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IEEE Conference on Decision and Control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

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TLDR: Traffic Light Detection using Fourier Domain Adaptation in Hostile WeatheR

arXiv

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arXiv 2024年

作者： Gakhar, Ishaan Guha, Aryesh Gupta, Aryaman Agarwal, Amit Toshniwal, Durga Verma, Ujjwal Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education India Department of Computer Science Engineering Manipal Institute of Technology Manipal Academy of Higher Education India Enterprise Analytics & Data Science Artificial Intelligence Center of Excellence Wells Fargo International Solutions Private Limited Center For Transportation Systems Indian Institute of Technology Roorkee India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education India

The scarcity of comprehensive datasets in the traffic light detection and recognition domain and the poor performance of state-of-the-art models under hostile weather conditions present significant challenges. To address these issues, this paper proposes a novel approach by merging two widely used datasets, LISA and S2TLD. The merged dataset is further processed to tackle class imbalance, a common problem in this domain. This merged dataset becomes our source domain. Synthetic rain and fog are added to the dataset to create our target domain. We employ Fourier Domain Adaptation (FDA) to create a final dataset with a minimized domain gap between the two datasets, helping the model trained on this final dataset adapt to rainy and foggy weather conditions. Additionally, we explore Semi-Supervised Learning (SSL) techniques to leverage the available data more effectively. Experimental results demonstrate that models trained on FDA-augmented images outperform those trained without FDA across confidence-dependent and independent metrics, like mAP50, mAP50-95, Precision, and Recall. The best-performing model, YOLOv8, achieved a Precision increase of 5.1860%, Recall increase of 14.8009%, mAP50 increase of 9.5074%, and mAP50- 95 increase of 19.5035%. On average, percentage increases of 7.6892% in Precision, 19.9069% in Recall, 15.8506% in mAP50, and 23.8099% in mAP50-95 were observed across all models, highlighting the effectiveness of FDA in mitigating the impact of adverse weather conditions on model performance. These improvements pave the way for real-world applications where reliable performance in challenging environmental conditions is critical. Impact Statement-This research addresses a critical challenge in autonomous driving systems-poor performance in adverse weather conditions-by introducing a novel method using Fourier Domain Adaptation (FDA). The FDA-enhanced models significantly improve the detection of traffic lights in rain and fog, which has broad implicatio

关键词： Rain

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Decentralized Peer-to-Peer Energy Trading for Prosumers Considering Demand Response Program

Decentralized Peer-to-Peer Energy Trading for Prosumers Cons...

引用

Smart Grid Conference (SGC)

作者： Mohammad Seyfi Mehdi Mehdinejad Behnam Mohammad-Ivatloo Heidarali Shayanfar Center of Excellence for Power Systems Automation and Operation School of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781665401661

In this paper, the peer-to-peer energy trading is performed between prosumers with energy surplus in the role of seller and prosumers with energy shortage in the role of buyer. In the proposed model, buyers, in addition to participating in local trading, can supply their demand shortage from the upstream market. These players can also participate in a price-based demand response program in a fully decentralized way to manage their energy and increase their welfare. The proposed problem is a fully decentralized problem that requires a decentralized approach to clear. In this paper, the decentralized alternating direction method of multipliers (ADMM) is used to solve the proposed problem. Numerical studies have been performed on a small distribution system with four sellers and three buyers. Numerical results show that the proposed model is a fully decentralized method, and the proposed approach ensures the convergence of negotiations and trading.

关键词： Demand response Convex functions Peer-to-peer computing Smart grids Numerical models Optimization Convergence

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Recent Advancements in Solid State Transformer-based EV Fast Charging Stations

Recent Advancements in Solid State Transformer-based EV Fast...

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International Conference on Power Electronics and Drive systems (PEDS)

作者： Ruvini De Seram Anindita Golder Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech University Oshawa ON Canada Ontario Tech-Automotive Center of Excellence (ACE) Ontario Tech University Oshawa ON Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

Growing deployments of ultra fast charging stations (XFCS) are putting a stress on the existing line-frequency transformer (LFT)-based medium voltage (MV) grid due to the high charging demands of exponentially growing electric vehicles (EV). The conventional LFT occupies a large space in the XFCS installation which introduces challenges in facilitating the XFCS infrastructure. As a result, solid-state transformer (SST)-based XFCS are being developed which can be directly connected to the MV grid and several advantages over LFT-based systems such as compactness, intelligence and availability of the DC link to connect renewable energy sources and storage system can be realized. As such, a detailed analysis of current developments in SST-based XFCS is presented and compared with one another in this paper. The challenges associated with the configurations are examined and direction for future research is identified.

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Learning Novel Skills from Language-Generated Demonstrations

arXiv

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arXiv 2024年

作者： Jin, Ao-Qun Xiang, Tian-Yu Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Cao, Yue Duan, Sheng-Bin Xie, Fu-Chao Hou, Zeng-Guang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Hubei Normal University Huangshi435002 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline’s capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le Binh Thai Pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

引用

作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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Technology Roadmap for Flexible Sensors

引用

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 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 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

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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