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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Peng Zhang Ziming Luo Sentang Wu School of Automation Science and Electrical Engineering Beihang University Beijing China General Department of Networks and Communications 54th Research Institute of the China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

This paper addresses the challenge of achieving resilient quantized consensus in multiagent systems operating within adversarial environments. Existing algorithms often rely on an assumption of the maximum adversarial agents in the multiagent systems or neighbors which may be impossible to appropriately estimate in practice. To address this limitation, we propose a novel filtering method that dynamically maintains a trusted neighbor set to identify and filter out suspicious states without requiring knowledge of the maximum adversaries. Additionally, we introduce a distributed resilient quantized control protocol tailored for regular agents, ensuring consensus despite limited communication and memory constraints that enforce agents to adopt integer values. We analyze a graph-theoretic condition that is both necessary and sufficient for our proposed control protocol to succeed. Finally, we validate our theoretical findings through simulations.

关键词： Protocols Parameter estimation Control engineering Filtering Heuristic algorithms Memory management Filtering algorithms

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Core Loss Separation Method for High Q Inductors

Core Loss Separation Method for High Q Inductors

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Wireless Power Week (WPW)

作者： Aaron Wadsworth Wenting Zhang Matthew G.S. Pearce Duleepa J. Thrimawithana Lei Zhao Zongzhen Li Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand College of Automation Chongqing University Chongqing China Jiangsu JITRI Advanced Energy Materials Research Institute Changzhou China

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

Magnetic components used in power electronics are typically the largest and least efficient part of the design. For designers of power magnetics it is necessary to have reliable measurements of loss so the appropriate core can be selected. However, this is a challenging task since small errors in phase measurements can cause large errors in core loss. This is particularly challenging for cores with a phase angle close to 90°, i.e. a high Q. In this work a loss separation method and new phase correction method are proposed. Experimental results show the proposed method has lower error than existing state of the art techniques.

关键词： Wireless communication Power measurement Phase measurement Magnetic cores Magnetic separation Core loss Measurement techniques

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Detecting Electricity Theft in Smart Grids: A Hybrid CNN-RF Approach

Detecting Electricity Theft in Smart Grids: A Hybrid CNN-RF ...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Safia Babikir Bashir Mena Maurice Farag Abdul-Kadir Hamid Ali Ahmed Ismail Ramesh C. Bansal A. Elnady Sustainable Energy and Power Systems Research Centre Research Institute of Sciences and Engineering (RISE) University of Sharjah Sharjah United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Electricity theft poses a significant economic and operational challenge to modern power grids, resulting in substantial financial losses and compromising grid reliability. Traditional detection methods, such as manual inspection and basic statistical analysis, are insufficient for addressing the complexity and scale of theft in smart grids. This study proposes a hybrid machine learning model, CNN-RF, combining convolutional neural networks (CNNs) for feature extraction and random forests (RF) for classification. Using a publicly available dataset spanning 12 months of hourly energy consumption data across 16 consumer categories, the model achieves superior accuracy of 88.84%, precision of 86.55%, recall of 88.84%, and an F1-score of 86.60% compared to standalone CNN, RF, and support vector machine (SVM) models. Specifically, CNN-RF improves accuracy by 1.6% over RF and 0.8% over CNN, demonstrating the advantage of integrating deep feature extraction with robust classification.

关键词： Radio frequency Support vector machines Deep learning Energy consumption Accuracy Electricity Feature extraction Data models Smart grids Convolutional neural networks

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Comparative Analysis of Online Monitoring Methods for Inertia in New Power System

Comparative Analysis of Online Monitoring Methods for Inerti...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Chen Fan Yalun Wang Qi Wang Yefeng Jiang Qibing Zhang Lin Shi Power Automation Department China Electric Power Research Institute Nanjing China School of Electrical Engineering Southeast University Nanjing China State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Due to the decrease of inertia caused by the grid-connected large-scale new energy in the new power system, the demand to evaluate the inertia of power system has become urgent, and the on-line monitoring of inertia based on PMU measurement data is considered as an effective method at present. Three typical online monitoring methods are discussed respectively which are ordinary calculation method, integral calculation method and differential calculation method. The theoretical calculation of these three typical methods is presented. Simulation analysis is carried out by IEEE39 nodes with load increase and cutting off of generator respectively, and the characteristics of each inertia calculation method are compared and analyzed, and a conclusion that the error of ordinary calculation method is smallest and most stable among these three methods, which is recommended. It can provide reference for online calculation and analysis of inertia in the future.

关键词： Power measurement Estimation Energy measurement System integration Power system stability Generators Stability analysis Power grids Phasor measurement units Monitoring

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A Triboelectric-Based Artificial Whisker for Reactive Obstacle Avoidance and Local Mapping

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research 2021年第1期2021卷 1485-1494页

作者： Peng Xu Xinyu Wang Siyuan Wang Tianyu Chen Jianhua Liu Jiaxi Zheng Wenxiang Li Minyi Xu Jin Tao Guangming Xie Marine Engineering College Dalian Maritime UniversityDalian 116026China College of Artificial Intelligence Nankai UniversityTianjin 300350China Department of Electrical Engineering and Automation Aalto UniversityEspoo 02150Finland Intelligent Biomimetic Design Lab College of EngineeringPeking UniversityBeijing 100871China Institute of Ocean Research Peking UniversityBeijing 100871China

Since designing efficient tactile sensors for autonomous robots is still a challenge,this paper proposes a perceptual system based on a bioinspired triboelectric whisker sensor(TWS)that is aimed at reactive obstacle avoidance and local mapping in unknown *** proposed TWS is based on a triboelectric nanogenerator(TENG)and mimics the structure of rat whisker *** operates to generate an output voltage via triboelectrification and electrostatic induction between the PTFE pellet and copper films(0.3 mm thickness),where a forced whisker shaft displaces a PTFE pellet(10 mm diameter).With the help of a biologically inspired structural design,the artificial whisker sensor can sense the contact position and approximate the external stimulation area,particularly in a dark *** highlight this sensor’s applicability and scalability,we demonstrate different functions,such as controlling LED lights,reactive obstacle avoidance,and local mapping of autonomous surface *** results show that the proposed TWS can be used as a tactile sensor for reactive obstacle avoidance and local mapping in robotics.

关键词： whisker obstacle autonomous

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NDDepth: Normal-Distance Assisted Monocular Depth Estimation

NDDepth: Normal-Distance Assisted Monocular Depth Estimation

引用

International Conference on Computer Vision (ICCV)

作者： Shuwei Shao Zhongcai Pei Weihai Chen Xingming Wu Zhengguo Li School of Automation Science and Electrical Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China SRO Department Institute for Infocomm Research A*STAR Singapore

Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep learning framework for monocular depth estimation by assuming that 3D scenes are constituted by piece-wise planes. Particularly, we introduce a new normal-distance head that outputs pixel-level surface normal and plane-to-origin distance for deriving depth at each position. Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint. We further integrate an additional depth head to improve the robustness of the proposed framework. To fully exploit the strengths of these two heads, we develop an effective contrastive iterative refinement module that refines depth in a complementary manner according to the depth uncertainty. Extensive experiments indicate that the proposed method exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI depth prediction online benchmark at the submission time. The source code is available at https://***/ShuweiShao/NDDepth.

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Nanocrystalline Core Loss in High Power IPT Systems for EV Charging Applications

Nanocrystalline Core Loss in High Power IPT Systems for EV C...

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Wireless Power Week (WPW)

作者： Wenting Zhang Seho Kim Duleepa J. Thrimawithana Grant A. Covic Lei Zhao Zongzhen Li Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand School of Automation Chongqing University Chongqing China Jiangsu JITRI Advanced Energy Materials Research Institute Changzhou China

ISBN: (数字)9798350349139

ISBN: (纸本)9798350349146

Mn-Zn ferrite is commonly used in inductive power transfer (IPT) systems to enhance magnetic coupling and reduce flux leakage. However, ferrite is not ideal for high power transfer due to the large volume of material needed for its low saturation flux density, brittleness, and high core loss leading to thermal issues. Nanocrystalline alloys are potential alternatives to replace ferrite in IPT pads to improve magnetic performance. This paper analyses the core loss and equivalent series resistance (ESR) of ferrite and nanocrystalline cores in high-power IPT pads based on the measurement of magnetic properties using the partial cancellation method. The experimental findings show that the ESR of IPT pads using nanocrystalline cores decreases with higher excitation current in the pads, contrary to the typical behaviour of IPT pads using ferrite.

关键词： Ferrites Wireless communication Magnetic flux leakage Magnetic cores Current measurement Core loss Metals

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High Gain Multilevel Boost Converter-Based Synergetic MPPT Controller for Standalone Photovoltaic Array

High Gain Multilevel Boost Converter-Based Synergetic MPPT C...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Afaf Zeita Melika Gaid Abdul-Kadir Hamid Mena Maurice Farag Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre Research Institute for Sciences and Engineering (RISE) University of Sharjah Sharjah United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Photovoltaic (PV) system output power is strongly influenced by temperature variations, shadowing, and irradiation. Monitoring the Maximum Power Point (MPP) under various climatic conditions is necessary to increase efficiency and optimize the output power that PV systems supply to the load. Numerous strategies have been put forth to maximize the power delivered to the load, such as Perturb and Observe (P&O), Incremental Conductance (IC), and Fractional Open-Circuit Voltage (FOCV). Furthermore, advanced Maximum Power Point Tracking (MPPT) techniques such as Synergetic Control (SC) and Artificial Intelligence (AI) have also been proposed for improved tracking performance. In this context, this paper provides a novel combination of nonlinear closed-loop SC with a Multi-Level Boost Converter (MLBC) to track the MPP of a standalone PV system. MLBCs hold useful benefits compared to traditional DC/DC converters in high voltage gain applications using smaller inductor values. A performance study of the proposed system proposed a tracking efficiency of 99.86% of MPPT at Standard Testing Conditions (STC) and under sudden changes in irradiance, temperature, and load. A faster response of 15 ms is recorded providing its superiority over contemporary trackers. The proposed system is robust when compared to traditional DC-DC single-level converters.

关键词： Maximum power point trackers Photovoltaic systems Radiation effects Power system dynamics Solar energy Power grids Artificial intelligence Standards Testing Shadow mapping

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The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

arXiv

引用

arXiv 2023年

作者： Zakani, Zeinab Moradi, Hadi Ghasemzadeh, Sogand Riazi, Maryam Mortazavi, Fatemeh Department of Psychology University of Tehran Iran Department of Robotics and Artificial Intelligence University of Tehran Iran Intelligent Systems Research Institute SKKU Iran Department of Electrical and Computer Engineering Boston University United States Department of Robotics and Artificial Intelligence University of Tehran Tehran Iran

Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. © 2023, CC BY-NC-ND.

关键词： Serious games

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Adaptive and Enhanced Hydrogen Production Using Dynamic Particle Swarm Optimization for PV-PEM Systems

Adaptive and Enhanced Hydrogen Production Using Dynamic Part...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Mena Maurice Farag Abdul-Kadir Hamid Safia Babikir Bashir Smart Automation and Communication Technologies Research Center (SACT) Research Institute of Sciences and Engineering (RISE) University of Sharjah Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This study addresses the inefficiencies of static models in solar-driven hydrogen production by proposing a dynamic Particle Swarm Optimization (PSO) framework for a 2.88 kW photovoltaic (PV)-proton exchange membrane (PEM) electrolyzer system under extreme summer conditions. Unlike static models with fixed parameters, the PSO-based approach dynamically optimizes electrolyzer input power in real time, adapting to fluctuating solar irradiance up to 1000 W/m 2 and ambient temperatures ranging from 25°C to 48°C. Simulation results demonstrate that the dynamic model outperforms static counterparts, achieving 11.63% higher total hydrogen production, with 28.06 kg and 25.14 kg for dynamic and static models, respectively, and 7.76% greater average hourly output. System efficiency improved from 64.97% to 69.29%, with penalty mechanisms ensuring operational stability by preventing voltage and thermal violations during rapid environmental shifts. This work uniquely addresses nonlinear efficiency losses caused by PV temperature degradation and electrolyzer power mismatches in harsh climates. The findings validate the superiority of real-time metaheuristic optimization, offering a scalable solution to enhance renewable energy utilization and grid resilience.

关键词： Solar irradiance Photovoltaic systems Adaptation models Hydrogen Production Real-time systems Nonlinear dynamical systems Particle swarm optimization Thermal stability Meteorology

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