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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Aichao Zhang Shunbo Lei Chongyang Wan Haoran Zhang Yuan Luo School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The emergence of peer-to-peer (P2P) energy trading market brings both benefits and risks to modern power system. Relying on information and communication technology (ICT), transaction in P2P energy trading market is vulnerable to false data injection attacks (FDIAs). Specifically, the malicious redistribution of short-term forecasting load data will mislead the economic dispatch (ED) in the ex-ante real-time (RT) market. Hence, the actual generation outputs deviate from the schedule, and the corrupt result of ED can affect the trading volume in RT market through a P2P framework. Accordingly, targeting on a specific agent in P2P market, we propose a strategy to inject a malicious forecasting data in order to maximize the trading profits for this agent. Moreover, since the P2P model often solved in a distributed way, which brings heavy computational burden, we conduct a preferred locational marginal price (LMP) to give a one-time solution. Eventually, we demonstrate the effectiveness of our attacking strategy based on the IEEE 9-bus test system.

关键词： Economics Schedules Energy Internet System integration Electricity supply industry Real-time systems Peer-to-peer computing Information and communication technology Forecasting Optimization

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PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments

arXiv

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arXiv 2023年

作者： Zhou, Rixin Xia, Ding Zhang, Yi Pang, Honglin Yang, Xi Li, Chuntao School of Artificial Intelligence Jilin University China The University of Tokyo Japan National University of Defense Technology College of Electrionic Engineering China School of Archaeology Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China Key Laboratory of Ancient Chinese Script Culture Relics and Artificial Intelligence Jilin Province China

In this paper, we propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem. Existing works use rule-based methods to match similar contour shapes or textures, which are always difficult to tune hyperparameters for extensive data and computationally time-consuming. Therefore, we propose a neural network that can effectively utilize neighbor textures with contour shape information to fundamentally improve performance. First, we employ a graph-based network to extract the local contour and texture features of fragments. Then, for the pair-searching task, we adopt a linear transformer-based module to integrate these local features and use contrastive loss to encode the global features of each fragment. For the pair-matching task, we design a weighted fusion module to dynamically fuse extracted local contour and texture features, and formulate a similarity matrix for each pair of fragments to calculate the matching score and infer the adjacent segment of contours. To faithfully evaluate our proposed network, we collect a real dataset and generate a simulated image fragment dataset through an algorithm we designed that tears complete images into irregular fragments. The experimental results show that our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time. Source codes and data are available at here. © 2023, CC BY.

关键词： Contourlet transform

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Microphysical Characteristics of the 2020 Record-Breaking Meiyu Rainfall in Anhui, China

SSRN

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SSRN 2024年

作者： Yang, Qiqi Zhang, Shuliang Chen, Yiheng Jin, Yuhan Fang, Hongyuan Nanjing Normal University China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou China Key Laboratory of VGE Ministry of Education Nanjing Normal University Nanjing China Suzhou Big Data Research Institute Suzhou China School of Water Conservancy and Transportation Zhengzhou University Zhengzhou China Department of Civil Engineering University of Bristol Bristol United Kingdom

The 2020 Meiyu season in Anhui, China, brought unprecedented rainfall, causing severe socio-economic and ecological disruptions. This study investigates the microphysical characteristics of raindrops during the 2020 record-breaking Meiyu event using minute-level data from six disdrometer stations. Light rain events lasted longer but contributed little to total rainfall, while brief, intense rain events delivered most of the precipitation. Throughout the entire event, the mean drop diameter (Dm) increased from 1.18 mm to 1.9 mm, and mean drop concentration (log10 Nw) rose from 2.92 to 4.08 with increasing rain rate. Novel μ–Λ and Z−R relationships tailored for this extreme event uncovered significant regional variability. The Z−R relationships for total, stratiform, and convective rainfall types revealed higher coefficients than those found in other Meiyu studies, indicating denser raindrop concentrations and larger drop sizes. Cutting-edge dual-polarization radar rainfall prediction models were developed, with relationships between Zdr, Zh, Kdp, and rain rate (R) showing exceptional accuracy, as evidenced by correlation coefficients nearing 1.0 and low RMSE and NMAE values. Furthermore, various KE–R relationships were established for estimating rainfall kinetic energy (KE), with Power Law relationships providing the most accurate representation for KEtime–R and Logarithmic relationships being most effective for KEmm–R, particularly for total and stratiform rainfall. These findings enhance understanding of rainfall microphysics in extreme Meiyu events, offering critical insights for improving flood management and soil erosion control in eastern China. © 2024, The Authors. All rights reserved.

关键词： Rain

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耐瑞特:一种基于群体学习方法的新型硝酸盐制剂,可发挥更好的机体保护作用

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Science Bulletin 2023年第8期68卷 838-850,M0004页

作者：潘雯胡耿李韶容李国情冯晓宇吴志芳张栋秦力铮王雪胡亮徐骏疾胡磊贾翌江温欣王劲松张春梅周建李文斌王晓刚王玉记王松灵 Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction Beijing Laboratory of Oral Health and Beijing Stomatological HospitalCapital Medical University Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang University Immunology Research Center for Oral and Systemic Health Beijing Friendship HospitalCapital Medical University Department of Dental Implantology The Affiliated Stomatological Hospital of Nanjing Medical University Department of Pediatric Dentistry School of StomatologyCapital Medical University Beijing Key Laboratory of Tolerance Induction and Organ Protection in Transplantation Department of Oral Maxillofacial Surgery Capital Medical University School of Stomatology School of Pharmaceutical Sciences Capital Medical University Beijing Area Major Laboratory of Peptide and Small Molecular Drugs Engineering Research Center of Endogenous Prophylactic of Ministry of Education of China1 Department of Biochemistry and Molecular Biology School of Basic Medical SciencesCapital Medical University1 Laboratory for Oral and General Health Integration and Translation Beijing Tiantan HospitalCapital Medical University1 Research Units of Tooth Development and Regeneration Chinese Academy of Medical Sciences

硝酸盐是一种机体不可或缺的营养物质,已应用于多种疾病的预防和治疗研究.但由于半衰期短,硝酸盐的临床应用受限.为了提高硝酸盐可用性,突破传统的依赖大规模高通量生物实验的药物配伍研发瓶颈,本文报道了一种基于群体学习方法的药物配... 详细信息

硝酸盐是一种机体不可或缺的营养物质,已应用于多种疾病的预防和治疗研究.但由于半衰期短,硝酸盐的临床应用受限.为了提高硝酸盐可用性,突破传统的依赖大规模高通量生物实验的药物配伍研发瓶颈,本文报道了一种基于群体学习方法的药物配伍预测系统,该系统预测维生素C为与硝酸盐配伍的首选药物.通过采用微囊化技术,筛选并优化缓释制剂工艺,以维生素C、硝酸钠及壳聚糖3000为芯材,果胶与羧甲基纤维素钠为壁材制备硝酸盐纳米颗粒,命名为耐瑞特(Nanonitrator).耐瑞特的更长循环期显著提高了硝酸盐对放射性唾液腺损伤的疗效,同时维持了安全性.相同剂量的耐瑞特比硝酸盐(包含或不包含维生素C)可显著增强PI3K-Akt信号通路转导,更好地维持细胞内稳态,提示了其广泛的临床应用潜力.此外,本文还提供了一种将无机化合物加入缓释纳米颗粒的新方法.

关键词： Inorganic nitrate Artificial intelligence Nanomedicine Chitosan Irradiation-induced submandibular gland injury Homeostasis

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A Remote Sensing Object Classification and Extraction Method Based on Improved U-Net

A Remote Sensing Object Classification and Extraction Method...

引用

data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Lili Wen Hui Qu Man Wu Junxiang Lai Wei Zhang Research Center for Carbon Sink and Low-Carbon Engineering in the Beibu Gulf of Guangxi Guangxi Academy of Marine Sciences line Nanning China National Marine Data and Information Service Tianjin China School of electrical engineering Guangxi University Nanning China Guangxi Key Laboratory of Marine Environmental Science Guangxi Academy of Sciences Nanning China Guangxi Key Laboratory of Intelligent Control and Operation and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

This paper addresses the problems of low accuracy and poor adaptability to complex scenes in traditional remote sensing object classification methods, and proposes a remote sensing object classification extraction method based on an improved U-Net. We retained the U-shaped symmetrical structure of U-Net and introduced the Swin Transformer block with self-attention mechanism to replace the original convolutional operation, enhancing the model's ability to extract global features. We also constructed a Bottleneck structure between the encoder and decoder to learn deep feature representations and form a new U-shaped network based on Transformer, improving the network's image semantic segmentation ability in scenarios with small targets and complexity. Experimental results show that the method proposed in this paper has an improvement of more than 9% in evaluation indicators such as recognition accuracy and recall rate compared with existing U-Net and Res-UNet methods, has high semantic segmentation accuracy and robustness, and provides new ideas and technical support for remote sensing image object classification extraction.

关键词： Hands Accuracy Semantic segmentation Land surface Transformers Feature extraction Robustness Decoding Remote sensing Testing

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Adaptive NN impedance control for an SEA-driven robot

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Science China(Information Sciences) 2020年第5期63卷 221-223页

作者： Xinbo YU Wei HE Yanan LI Chengqian XUE Yongkun SUN Yu WANG School of Automation and Electrical Engineering University of Science and Technology Beijing Institute of Artificial Intelligence University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationUniversity of Science and Technology Beijing Department of Engineering and Design University of Sussex Institute of Automation Chinese Academy of Sciences

Dear editor,Series elastic actuators (SEAs) have been deliberately selected in collaborative robots intended for safe physical interaction with humans or unstructured environments [1]. This passive mechanical compliance guarantees an inertial decoupling between the link and actuator, thus decreasing kinetic energy involved in unexpected collisions with environments [2]. SEAs are widely used in cooper-

关键词： simulation Adaptive NN impedance control for an SEA-driven robot

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Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Ni, Wanli Xu, Jin Yue, Xinwei Tao, Xiaofeng Niyato, Dusit School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS;ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity;and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Diffusion Model-based Metaverse Rendering in UAV-Enabled Edge Networks With Dual Connectivity

Diffusion Model-based Metaverse Rendering in UAV-Enabled Edg...

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IEEE Conference on Wireless Communications and Networking

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yuling Chen Yang Zhang Luchao Han Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China National Natural Science Foundation of China China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

关键词： Costs Metaverse Computational modeling System performance Rendering (computer graphics) Autonomous aerial vehicles User experience

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Zero-knowledge Proof-based Practical Federated Learning on Blockchain

arXiv

引用

arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Li, Meng Liu, Jiamou Zhu, Liehuang Russello, Giovanni Asghar, Muhammad Rizwan The School of Computer Science The University of Auckland Auckland New Zealand The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China The Department of Computer Science University of Surrey The School of Computer Science The University of Auckland Auckland New Zealand

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Modeling and Regulating Electric Vehicles Carbon Flow in Coupled Transportation-Power Networks

Modeling and Regulating Electric Vehicles Carbon Flow in Cou...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Weimin Wu Shunbo Lei Chongyang Wan Yanting Huang Yiting Xiong Yuan Luo School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Controlling indirect carbon emissions from electric vehicles (EVs) is essential to achieving a carbon-neutral future for the transportation sector, given the long-term goal of attaining 100% clean electricity. However, the interdependence between power and traffic flows, along with the differing time scales of power system dispatch and traffic flow variation, makes the analysis of spatio-temporal dynamics of indirect carbon emissions within the coupled transportation-power network challenging. Therefore, this paper proposes a novel spatio-temporal dynamic traffic flow model (STDT) that is synchronized with the time scales of power system dispatch and captures the interdependence between EV route choices in the transportation network and charging power from the power network. Then, by combining the time-varying local marginal carbon emissions at each charging station with STDT, a new concept called EV-CF (carbon flow of EVs) can be introduced to analyze the spatio-temporal variation in indirect carbon emissions caused by EV charging and transportation. The spatio-temporal EV-CF can be regulated to minimize indirect carbon emissions of a coupled transportation-power network while ensuring transportation requirements. The proposed model and regulation method are verified through case studies.

关键词： Electricity Power system dynamics Transportation Energy Internet Carbon dioxide System integration Electrification Regulation Synchronization Vehicle dynamics

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