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SSRN 2024年

作者： Sheng, Jingran Liang, Bo Wang, Lin Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

With the ongoing evolution of scientific research collaboration in depth and breadth, the age distribution among paper collaborators is becoming increasingly varied. Consequently, the question of whether this age distribution affects the quality of collaborative papers has emerged as a significant topic in recent research. In this study, we have compiled and established a comprehensive scientific database, comprising more than 3.5 million papers. These papers, featuring multiple attribute information, originate from over 180 countries. We define the age diversity of a paper based on the academic ages of its co-authors and examine the relationship between age diversity and citation impact using regression analyses. Our findings reveal a clear upward trend in the age diversity of authors over time. The results indicate that the average age diversity of highly cited papers is consistently higher than that of general papers across the entire timeline, spanning four disciplines: economics, engineering, computer science, and physics. Moreover, we demonstrate a significantly positive correlation between age diversity and citation impact. In the aforementioned four disciplines, an increase in the collaborators’ age diversity is associated with a corresponding rise in the paper’s citation impact. This study contributes to a deeper understanding of age-related dynamics in scientific collaboration. It also offers insights into the age distribution of teams, providing practical suggestions for researchers engaged in collaborative endeavors. © 2024, The Authors. All rights reserved.

关键词： Regression analysis

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Analysis of Maximum PowerPoint Tracking (MPPT) Adaptability in Inverters of the Three-Phase Photovoltaic systems Integrated into the Electrical Grid of Congo-Brazzaville

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Journal of Power and Energy engineering 2024年第11期12卷 125-152页

作者： Rostand Martialy Davy Loembe Souamy Mavie Grace Mimiesse Brel Levallois Ndzah Yombi Zonzolo Guoping Jiang Wanghong Hua Xubao Wen Laboratory of Electrical and Electronic Engineering (LGEE) National Higher Polytechnic School Marien Ngouabi University Brazzaville Republic of the Congo National Institute of Research in Natural and Exact Sciences (IRSEN) Brazzaville Republic of the Congo College of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Laboratory of Control Theory and Control Engineering Hohai University College of Energy and Electrical Engineering Nanjing China Jiangsu Provincial Key Laboratory for Novel Technology Department of Computers Sciences and Technology Nanjing University Nanjing China

This paper investigates the adaptability of Maximum Power Point Tracking (MPPT) algorithms in single-stage three-phase photovoltaic (PV) systems connected to the grid of Congo-Brazzaville and compares the attributes of various conventional, significance and novelty of controller system of the proposed of method and improved Incremental Conductance algorithms, Perturbation and Observation Techniques, and other Maximum Power Point Tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system although the control of this structure is relatively advanced technology but the conversion efficiency is difficult to improve due to increase in transformation series. The single stage topology has a simple topology with high reliability and efficiency because of high power consumption, but control algorithm is more complex because of its power convert main circuit a new strategy is being developed. This paper describes a method for maximum power point tracking (MPPT) in the single-stage and three single-phase PV grid-connected system. In the paper, the nonlinear output characteristics of the PV including I-V & P-V are obtained in changed solar insulations or temperature based on MATLAB, and the MPPT algorithm which is based on the P & O algorithm method, compared with Incremental Conductance, is also described, a dimensioning of the impedance adapter for better stabilization. A comparison SPWM and SVPWM control methods in the case of a grid connection applied to the electrical grid of Republic of Congo and their influences on the dynamic performance of the system and their impact in reducing the harmonic rate for better injection into the grid. The simulation model of three single-phase PV grid-connected system is built, and simulation results show the MPPT algorithm has excellent dynamic and static performances, which verifies the Incremental Conductance is effective f

关键词： Photovoltaic Array Congo Maximum Power Point Tracking Perturbation Observation Incremental Conductance Partial Shading Conditions Performance Evaluation State Flow

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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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Study on the Improved Water Flow Prediction Based on Classification-Regression Approach for Amphibious Spherical Robots

Study on the Improved Water Flow Prediction Based on Classif...

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IEEE International Conference on Mechatronics and Automation

作者： Jun Leng Shuxiang Guo Chunying Li Sheng Cao School of Control Science and Engineering Shandong University Jinan China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Inspired by the lateral line systems found in aquatic organisms, an artificial lateral line system (ALLS) for underwater robots was developed. This technology was integrated into Autonomous Underwater Vehicles (AUVs) to enhance their environmental awareness and navigation. This paper was focused on improving water flow prediction performance for a amphibious spherical robot (ASR) equipped with a ALLS comprising 12 pressure sensors. To address the challenge of the initial method relying on polynomial fitting based on data from a single sensor, which encounters difficulties when the flow direction falls between sensor angles. Two strategies were explored: increasing the quantity of sensors and implementing a Classification-Regression approach. Experimental simulations using Computational Fluid Dynamics (CFD) software were conducted to investigate the effectiveness of these strategies. Experimental results indicated that the classification-regression method significantly enhanced prediction accuracy compared to the original approach and offered a cost-effective alternative to increasing sensor quantity. This research contributed to the advancement of underwater robotics by enhancing perception capabilities, which were crucial for navigating challenging underwater environments efficiently.

关键词： Autonomous underwater vehicles Analytical models Accuracy Navigation Computational fluid dynamics Fitting Robot sensing systems

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Analysis of Power system Harmonic Effects Based on Data Features and Gene Expression Programming 9

Analysis of Power System Harmonic Effects Based on Data Feat...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Xia, Yuhang Qing, Song Liu, Yingjie Liu, Yijun Zhou, Lin Jia, Jinshuo Huang, Zhengwen Engineering Project Management Center Chengdu Power Supply Company of Sichuan Electric Power Company Chengdu China Chengdu Power Supply Company Chengdu Power Supply Company of Sichuan Electric Power Company Chengdu China High Voltage Cable Oper. and Maintenance Ctr. Chengdu Pwr. Supp. Co. of Sichuan Elec. Power Company Chengdu China Chongqing University of Posts and Telecommunications Group of Artificial Intelligence and System Optimization Chongqing China Brunel University Department of Electronic and Computer Engineering London United Kingdom

ISBN: (纸本)9798350309638

In order to quantitatively recognize the harmonic impacts of harmonic source users at the common connection point (PCC), a method to calculate the system harmonic impedance and quantify the harmonic impacts by using gene expression programming to analyses data features was proposed in this paper. Firstly, the harmonic voltage and current data satisfying the analysis conditions were selected by using the time series segmentation method. The system harmonic impedance phase angle was obtained by using the characteristic of zero covariance of independent random variables, and then the harmonic phase angle was implanted into the existing data correlation analysis model. When the user harmonic current fluctuated, the accurate system harmonic impedance module was calculated by the user harmonic current fluctuation. When the user's harmonic current was stable, the harmonic impacts was quantified by the fluctuation of system harmonic voltage. In this paper, the user harmonic impedance was also given in thoughts in the entire process, which reduced the error caused by ignoring the user harmonic impedance in the traditional method. Some potential Evolutionary Algorithm based solutions which could be further applied in this domain was also reviewed. As a particular novelty of this work, the possibility and feasibility of employing gene expression programming into the conventional data correlation analysis work in power system. Simulation analysis and practical engineering examples showed that this method can effectively suppress the influence of system harmonic change and user harmonic impedance compared with the existing methods and obtained more accurate harmonic responsibility division results. © 2024 IEEE.

关键词： Data mining

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #08-03 Innovis Singapore 138634 Republic of Singapore；Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States；Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden；Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Optimal Power system Scheduling Considering Uncertainty-Induced Generation Unit Output Curtailment

Optimal Power System Scheduling Considering Uncertainty-Indu...

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system Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Wei Hu Ning Mi Hailiang Zhong Weihan Chen Kun Ma Tianxiang Geng Yun Wang Bo Su Long Yang State Key Laboratory of Power System and Generation Equipment(Department of Electrical Engineering) Tsinghua University Beijing China Dispatching Control Center State Grid Ningxia Electric Power Co. Ltd. Yinchuan China Department of Electrical Engineering and Applied Electronic Technology Tsinghua University Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Under the call of the national “dual carbon” goals, the growth of the proportion of new energy generation has become an inevitable trend in energy structure adjustment. The “static parameter” model, which traditional thermal power units rely on, can no longer accurately reflect their output uncertainty, urgently requiring a new optimization scheduling strategy to cope with this change. This study proposes an optimized scheduling model for power generation systems that considers the output curtailment and unplanned outages of thermal power units. It employs non-parametric kernel density estimation techniques to model the uncertainty of thermal power unit output and uses chance constraints to define the fluctuation range of generation output and the system’s reliability. The model adopts a scenario generation method based on Copula theory to construct output scenarios and further employs reduction techniques to optimize the number of scenarios. Moreover, this paper introduces Conditional Value at Risk as a key indicator to measure the cost of system uncertainty risk, thereby providing a more comprehensive perspective in risk assessment. Simulation experiments on the IEEE 30-node standard system demonstrate that compared to traditional optimization scheduling models relying on static parameters, the proposed model can more effectively balance the output fluctuations of different thermal power units, significantly reducing the risk of curtailment due to supply shortages. This study offers a new theoretical basis and practical guidance for optimizing the scheduling of power systems under the context of large-scale integration of new energy and economic fluctuations.

关键词： Uncertainty Fluctuations Costs Estimation Thermal management systems simulation Scenario generation Kernel Optimization Standards

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Yanbo Xie, Hongle Wang, Hesheng Wang, Jingchuan Wang, Danwei Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis. © 2024, CC BY-NC-SA.

关键词： Geometry

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Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes

arXiv

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arXiv 2024年

作者： Deng, Tianchen Wang, Nailin Wang, Chongdi Yuan, Shenghai Wang, Jingchuan Wang, Danwei Chen, Weidong The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in large-scale scenes due to three core challenges: (a) inaccurate depth input. Accurate depth input is impossible to get in real-world large-scale scenes. (b) inaccurate pose estimation. Existing methods are not robust enough with the growth of cumulative errors in large scenes and long sequence. (c) insufficient scene representation capability. A single global radiance field lacks the capacity to effectively scale to large-scale scenes. To this end, we propose an incremental joint learning framework, which can achieve accurate depth, pose estimation, and large-scale scene reconstruction. A vision transformer-based network is adopted as the backbone to enhance performance in scale information estimation. For pose estimation, a feature-metric bundle adjustment (FBA) method is designed for accurate and robust camera tracking in large-scale scenes. In terms of implicit scene representation, we propose an incremental scene representation method to construct the entire large-scale scene as multiple local radiance fields to enhance the scalability of 3D scene representation. Extended experiments have been conducted to demonstrate the effectiveness and accuracy of our method in depth estimation, pose estimation, and large-scale scene reconstruction. The code will be open-sourced upon paper acceptance. © 2024, CC BY-NC-SA.

关键词： Depth perception

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