IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第10页

The Role of Cyber-Physical-Social Systems in Smart Energy Future 网络-物理-社会系统在未来智能能源中的作用

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-01-23 DOI: 10.1109/TICPS.2024.3357666

Xinghuo Yu;Nian Liu;Yusheng Xue

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International Collaboration: Mainstreaming Artificial Intelligence and Cyberphysical Systems for Carbon Neutrality 国际合作：将人工智能和网络物理系统纳入主流，实现碳中和

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-01-15 DOI: 10.1109/TICPS.2024.3351624

Thorsten Jelinek;Amit Bhave;Nicolas Buchoud;Michael Max Bühler;Patrick Glauner;Oliver Inderwildi;Markus Kraft;Charles Mok;Konrad Nübel;Axel Voss

{"title":"International Collaboration: Mainstreaming Artificial Intelligence and Cyberphysical Systems for Carbon Neutrality","authors":"Thorsten Jelinek;Amit Bhave;Nicolas Buchoud;Michael Max Bühler;Patrick Glauner;Oliver Inderwildi;Markus Kraft;Charles Mok;Konrad Nübel;Axel Voss","doi":"10.1109/TICPS.2024.3351624","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3351624","url":null,"abstract":"Cyberphysical systems together with Artificial Intelligence play vital roles in reducing, eliminating, and removing greenhouse gas emissions across sectors. Electrification with renewables introduces complexity in systems in the deployment, integration, and efficient orchestration of electrified economic systems. AI-driven cyberphysical systems are uniquely suited to tackle this complexity, potentially accelerating the transition towards a low-carbon economy. The objective of this policy brief is to advocate for the mainstreaming of AI-driven cyberphysical systems for climate change risk mitigation and adaptation. To effectively and more rapidly realize the Intelligent Decarbonation potential, the concept of AI-driven cyberphysical systems must be elevated to a global level of collaboration and coordination, fostering research and development, capacity building, as well as knowledge and technology transfer. Drawing on a multidisciplinary, international study about intelligent decarbonization use cases, this brief also highlights factors impeding the transition to carbon neutrality and risks associated with technology determinism. The importance of governance is emphasized to avoid unwanted path dependency and avert a technology-solutionist approach dominating climate policy that delivers limited results. Given only 12% of the Sustainable Development Goals have been realized, a condensed version of this policy brief was submitted to the India T20, a G20 engagement group, urging global collaboration to prioritize AI-driven CPSs.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"26-34"},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10399830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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2023 Index IEEE Transactions on Industrial Cyber-Physical Systems Vol.1 2023 索引 IEEE《工业网络物理系统论文集》第 1 卷

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-12-28 DOI: 10.1109/TICPS.2023.3345558

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IEEE Industrial Electronics Society Information IEEE工业电子学会信息

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-12-06 DOI: 10.1109/TICPS.2023.3285337

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A Two-Level Fusion Framework for Cyber-Physical Anomaly Detection 网络物理异常检测的两级融合框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-29 DOI: 10.1109/TICPS.2023.3336608

Simone Guarino;Francesco Vitale;Francesco Flammini;Luca Faramondi;Nicola Mazzocca;Roberto Setola

{"title":"A Two-Level Fusion Framework for Cyber-Physical Anomaly Detection","authors":"Simone Guarino;Francesco Vitale;Francesco Flammini;Luca Faramondi;Nicola Mazzocca;Roberto Setola","doi":"10.1109/TICPS.2023.3336608","DOIUrl":"https://doi.org/10.1109/TICPS.2023.3336608","url":null,"abstract":"Industrial Cyber-Physical Systems (ICPSs) generate cyber and physical data whose joint elaboration can provide insight into ICPSs' operating conditions. Cyber-Physical Anomaly Detection (CPAD) addresses the joint analysis of cyber and physical threats through multi-source and multi-modal data analysis. CPAD is often tailored to specific anomaly types and may use opaque deep learning models, impairing flexibility and explainability. In light of these challenges, we propose a two-level fusion framework for modeling and deploying CPAD in distributed ICPSs. The first detector-level fusion involves deploying CPAD detectors to several distributed ICPS segments and training them through data/decision fusion techniques with historical cyber-physical data. When the distributed ICPS is operational, thus collecting new cyber-physical data, ICPS segments' trained CPAD detectors provide pieces of evidence that go through the second ensemble-level fusion, for which we propose an explainable decision fusion technique based on Time-Varying Dynamic Bayesian networks. The evaluation involves the comprehensive application of the framework to a real hardware-in-the-loop case-study in a laboratory environment. The proposed ensemble-level fusion outperforms the state-of-the-art decision fusion techniques while providing explainable results.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Demand-Side Joint Electricity and Carbon Trading Mechanism 需求方电力和碳交易联合机制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-28 DOI: 10.1109/TICPS.2023.3335328

Haochen Hua;Xingying Chen;Lei Gan;Jiaxiang Sun;Nanqing Dong;Di Liu;Zhaoming Qin;Kang Li;Shiyan Hu

{"title":"Demand-Side Joint Electricity and Carbon Trading Mechanism","authors":"Haochen Hua;Xingying Chen;Lei Gan;Jiaxiang Sun;Nanqing Dong;Di Liu;Zhaoming Qin;Kang Li;Shiyan Hu","doi":"10.1109/TICPS.2023.3335328","DOIUrl":"https://doi.org/10.1109/TICPS.2023.3335328","url":null,"abstract":"Decarbonization of the whole energy chain has been recognized as a measure to tackle the global challenge of climate change, and significant progress has already been made on the generation side to integrate renewable energy. However, the demand side is the single largest underlying factor in shaping decarbonization roadmap. Hence, the carbon emission cost should also be shared by the users according to their power consumption. In this paper, a joint electricity-carbon trading framework is designed to reduce the carbon emission through trading and demand response. A delayed carbon emission liability settlement for asynchronous markets is proposed to ameliorate the users’ optimal decision from single-point optimization to interval-based optimization. To develop the optimal strategy of trading within the proposed mechanism, an improved proximal policy optimization (PPO) algorithm based on Monte Carlo reward sampling is applied. Simulation studies reveal that, compared with the market without carbon trading and users without delayed settlement, the proposed mechanism has achieved a carbon emission reduction by 40.7% and 12.7% respectively. Simulations also show the algorithm's training efficiency can be significantly improved with the proposed Monte Carlo sampling method.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"14-25"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139090395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber-Physical Zero Trust Architecture for Industrial Cyber-Physical Systems 面向工业信息物理系统的信息物理零信任架构

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-28 DOI: 10.1109/TICPS.2023.3333850

Xiaomeng Feng;Shiyan Hu

{"title":"Cyber-Physical Zero Trust Architecture for Industrial Cyber-Physical Systems","authors":"Xiaomeng Feng;Shiyan Hu","doi":"10.1109/TICPS.2023.3333850","DOIUrl":"https://doi.org/10.1109/TICPS.2023.3333850","url":null,"abstract":"In recent years, zero trust architecture (ZTA) has become an emerging security architecture. When deploying to industrial systems, an important consideration of the ZTA is the effective modeling of the cross-layer penetration between cyber and physical layers. An ineffective model of cross-layer penetration can lead to inferior performance in mitigating cross-layer failures. To tackle this issue, this paper develops a subset of the ZTA dedicated to industrial cyber-physical systems (ICPS), called the Cyber-Physical-ZTA, to model cross-layer penetration. Its uniqueness mainly consists of two innovative techniques, namely, a multi-layer access control engine and an integrated physical model-based and data-driven policy optimizer. The multi-layer access control engine can evaluate the trust scores for each component considering their cross-layer impact, while the integration of data-driven and model-based approaches can improve efficiency in optimizing access policies. Our simulations are conducted to demonstrate the effectiveness of Cyber-Physical-ZTA. In comparison to the standard ZTA, with no rules added to detect cross-layer penetration, the multi-access policy engine of the Cyber-Physical-ZTA increases the detection probability against false data injection (FDI) attacks by more than 31%.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"1 ","pages":"394-405"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138468161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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1-Order-Smooth Explicit-Time Nonsingular Terminal Sliding Mode Control of Industrial Cyber-Physical Systems Against Cyber-Attacks 工业信息物理系统抗网络攻击的1阶光滑显式时间非奇异终端滑模控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-13 DOI: 10.1109/TICPS.2023.3332026

Wen Yan;Tao Zhao;Haixin Yang;Xin Wang;Ben Niu

{"title":"1-Order-Smooth Explicit-Time Nonsingular Terminal Sliding Mode Control of Industrial Cyber-Physical Systems Against Cyber-Attacks","authors":"Wen Yan;Tao Zhao;Haixin Yang;Xin Wang;Ben Niu","doi":"10.1109/TICPS.2023.3332026","DOIUrl":"10.1109/TICPS.2023.3332026","url":null,"abstract":"In the existing terminal sliding mode tracking controller design of the industrial cyber-physical systems (ICPSs) against cyber-attacks, the convergence time and tracking precision can not be predefined simultaneously due to singular problem. To solve this problem, for ICPSs under randomly occurring injection attacks, this paper presented a novel explicit-time nonsingular terminal sliding mode control (TSMC) method. Firstly, a novel 1-order-smooth symmetric system structure was found to construct an explicit-time stable system. It was 1-order-smooth at the steady-state boundary. Then, the singularity of the explicit-time terminal sliding mode nominal controller was analyzed from three aspects: narrow sense, broad sense and practical sense. Finally, based on above analysis, a novel 2-order nonsingular explicit-time TSMC method for ICPSs was proposed without the cost of convergence time and precision. Compared with other related methods, this method had more controllable convergence performance and smoother control input. Theoretical results were verified by simulation and experiment.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"1 ","pages":"371-380"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Distributed Model Predictive Control for Coupled Nonlinear Systems via Two-Channel Event- Triggered Transmission Scheme 基于双通道事件触发传输的耦合非线性系统分布式模型预测控制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-13 DOI: 10.1109/TICPS.2023.3332313

Rui Guo;Jianwen Feng;Jingyi Wang;Yi Zhao

{"title":"Distributed Model Predictive Control for Coupled Nonlinear Systems via Two-Channel Event- Triggered Transmission Scheme","authors":"Rui Guo;Jianwen Feng;Jingyi Wang;Yi Zhao","doi":"10.1109/TICPS.2023.3332313","DOIUrl":"10.1109/TICPS.2023.3332313","url":null,"abstract":"This paper investigates the issue of event-triggered distributed model predictive control for coupled nonlinear systems with additive disturbances. Specifically, this paper proposes two event-triggered strategies, which are incorporated into the sensor and model predictive control (MPC) based controller for each subsystem, respectively. A limited-information-based control scheme is constructed using two-channel even-triggered transmissions. The scheme proposed achieves efficient reduction in both the transmission rates of the sensor and the resource consumption associated with optimization problem, as well as, enhances the real-world operational capability through the utilization of a sample-and-hold technique. This technique allows the actual control inputs to be derived by discretizing the continuous optimal control trajectory. This paper shows rigorously that the mutual influences invoked by dynamic coupling are bounded and the Zeno behavior is excluded entirely. Also, the sufficient conditions are developed to ensure the algorithm feasibility and the convergence of the overall system to a bounded set. Finally, a practical example is presented and comparisons are made to demonstrate the efficiency of the proposed algorithm.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"1 ","pages":"381-393"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135660184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Industrial Big Data Analytical System in Industrial Cyber-Physical Systems Based on Coarse-to-Fine Deep Network 基于粗细深度网络的工业信息物理系统大数据分析系统

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2023-11-09 DOI: 10.1109/TICPS.2023.3331331

Ruonan Liu;Quanhu Zhang;Yu Wang;Zengxiang Li;Dongyue Chen;Steven X. Ding;Qinghua Hu;Boyuan Yang

{"title":"Industrial Big Data Analytical System in Industrial Cyber-Physical Systems Based on Coarse-to-Fine Deep Network","authors":"Ruonan Liu;Quanhu Zhang;Yu Wang;Zengxiang Li;Dongyue Chen;Steven X. Ding;Qinghua Hu;Boyuan Yang","doi":"10.1109/TICPS.2023.3331331","DOIUrl":"10.1109/TICPS.2023.3331331","url":null,"abstract":"In smart factories, there have been increasing requirements for industrial Big Data analysis of complex systems. With the rapid development of industrial cyber-physical systems (ICPS) and communication techniques, the scale and complexity of industrial data are growing explosively, which not only provides massive operational information of industrial systems but also brings challenges in Big Data analysis. In this paper, to overcome the intra/inter-class distance unbalance and local minima problems in traditional deep learning-based methods, an industrial Big Data analytical system based on a coarse-to-fine network (CTFN) is proposed for intelligent industrial Big Data analysis and condition monitoring of complex system. In addition, considering the gap between semantic comprehension and natural characteristics of different failures, a structure learning algorithm is proposed to get rid of the complicated hyper-parameters and implement intelligentization authentically. Finally, an experimental verification was carried on a nuclear power system dataset with 362,994 samples from 66 fault categories. The results demonstrate the effectiveness and superiority of the proposed method in condition monitoring of industrial systems, which provides a promising tool for industrial Big Data analysis in ICPS.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"1 ","pages":"359-370"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0