system operating cost and maximize the photovoltaic absorption rate. Finally, in order to better solve the established multi-objective optimization model, an improved L-NSGA-III algorithm is proposed. The population crossover and mutation strategies based on adaptive strategies are added to the NSGA-III algorithm, which not only improves the accuracy of the algorithm, but also enhances the stability of the algorithm. The experimental results demonstrate that the proposed model can realize the coordinated operation of photovoltaic power generation system (PV) and distribution network, under the premise that the local consumption of distributed photovoltaic energy is not less than 95%, the operating cost of the system is minimized. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy System

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Synthesis of Dynamic Virtual-Inertia for Microgrid System Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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2025 International Conference on Intelligent control, Computing and Communications, IC3 2025

作者： Justo, Jackson J. Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Makolo, Peter Bansal, Ramesh C. Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia University of Dar es Salaam Department of Electrical Engineering P.O. Box 33335 Dar es Salaam Tanzania United Republic of University of Sharjah Depatment of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331527495

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied. © 2025 IEEE.

关键词： Solar fuels

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Identification Method of Series Arc Fault in Electric Bicycle Battery Charging Circuit 11th

Identification Method of Series Arc Fault in Electric Bicycl...

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11th Frontier Academic Forum of electrical engineering, FAFEE 2024

作者： Wang, Zhiyong Feng, Jingyu Ge, Shiwei Jv, Guanbo Xie, Zhenhua Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125105 China Zhejiang Tengen Electric Co. Ltd Yueqing325600 China Zhejiang Zhongke City Safety Fire Technology Co. Ltd Yueqing325600 China Zhejiang Institute of Mechanical & Electrical Engineering Design Co. Ltd Hangzhou310051 China

ISBN: (纸本)9789819788156

In recent years, fire accidents caused by the fault of the electric bicycle charging circuit have occurred frequently. In order to identify the occurrence of arc fault in the battery charging stage, a feature identification method of AC series arc fault based on the combination of variational mode decomposition (VMD) and multi-scale sample entropy was proposed. The series arc fault experiments in the electric bicycle battery charging circuit was carried out by using a self-developed arc fault generator, and the measured circuit current signal was decomposed by VMD. The multi-scale sample entropy was calculated by using the decomposed IMF components and used as the series arc fault features. Combined with an optimized support vector machine, the series arc fault was identified. The results indicated that the method proposed in the paper can efficiently identify the arc fault in the battery charging stage. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Variational mode decomposition

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