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Point-SCT: A Multi-Scale Spatial Convolution-Swin Transformer Network for Point Cloud Ground Filtering in Complex Mountainous Terrains

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Li, Jingxiang Tang, Fuquan Ma, Lingfei Zhu, Chao Gong, Zheng Ruhaiyem, Nur Intan Raihana Li, Jonathan Department of Geomatics Xi’An University of Science and Technology Xi’an710054 China East China Normal University School of Geospatial Artificial Intelligence Shanghai200241 China Jimei University School of Computer Engineering Xiamen361021 China Universiti Sains Malaysia School of Computer Sciences Penang Gelugor11800 Malaysia University of Waterloo Department of Geography and Environmental Environment Department of Systems Design Engineering WaterlooONN2L 3G1 Canada

Deep learning-based point cloud segmentation methods have been extensively explored, but the majority focus either on local or global feature learning, with few integrating both. These integrated approaches have not been sufficiently explored in complex mountainous scenes with low feature heterogeneity. To address this gap, we propose a novel point-based multi-scale spatial Convolution-Swin Transformer network (Point-SCT). Point-SCT combines convolutional local geometric detail capture with global relationship modeling via dynamic window interactions in Transformer, enhancing ground filtering accuracy in challenging mountainous scenes. The encoder incorporates convolution-based Multi-Scale Local Feature Aggregation (MLFA) approach, integrating Local Geometric Feature Encoding (LGSE) and Diluent Pooling (DP) strategies to effectively aggregate local detailed geometric features while suppressing irrelevant feature vectors and enhancing the representation of low-heterogeneity feature. Additionally, the dynamic spatial window strategy within the Transformer facilitates the capture of long-range feature dependencies. To mitigate noise introduced by RGB in point cloud overlays and sharpen geometric distinctions between the ground and low-lying vegetation, we introduce Boundary Detector, Curvature, and Average Elevation (BCE) as prior inputs, replacing RGB. Finally, quantitative and qualitative analyses of Point-SCT are conducted on an airborne laser scanning (ALS) dataset from a mountainous area, with ablation studies validating the effectiveness of LGSE, DP and BCE. The comprehensive experiments demonstrate that Point-SCT robustly segments ground points in complex mountainous scenes, achieving state-of-the-art levels of accuracy and generalization. © 1980-2012 IEEE.

关键词： Image segmentation

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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A flexible framework for web-based virtual reality presentation of cultural heritage

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AIP Conference Proceedings 2021年第1期2333卷

作者： Dimo Chotrov Angel Bachvarov 1Technical University Sofia Faculty of Computer Systems and Technologies Department Programming and Computer Technologies Bulgaria 2Technical University Sofia Faculty of Mechanical Engineering Department Automation of Discrete Production Engineering Bulgaria

The Virtual Reality (VR) experiences are great for an engaging presentation creating wide public awareness of cultural heritage, especially if the experience is built on a web-based technology. One of the still posing challenges is that once an application has been developed, for example for a virtual museum or a virtual tour, it usually remains fixed to an embedded in the application model. Adding new functionalities or interaction paradigms needs additional development and new deployment. In this paper, we propose a framework, which achieves the decoupling of an experience from the used models and interaction paradigms through scene and input templates and configuration files. This allows an easy way for generation of a variety of experiences even by people with no or limited programming skills.

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Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications

arXiv

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arXiv 2022年

作者： Rožanec, Jože M. Novalija, Inna Zajec, Patrik Kenda, Klemen Tavakoli, Hooman Suh, Sungho Veliou, Entso Papamartzivanos, Dimitrios Giannetsos, Thanassis Menesidou, Sofia Anna Alonso, Ruben Cauli, Nino Meloni, Antonello Recupero, Diego Reforgiato Kyriazis, Dimosthenis Sofianidis, Georgios Theodoropoulos, Spyros Fortuna, Blaž Mladenić, Dunja Soldatos, John Jožef Stefan International Postgraduate School Jamova 39 Ljubljana1000 Slovenia Jožef Stefan Institute Jamova 39 Ljubljana1000 Slovenia Qlector d.o.o. Rovšnikova 7 Ljubljana1000 Slovenia Ubitech Ltd Digital Security & Trusted Computing Group Athens Greece Department of Informatics and Computer Engineering University of West Attica Agiou Spyridonos Street Egaleo Athens12243 Greece R2M Solution Srl Pavia Italy Department of Computer Science University of Cagliari Cagliari Italy Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece INTRASOFT International 19.5 KM Markopoulou Ave. Peania GR 19002 Greece Kaiserslautern67663 Germany

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focusing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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On the Security of LEO Satellite Communication systems: Vulnerabilities, Countermeasures, and Future Trends

TechRxiv

引用

TechRxiv 2022年

作者： Yue, Pingyue An, Jianping Zhang, Jiankang Pan, Gaofeng Wang, Shuai Xiao, Pei Hanzo, Lajos School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Department of Computing and Informatics Bournemouth University BournemouthBH12 5BB United Kingdom 5GIC & 6GIC Institute for Communication Systems University of Surrey GU2 7XH United Kingdom School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Low Earth Orbit (LEO) satellite systems undergo a period of rapid development driven by the ever-increasing user demands, reduced costs, and technological progress. Since there is a paucity of literature on the security issues of LEO Satellite Communication systems (SCSs), we aim for filling this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and summarize their unique security vulnerabilities. In light of this, we further discuss their security vulnerabilities, including the issues of passive and active eavesdropping attacks, interference scenarios, single event upsets, and space debris. Subsequently, we discuss the corresponding active and passive security countermeasures, followed by unveiling a range of trade-offs, security vulnerabilities and their countermeasures. Furthermore, we shed light on several promising future research directions for enhancing the security of LEO SCSs, such as secure quantum communications, three-dimensional virtual arrays, artificial intelligence-based security measures, space-based blockchain, and intelligent reflecting surface enabled secure transmission. Finally, the take-away messages of this paper are crystallized in our concluding design guidelines. © 2022, CC BY-NC-SA.

关键词： Satellite communication systems

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Piquasso: A Photonic Quantum computer Simulation Software Platform

arXiv

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arXiv 2024年

作者： Kolarovszki, Zoltán Rybotycki, Tomasz Rakyta, Péter Kaposi, Ágoston Poór, Boldizsár Jóczik, Szabolcs Nagy, Dániel T.R. Varga, Henrik El-Safty, Kareem H. Morse, Gregory Oszmaniec, Michal Kozsik, Tamás Zimborás, Zoltán Quantum Computing and Quantum Information Research Group HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33 BudapestH-1525 Hungary Department of Programming Languages and Compilers Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Center for Theoretical Physics Polish Academy of Sciences Al. Lotników 32/46 Warszawa02-668 Poland Department of Physics of Complex Systems Eötvös Loránd University Pázmány Péter sétány 1/a BudapestH-1117 Hungary Quantinuum 17 Beaumont Street OxfordOX1 2NA United Kingdom Robert Bosch Kft. Gyömrői út 104. BudapestH-1103 Hungary Department of Computer Engineering Technical University of Munich Arcisstraße 21 München80333 Germany Algorithmiq Ltd. Kanavakatu 3C Helsinki00160 Finland

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Multi-objective trajectory design for overtaking maneuvers of automated vehicles

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IFAC-PapersOnLine 2020年第2期53卷 15566-15571页

作者： Tamás Hegedüs Balázs Németh Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2H-1111 Budapest Hungary. Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary.

The paper proposes a trajectory design method for overtaking maneuvers, in which several performances are incorporated. Thus, it leads to a multi-objective optimization task. It is based on a simple mathematical representation of feasible trajectories by using a potential field approach. The solution of the complex computations in the optimization is approximated by neural networks. The proposed solution is based on the idea that there are a large number of collision free feasible trajectories. The goal of the method is to find the trajectory, which is suitable for the specified objectives. The result of the paper is an integrated decision for both longitudinal and lateral directions, such as longitudinal acceleration command and the values of the defined path. The effectiveness of the method is presented through CarMaker simulation environment.

关键词： automated vehicles overtaking strategy potential field neural network

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Digital Twins of Business Processes: A Research Manifesto

arXiv

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arXiv 2024年

作者： Fornari, Fabrizio Compagnucci, Ivan De Donato, Massimo Callisto Bertrand, Yannis Beyel, Harry H. Carrión, Emilio Franceschetti, Marco Groher, Wolfgang Grüger, Joscha Kilic, Emre Koschmider, Agnes Leotta, Francesco Li, Chiao-Yun Lugaresi, Giovanni Malburg, Lukas Mangler, Juergen Mecella, Massimo Pastor, Oscar Riss, Uwe Seiger, Ronny Serral, Estefania Torres, Victoria Valderas, Pedro University of Camerino School of Science and Technology Computer Science Department Via Madonna delle Carceri 7 Camerino62032 Italy Gran Sasso Science Institute Computer Science Department Viale Francesco Crispi 7 L’Aquila67100 Italy University of St.Gallen Institute of Computer Science Rosenbergstrasse 30 St.Gallen9000 Switzerland University of Bayreuth Business Informatics and Process Analytics Wittelsbacherring 8 Bayreuth95444 Germany Fraunhofer Institute for Applied Information Technology Schloss Birlinghoven Sankt Augustin53757 Germany RWTH Aachen University Ahornstraße 55 Aachen52074 Germany Mercadona Tech Mercadona Plaza de Amèrica 2 Valencia46004 Spain PROS Research Center - VRAIN Institute Universitat Politècnica de València Camino de Vera Valencia46022 Spain TU Dortmund University Leonhard-Euler-Str. 5 Dortmund44227 Germany KU Leuven Warmoesberg 26 Brussels1000 Belgium Sapienza Università di Roma DIAG via Ariosto 25 Rome00185 Italy KU Leuven Faculty of Engineering Technology Celestijnenlaan 300 Leuven3001 Belgium Branch Trier University Behringstraße 21 Trier54296 Germany Artificial Intelligence and Intelligent Information Systems Trier University Universitätsring 15 Trier54296 Germany OST Eastern Switzerland University of Applied Sciences Rosenbergstrasse 59 St. Gallen9001 Switzerland TUM School of Computation Information and Technology Boltzmannstraße 3 Garching bei München85748 Germany

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay. © 2024, CC BY-NC-ND.

关键词： Virtual corporation

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*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

arXiv

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing Elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： Data visualization

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Model Predictive Costate Control for Power Allocation of Dual-Source Electric Vehicles

SSRN

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SSRN 2022年

作者： Nguyen, Hoai-Linh T. Nguyen, Bao-Huy Vo-Duy, Thanh Trovão, João Pedro F. Ta, Minh C. Laboratory for Electric Vehicles School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Department of Electrical Engineering and Computer Engineering University of Sherbrooke SherbrookeQCJ1K 2R1 Canada Polytechnic of Coimbra Coimbra Institute of Engineering R. Pedro Nunes Coimbra3030-199 Portugal Institute for Systems and Computers Engineering at Coimbra R. Silvio Lima Coimbra3030-194 Portugal

Electric vehicle (EV) can be fed by hybrid energy storage system (HESS) that must be coordinated by energy management strategy (EMS) . Optimization-based strategies are often of interest, however, the complexity of these EMSs may prevent them from real-world implementation. In our previous study, we developed an alternative approach of using Pontryagin’s minimum principle (alt-PMP) to linearize the nonlinear optimal control problem of energy management for HESS. Based on this innovative alt-PMP method, this paper proposes a real-time power allocation strategy for battery/supercapacitor EVs by adapting the costate variable of alt-PMP using model predictive control (MPC) framework. It should be noted that the novel strategy is distinguished from the previous works in literature using MPC. In this article, the MPC method is employed to control the costate variable of the PMP-based optimal solution. Gaining from the linearization of alt-PMP method, the linear MPC scheme is applicable to conduct a high-performance optimization-based EMS. To illustrate the appropriateness of the approach, a PI costate variable control strategy is also conducted and compared to the MPC scheme. The results figure out that the proposed scheme reduces up to 34.6% of long-term battery stress and 49% of current oscillation over an unknown driving cycle. © 2022, The Authors. All rights reserved.

关键词： Supercapacitor

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