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Utilization of Energy Storage and Hydrogen in Power and Energy systems:Viewpoints from Five Aspects

CSEE Journal of Power and Energy systems 2023年第1期9卷 1-7页

作者： Yonghua Song Mohammad Shahidehpour Saifur Rahman Nigel Brandon Kai Strunz Jin Lin Yuxuan Zhao State Key Laboratory of Internet of Things for Smart City University of MacaoTaipaMacao 999078China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Galvin Center for Electricity Innovation Illinois Institute of TechnologyChicagoIL 60616USA Department of Advanced Research Institute Virginia Polytechnic Institute and State UniversityArlingtonVA 22203USA Department of Earth Science and Engineering Imperial College LondonLondon SW72AZU.K Department of Sustainable Electric Networks and Sources of Energy Technische Universitt Berlin10587 BerlinGermany State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon *** renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization ***,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power *** storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of *** the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage ***,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were *** experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.

关键词： Electrolysis electric vehicle energy storage flow battery fuel cell electric vehicle hydrogen energy

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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AN ENDPOINT ESTIMATE FOR THE MAXIMAL CALDERÓN COMMUTATOR WITH ROUGH KERNEL

arXiv

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arXiv 2024年

作者： Hu, Guoen Lai, Xudong Tao, Xiangxing Xue, Qingying Department of Mathematics School of Science Zhejiang University of Science and Technology Hangzhou310023 China Institute for Advanced Study in Mathematics Harbin Institute of Technology Harbin150001 China School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China

In this paper, the authors consider the endpoint estimates for the maximal Calderón commutator defined by TΩ∗, af(x) = sup ǫ>0⃓⃓⃓ Z|x−y|>ǫ |xΩ(−xy−|dy+1) (a(x) − a(y))f(y)dy ⃓⃓⃓, where Ω is homogeneous of degree zero, integrable on Sd−1 and has vanishing moment of order one, a be a function on Rd such that ∇a ∈ L∞(Rd). The authors prove that if Ω ∈ L log L(Sd−1), then TΩ∗, a satisfies an endpoint estimate of L log log L *** Codes 42B20 Copyright © 2024, The Authors. All rights reserved.

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ERROR ESTIMATES OF A REGULARIZED FINITE DIFFERENCE METHOD FOR THE LOGARITHMIC SCHRÖDINGER EQUATION WITH DIRAC DELTA POTENTIAL

arXiv

引用

arXiv 2024年

作者： Zhou, Xuanxuan Wang, Tingchun Wu, Yong Cai, Yongyong School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing210094 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Laboratory of Mathematics and Complex Systems Ministry of Education School of Mathematical Sciences Beijing Normal University Beijing100875 China

In this paper, we introduce a conservative Crank-Nicolson-type finite difference schemes for the regularized logarithmic Schrödinger equation (RLSE) with Dirac delta potential in 1D. The regularized logarithmic Schrödinger equation with a small regularized parameter 0 1 error estimates and the conservative properties of the finite difference schemes are investigated. The Crank-Nicolson finite difference methods enjoy the second-order convergence rate in time and space. Numerical examples are provided to support our analysis and show the accuracy and efficiency of the numerical *** Codes 35Q41, 65M70, 65N35 Copyright © 2024, The Authors. All rights reserved.

关键词： Finite difference method

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Ma, Ning Yan, Long The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 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 The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing100070 China

ISBN: (纸本)9781665481090

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (612 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians. © 2022 IEEE.

关键词： Medical imaging

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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yonglin Tian Fei Lin Xinyuan Zhang Jingwei Ge Yutong Wang Xingyuan Dai Yisheng Lv Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems and State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Modeling Full Information with Graph Network for Joint Entity-Relation Extraction 21

Modeling Full Information with Graph Network for Joint Entit...

引用

Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Qian Wan Luona Wei Xinhai Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China College of Systems Engineering National University of Defense Technology China Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

ISBN: (纸本)9781450385053

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the CoNLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model.

关键词： Natural Language Processing Named Entity Recognition Graph Network Deep Learning Relation Extraction

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Privacy sensitive large-margin model for face de-identification 1st

Privacy sensitive large-margin model for face de-identifica...

引用

1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Guo, Zhiqiang Liu, Huigui Kuang, Zhenzhong Nakashima, Yuta Babaguchi, Noboru Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Osaka University Osaka Japan

ISBN: (纸本)9789811576690

There is an increasing concern of face privacy protection along with the wide application of big media data and social networks due to free online data release. Although some pioneering works obtained some achievements, they are not sufficient enough for sanitizing the sensitive identity information. In this paper, we propose a generative approach to de-identify face images yet preserving the non-sensitive information for data reusability. To ensure a high privacy level, we introduce a large-margin model for the synthesized new identities by keeping a safe distance with both the input identity and existing identities. Besides, we show that our face de-identification operation follows the (Formula Presented)-differential privacy rule which can provide a rigorous privacy notion in theory. We evaluate the proposed approach using the vggface dataset and compare with several state-of-the-art methods. The results show that our approach outperforms previous solutions for effective face privacy protection while preserving the major utilities. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Privacy-preserving techniques

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Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

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Journal of Social Computing 2022年第4期3卷 287-302页

作者： Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen the Shanghai Key Lab of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200438China the Faculty of Social Sciences Tampere UniversityTampere 33100Finland School of Information Science and Technology Fudan UniversityShanghai 200438China Shanghai Artificial Intelligence Laboratory Shanghai 200232China. School of Social Development and Public Policy Fudan UniversityShanghai 200433China. Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China.

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social *** this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography *** have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research *** findings will be helpful for researchers and practitioners working in relevant fields.

关键词： social computing Digital Bibliography and Library Project(DBLP) bibliometric evolution visualization

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