检索结果-内蒙古大学图书馆

Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

IEEE Transactions on Smart Grid 2025年

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan 430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou 310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science Brisbane 4001 QLD Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： DC microgrid finite-time stability privacy-preserving secondary control

来源：评论

学校读者我要写书评

暂无评论

Classification System Based on Data Mining Technique for Flat-Colored Textiles

SSRN

引用

SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

来源：评论

学校读者我要写书评

暂无评论

Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

引用

IEEE Internet of Things Journal 2025年

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

来源：评论

学校读者我要写书评

暂无评论

H_∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou 221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China National Taiwan Ocean University (NTOU) Department of Marine Engineering Keelung 202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan University of Agder (UiA) Center for AI Research (CAIR) Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing rese... 详细信息

This article investigates the design of robust H_∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H_∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Consumer Electronics Applications Delayed Fuzzy Robust Filtering Markov fuzzy systems Modedependent Lyapunov–Krasovskii functional Time–Delayed Systems Wearable Technology

来源：评论

学校读者我要写书评

暂无评论

UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

引用

arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Long-fiber Sagnac interferometers for twin-field quantum key distribution networks

引用

Physical Review Applied 2025年第3期23卷 034040-034040页

作者： Reem Mandil Li Qian Hoi-Kwong Lo Centre for Quantum Information and Quantum Control Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Centre for Quantum Information and Quantum Control Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada

A Sagnac-loop structure can help overcome the major difficulty in the practical implementation of a twin-field quantum key distribution (TFQKD) network, namely, the need to stabilize the phase of a quantum state over many kilometers of fiber. Unfortunately, Rayleigh backscattering noise limits the signal-to-noise ratio for Sagnac systems containing long fibers and lossy photonic devices. Here, we solve this problem by sending optical pulses in long on-off bursts and using time postselection on measurements taken with free-run single-photon avalanche detectors. We also investigate the impact of the residual phase noise uncompensated by the Sagnac structure and find that the variance of the phase noise scales as loop length to the third power, verifying an existing calculation in the literature. We measure the interference visibility in Sagnac loops of varying length without active phase stabilization and achieve >97% visibility in 200-km ultralow-loss fiber, which is, to our knowledge, the longest fiber Sagnac interferometer demonstrated. Our results suggest that a Sagnac system is feasible for long-distance TFQKD networks, a key step towards the practical implementation of metropolitan quantum networks.

关键词： Interferometers

来源：评论

学校读者我要写书评

暂无评论

A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

引用

IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Data Identification Optimal control for Quantized Linear Systems under DoS Attacks

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, Mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

来源：评论

学校读者我要写书评

暂无评论

Event-aided Semantic Scene Completion

arXiv

引用

arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Dynamic Quasi-Hyperbolic Momentum Iterative Attack With Small Perturbation for 4D-Flight Trajectory Prediction

引用

IEEE Internet of Things Journal 2025年

作者： Zhao, Zhengyang Wang, Buhong Yao, Xuan Tian, Jiwei Dong, Ruochen Zhu, Peican Air Force Engineering University School of Information and Navigation Xi’an710082 China University of Manchester Department of Computer Science ManchesterM13 9PL United Kingdom Air Force Engineering University Air Traffic Control and Navigation College Xi’an710005 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi’an China

With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time series prediction (TSP) models for 4D-FTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the Dynamic Quasi-Hyperbolic Momentum Iterative Attack with Small Perturbation (DQM-Attack). This method leverages quasi-hyperbolic momentum (QHM) and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the Manifold Smooth Module (MSM) and Sparse Smooth Reinforcement Learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM). © 2014 IEEE.

关键词： Flight paths

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：