IET Cyber-Physical Systems: Theory and Applications最新文献

{"title":"Status, challenges, and promises of data-driven battery lifetime prediction under cyber-physical system context","authors":"Yang Liu,&nbsp;Sihui Chen,&nbsp;Peiyi Li,&nbsp;Jiayu Wan,&nbsp;Xin Li","doi":"10.1049/cps2.12086","DOIUrl":"10.1049/cps2.12086","url":null,"abstract":"<p>Energy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber-physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data-driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"207-217"},"PeriodicalIF":1.7,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140471755","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}

{"title":"Oversampling and undersampling for intrusion detection system in the supervisory control and data acquisition IEC 60870-5-104","authors":"M. Agus Syamsul Arifin,&nbsp;Deris Stiawan,&nbsp;Bhakti Yudho Suprapto,&nbsp;Susanto Susanto,&nbsp;Tasmi Salim,&nbsp;Mohd Yazid Idris,&nbsp;Rahmat Budiarto","doi":"10.1049/cps2.12085","DOIUrl":"10.1049/cps2.12085","url":null,"abstract":"<p>Supervisory control and data acquisition systems are critical in Industry 4.0 for controlling and monitoring industrial processes. However, these systems are vulnerable to various attacks, and therefore, intelligent and robust intrusion detection systems as security tools are necessary for ensuring security. Machine learning-based intrusion detection systems require datasets with balanced class distribution, but in practice, imbalanced class distribution is unavoidable. A dataset created by running a supervisory control and data acquisition IEC 60870-5-104 (IEC 104) protocol on a testbed network is presented. The dataset includes normal and attacks traffic data such as port scan, brute force, and Denial of service attacks. Various types of Denial of service attacks are generated to create a robust and specific dataset for training the intrusion detection system model. Three popular techniques for handling class imbalance, that is, random over-sampling, random under-sampling, and synthetic minority oversampling, are implemented to select the best dataset for the experiment. Gradient boosting, decision tree, and random forest algorithms are used as classifiers for the intrusion detection system models. Experimental results indicate that the intrusion detection system model using decision tree and random forest classifiers using random under-sampling achieved the highest accuracy of 99.05%. The intrusion detection system model's performance is verified using various metrics such as recall, precision, F1-Score, receiver operating characteristics curves, and area under the curve. Additionally, 10-fold cross-validation shows no indication of overfitting in the created intrusion detection system model.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"282-292"},"PeriodicalIF":1.7,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386040","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}

{"title":"Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique","authors":"Dipta Neogi,&nbsp;Mahirul Alam Chowdhury,&nbsp;Mst. Moriom Akter,&nbsp;Md. Ishan Arefin Hossain","doi":"10.1049/cps2.12083","DOIUrl":"10.1049/cps2.12083","url":null,"abstract":"<p>Testing the visual field is a valuable diagnostic tool for identifying eye conditions such as cataract, glaucoma, and retinal disease. Its quick and straightforward testing process has become an essential component in our efforts to prevent blindness. Still, the device must be accessible to the general masses. This research has developed a machine learning model that can work with Edge devices like smartphones. As a result, it is opening the opportunity to integrate the disease-detecting model into multiple Edge devices to automate their operation. The authors intend to use convolutional neural network (CNN) and deep learning to deduce which optimisers have the best results when detecting cataracts from live photos of eyes. This is done by comparing different models and optimisers. Using these methods, a reliable model can be obtained that detects cataracts. The proposed TensorFlow Lite model constructed by combining CNN layers and Adam in this study is called Optimised Light Weight Sequential Deep Learning Model (SDLM). SDLM is trained using a smaller number of CNN layers and parameters, which gives SDLM its compatibility, fast execution time, and low memory requirements. The proposed Android app, I-Scan, uses SDLM in the form of TensorFlow Lite for demonstration of the model in Edge devices.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"269-281"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394799","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}

{"title":"Real-time implementation for vulnerability of power components under switching attack based on sliding mode","authors":"Seema Yadav,&nbsp;Nand Kishor,&nbsp;Shubhi Purwar,&nbsp;Saikat Chakrabarti,&nbsp;Petra Raussi,&nbsp;Avinash Kumar","doi":"10.1049/cps2.12084","DOIUrl":"https://doi.org/10.1049/cps2.12084","url":null,"abstract":"<p>In recent years, cyber security-related studies in the power grid have drawn wide attention, with much focus on its detection, mainly for data injection type of attacks. The vulnerability of power components as a result of attack and their impact on generator dynamics have been largely ignored so far. With the aim of addressing some of these issues, the authors propose a novel approach using real-time sliding surface-based switching attack (SA) construction. This approach targets the circuit breaker, excitation system, and governor system of the generator. The vulnerability of these power components to cyber-physical attacks and assessment of their potential impact on the stability of generator are discussed. The study is presented to show the progression of cascading generator dynamics on account of single or multiple time instants of SA launched on these power components. The results are discussed according to criteria in terms of deviations in rotor speed of the generator and identify some of possible combinations of power components that are most critical to grid stability. The proposed study is implemented on standard IEEE 3-machine, 9-bus network in real-time digital simulator via transmission control protocol/internet protocol (TCP/IP) communication network established as cyber-physical system. The sliding surface-based SA algorithm developed in MATLAB is launched from another computer.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 4","pages":"375-391"},"PeriodicalIF":1.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253747","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}

{"title":"Learning the geometry of short-circuit faults in power systems for real-time fault detection and classification","authors":"Juan Pablo Naranjo Cuéllar,&nbsp;Gustavo Ramos López,&nbsp;Luis Felipe Giraldo Trujillo","doi":"10.1049/cps2.12074","DOIUrl":"https://doi.org/10.1049/cps2.12074","url":null,"abstract":"<p>Given the short time intervals in which short-circuit faults occur in a power system, a certain time delay between the moment of a fault's inception in the system to the moment in which the fault is actually detected is introduced. In this small time margin, the high amplitudes of the fault current can deal significant damage to the power system. A technique to characterise different types of short circuit faults in a power system for real-time detection, namely AB, BC, CA, ABC, AG, BG and CG faults (and normal operation), is presented based on the geometry of the curve generated by the Clarke transform of the three-phase voltages of the power system. The process was conducted in real time using the <i>HIL402</i> system and a Raspberry Pi 3, and all programming done in the Python programming language. It was concluded that the tested types of faults can be accurately characterised using the eigenvalues and eigenvectors of the matrix that characterises an ellipse associated with each fault: eigenvalues can be used to determine the fault inception distance and eigenvectors can be used to determine the type of fault that occurred. Next, the design of a machine learning model was done based on the previously mentioned characterisation technique. The model was embedded into a Raspberry Pi 3, thus enabling fault detection and classification in a base power system in real time. Finally, the accuracy of the model was tested under different measurement conditions, yielding satisfactory results for a selected set of conditions and overcoming the shortcomings presented in the current research, which do not perform detection and classification in real time.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"8 4","pages":"289-306"},"PeriodicalIF":1.5,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138578065","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}

{"title":"Guest Editorial: Optimisation, control and AI technology for digital and low-carbon power systems","authors":"Pathmanathan Naidoo,&nbsp;Maria Cristina Tavares,&nbsp;Junwei Cao,&nbsp;Yi Ding,&nbsp;Haochen Hua","doi":"10.1049/cps2.12082","DOIUrl":"https://doi.org/10.1049/cps2.12082","url":null,"abstract":"&lt;p&gt;Modern power systems are facing a growing integration of distributed energy resources (DERs), mainly driven by energy transition, decarbonisation and economic benefits. The deployment of Internet of Things devices transforms the conventional power system into a digitised, cyber, intelligent one, which plays a significant role in grid control and operation and enables numerous smart-grid applications.&lt;/p&gt;&lt;p&gt;The stochastic nature of distributed renewable power generation poses challenges for a power systems operation, while coordinating the dispatch and control of various DERs to reduce operating costs and carbon emissions is essential to improve energy utilisation efficiency. Also, the large-scale connection of DERs increases the complexity of distribution networks, which require more advanced and efficient approaches for system analysis, fault diagnosis and operational optimisation. In this sense, smart monitoring and control systems can also be applied to transmission power networks, enhancing safety and robustness.&lt;/p&gt;&lt;p&gt;Energy internet technology has laid a solid foundation for data-driven analysis, allowing power systems to enter a ‘data-intensive’ era. Currently, huge amounts of data from various sources have been a driving force, enabling big data analytics and artificial intelligence on smart-grid applications, such as planning, operation, energy management, trading, system reliability and resiliency enhancement, system identification and monitoring, fault intelligent perception and diagnosis, and cyber and physical security.&lt;/p&gt;&lt;p&gt;This Special Issue publishes state-of-the-art works related to all aspects of theories and methodologies in optimisation, control and AI technology for digital and low-carbon power systems.&lt;/p&gt;&lt;p&gt;The stochastic nature of distributed renewable generation makes the operation of power systems face the challenge of uncertainty. Thereby, it is of great significance to monitor and identify the real-time state of the new power system. The paper, ‘The real-time state identification of the electricity-heat system based on borderline-SMOTE and XGBoost’ by X. Pei et al., proposes a state identification method based on multi-class data equalisation and extreme gradient boost for systems. The optimal hyperparameters of the model are obtained based on the K-fold cross-validation and grid search.&lt;/p&gt;&lt;p&gt;Reducing carbon emissions is one of the goals of modern power systems operation. Power generation by natural gas, compared with that by coal, has the characteristics of cleanness, efficiency and low carbon. This makes gas-fired power plants popular for undertaking peak regulation tasks in the power systems. The paper, ‘Key problems of gas-fired power plants participating in peak load regulation: a review’ by G. Wang et al., reviews the key problems faced by gas-fired power plants participating in peak load regulation. This paper provides suggestions for the coordinated development of electricity and carbon market in the futur","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"8 4","pages":"219-221"},"PeriodicalIF":1.5,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138578182","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}

{"title":"Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach","authors":"Bijan Nasiri,&nbsp;Farhad Bayat,&nbsp;MohammadAli Mohammadkhani,&nbsp;Andrzej Bartoszewicz","doi":"10.1049/cps2.12081","DOIUrl":"10.1049/cps2.12081","url":null,"abstract":"<p>A predictive queue management method is proposed for constrained congestion control in internet routers in the face of communication delays. The proposed method uses the queue and router models, input traffic rate, and queue length to precisely characterise the entire process. The model that has been built is then used to construct an optimal constrained active queue management (CAQM) strategy using the model predictive control method. Important factors, such as link capacity, Transmission Control Protocol (TCP) sessions, round-trip time, and a few others, have been selected and used to linearise the interconnection of TCP. Then, an efficient MPC-based structure to manage the CAQM in the face of unknown disturbances is designed. Simulations are used to validate the proposed method's effectiveness and robustness.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"258-268"},"PeriodicalIF":1.7,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136014706","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}

{"title":"Multiple dependence representation of attention graph convolutional network relation extraction model","authors":"Zhao Liangfu,&nbsp;Xiong Yujie,&nbsp;Gao Yongbin,&nbsp;Yu Wenjun","doi":"10.1049/cps2.12080","DOIUrl":"10.1049/cps2.12080","url":null,"abstract":"<p>Dependency analysis can better help neural network to capture semantic features in sentences, so as to extract entity relation. Currently, hard pruning strategies and soft pruning strategies based on dependency tree structure coding have been proposed to balance beneficial additional information and adverse interference in extraction tasks. A new model based on graph convolutional networks, which uses a variety of representations describing dependency trees from different perspectives and combining these representations to obtain a better sentence representation for relation classification is proposed. A newly defined module is added, and this module uses the attention mechanism to capture deeper semantic features from the context representation as the global semantic features of the input text, thus helping the model to capture deeper semantic information at the sentence level for relational extraction tasks. In order to get more information about a given entity pair from the input sentence, the authors also model implicit co-references (references) to entities. This model can extract semantic features related to the relationship between entities from sentences to the maximum extent. The results show that the model in this paper achieves good results on SemEval2010-Task8 and KBP37 datasets.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"247-257"},"PeriodicalIF":1.7,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590945","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}

{"title":"Selective real-time adversarial perturbations against deep reinforcement learning agents","authors":"Hongjin Yao,&nbsp;Yisheng Li,&nbsp;Yunpeng Sun,&nbsp;Zhichao Lian","doi":"10.1049/cps2.12065","DOIUrl":"10.1049/cps2.12065","url":null,"abstract":"<p>Recent work has shown that deep reinforcement learning (DRL) is vulnerable to adversarial attacks, so that exploiting vulnerabilities in DRL systems through adversarial attack techniques has become a necessary prerequisite for building robust DRL systems. Compared to traditional deep learning systems, DRL systems are characterised by long sequential decisions rather than one-step decision, so attackers must perform multi-step attacks on them. To successfully attack a DRL system, the number of attacks must be minimised to avoid detecting by the victim agent and to ensure the effectiveness of the attack. Some selective attack methods proposed in recent researches, that is, attacking an agent at partial time steps, are not applicable to real-time attack scenarios, although they can avoid detecting by the victim agent. A real-time selective attack method that is applicable to environments with discrete action spaces is proposed. Firstly, the optimal attack threshold <i>T</i> for performing selective attacks in the environment <i>Env</i> is determined. Then, the observation states corresponding to when the value of the action preference function of the victim agent in multiple eposides exceeds the threshold <i>T</i> are added to the training set according to this threshold. Finally, a universal perturbation is generated based on this training set, and it is used to perform real-time selective attacks on the victim agent. Comparative experiments show that our attack method can perform real-time attacks while maintaining the attack effect and stealthiness.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 1","pages":"41-49"},"PeriodicalIF":1.5,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061808","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}

{"title":"Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images","authors":"Yongquan Wu,&nbsp;Xuan Zhao,&nbsp;Xinsheng Zhang,&nbsp;Tao Long,&nbsp;Ping Luo","doi":"10.1049/cps2.12076","DOIUrl":"10.1049/cps2.12076","url":null,"abstract":"<p>Single-image dehazing is a highly challenging ill-posed task in the field of computer vision. To address this, a new image dehazing model with feature attention, named feature attention gated context aggregation network (FAGCA-Net), is proposed to tackle the issues of incomplete or over-dehazing caused by the original model's inability to handle non-uniform haze density distributions. A feature attention module that combines channel attention and spatial attention is introduced. Additionally, the authors propose a new extended attention convolutional block, which not only addresses the grid artefacts caused by the extended convolution but also provides added flexibility in handling different types of feature information. At the same time, in addition to the input image itself, incorporating the dark channel and edge channel of the image as the final input of the model is helpful for the model learning process. To demonstrate the robustness of the new model, it is applied to two completely different dehazing datasets, and it achieves significant dehazing performance improvement over the original model. Finally, to verify the effectiveness of the model in practical production processes, the authors apply it as an image preprocessing step to a set of UAV (Unmanned Aerial Vehicle) images of foreign objects. The result shows that the UAV images after being processed by FAGCA-Net for haze removal have a better impact on subsequent usage.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"9 3","pages":"218-227"},"PeriodicalIF":1.7,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375950","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}