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A real-time data acquisition method of industrial production line based on OPC technology

International Journal of Internet Manufacturing and Services 2023年第1期9卷 16-31页

作者： Xu, Airong He, Panpan Meng, Kaiyuan Xue, Jijun Wang, Chao College of Mechanical Engineering Xi’an Shiyou University Xi’an710065 China Software Research Institute College of Computer Science Xi’an Shiyou University Xi’an710065 China

In view of the low timeliness and accuracy of traditional production line data acquisition methods, this study proposed a real-time data acquisition method of industrial production line based on OPC technology. Considering the heterogeneity of industrial production line data sources, a data transmission scheme is generated based on OPC technology and multi-point communication with Modbus protocol after data preprocessing by piecewise linear interpolation method. Then, the improved decision tree algorithm is used to design the data processing mode. Finally, the current state of industrial production line data is judged by comparing the threshold value, and the real-time data collection of industrial production line is completed after the collection interval is determined. Experimental results show that the acquisition accuracy of this method is up to 96.8%, the maximum acquisition time is only 12.5 s, and the highest packet loss rate is only 1.89%, indicating that this method improves the acquisition quality. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Data acquisition

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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Ubiquitous WiFi and Acoustic Sensing:Principles,Technologies,and Applications

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Journal of computer science & Technology 2023年第1期38卷 25-63页

作者：黄佳玲王云舒邹永攀伍楷舜倪明选 The IoT Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060 China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511455China The Hong Kong University of Science and Technology Hong KongChina

With the increasing pervasiveness of mobile devices such as smartphones,smart TVs,and wearables,smart sensing,transforming the physical world into digital information based on various sensing medias,has drawn researchers’great *** different sensing medias,WiFi and acoustic signals stand out due to their ubiquity and zero hardware *** on different basic principles,researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition,motion tracking,indoor localization,health monitoring,and the *** enable readers to get a comprehensive understanding of ubiquitous wireless sensing,we conduct a survey of existing work to introduce their underlying principles,proposed technologies,and practical *** we also discuss some open issues of this research *** survey reals that as a promising research direction,WiFi and acoustic sensing technologies can bring about fancy applications,but still have limitations in hardware restriction,robustness,and applicability.

关键词： WiFi sensing acoustic sensing human-computer interaction human activity recognition

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A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

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computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

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Improve Robustness of Safe Reinforcement Learning Against Adversarial Attacks 4

Improve Robustness of Safe Reinforcement Learning Against Ad...

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4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Xiaoyan Zhang, Yujuan Huang, Lan College of Computer Science and Technology Jilin University Changchun China Jilin University Software Engineering Changchun China

ISBN: (纸本)9798331531409

Models trained with adversarial attack can be significantly improved stability and performance when faced with new uncertain environment. In this paper, we propose the robust training framework based on Wasserstein SA-MDP for safe RL, which combines ideas from reinforcement learning, adversarial examples generation, Wasserstein metric and robust training. This framework can improve the robustness of safe RL models against adversarial attacks by considering the dynamics of the adversarial interaction as a sequential decision-making process. We made the experiment in safety gym based on the proposed framework and report the experimental results. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series 7

A Novel Data Mining Framework for Discovering Association Ru...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Shengwei Lyu, Jianhua Zhang, Baili School Of Software Southeast University Nanjing China College Of Computer Science And Engineering Southeast University Nanjing China

ISBN: (纸本)9798350361445

As a significant field within data mining, association rule mining plays a pivotal role in unveiling relationships between items within a dataset. Existing approaches in multivariate time series (MTS) association rule mining primarily emphasize the generation of association rules by combining features from different variables, but neglect to adequately consider the temporal relationship between these features and ignore the trend information of variables, resulting in an incomplete capture of the essential characteristics inherent in MTS data. In this paper, we introduce a novel framework for association rule mining in multivariate time series. First, we discretize the multivariate time series into the symbolic sequence using trend-based symbolic aggregate approximation(TSAX). Then, a rule mining method is introduced to discover association rules that consider the temporal relationship among symbols of different variables. The results of experiments demonstrate the discovery of significant rules within the 'Weather&Traffic' dataset which gives the traffic conditions in France depending on the weather. Further, we also provide the outcomes of the sensitivity analysis conducted on our method, illustrating that significant rules can be mined by adjusting the parameters of TSAX to different settings. The experimental results highlight the utility of our proposed framework in discovering temporal association rules within multivariate time series datasets. © 2024 IEEE.

关键词： Association rules

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ALQ: An Efficient Active Learning Recommendation Algorithm 6

ALQ: An Efficient Active Learning Recommendation Algorithm

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Song, Ao Lei, Zhou Chen, Shengbo Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350316520

With the rapid development of the Internet, the problem of information overload is becoming more and more serious, which makes it difficult for users to choose the content they are interested in from a large number of information. With the emergence of recommendation systems, the historical interaction information between users and information and items is used to provide users with topics of interest, which not only alleviates the problem of information overload, but also brings great satisfaction to users. However, training a model that can make accurate recommendations is not an easy task. In addition to an appropriate training strategy, it also requires a large amount of data support. Getting data, especially labeled data, takes a lot of time and money. Therefore, this paper proposes a new active learning algorithm. Firstly, the recommendation problem was transformed into a PU learning problem, and then the method was introduced in the positive sample learning process to achieve the purpose of increasing positive sample data, thereby reducing the cost of data labeling. Through experiments, the proposed method reduces the cost of data labeling in the recommendation task and further verifies the rationality of transforming the recommendation problem into a PU learning problem. © 2024 IEEE.

关键词： Recommender systems

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Anomaly Detection in Spatial Information Networks: A Security Knowledge Graph-based Approach

Anomaly Detection in Spatial Information Networks: A Securit...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Tan, Yayue Zhang, Yaqi Liu, Hui Tang, Yongqing College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350372274

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network securityoriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience. ©2024 IEEE.

关键词： Knowledge graph

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing

Group Signature with Time-Bound Keys for Secure E-health Rec...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Junze Hu, Chunqiang Ruan, Conghao Cai, Bin Xiang, Tao Chongqing University School of Big Data & Software Engineering China Chongqing University College of Computer Science China

ISBN: (纸本)9798350386226

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient. © 2024 IEEE.

关键词： Electronic health record

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module 4

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

ISBN: (纸本)9798350363586

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments © 2024 IEEE.

关键词： Adversarial machine learning

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