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Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1725-1737页

作者： Weiqing Sun Wei Liu Jie Zhang Kunpeng Tian School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power *** energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ***,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of *** paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power *** upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the *** fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of ***,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation *** the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed *** simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road ***,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

关键词： Mobile energy storage system economic dispatch bi-level optimization model road congestion fuzzy constraint

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LiDUT-Depth: A Lightweight Self-supervised Depth Estimation Model Featuring Dynamic Upsampling and Triplet Loss Optimization 27th

LiDUT-Depth: A Lightweight Self-supervised Depth Estimation ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Hao Fang, Zhijun Shao, Xuan Jiang, Xiaoyan Hwang, Jenq-Neng School of Computer Science and Technology Donghua University Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Department of Electrical and Computer Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783031784439

Lightweight yet reliable depth estimation models that can deployed on edge devices are crucial for the practical application of fields such as autonomous driving, robot navigation, and augmented reality. However, previous research often fails to balance accuracy and complexity well. Existing lightweight models still have relatively high error rates in specific scenarios, which makes them unsuitable for industrial applications. Hence, we propose LiDUT-Depth, a lightweight architecture for self-supervised monocular depth estimation that is enhanced through dynamic upsampling and triplet loss optimization, which achieves competitive results with smaller model sizes and lower computational complexity. Specifically, an efficient dynamic upsampling module (EDU Module) is employed to preserve detailed local features, thereby obtaining more accurate depth estimation results. In addition, an improved semantic-aware triplet loss (SaTri Loss) is integrated into the training loss, significantly improving depth estimation accuracy in gradient-rich regions. Experiments show that our architecture achieves a considerably enhanced accuracy compared with previous works with a much lower model size. Our codes and models are available here. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

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Blockchain based federated learning for intrusion detection for Internet of Things

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Frontiers of computer science 2024年第5期18卷 109-119页

作者： Nan SUN Wei WANG Yongxin TONG Kexin LIU School of Cyber Science and Technology Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.

关键词： intrusion detection federated learning new attacks discovering blockchain

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Large-signal Stability Analysis of Three-phase Grid-following Inverters

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IEEE Transactions on Energy Conversion 2023年 1-15页

作者： Pal, Diptak Panigrahi, Bijaya Ketan Johnson, Brian Venkatramanan, D. Dhople, Sairaj Department of Electrical & Computer Engineering National University of Singapore Singapore Indian Institute of Technology Delhi New Delhi India Chandra Department of Electrical & Computer Engineering The University of Texas at Austin Austin TX USA Department of Energy Science & Engineering Indian Institute of Technology Bombay Mumbai India Department of Electrical & Computer Engineering University of Minnesota Minneapolis MN USA

This work analytically establishes a multi-variable energy function for a three-phase grid-following inverter leveraging a unified equivalent-circuit model for its physical- and control-layer subsystems. This is a significant contribution to the prior art in which analytical approaches to large-signal stability for inverters have largely been attempted with simplified models. Central to our effort is to cast physical- and control-layer dynamics of each dynamical subsystem as an equivalent circuit consisting of familiar circuit elements adopting a positive-sequence modeling framework. An energy function for the inverter is then constructively synthesized by summing the energy functions across the various subsystems that are readily derived from circuit-theoretic principles. Numerical simulations are presented to validate the equivalent-circuit model of the inverter as well as the efficacy of the synthesized energy function in characterizing large-signal stability following a disturbance. IEEE

关键词： Stability criteria

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Deployment of Digital Twin in Robotics and Autonomous Driving Using ROS Architecture

Automation, Robotics and Communications for Industry 4.0/5.0

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Automation, Robotics and Communications for Industry 4.0/5.0 2023年 2023卷 10-12页

作者： Hussain, Muhammad Ishfaq Rafique, Muhammad Aasim Sayfullokh Khurbaev Pyo, Junbom Kim, Taweook D’ Auriol, Brain J. Jeon, Moongu School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju500-712 Korea Republic of

In robotics and autonomous driving applications, safety and reliability are major concerns. Testing and authentication of autonomous driving and robotic applications in a simulation environment exist. Visualization and performing some operations with the real-time data of autonomous driving and robots can be dealt with Digital twin technology. Digital twin technology provides us the real-time ongoing operations on the remote machine as well. Sending the commands and performing some operations on the real autonomous robot or vehicle using the cloud machine (twin) required smooth network communication. While communication over the network the network’s latency should be very low to deal with life-critical applications like robotics and autonomous driving. In this research we designed a prototype of Digital twin, using the robot operating systems (ROS) based publisher-subscriber pattern. However, the network latency is quite high while communicating over the network. Dealing with this problem, we also proposed a vehicular edge computing device to minimize network latency. We compare the results by analyzing the received timestamps in real time on the autonomous robot and on the remote machine timestamps to empower the credibility and reliability of the proposed work. © 2023, International Frequency Sensor Association (IFSA). All rights reserved.

关键词： Robot Operating System

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引用

International Journal of computers and Applications 2023年第5期45卷 379-390页

作者： Liu, J. Shao, H.J. Deng, X. Jiang, Y.T. School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Electrical and Computer Engineering University of Nevada Las VegasNV United States

The paper discusses the high computational costs associated with convolutional neural networks (CNNs) in real-world applications due to their complex structure, primarily in hidden layers. To overcome this issue, the paper proposes a novel channel pruning technique that leverages the correlation topology of feature maps generated by each CNNs layer to construct a network with fewer nodes, reducing computational costs significantly. Redundant channels exhibit a high degree of topological similarity and tend to increase as the number of network layers rises. Removing the channel corresponding to highly correlated feature maps allows retrieval of the ‘base’ set of characteristics needed by subsequent layers. The proposed channel pruning technique provides a promising approach to reducing the computational costs of deep convolutional neural networks while maintaining high performance levels. By designing a network structure optimized for specific input data types, the method results in more efficient and effective machine learning models. The pruning operation requires fine-tuning to optimize network performance, and experiments using X-ray, chest CT, and MNIST images show that the pruned network can eliminate approximately 80% of redundant channels with minimal performance deterioration (maintaining original CNNs performance at 99.2%). © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Topology

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Topology Optimization in Three-Phase C-Core SRMs 50

Topology Optimization in Three-Phase C-Core SRMs

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Davarpanah, Gholamreza Mohammadi, Sajjad Kirtley, James L. Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge United States

ISBN: (纸本)9781665464543

This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical loading capability, leading to a higher torque density. The general formulation is discussed. Additionally, the shorter flux path within the design leads to a reduced core loss. The finite element method is employed in the design and field analysis. A comparison with a conventional motor shows the superiority of the proposed configuration. The optimized design is prototyped and tested. Both static and dynamic torques are extracted. A good correlation between simulation and experimental results is observed. © 2024 IEEE.

关键词： Topology

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