IET Cyber-Physical Systems: Theory and Applications最新文献_第2页

Intuitive Multi-Scale Visual Feature Fusion for Automated Classification of Human Activity 基于直观多尺度视觉特征融合的人类活动自动分类

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-06-08 DOI: 10.1049/cps2.70054

Vinay Gautam, Gaganpreet Kaur, Anurag Sinha, Ashish Kumar Verma, Anindya Nag

{"title":"Intuitive Multi-Scale Visual Feature Fusion for Automated Classification of Human Activity","authors":"Vinay Gautam, Gaganpreet Kaur, Anurag Sinha, Ashish Kumar Verma, Anindya Nag","doi":"10.1049/cps2.70054","DOIUrl":"https://doi.org/10.1049/cps2.70054","url":null,"abstract":"Human activity recognition (HAR) with the help of wearable sensors has become a major research focus because of its broad application areas, such as healthcare monitoring, smart homes and human computer interaction. Yet, it is not easy to recognise activities accurately by using multivariate sensor data because the sensors can produce noisy signals, there can be redundant features and complex temporal dependencies make the task difficult. In our paper, we suggest a deep learning method that combines sensor-to-image conversion, feature-level fusion, dimensionality reduction and multi-scale classification to solve the above issues. Firstly, raw multivariate sensor signals are transformed into structured image representations with the use of spectrogram-based encoding, thus enabling convolutional neural networks to grasp spatial patterns in temporal data quite well. Two deep architectures that complement each other, namely Inception and Xception, are used to obtain significant features from the generated images. Next, as a way of feature-level fusion, the feature vectors extracted from the two networks are joined to harness the complementary information contained in both networks. After that, principal component analysis (PCA) is used to get a small reduced fused feature (RFF) representation in order to minimise feature redundancy and computational complexity. This reduced feature space is later processed through a common multi-scale convolutional front-end with kernel sizes of 3, 5 and 7, and then CNN, LSTM and RNN classifiers are used to represent spatial temporal activity patterns. As shown by the tests on the WISDM, UCI-HAR and PAMAP2 datasets, the proposed method can achieve excellent results with accuracies of 98.88%, 98.71% and 98.71%, respectively.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237522","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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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-26

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Detection of Multi-Target False Data Injection Attacks in Networked Microgrids Considering Renewable Energy Uncertainties 考虑可再生能源不确定性的网络化微电网多目标虚假数据注入攻击检测

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-26 DOI: 10.1049/cps2.70049

Kaishun Xiahou, Kun Qu, Dingjie Lin, Yang Liu, Ren Liu, Hengdao Guo

{"title":"Detection of Multi-Target False Data Injection Attacks in Networked Microgrids Considering Renewable Energy Uncertainties","authors":"Kaishun Xiahou, Kun Qu, Dingjie Lin, Yang Liu, Ren Liu, Hengdao Guo","doi":"10.1049/cps2.70049","DOIUrl":"https://doi.org/10.1049/cps2.70049","url":null,"abstract":"To address the detection issue of false data injection attacks (FDIAs) in networked microgrids (NMGs) with renewable energy integration and secondary frequency regulation, this paper proposes a novel detection scheme based on delay unknown input observers (DUIOs). Firstly, a comprehensive dynamic NMG model is developed under the cyber-physical integration framework, incorporating renewable energy systems, hybrid energy storage systems and dynamic frequency control for multi-microgrid interactions. This model establishes a foundation for analysing multi-target FDIAs across different channels and control areas. Secondly, an innovative DUIO is designed that incorporates renewable energy uncertainties and load prediction errors into the observer design, achieving accurate system state estimation under unknown disturbances through the introduction of an observer delay parameter. Finally, a multi-target detection framework based on residual analysis is proposed, enabling precise attack localisation across different channels and regions through multiple parallel observers and a moving accumulation residual evaluation mechanism. Simulations conducted on a three-area NMG validate the effectiveness of the proposed scheme under various attack scenarios, including single-area multi-target attacks, multi-area multi-target attacks and coordinated attacks, while maintaining robustness against renewable energy fluctuations.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091923","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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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-25

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Uncertainty-Quantified Probabilistic Forecasting and Reinforcement Learning for Short-Term Hydropower Scheduling 水电短期调度的不确定性量化概率预测与强化学习

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-25 DOI: 10.1049/cps2.70042

Xin Wang, Zengchao Wang, Yuan La

{"title":"Uncertainty-Quantified Probabilistic Forecasting and Reinforcement Learning for Short-Term Hydropower Scheduling","authors":"Xin Wang, Zengchao Wang, Yuan La","doi":"10.1049/cps2.70042","DOIUrl":"https://doi.org/10.1049/cps2.70042","url":null,"abstract":"Short-term hydropower scheduling is inherently affected by uncertainties in both inflow and electricity demand, which challenge the reliability of management strategies. Deterministic approaches struggle to maintain feasible and economically efficient schedules, especially when a rare or extreme scenario occurs. In this paper, an integrated framework combining probabilistic forecasting with safe reinforcement learning is proposed to enhance the short-term hydropower scheduling strategy. LightGBM and temporal fusion transformer are combined into a multimodel forecasting layer to generate calibrated probabilistic predictions. The forecasts are transformed into risk-aware constraints for a deep reinforcement learning agent to optimise reservoir operations. Experiments under three different scenarios are conducted to demonstrate the effectiveness of the proposed framework. Probabilistic forecasting provides well-calibrated uncertainty bounds that adapt to both stationary and highly volatile conditions. The proposed approach can achieve higher cumulative rewards, maintain operational feasibility under compound disturbances and exhibit strong adaptability to nonstationary and biased forecasting regimes.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094378","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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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-22

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Guest Editorial: Artificial Intelligence and Industrial Cyber-Physical Security 嘉宾评论：人工智能与工业网络物理安全

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-22 DOI: 10.1049/cps2.70052

Neetesh Saxena, Bong Jun Choi, Kim-Kwang Raymond Choo, Ali Dehghantanha

{"title":"Guest Editorial: Artificial Intelligence and Industrial Cyber-Physical Security","authors":"Neetesh Saxena, Bong Jun Choi, Kim-Kwang Raymond Choo, Ali Dehghantanha","doi":"10.1049/cps2.70052","DOIUrl":"https://doi.org/10.1049/cps2.70052","url":null,"abstract":"Cyber-Physical Systems (CPS) are central to modern smart infrastructures, enabling intelligent processes that handle large volumes of data while ensuring security, safety, reliability, and resilience. The integration of Artificial Intelligence (AI), particularly deep learning, with CPS and Internet of Things (IoT) technologies is driving advancements in Industry 4.0, smart grids, and intelligent transportation systems. AI enhances cybersecurity by enabling anomaly detection, continuous monitoring, network scanning, log analysis, and classification of data into legitimate and malicious categories, allowing real-time threat identification and proactive mitigation. At the same time, the adoption of CPS and Industrial Internet of Things (IIoT) introduces significant cybersecurity challenges, as security incidents can directly affect humans and critical assets. This convergence of AI and cybersecurity provides a dual advantage: improving operational efficiency while reinforcing defences against evolving cyber-physical threats. By enabling real-time threat detection, resilient response, and robust protection of both infrastructure and AI algorithms, CPS can maintain safety, reliability, and performance under complex cyber and physical stressors. This special issue highlights research at the intersection of AI, cybersecurity, and CPS/IIoT, focusing on innovations that enhance resilience, protect critical systems, and support the sustainable growth of smart industrial and urban ecosystems.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087332","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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Multi-Source Fusion Deep Learning for Predicting Satellite Communication Link Quality in Environmental Monitoring Cyber-Physical Systems 环境监测信息物理系统中卫星通信链路质量预测的多源融合深度学习

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-09 DOI: 10.1049/cps2.70040

Yuewei Wang, Kai Zhang, Jiahao Yuan, Shiyan Hu, Xiaodao Chen

{"title":"Multi-Source Fusion Deep Learning for Predicting Satellite Communication Link Quality in Environmental Monitoring Cyber-Physical Systems","authors":"Yuewei Wang, Kai Zhang, Jiahao Yuan, Shiyan Hu, Xiaodao Chen","doi":"10.1049/cps2.70040","DOIUrl":"https://doi.org/10.1049/cps2.70040","url":null,"abstract":"Remote environmental monitoring covers large and heterogeneous regions, yet large target areas lack communication stations, creating a persistent backhaul constraint. To provide connectivity, monitoring-oriented cyber–physical systems adopt low earth orbit satellite links. However, a LEO satellite offers only short, intermittent contact windows, and both window availability and in-window link quality vary with satellite pass geometry and weather driven disturbances. In this paper, a multimodal fusion model, Residual Multi-Modal Temporal Fusion Network (RMM-TFNet) that integrates numerical meteorological variables, historical communication statistics, and sky images is proposed to predict access window availability and signal quality. The architecture employs two modality specific encoders and combines their representations through two complementary fusion modules. A series of controlled data transmission experiments were conducted, yielding a corpus of communication link measurements in practical situations. The proposed model is found to achieve higher predictive accuracy than competitive deep learning baselines by evaluating on these measurements.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072855","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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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-05-09

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A Reinforcement Learning Engine With Reduced Action and State Space for Scalable Cyber-Physical Optimal Response 一种具有简化动作和状态空间的可扩展网络物理最优响应强化学习引擎

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IET Cyber-Physical Systems: Theory and Applications Pub Date : 2026-04-30 DOI: 10.1049/cps2.70051

Shining Sun, Khandaker Akramul Haque, Xiang Huo, Leen Al Homoud, Shamina Hossain-McKenzie, Ana Goulart, Katherine Davis

{"title":"A Reinforcement Learning Engine With Reduced Action and State Space for Scalable Cyber-Physical Optimal Response","authors":"Shining Sun, Khandaker Akramul Haque, Xiang Huo, Leen Al Homoud, Shamina Hossain-McKenzie, Ana Goulart, Katherine Davis","doi":"10.1049/cps2.70051","DOIUrl":"https://doi.org/10.1049/cps2.70051","url":null,"abstract":"Numerous research studies have been conducted to enhance the resilience of cyber-physical systems (CPSs) by detecting potential cyber or physical disturbances. However, the development of scalable and optimal response measures under power system contingencies based on fusing cyber-physical data is still in an early stage. To address this research gap, this paper introduces a power system response engine based on reinforcement learning (RL) and role and interaction discovery (RID) techniques. RL-RID-GridResponder is designed to automatically detect the contingency and assist with the decision-making process to ensure optimal power system operation. The RL-RID-GridResponder learns via an RL-based structure and achieves enhanced scalability by integrating an RID module with reduced action and state spaces. The applicability of RL-RID-GridResponder in providing scalable and optimal responses for CPSs is demonstrated on power systems during cyber disturbances. Moreover, simulations are conducted on a Volt-Var regulation problem using the augmented WSCC 9-bus, augmented IEEE 24-bus and IEEE 8500-node systems based on fused cyber and physical datasets. The results show that the proposed RL-RID-GridResponder can provide fast and accurate responses to ensure optimal power system operation under cyber intrusions, such as DoS, and can extend to other system contingencies, such as line outages and load losses.","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098364","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}

引用次数: 0