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An Enhancing Stability Control Strategy of VSG-Based Power Source Under PWM Rectifier Load

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Gaoxiang Liu, Bowen Song, Wenhao Jining, Chen Liu, Xiao Guangxi University Guangxi Key Laboratory of Power System Optimization and Energy Technology School of Electrical Engineering Nanning530004 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530200 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Nanning530004 China

Under PWM rectifier load, the power source based on virtual synchronous generator (VSG) is prone to cause mid-frequency oscillation. Firstly, the system oscillation mechanism and characteristic of the VSG-based power source are analyzed based on impedance method. Due to the negative resistance and capacitive characteristic in the middle frequency range, the PWM rectifier load is prone to interact with the VSG-based power source, which can lead to impedance resonance. Secondly, to improve the stability of the VSG-based power source under the PWM rectifier load, a voltage differentiation feedforward control is proposed. In addition, since the mid-frequency impedance magnitude decrease obviously, the improved VSG-based power source is closer to the ideal voltage source, which can automatically suppress the middle harmonics induced by uncontrollable rectifier load. Finally, the proposed control and the corresponding theoretical analysis are verified by simulations and experiments. © 1975-2011 IEEE.

关键词： Synchronous generators

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Multi-Relational Variational Contrastive Learning for Next POI Recommendation

Multi-Relational Variational Contrastive Learning for Next P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin Chen Peipei Wang Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Next point-of-interest (POI) recommendation aims to predict the next interested POI to the user based on their historical check-in data in location-based social services. Most existing studies have attempted to model user visiting behaviors via sequence-based and graph-based models, and have achieved impressive performance. However, there is still room to explore the implicit transition preferences and contextual multiple semantic relationships among various POIs. To this end, we propose a novel graph-based Multi-Relational Variational Contrastive Learning (MRVCL) method for next POI recommendation, which captures local similarity associations and global contextual dependencies among POIs. Specifically, an order-free local-relational adaptive weighting module is designed to alleviate the problem of insufficient utilization of multi-hop neighbor information caused by the limit of neighbor order of nodes. We then develop a contextaware global-relational encoding module to capture implicit semantic sequential relationships. Finally, generative variational-contrastive learning is employed to construct a continuous representation of the latent features and reinforce the quality of representation learning. Extensive experiments on two real-world datasets validate that our MRVCL outperforms existing state-of-the-art methods on various evaluation metrics. To facilitate future research, our code and data are open-sourced at https://***/LinCH-en/MRVCL.

关键词： Representation learning Measurement Correlation Semantics Neural networks Contrastive learning Signal processing Transformers Encoding Speech processing

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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Engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity

arXiv

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arXiv 2024年

作者： Zhang, Xin Zhang, Linhai Zhou, Deyu Xu, Guoqiang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China SANY Group Co. Ltd China

Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels. Recently, the emergence of LLMs has introduced new solutions to such problems by leveraging graph structures to generate supplementary user profiles. However, previous approaches have not fully utilized the graph understanding capabilities of LLMs and have struggled to adapt to complex streaming data environments. In this work, we propose a fine-grained streaming data synthesis framework that categorizes sparse users into three categories: Mid-tail, Long-tail, and Extreme. Specifically, we design LLMs to comprehensively understand three key graph elements in streaming data, including Local-global Graph Understanding, Second-Order Relationship Extraction, and Product Attribute Understanding, which enables the generation of high-quality synthetic data to effectively address sparsity across different categories. Experimental results on three real datasets demonstrate significant performance improvements, with synthesized data contributing to MSE reductions of 45.85%, 3.16%, and 62.21%, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Sentiment analysis

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

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Application of quantum genetic algorithm in high noise laser image security

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Optoelectronics Letters 2022年第1期18卷 59-64页

作者： MAN Zhenlong LI Jinqing DI Xiaoqiang MU Yining School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Jilin Province Key Laboratory of Network and Information Security Changchun 130022China Information Center of Changchun University of Science and Technology Changchun 130022China School of Science Changchun University of Science and TechnologyChangchun 130022China

Aiming at the security problem of range gated laser imaging in high noise background,a range gated laser image encryption scheme based on the quantum genetic algorithm(QGA)is *** to the fuzziness of the laser image itself,the randomness and security of the key become more and more important in *** this paper,the chaotic sequence is used as the parent chromosome of the QGA,and the random number satisfying the encryption algorithm is obtained by an iterative genetic *** further improve the security of laser images,some random pixels are stochastically inserted around the laser image before *** random pixels are scrambled together with the ***,an adaptive diffusion method is designed to completely change the original statistical information of the *** simulation and performance analysis show that the scheme has high security.

关键词： image algorithm quantum

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong information

Wrong-of-Thought: An Integrated Reasoning Framework with Mul...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China Yunnan University Kunming China

ISBN: (纸本)9798891761681

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024 Association for Computational Linguistics.

关键词：

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Cross-chain Transaction Tracking Protocol based on Multi-dimensional Digital Watermarking Fingerprints

Cross-chain Transaction Tracking Protocol based on Multi-dim...

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Frontiers Technology of information and computer (ICFTIC), IEEE International Conference on

作者： Duanzhen Li Shujiang Xu Haochen He Hongrui Xue Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Cross-chain technology plays a critical role in the process of interconnecting blockchains. However, all attention has been focused on how to achieve efficient cross-chain interoperability protocols, thus neglecting the most important security issues in cross-chain data interaction. Frequent network attacks and data privacy leaks have sounded the alarm for the safety of cross-chain data transactions. How to solve the problem of data ownership tracking in cross-chain transactions has become the top priority. This paper designs a cross-chain transaction tracking protocol based on multi-dimensional digital watermark fingerprints, using robust watermark, fragile watermark, and user identity key to achieve multi-dimensional tracking and supervision of cross-chain data transactions. Finally, this paper analyzes the feasibility and efficiency of the proposed protocol through experiments.

关键词： Data privacy Protocols Watermarking Fingerprint recognition Blockchains Safety Discrete wavelet transforms Interoperability

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Yujie Fan Huan Luo Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes.

关键词： Training Image segmentation Vocabulary Visualization Three-dimensional displays Semantics Natural languages Neural radiance field Feature extraction Robustness

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