IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第5页

Confident Information Coverage Reliability Evaluation for Sensor Networks of Openly Deployed ICP Systems 对开放部署的 ICP 系统传感器网络进行可靠的信息覆盖可靠性评估

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-30 DOI: 10.1109/TICPS.2024.3455273

Suning Chen;Yuanyuan Yi;Lingzhi Yi;Shenghao Liu;Xianjun Deng;Yunzhi Xia;Xiaoxuan Fan;Laurence T. Yang;Jong Hyuk Park

{"title":"Confident Information Coverage Reliability Evaluation for Sensor Networks of Openly Deployed ICP Systems","authors":"Suning Chen;Yuanyuan Yi;Lingzhi Yi;Shenghao Liu;Xianjun Deng;Yunzhi Xia;Xiaoxuan Fan;Laurence T. Yang;Jong Hyuk Park","doi":"10.1109/TICPS.2024.3455273","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3455273","url":null,"abstract":"Industrial Cyber-Physical (ICP) systems are integration of computation and physical processes to help achieve operational excellence. As sensors and actuators compose the openly deployed ICP systems and are often susceptible to interference, it puts forward higher requirements on reliability. Reliable coverage ensures sensors acquiring information and therefore coverage reliability of sensor network is studied from various perspectives. However, most existing reliability evaluation methods ignore the belief degree of coverage which influences the reliability to an extent. To evaluate the coverage reliability from the perspective of belief degree, we firstly define Confident Information Coverage Belief Reliability (CICBR) based on the Confident Information Coverage (CIC) model and D-S evidence theory, which comprehensively considers the coverage rate, interference from the environment, and belief degree of the result. Next, to evaluate the belief degree more reasonably, an Evidence-discount-based basic probability assignment generating method is proposed based on D-S theory to describe the interference in the belief degree. Moreover, a belief evaluation algorithm is proposed to calculate the belief degree of the coverage. Finally, an evidence-Discount-based Confident Information Coverage Belief Reliability Evaluation (D-CICBRE) algorithm is proposed to evaluate CICBR. The simulation result indicates that the proposed method can obtain higher and more reasonable reliability. Therefore, the proposed D-CICBRE algorithm can be used to evaluate the reliability of ICP systems more accurately and reasonably.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"565-574"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450974","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

A Decoder-Free Reconstruction Method for Semi-Supervised Rail Surface Defect Detection 半监督轨道表面缺陷检测的无解码器重构方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-25 DOI: 10.1109/TICPS.2024.3456758

Chen Liu;Zhenyu Shi;Shibo He;Shunpu Tang;Qianqian Yang

{"title":"A Decoder-Free Reconstruction Method for Semi-Supervised Rail Surface Defect Detection","authors":"Chen Liu;Zhenyu Shi;Shibo He;Shunpu Tang;Qianqian Yang","doi":"10.1109/TICPS.2024.3456758","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3456758","url":null,"abstract":"Detecting defects on railway tracks is critical for the operation of high-speed trains. Despite a plethora of machine vision-based methods designed to tackle this problem, the majority adopt a supervised setting and demand considerable labeled training data, inclusive of defective samples, which is expensive and impractical. In this paper, we propose an <underline>I</u>nvertible <underline>R</u>econstruction neural <underline>N</u>etwork (IRNet) for semi-supervised rail surface defect detection, where only normal images are accessible during training. Firstly, we devise an information-preserving feature encoder comprising several invertible blocks. This structure safeguards subtle visual patterns distinguishing normal and defective images from being obscured by background information, guaranteed by its mathematical reversibility property. Second, to overcome the overgeneralization issue of conventional autoencoders caused by imperfectly crafted decoders, we propose a novel decoder-free reconstruction workflow based on the invertible feature encoder. Specifically, we force one portion of extracted features to approach a predefined constant tensor during the training stage by minimizing their mean squared error. Next, we feed the remained features and the predefined constant tensor backward into the encoder to reconstruct the original images. During the testing phase, we formulate an anomaly score that consolidates the reconstruction error and mean squared error to spot defects. Extensive experiments are conducted on 4 real-world datasets. Our method consistently outperforms state-of-the-art techniques, demonstrating an average increase of 8.5% on the F1 score.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"3 ","pages":"285-295"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716499","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

Event-Triggered Automatic Parking Control for Unmanned Vehicles Against DoS Attacks 事件触发式无人飞行器自动泊车控制，对抗 DoS 攻击

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-18 DOI: 10.1109/TICPS.2024.3463613

Huarong Zhao;Zhiwei Zhao;Dezhi Xu;Hongnian Yu

引用次数: 0

Industrial Metaverse-Powered Interactive and Self-Healing Control Method for Combustion System 工业元宇宙驱动的燃烧系统交互式自愈控制方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-16 DOI: 10.1109/TICPS.2024.3462675

Yinsong Wang;Ke Li;Jianfang Jiao;Jiale Xie;Guang Wang;Meng Liu

{"title":"Industrial Metaverse-Powered Interactive and Self-Healing Control Method for Combustion System","authors":"Yinsong Wang;Ke Li;Jianfang Jiao;Jiale Xie;Guang Wang;Meng Liu","doi":"10.1109/TICPS.2024.3462675","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3462675","url":null,"abstract":"To improve the active immunity and robustness of the combustion system of the thermal power unit following network attacks on industrial cyber-physical system controllers, this study applies the advanced possibilities of the industrial metaverse to industrial intelligent control and proposes a conceptual model architecture of industrial metaverse powered interactive and self-healing control (I-Metaverse-C) method. It has three technical features: namely, pluralistic coexistence, intelligent control, and value interoperability, to design a self-healing controller and an imitation expert operating experience model based on industrial digital twin under I-Metaverse-C. First, based on Newton's law of motion, the I/O data of the combustion system are extracted to establish the motion model of pluralistic control process coexistence in the I-Metaverse-C system. Second, a self-healing control system is established based on digital twin technology under the model architecture of I-Metaverse-C, and key physical process variables are determined to design the velocity and acceleration self-healing factors. Third, the imitation expert operating experience model of autonomous learning expert operating experience in value interoperability and seamless human-in-the-loop interaction is developed. Finally, theoretical proof and experiments comparing the combustion system after a network attack are conducted. The experimental findings indicate that the I-Metaverse-C improves the safety, stability, rapidity, and accuracy of the adjustment process of the combustion system during it is attacked by a network and that the imitation-expert operating experience model endows I-Metaverse-C with the capability to learn from expert experience.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"484-494"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368556","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

Early-Stage Electrical Fault Identification for Traction Transformers Using Vibration Signals Based on Dual-Attention Convolutional Network 基于双注意力卷积网络的牵引变压器振动信号早期电气故障识别技术

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-06 DOI: 10.1109/TICPS.2024.3455271

Jingjian Yang;Gang Zhang;Zhongbei Tian;Edward Stewart;Zhigang Liu

{"title":"Early-Stage Electrical Fault Identification for Traction Transformers Using Vibration Signals Based on Dual-Attention Convolutional Network","authors":"Jingjian Yang;Gang Zhang;Zhongbei Tian;Edward Stewart;Zhigang Liu","doi":"10.1109/TICPS.2024.3455271","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3455271","url":null,"abstract":"Vibration signals typically contain a wealth of information on equipment status, making them widely used in the fault diagnosis for transformers. However, electrical faults, especially early-stage inter-turn short circuit faults are not characterized on vibration signals, which causes difficulties in precisely identifying the signals. To solve this issue, a dual attention-based neural network is proposed in this paper. A novel attention mechanism (AM) called similarity attention (SA) is designed in this network. It is then embedded in a convolutional neural network (CNN) with the conventional channel attention (CA) to form a dual-attention module (DAM). This module uses multi-channel receptive fields to automatically extract fault features from vibration signals. It subsequently expands and adaptively weights each receptive field and channel through the SA and CA blocks. By using the DAM, the unobvious fault-related features can be extracted effectively. Finally, these features are input into an eXtreme Gradient Boosting (XGBoost) classifier to achieve high-accuracy fault detection. The effectiveness of the method is verified using a 50 kVA traction transformer experimental platform. Moreover, the superiority of this method has been compared with other methods. The comparison results indicate that the proposed method can successfully categorize various early-stage faults with an accuracy rate of over 96% and unaffected by load fluctuations.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"471-483"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316491","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

A Change Detection Approach Based on Multi-Temporal SAR Images for Railway Perimeter Intelligent Perception 基于多时相合成孔径雷达图像的铁路周界智能感知变化检测方法

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-02 DOI: 10.1109/TICPS.2024.3452644

Hairong Dong;Tian Wang;Haifeng Song;Zhen Liu;Donghua Zhou

{"title":"A Change Detection Approach Based on Multi-Temporal SAR Images for Railway Perimeter Intelligent Perception","authors":"Hairong Dong;Tian Wang;Haifeng Song;Zhen Liu;Donghua Zhou","doi":"10.1109/TICPS.2024.3452644","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3452644","url":null,"abstract":"Accurately and promptly change detection (CD) of railway perimeter is significant to rail transportation cyber-physical systems (CPSs) safety and operating environment monitoring. Synthetic aperture radar (SAR) images have been demonstrated to offer advantageous information for change detection. However, it remains a challenge in SAR scene parsing due to the speckle noise limitations. Furthermore, fine-grained discriminant information needs to be introduced in high-similarity feature analysis. In this paper, an unsupervised multi-temporal contrastive enhancement dual-domain network (MCEDNet) is proposed. First, pre-classification is performed to generate pseudo-labels for training samples. Subsequently, the sample features are enhanced through the dual-domain network, in which the spatial feature attention module (SFAM) is used to improve the semantic representation of the central region, and frequency information is represented by multi-spectral attention. Finally, multi-temporal contrastive learning is developed to refine the final output. The experimental results on SAR datasets demonstrate the effectiveness and generalization of the proposed MCEDNet.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"435-445"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230842","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

An Intrusion Detection and Mitigation Framework for Automatic Generation Control Systems 自动发电控制系统入侵检测和缓解框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-09-02 DOI: 10.1109/TICPS.2024.3452681

Fazel Mohammadi;Mehrdad Saif

{"title":"An Intrusion Detection and Mitigation Framework for Automatic Generation Control Systems","authors":"Fazel Mohammadi;Mehrdad Saif","doi":"10.1109/TICPS.2024.3452681","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3452681","url":null,"abstract":"The main role of Automatic Generation Control (AGC) is to maintain power grids frequency within specified operating limits. Due to the fact that AGC is the sole automatic feedback control loop between physical and cyber infrastructure in modern power systems and the data required by the AGC system is transferred to a control center through communication links, it can be highly vulnerable to malicious attacks. Therefore, AGC systems should be well-protected against cyberattacks, e.g., False Data Injection (FDI) attacks. In this paper, an intrusion detection and mitigation framework for AGC systems based on a modified Goertzel algorithm is proposed. Compared with the existing intrusion detection and mitigation strategies, the major superiorities of the proposed framework are less computational burden, high accuracy, and rapid detection and mitigation of FDI attacks, which are considered unknown inputs. The proposed framework is validated on a two-area interconnected power systems model and the IEEE 39-bus test system. The dynamic simulation results under different testing conditions verify the applicability and effectiveness of the proposed framework.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"412-421"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165037","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

Individualized Forecasting of Gas Consumption Guided by Smart Meter Data Through Transform Integrated Neural Network 通过变换集成神经网络，以智能电表数据为指导，对天然气消耗量进行个性化预测

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-30 DOI: 10.1109/TICPS.2024.3452049

Xudong Hu;Biplab Sikdar

{"title":"Individualized Forecasting of Gas Consumption Guided by Smart Meter Data Through Transform Integrated Neural Network","authors":"Xudong Hu;Biplab Sikdar","doi":"10.1109/TICPS.2024.3452049","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3452049","url":null,"abstract":"The accurate, extended period prediction of individual customer energy consumption is critical for utility providers. Machine learning techniques, particularly neural networks, have proven effective in predicting household energy consumption by identifying correlations and patterns. However, these predictions often generalize across the entire dataset, neglecting the distinct behaviors of specific sub-groups. This paper presents an innovative transformation architecture aimed at enhancing the prediction of gas consumption for multiple households or population subgroups concurrently. The adaptability of the transformation layer to various neural network frameworks allows for broader applicability. The model's performance is assessed based on prediction accuracy and efficiency. Furthermore, as the transformation layer may introduce private information during training, we also evaluate the robustness of the model against inference attacks and its resilience to Additive White Gaussian Noise (AWGN) and adversarial examples. Our results demonstrate that the proposed approach not only achieves parallel prediction with high accuracy but also maintains the ability to forecast consumption over an extended period without the need for recent meter readings.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"422-434"},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230822","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

A Novel Adversarial FDI Attack and Defense Mechanism for Smart Grid Demand-Response Mechanisms 智能电网需求响应机制的新型对抗性 FDI 攻防机制

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-23 DOI: 10.1109/TICPS.2024.3448380

Guihai Zhang;Biplab Sikdar

{"title":"A Novel Adversarial FDI Attack and Defense Mechanism for Smart Grid Demand-Response Mechanisms","authors":"Guihai Zhang;Biplab Sikdar","doi":"10.1109/TICPS.2024.3448380","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3448380","url":null,"abstract":"This article focuses on enhancing the cybersecurity of cyber-physical systems, with a particular emphasis on the False Data Injection (FDI) attack within the Demand Response (DR) mechanism in smart grids. DR seeks to introduce flexibility in consumers' electricity consumption through dynamic pricing or financial incentives, aiming to optimize the equilibrium between supply and demand. The vulnerability of DR to FDI attacks becomes particularly evident when considering its reliance on accurate demand data. In emphasizing the importance of fortifying DR's security against FDI, the Ensemble and Transfer Adversarial Attack (ETAA) based on Adversarial Machine Learning (AML) techniques is proposed. This method facilitates the injection of false data with reduced detectability by existing neural network-based detection method. With the general framework of ETAA, any gradient-based adversarial attack method can be integrated to achieve enhanced attack transferability across diverse detection models. To counteract such attacks, the training process of detection models is refined through three key steps: Gaussian noise injection, latent feature combination and probability margin enlargement. Evaluation results demonstrate that the ETAA method executes FDI attacks with a higher success rate compared to benchmark methods. Furthermore, defensive training contributes to elevating the performance of detection models, ensuring higher standard accuracy, and reducing the success rate of AML attacks. This paper underscores the critical need to enhance the security of DR mechanisms to mitigate the impact of sophisticated FDI attacks on the robustness of smart grids.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"380-390"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137571","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

An Efficient Profit-Aware Scalable Vehicle Dispatch Framework for On-Demand Ridesharing 面向按需共享乘车的高效盈利感知可扩展车辆调度框架

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2024-08-21 DOI: 10.1109/TICPS.2024.3446957

Benjamin Lartey;Wendwosen Bedada;Xuyang Yan;Abdollah Homaifar;Ali Karimoddini;Edward Tunstel

{"title":"An Efficient Profit-Aware Scalable Vehicle Dispatch Framework for On-Demand Ridesharing","authors":"Benjamin Lartey;Wendwosen Bedada;Xuyang Yan;Abdollah Homaifar;Ali Karimoddini;Edward Tunstel","doi":"10.1109/TICPS.2024.3446957","DOIUrl":"https://doi.org/10.1109/TICPS.2024.3446957","url":null,"abstract":"On-demand ridesharing is a promising avenue to transform urban mobility by providing effective, low-cost transportation services to passengers in real-time while increasing transit companies' profits. However, existing works suffer from the lack of balance between the system profit and the response time. Therefore, this paper proposes a computationally efficient, dynamic vehicle dispatch framework while optimizing system profit. We implemented a one-to-one assignment method coupled with a feasible schedule pruning strategy to enhance the proposed method's performance. Additionally, a profit optimization mechanism that dynamically estimates the fare per passenger based on travel time is presented. This fare estimation strategy is more realistic since it incorporates varying traffic conditions. Extensive experiments conducted on the New York City taxicab open-source data demonstrate that the proposed framework is up to ten times faster than the state-of-the-art method and achieves comparable profit. Moreover, the proposed approach proved scalable and efficient for large problem instances.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"2 ","pages":"542-555"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443000","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