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A multiple sensitive attributes data publishing method with guaranteed information utility

CAAI Transactions on Intelligence technology 2023年第2期8卷 288-296页

作者： Haibin Zhu Tong Yi Songtao Shang Minyong Shi Zhucheng Li Wenqian Shang Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhouChina School of Computer Science Communication University of ChinaBeijingChina Business College Beijing Union UniversityBeijingChina

Data publishing methods can provide available information for analysis while preserving *** multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression *** category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for ***,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information *** acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information *** experimental results verify the practice of the proposed data publishing method with multiple sensitive *** proposed method can guarantee information utility when compared with previous ones.

关键词： data analysis data privacy security of data

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Longitudinal velocity control of autonomous driving based on extended state observer

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CAAI Transactions on Intelligence technology 2025年第1期10卷 36-46页

作者： Hongbo Gao Hanqing Yang Xiaoyu Zhang Xiangyun Ren Fenghua Liang Ruidong Yan Qingchao Liu Mingmao Hu Fang Zhang Jiabing Gao Siyu Bao Keqiang Li Deyi Li Danwei Wang Department of Automation University of Science and Technology of ChinaHefeiChina State Key Laboratory of Intelligent Vehicle Safety Technology ChongqingChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Traffic and Transportation Beijing Jiaotong UniversityBeijingChina Automotive Engineering Research Institute Jiangsu UniversityZhenjiangChina School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore School of Automotive Engineering Hubei University of Automotive TechnologyShiyanChina Beijing Idriverplus Technology Co. Ltd.BeijingChina Chery Automobile Co. Ltd.WuhuChina State Key Laboratory of Automotive Safety and Energy Tsinghua UniversityBeijingChina Department of Computer Science and Technology Tsinghua UniversityBeijingChina

Active Disturbance Rejection Control(ADRC)possesses robust disturbance rejection capabilities,making it well-suited for longitudinal velocity ***,the conventional Extended State Observer(ESO)in ADRC fails to fully exploit feedback from first-order and higher-order estimation errors and tracking error simultaneously,thereby diminishing the control performance of *** address this limitation,an enhanced car-following algorithm utilising ADRC is proposed,which integrates the improved ESO with a feedback *** comparison to the conventional ESO,the enhanced version effectively utilises multi-order estimation and tracking ***,it enhances convergence rates by incorporating feedback from higher-order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error *** improved ESO significantly enhances the disturbance rejection performance of ***,the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

关键词： active disturbance rejection control(ADRC) autonomous vehicle extended state observer(ESO) intelligent control longitudinal velocity control

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Joint Beamforming Design for Semi-IRS-Aided Secure ISAC with Assistance of Sensing 16

Joint Beamforming Design for Semi-IRS-Aided Secure ISAC with...

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16th International Conference on Wireless communications and Signal Processing, WCSP 2024

作者： Wei, Wenjing Pang, Xiaowei Qin, Xiaoqi Xing, Chengwen Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

ISBN: (纸本)9798350390643

In this paper, we investigate the sensing-assisted secure transmission for an integrated sensing and commu-nication (ISAC) system, where the semi-intelligent reflecting surface (IRS) is deployed to facilitate the secure communication and sensing. First, we adopt the multiple signal classification algorithm to estimate the target's direction with Cramér-Rao bound (CRB) serving as the performance metric. After obtaining the information about the target, which may be a potential eavesdropper, the expression of secrecy rate related to the eavesdropper's estimation accuracy can be formulated. Then, we formulate a weighted optimization problem to maximize the secrecy rate and minimize the CRB by jointly designing the active beamforming, artificial noise and phase shifts of IRS. In particular, by considering the estimation errors, we introduce a series of beampattern constraints about the main beam in order to cover all possible angles of target, ensuring that the security is guaranteed in all directions. Accordingly, the sensing function can improve the secrecy performance through the increasing estimation accuracy in each iteration. Simulation results reveal effectiveness of the proposed scheme and the utility of sensing as a facilitator for physical layer security. © 2024 IEEE.

关键词： Cramer-Rao bounds

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Frequency Agile Strategy Design of Radar Network for Wideband Sweep Jamming via Coalition Formation Game

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 7906-7919页

作者： Liao, Maosen Xiong, Kui Zhang, Long Yu, Xianxiang Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100089 China School of Information and Communication Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Chengdu611731 China

This article proposes an intelligent frequency agile design strategy for radar network using coalition game theory to counter wideband sweep jamming, which is formulated as a two-stage decision-making problem. In the first stage, the entire frequency agile range is divided into several non-overlapping agile subrange, and each radar selects a frequency agile subrange to join, which is solved by the coalition formation algorithm. Then, after achieving a stable coalition partition, a distributed multi-agent reinforcement learning algorithm is developed to address the specific carrier frequency selection problem within each coalition. By combining these two stages of the frequency agile design strategy, a joint coalition formation game and distributed Q-learning algorithm (CFDQ) is proposed. Finally, numerical simulations are conducted to illustrate the superiority of the proposed algorithm over several typical frequency agile strategies. © 1965-2011 IEEE.

关键词： Jamming

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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Data-Driven Modeling and Working Condition Prediction in Process Industry Production 21

Data-Driven Modeling and Working Condition Prediction in Pro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Zhang, Meifang Wang, Ziqi Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

In the production process of the process industry, precise adjustment of working conditions presents a challenge due to the complexity of processes and unknown disturbances. Central control operators need to adjust setpoints based on deviations in process parameters and monitor target values to maintain system stability. However, many operating procedures excessively rely on human experience, increasing the uncertainty of the production process. In addition, the expert knowledge is not fully embedded in accumulated operations, limiting its potential in decision support. Therefore, data-driven modeling of production processes is essential for developing industrial expert systems to realize intelligent manufacturing. This work proposes a work condition prediction framework based on an Operation Mode Library (OML) to realize Working Condition Prediction (WCP), called for OML-WCP short. Taking the cement rotary kiln adjustment process as an example, a stable OML is constructed using Gaussian mixture clustering technology. Experimental results with real-life operation data of a cement plant reveal that the prediction accuracy of OML-WCP outperforms the existing methods. Moreover, the continuous accumulation of operating mode libraries can improve prediction accuracy in practical annlications. © 2024 IEEE.

关键词： Cements

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Digital Defect Simulation-based Data Generation for Visual Quality Inspection 23

Digital Defect Simulation-based Data Generation for Visual Q...

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15th International Conference on computer Modeling and Simulation, ICCMS 2023

作者： Wang, Xiaohui Xie, Yulai Zhang, Yang Pi, Xiaoning Ren, Fang School of Computer and Communication Engineering University of Science and Technology Beijing China Hitachi China Research Laboratory China

ISBN: (纸本)9798400707919

With the continuous advancement of computer vision (CV) technology, visual inspection techniques in the quality inspection industry have been rapidly developing. Data is an indispensable factor in visual detection techniques, and the demand for data is gradually increasing. Studies are progressively focusing on data generation solutions to provide high-quality data for the development of quality inspection techniques. This paper presents a morphology-based defect classification approach for visual defect simulation. Based on this classification, a digital defect simulation-based data generation solution is proposed to simulate the defects of industrial products. A new dataset SimNEU-DET is created to evaluate the proposed method, based on the open-source dataset NEU-DET. The effectiveness of the dataset is demonstrated using the object detection algorithm YOLOv5. © 2023 ACM.

关键词： Defects

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Long-term Water Quality Prediction Based on Multimodal Fusion and Temporal 2D-variation 21

Long-term Water Quality Prediction Based on Multimodal Fusio...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wu, Xiangxi Wang, Ziqi Li, Yibo Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

The global water environment confronts numerous challenges, e.g., water pollution, overexploitation, and ecological degradation. Comprehensive protection and management are imperative for sustainable water resource utilization. Water quality predictions provide timely warning of future water quality problems and enable early action to avoid deterioration. As science and technology are increasingly applied in comprehensive water environment management, a diverse array of multimodal data is gathered from various sources, including remote sensing images and hydrological time series. However, current water quality prediction methods, e.g., statistical, machine learning, and deep learning methods fail to utilize multimodal data to enhance their accuracy of water quality prediction. To solve the above problem, this work proposes a multi-factor and long-term water quality prediction model based on multimodal data fusion named Low-rank Multimodal Fusion TimesNet (LMF-TimesN et), It first extracts features from hydrological time series and remote sensing images, respectively. Then, they are fused with the low-rank multimodal fusion network to extract diverse information. Finally, TimesNet is adopted to integrate fused multimodal water environment information for water quality prediction. Experimental results on a real-world dataset show that LMF - TimesNet achieves higher prediction accuracy and generalization ability than its state-of-the-art peers. © 2024 IEEE.

关键词： Data quality

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Toward next-generation networks:A blockchain-based approach for core network architecture and roaming identity verification

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Digital communications and Networks 2025年第2期11卷 326-336页

作者： Yi Gong Boyuan Yu Lei Yang Fanke Meng Lei Liu Xinjue Hu Zhan Xu School of Information and Communication Engineering Beijing Information Science&Technology UniversityBeijingChina Xi'an University of Posts and Telecommunications Xi'anChina Guangzhou Institute of Technology Xidian UniversityGuangzhouChina Intelligent Transportation Systems Research Center Wuhan University of TechnologyWuhanChina State Key Laboratory of Maritime Technology and Safety WuhanChina

With the evolution of next-generation communication networks,ensuring robust Core Network(CN)architecture and data security has become *** paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication *** centralized network architectures are vulnerable to Distributed Denial of Service(DDoS)attacks,particularly in roaming scenarios where there is also a risk of private data leakage,which imposes significant operational *** address these issues,we introduce the Blockchain-Enhanced Core Network Architecture(BECNA)and the Secure Decentralized Identity Authentication Scheme(SDIDAS).The BECNA utilizes blockchain technology to decentralize data storage,enhancing network security,stability,and reliability by mitigating Single Points of Failure(SPoF).The SDIDAS utilizes Decentralized Identity(DID)technology to secure user identity data and streamline authentication in roaming scenarios,significantly reducing the risk of data breaches during cross-network *** framework employs Ethereum,free5GC,Wireshark,and UERANSIM tools to create a robust,tamper-evident system model.A comprehensive security analysis confirms substantial improvements in user privacy and network *** results indicate that our approach enhances communication CNs security and reliability,while also ensuring data security.

关键词： Blockchain Core network Privacy data protection Decentralized identity Roaming identity verification

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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