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International Conference on Applied Electronics, AE

作者： Emília Bubeníková Peter Holečko Alžbeta Kanáliková Peter Peniak Juraj Kekelák Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovak Republic

ISBN: (数字)9798350350739

ISBN: (纸本)9798350362299

Our article deals with the Edge device model focusing on high-availability of Long-Range Wide Area (LoRaWAN) sensors for business-critical applications. We focus on creating a suitable Edge device model that ensures the high availability of sensor data for specific critical applications. The model was implemented in Node-RED software, based on the MQTT protocol, and subsequently validated to confirm the required duplication of LoRaWAN sensors values for application. In case of single failure, Edge model enables uninterrupted flow of sensors data for their processing by critical applications.

关键词： LoRaWAN Focusing Data models Software Sensors

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Testing Strength Based on the Principle of Robotic Arm Wrestling

Testing Strength Based on the Principle of Robotic Arm Wrest...

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International Conference on Applied Electronics, AE

作者： Emília Bubeníková Jozef Hrbček Juraj Ždánsky Dušan Nemec Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovak Republic

ISBN: (数字)9798350350739

ISBN: (纸本)9798350362299

This article describes the design and development of an arm that measures human physical condition controlled by a Programmable Logic controller (PLC), ensuring system control, adjustment, and display of necessary results. A user-friendly interface, both local and remote, will be designed and implemented. The system incorporates safety features to ensure the safety of individuals. The design and implementation are based on a digital twin linked to the control system, including simulation. This approach accelerates the development of application implementation with the possibility of creating control programs for the PLC and fine-tuning the system during the design phase. Safety functions ensuring the safety of tested individuals are applied. The developed system will be portable and will serve for scientific experiments, presentation activities, and as an educational tool.

关键词： Force measurement Programmable logic devices Force Manipulators control systems Safety Digital twins Testing

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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Computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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Infrared Hot Spot Detection of Photovoltaic Arrays Based on Optimization and Improvement Using YOLOv8 1st

Infrared Hot Spot Detection of Photovoltaic Arrays Based on ...

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1st Electrical Artificial Intelligence Conference, EAIC 2024

作者： Peng, ZiRan Wang, SiYua Xiao, Shenping Department of Electrical and Information Engineering Hunan University of Technology Zhuzhou412007 China Hunan Key Laboratory of Electric Drive Control and Intelligent Equipment Hunan University of Technology Zhuzhou412007 China

ISBN: (纸本)9789819640621

Detecting hot spots on solar cells in mountainous photovoltaic power plants is challenging due to high false and missed detection rates. This paper proposes S-YOLO, a lightweight model based on YOLOv8, for efficient hot spot detection. The model introduces a Spatial Confusion Backbone to reduce parameters and computational complexity while enhancing feature extraction with attention mechanisms. A Multi-scale Feature Pyramid Network (MSBFPN) improves small-target detection, and the SIoU loss function replaces CIoU for faster convergence and precise regression. Compared to YOLOv8, S-YOLO reduces weights by 41.3%, parameters by 21.2%, and increases accuracy by 2.3%. Its lightweight design and high accuracy make it ideal for drone-based inspections in complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrared detection Loss function SIoU Network model Solar cells YOLOv8

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments 13

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mavrovouniotis, Michalis Li, Changhe Yazdani, Danial Handjimitsis, Diofantos Eratosthenes Centre of Excellence Limassol Cyprus Cyprus University of Technology Department of Civil Engineering and Geomatics Limassol Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China University of Technology Sydney Faculty of Engineering & Information Technology Sydney Australia

ISBN: (纸本)9798350308365

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum. © 2024 IEEE.

关键词： Ant colony optimization

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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A Novel Fuzzy PIDF Enhancing PIDF controller Tuned in Two Stages by TLBO and PSO Algorithms for Reliable AVR Performance

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IEEE Access 2025年 13卷 93505-93522页

作者： Mokhtar Shouran Mohammed Alenezi Libyan Centre for Engineering Research and Information Technology Bani Waled Libya Department of Control Engineering College of Electronic Technology Bani Walid Libya School of Engineering Cardiff University Cardiff U.K.

Reliability in control engineering remains a fundamental consideration in the design of controllers for various applications. This study proposes an innovative control design for Automatic Voltage Regulator (AVR) that integrates Fuzzy Proportional-Integral-Derivative with Filter action (Fuzzy PIDF) enhancing PIDF controllers (PIDF + Fuzzy PIDF), which is tuned in a two-stage framework. This framework leverages Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms to find the optimum values of the PIDF and Fuzzy-PIDF controllers’ gains, respectively. The dual-stage design ensures that the controller maintains functionality even if one stage experiences partial failure, thereby enhancing the reliability of both the controller and the system under control. The tuning process for both stages employs the well-known Integral Time Absolute Error (ITAE) cost function in conjunction with the TLBO and PSO algorithms. The reliability of the proposed controller is rigorously tested, demonstrating its ability to deliver robust performance even when operating partially. A comprehensive comparative analysis with existing techniques in the literature demonstrates the superior performance of the proposed PIDF plus Fuzzy PIDF controller. The results show significant improvements in transient response metrics: overshoot is reduced from 0.1559 pu to 0.0211 pu, undershoot from 0.0954 pu to 0.006 pu, and settling time from 0.7568 seconds to 0.2299 seconds. These advancements underscore the controller’s enhanced dynamic stability and precision compared to prior methodologies. Additionally, the robustness of the PIDF + Fuzzy-PIDF controller is evaluated under conditions of system uncertainty and varying load disturbances, with validation conducted in the MATLAB and Simulink environments. The results affirm the controller’s superiority, robustness, and reliability, establishing its potential for successful implementation in real-world applications. Thi

关键词： Reliability Power system stability Voltage control Power system reliability Optimization Tuning Adaptive control Renewable energy sources Power system dynamics Robustness

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