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arXiv 2023年

作者： Yamamoto, Kenjiro Ito, Hiroshi Ichiwara, Hideyuki Mori, Hiroki Ogata, Tetsuya Robotics Research Department Connective Automation Innovation Center R&D Group Hitachi Ltd Germany Institute for AI and Robotics Future Robotics Organization Japan Faculty of Science and Engineering Waseda University Japan

While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due to their dynamic properties, requires learning a wide range of factors such as the object’s position, movement speed, and grasping timing. We propose a data augmentation method for enabling a robot to grasp moving objects with different speeds and grasping timings at low cost. Specifically, the robot is taught to grasp an object moving at low speed using teleoperation, and multiple data with different speeds and grasping timings are generated by down-sampling and padding the robot sensor data in the time-series direction. By learning multiple sensor data in a time series, the robot can generate motions while adjusting the grasping timing for unlearned movement speeds and sudden speed changes. We have shown using a real robot that this data augmentation method facilitates learning the relationship between object position and velocity and enables the robot to perform robust grasping motions for unlearned positions and objects with dynamically changing positions and velocities. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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Adaptive and Enhanced Hydrogen Production Using Dynamic Particle Swarm Optimization for PV-PEM Systems 7

Adaptive and Enhanced Hydrogen Production Using Dynamic Part...

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Farag, Mena Maurice Hamid, Abdul-Kadir Bashir, Safia Babikir Smart Automation and Communication Technologies Research Center (SACT) Research Institute of Sciences and Engineering (RISE) University of Sharjah PO Box 27272 Sharjah United Arab Emirates Sustainable Energy and Power Systems Research Centre University of Sharjah Sharjah United Arab Emirates College of Engineering University of Sharjah Department of Electrical Engineering PO Box 27272 Sharjah United Arab Emirates

ISBN: (纸本)9798331531836

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/m² 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. © 2025 IEEE.

关键词： Green Hydrogen Metaheuristic control strategies Particle Swarm Optimization (PSO) Photovoltaic systems Real-time energy management

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High Gain Multilevel Boost Converter-Based Synergetic MPPT Controller for Standalone Photovoltaic Array 7

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

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Gaid, Afaf Zeita Melika Hamid, Abdul-Kadir Farag, Mena Maurice College of Engineering University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates Research Institute for Sciences and Engineering (RISE) University of Sharjah Sustainable Energy and Power Systems Research Centre PO Box 27272 Sharjah United Arab Emirates University of Sharjah Smart Automation and Communication Technologies Research Center (SACT) PO Box 27272 Sharjah United Arab Emirates

ISBN: (纸本)9798331531836

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. © 2025 IEEE.

关键词： DC power grid Maximum Power Point Tracking Multi-Level Boost Converters PV Array Solar Energy Synergetic Control

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Solar-driven photothermal catalytic CO_(2) conversion:a review

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Rare Metals 2024年第7期43卷 2913-2939页

作者： Bachirou Guene Lougou Bo-Xi Geng Ru-Ming Pan Wei Wang Tian-Tian Yan Fang-Hua Li Hao Zhang Oraléou SanguéDjandja Yong Shuai Meisam Tabatabaei Daniel Sabi Takou School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin 150090China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Guangdong Provincial Key Laboratory of Distributed Energy Systems Engineering Research Center of None-Food Biomass Efficient Pyrolysis and Utilization Technology of Guangdong Higher Education InstitutesDongguan University of TechnologyDongguan 523808China Higher Institution Centre of Excellence(HICoE) Institute of Tropical Aquaculture and Fisheries(AKUATROP)Universiti Malaysia Terengganu21030 Kuala NerusMalaysia Department of Biomaterials Saveetha Dental CollegeSaveetha Institute of Medical and Technical SciencesChennai600077India école Polytechnique d'Abomey-Calavi University of Abomey-Calavi2009CotonouBenin

It is highly desirable to seek green and sustainable technologies,such as employing photo thermal effects to drive energy catalysis processes to address the high energy demand and associated environmental impacts induced by the current *** photothermocatalysis process is an emerging research area with great potential in efficiently converting solar energy through various catalytic ***,achieving simultaneously high conversion efficiency,cyclability,and durability is still a daunting ***,tremendous work is still needed to enhance solar photo thermal catalytic conversion and promote its large-scale *** review developed the principles of coupling solar photon and thermal fields underlying the photothermal effect,exploration of efficient nanocatalysts,development of optofluidic reactor model,and photo thermal synergistic-driven CO_(2) reduction *** ultimate goal was to provide an effective approach that can effectively convert solar energy into photocarriers/hot-electrons and heat,and importantly,can couple them to regulate catalysis reaction pathways toward the production of value-added fuel and chemical energy.

关键词： Photochemistry Photothermal CO_(2)catalytic conversion Nanomaterial Solar fuels and chemicals

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Energy Management Using Smart Inverter

Energy Management Using Smart Inverter

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Digvijay B Kanase Sachin P Jadhav Dushyant P Patil Shashikant Prasad Swaraj Kadam Digambar P. Patil Department of Electrical Engineering Dr. D. Y. Patil Institute of Technology Pune Maharashtra India Department of Automation and Robotics Dr. D. Y. Patil Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

In the digital age, nearly all activities require a consistent power supply to function. Homes may experience prolonged power outages due to major disruptions in the local or regional electrical grid, often caused by severe weather, car accidents, or fallen trees. Inverters are commonly used as backup power sources in homes and are frequently employed as secondary power sources in both residential and commercial settings. The primary function of a solar inverter is to convert the direct current (DC) electricity generated by photovoltaic cells into alternating current (AC) power. Smart inverters further enhance this by utilizing batteries to store excess energy. These inverters optimize power management and ensure efficient operation. Maximum Power Point Tracking (MPPT) technology is implemented to maximize energy extraction from solar panels, thereby improving overall system efficiency. The system also supports remote monitoring and management, allowing for optimized battery storage and improved performance.

关键词： Maximum power point trackers Renewable energy sources Power system management Production Inverters Power system reliability Batteries Solar panels Reliability Remote monitoring

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Adaptive Beamforming Based on Interference-Plus-Noise Covariance Matrix Reconstruction for Speech Separation

Adaptive Beamforming Based on Interference-Plus-Noise Covari...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yongxiong Xiao Shiqiang Zhu Te Li Minhong Wan Wei Song Jason Gu Qiang Fu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

Estimating the interference-plus-noise covariance matrix (INCM) is critical for the robustness of the minimum variance distortionless response (MVDR) beamformer. Existing INCM reconstruction methods are computationally intensive and not suitable for real-time speech separation. We propose a singular value decomposition (SVD) based INCM reconstruction method for speech separation. The spatial covariance matrix (SCM) for each source is obtained by rank-1 approximation using the nominal steering vector (SV) or the pre-measured relative transfer function (RTF). The INCM used to separate each source is reconstructed as the sum of the covariance matrices of interference and spherical isotropic noise. The proposed method is evaluated using the mixed signal received by a circular array with six microphones placed in a simulated reverberation chamber. The results show that the proposed method has comparable sound quality performance to the reference method, but requires much less computation.

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Based on an improved Zero-DCE++ low-light enhanced object detection algorithm

Based on an improved Zero-DCE++ low-light enhanced object de...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Bai, Xuecheng Gao, Hongwei Zhao, Zhanpeng Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China Xi'an Modern Control Technology Research Institute Xi'an710100 China

ISBN: (纸本)9781510689176

Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information loss. This study presents an enhanced low-light image detection model based on an improved Zero-DCE++ algorithm, integrated with YOLOv8 for high-speed, accurate detection. Key improvements include the CBAM attention mechanism to enhance feature extraction in low-light areas, DCNv2 for capturing complex object details, and a Local Region Brightness Curve Enhancement Module to prevent overexposure in well-lit areas. The proposed model was tested on the Night Drone dataset, where it achieved superior performance in image quality and detection accuracy compared to state-of-the-art methods. This approach demonstrates significant potential for applications requiring reliable object detection in challenging low-light conditions. © 2025 SPIE.

关键词： Semantic Segmentation

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Distributed cooperative control for parallel direct current–direct current buck converters based on multi-agent consensus theory

Distributed cooperative control for parallel direct current...

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作者： Zhu, Min Wang, He Cheng, Yingying Jia, Ruting Yu, Miao School of Electrical Engineering and Automation Hefei University of Technology Hefei China Engineering Technology Research Center of Industrial Automation Hefei China Department of Electrical and Computer Engineering California State University at Northridge NorthridgeCA United States China Energy Engineering Group Anhui Electric Power Design Institute Co. Ltd. Hefei China

The voltage regulation and power sharing for parallel direct current–direct current (DC–DC) buck converters is studied in this paper by using the multi-agent consensus theory. At the first step, the control problem of parallel DC–DC buck converters is modeled as the consensus problem of multi-agent system. Different from the most existing algorithms for parallel DC–DC buck converters, a new distributed cooperative control algorithm is proposed to implement plug-and-play and to enhance the system's flexibility. For the current-loop, a finite-time control algorithm is employed to improve the dynamic response for the current tracking. The rigors stability analysis is given and the simulation result is presented. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Multi agent systems

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RFFCE: Residual Feature Fusion and Confidence Evaluation Network for 6DoF Pose Estimation

RFFCE: Residual Feature Fusion and Confidence Evaluation Net...

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IEEE International Conference on robotics and automation (ICRA)

作者： Qiwei Meng Shanshan Ji Shiqiang Zhu Tianlei Jin Te Li Jason Gu Wei Song Research Center for Intelligent Robotics Zhejiang Lab Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In this paper, we propose a novel RGBD-based object 6DoF pose estimation network - RFFCE. It is a two-stage method that firstly leverages deep neural networks for feature extraction and object points matching, and then the geometric principles are utilized for final pose computation. Our approach consists of three primary innovations: residual feature fusion for representative RGBD feature extraction; confidence evaluation and confidence-based paired points offsets regression for self-evaluation and self-optimization respectively. Their effectiveness is verified through an ablation study, and our RFFCE achieves the SOTA performance on LineMOD, Occlusion-LineMOD and YCB-Video datasets. Additionally, we also conduct a real-world object grasping experiment for visualization and qualitative evaluation of the RFFCE.

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Optimal Placement of Voltage Regulator and Capacitor for Distribution System

Optimal Placement of Voltage Regulator and Capacitor for Dis...

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2021 International Conference on System, Computation, automation and Networking, ICSCAN 2021

作者： Rajalakshmi, M. Addisu, Molla Jeyakkannan, N. Renuka Devi, G. Karthik, C. Jyothi Engineering College Department of Mechatronics Thrissur India Woldia University Department of Electrical and Computer Engineering Ethiopia Manakula Vinayagar Institute of Technology Deprtment of Robotics and Automation Puduchery India

ISBN: (纸本)9781665439862

The radial distribution system (RDS) is the ultimate link between the high-voltage transmission system (HVT) and the user in most electrical systems in Ethiopia. The Distribution System (DS) is divided into different areas for proper operation and supply of electricity demand. The study in this paper applies specifically to the Gondar feeder, which consists of 60 nodes with 3,413 MW of total capacity, in power distribution lines. For the modulation of voltages and DS node power loss indices, the fuzzy membership functions are used. A fluid expert system (FES) with a collection of heuristic legislation will determine the voltage regulator and the capacitor suitability index. The proposed method discusses and compares the effectiveness and results obtained from the voltage control system and the optimal placement of capacitors. © 2021 IEEE.

关键词： Voltage regulators

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