IEEE Transactions on Industrial Informatics最新文献

WTC3D: An Efficient Neural Network for Noncontact Pulse Acquisition in Internet of Medical Things WTC3D：医疗物联网中用于非接触式脉搏采集的高效神经网络

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-15 DOI: 10.1109/tii.2024.3485749

Changchen Zhao, Pengcheng Cao, Meng Hu, Bin Huang, Huiling Chen, Jing Li

引用次数: 0

A Robust ADMM-Enabled Optimization Framework for Decentralized Coordination of Microgrids 微电网分散协调的稳健 ADMM 优化框架

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-15 DOI: 10.1109/tii.2024.3478274

Seyed Amir Mansouri, Emad Nematbakhsh, Andrés Ramos, Marcos Tostado-Véliz, José A. Aguado, Jamshid Aghaei

引用次数: 0

An Enhanced Matrix Pencil Method for Parameter Identification of Sub-/Super-Synchronous Oscillations Using Synchrophasors 利用同步信号识别次/超同步振荡参数的增强型矩阵铅笔方法

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-14 DOI: 10.1109/tii.2024.3485797

Xiaoxue Zhang, Fang Zhang, Jinghan He, Wenzhong Gao

引用次数: 0

A Discrete-Time Equivalent-Input-Disturbance Approach Based on Joint Minimum-Variance Unbiased Disturbance and State Estimation 基于最小方差无偏扰动和状态联合估计的离散时间等效输入扰动方法

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-14 DOI: 10.1109/tii.2024.3488794

Mingyuan Xie, Jinhua She, Zhen-Tao Liu, Daiki Sato, Seiichi Kawata

引用次数: 0

Subpixel Vision Measurement Method for Rectangular-Pin SMDs Based on Asymmetric Gaussian Gradient Edge Profile 基于非对称高斯梯度边缘轮廓的矩形引脚 SMD 亚像素视觉测量方法

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-14 DOI: 10.1109/tii.2024.3488789

Weihua Liu, Yimin Wang, Juan J. Rodriguez-Andina, Xinghu Yu

引用次数: 0

Finite-Rate Distributed Secondary Control Over Digital Communication Networks Using an Event-Triggered Quantized Algorithm for Islanded Modern Microgrids Utilizing Inverter-Based Resources 使用事件触发量化算法的数字通信网络上的有限速率分布式二级控制，适用于利用基于逆变器资源的岛式现代微电网

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2024-11-14 DOI: 10.1109/tii.2024.3412164

Amir Afshari, Mohammad Raeispour, Masoud Davari, Weinan Gao, Frede Blaabjerg, Tianyou Chai

引用次数: 0

Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT 工业物联网中值得信赖和可靠的基于深度学习的网络攻击检测

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2022-07-13 DOI: 10.1109/TII.2022.3190352

Fazlullah Khan;Ryan Alturki;Md Arafatur Rahman;Spyridon Mastorakis;Imran Razzak;Syed Tauhidullah Shah

{"title":"Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT","authors":"Fazlullah Khan;Ryan Alturki;Md Arafatur Rahman;Spyridon Mastorakis;Imran Razzak;Syed Tauhidullah Shah","doi":"10.1109/TII.2022.3190352","DOIUrl":"10.1109/TII.2022.3190352","url":null,"abstract":"A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"19 1","pages":"1030-1038"},"PeriodicalIF":12.3,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9829330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10221442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Deep Reinforcement Learning for Edge Service Placement in Softwarized Industrial Cyber-Physical System. 软件工业信息物理系统中边缘服务放置的深度强化学习。

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2021-08-01 DOI: 10.1109/tii.2020.3041713

Yixue Hao, Min Chen, Hamid Gharavi, Yin Zhang, Kai Hwang

{"title":"Deep Reinforcement Learning for Edge Service Placement in Softwarized Industrial Cyber-Physical System.","authors":"Yixue Hao, Min Chen, Hamid Gharavi, Yin Zhang, Kai Hwang","doi":"10.1109/tii.2020.3041713","DOIUrl":"https://doi.org/10.1109/tii.2020.3041713","url":null,"abstract":"<p><p>Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied. Furthermore, none of existing schemes can optimize service placement, workload scheduling, and resource allocation under uncertain service demands. To address these issues, we first formulate a joint optimization problem of service placement, workload scheduling, and resource allocation in order to minimize service response delay. We then propose an improved deep Q-network (DQN)-based service placement algorithm. The proposed algorithm can achieve an optimal resource allocation by means of convex optimization where the service placement and workload scheduling decisions are assisted by means of DQN technology. The experimental results verify that the proposed algorithm, compared with existing algorithms, can reduce the average service response time by 8-10%.</p>","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"17 8","pages":""},"PeriodicalIF":12.3,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/tii.2020.3041713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10640010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Model-Driven Safety Analysis of Closed-Loop Medical Systems 闭环医疗系统的模型驱动安全分析

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2012-10-26 DOI: 10.1109/TII.2012.2226594

Miroslav Pajic;Rahul Mangharam;Oleg Sokolsky;David Arney;Julian Goldman;Insup Lee

引用次数: 111

On Design and Implementation of Neural-Machine Interface for Artificial Legs 人工腿神经机接口的设计与实现

IF 12.3 1区计算机科学

IEEE Transactions on Industrial Informatics Pub Date : 2011-09-05 DOI: 10.1109/TII.2011.2166770

Xiaorong Zhang;Yuhong Liu;Fan Zhang;Jin Ren;Yan Lindsay Sun;Qing Yang;He Huang

{"title":"On Design and Implementation of Neural-Machine Interface for Artificial Legs","authors":"Xiaorong Zhang;Yuhong Liu;Fan Zhang;Jin Ren;Yan Lindsay Sun;Qing Yang;He Huang","doi":"10.1109/TII.2011.2166770","DOIUrl":"10.1109/TII.2011.2166770","url":null,"abstract":"The quality-of-life of leg amputees can be improved dramatically by using a cyber-physical system (CPS) that controls artificial legs based on neural signals representing amputees' intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system—a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a postprocessing scheme, was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real-time testing. Real-time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"8 2","pages":"418-429"},"PeriodicalIF":12.3,"publicationDate":"2011-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TII.2011.2166770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30505239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 57