Article
Automation & Control Systems
Yi Sun, Jie Liu, Keping Yu, Mamoun Alazab, Kaixiang Lin
Summary: This article investigates how to securely access patients' records from previous case-databases while protecting patient privacy. The proposed privacy-preserving medical record searching scheme based on ElGamal Blind Signature allows patients to make self-helped medical diagnosis and intelligently obtain target searching information. The scheme greatly improves the timeliness of information acquisition and meets the high-speed information sharing requirements, while ensuring bilateral security for patient privacy and case-database privacy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Bin Cao, Yanlong Yan, Yu Wang, Xin Liu, Jerry Chun-Wei Lin, Arun Kumar Sangaiah, Zhihan Lv
Summary: The wide area measurement system based on synchronous phasor measurement technology is crucial for dynamic monitoring and wide area protection in modern power systems. This article discusses incomplete observability under single PMU loss and its impact on PMU placement, proposing an improved two-archive algorithm for placement optimization and a fuzzy decision-making method for selecting the best solution. Tests on IEEE bus systems and the Polish 2383-bus system confirm the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Weixin Zhang, Changzheng Shao, Bo Hu, Jiahao Zhou, Maosen Cao, Kaigui Xie, Pierluigi Siano, Wenyuan Li
Summary: This article proposes a proactive security-constrained unit commitment model solved by an approximate dynamic programming algorithm to improve system resilience during typhoon disasters, while meeting computational time requirements.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Industrial
Jiewu Leng, Weinan Sha, Baicun Wang, Pai Zheng, Cunbo Zhuang, Qiang Liu, Thorsten Wuest, Dimitris Mourtzis, Lihui Wang
Summary: Industry 5.0 aims to prioritize human well-being in manufacturing systems, achieving social goals beyond employment and growth for the sustainable development of humanity. However, research on Industry 5.0 is still in its early stages and lacks systematic exploration. This paper reviews the evolution and characteristics of Industry 5.0, discusses its connotation system and diversified essence, and proposes a tri-dimensional system architecture for its implementation. It also examines key enablers, future implementation paths, potential applications, and challenges. The limitations of current research are discussed, and potential future research directions are highlighted.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Automation & Control Systems
Imran Ahmed, Gwanggil Jeon, Francesco Piccialli
Summary: This article provides a comprehensive survey of AI and XAI-based methods in the context of Industry 4.0. It discusses the technologies enabling Industry 4.0, investigates the main methods used in the literature, and addresses the future research directions and the importance of responsible and human-centric AI and XAI systems.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Yang Li, Jiazheng Li, Yi Wang
Summary: This article proposes a novel federated deep generative learning framework, Fed-LSGAN, for generating high-quality scenarios in power systems with high-penetration renewables. The framework integrates federated learning and least square generative adversarial networks (LSGANs) to achieve privacy-preserving scenario generation and improve generation quality using the least squares loss function.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Chaosheng Feng, Bin Liu, Keping Yu, Sotirios K. Goudos, Shaohua Wan
Summary: Motivated by Industry 4.0, 5G-enabled drones have been widely applied, but the open nature of 5G networks threatens data sharing and privacy. To address these issues, a blockchain-empowered decentralized federated learning framework is proposed, enabling cross-domain authentication and global model updates, achieving efficient authentication and accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Dmitry Ivanov
Summary: Industry 5.0 is the combination of organisational principles and technologies to design and manage operations and supply chains as resilient, sustainable, and human-centric systems. It encompasses multiple dimensions including technological principles, coverage areas, and levels spanning across society, networks, and plants. Industry 5.0 frames a new triple bottom line and understanding of value, highlighting its significance for future industrial development.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Marin Jovanovic, David Sjodin, Vinit Parida
Summary: This study examines the development of digital platforms in the industrial manufacturing context and identifies three platform archetypes. The study finds that each platform archetype gradually evolves in terms of architecture, services, and governance, and they influence each other. Furthermore, each platform archetype has a specific innovation mechanism that contributes to the discovery of platform services and the expansion of platform value. The study extends the co-evolution perspective in platform ecosystem and digital servitization literature.
Article
Automation & Control Systems
Hairong Lin, Chunhua Wang, Li Cui, Yichuang Sun, Cong Xu, Fei Yu
Summary: This article focuses on coupled neural networks with brain-like chaotic dynamics and their application in biomedical image encryption. A memristive-coupled neural network (MCNN) model is proposed and its dynamical behaviors are studied. Numerical results show that the MCNN can generate highly complex hyperchaotic attractors and boost the attractor positions by switching their initial states. A biomedical image encryption scheme is designed and its performance is evaluated.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink
Summary: A novel hybrid framework is proposed to combine physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems, improving prediction horizon by 127% compared to purely data-driven approaches. Physics-based performance models are used to infer unobservable model parameters related to system health and combined with sensor readings as input to a deep neural network, demonstrating superior performance over traditional data-driven methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Yougang Sun, Junqi Xu, Guobin Lin, Wen Ji, Lukun Wang
Summary: This article proposes a control method for the maglev vehicle system using an amplitude saturation controller (ASC) and a neural network-based supervisor controller (NNBSC). The effectiveness and robustness of the method are validated through simulations and hardware experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Anmin Fu, Xianglong Zhang, Naixue Xiong, Yansong Gao, Huaqun Wang, Jing Zhang
Summary: This article proposes VFL, a verifiable federated learning approach with privacy-preserving for big data in industrial IoT. By using Lagrange interpolation for verifying the aggregated gradients and employing blinding technology to protect the privacy gradients, VFL achieves high accuracy and efficiency while preserving privacy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Liu Yang, Keping Yu, Simon X. Yang, Chinmay Chakraborty, Yinzhi Lu, Tan Guo
Summary: This article presents an intelligent trust cloud management method for secure and reliable communication in 5G edge computing and device-to-device enabled Internet of Medical Things (IoMT) systems. The method includes constructing standard trust clouds, establishing individual trust clouds, proposing a trust classification scheme, and implementing a trust cloud update mechanism. Simulation results show that this method effectively addresses trust uncertainty and improves the detection accuracy of malicious devices.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Jiusi Zhang, Yuchen Jiang, Shimeng Wu, Xiang Li, Hao Luo, Shen Yin
Summary: This paper introduces a novel bidirectional GRU model with temporal self-attention mechanism for predicting remaining useful life (RUL). Experimental results demonstrate its superiority over existing machine learning and deep learning methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Jin Wang, Hui Han, Hao Li, Shiming He, Pradip Kumar Sharma, Lydia Chen
Summary: This article introduces a multiple-strategies differential privacy framework on sparse tensor factorization (MDPSTF) for analysis of high-order, high-dimension, and sparse tensor (HOHDST) network traffic data. MDPSTF utilizes three differential privacy mechanisms to provide general data protection for HOHDST network traffic data with high-security promise and high recovery accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Bin Jia, Xiaosong Zhang, Jiewen Liu, Yang Zhang, Ke Huang, Yongquan Liang
Summary: This article presents a blockchain-enabled federated learning application model and data protection methods based on differential privacy and homomorphic encryption. Experimental results demonstrate that the method has better performance in data sharing and model sharing.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Ke Zhang, Jiayu Cao, Yan Zhang
Summary: Technological advancements in urban informatics and vehicular intelligence have made smart vehicles ubiquitous edge computing platforms for various applications. However, the different capacities of smart vehicles, diverse application requirements, and unpredictable vehicular topology pose challenges for efficient edge computing services. To address these challenges, we propose incorporating digital twin technology and artificial intelligence into a vehicular edge computing network, enabling centralized service matching and distributed task offloading and resource allocation using multiagent deep reinforcement learning. We also introduce a coordination graph-driven task offloading scheme that integrates service matching and intelligent offloading scheduling in both digital twin and physical networks to minimize costs. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Chao Liu, Leopold Le Roux, Carolin Korner, Olivier Tabaste, Franck Lacan, Samuel Bigot
Summary: Metal Additive Manufacturing (AM) has gained attention due to its advantages, but the complex relationships limit its widespread use. To facilitate its development, this paper proposes a Digital Twin-enabled collaborative data management framework, which has been validated through practical implementation.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Automation & Control Systems
Weidang Lu, Yu Ding, Yuan Gao, Su Hu, Yuan Wu, Nan Zhao, Yi Gong
Summary: This article proposes a secure communication scheme for the dual-UAV-MEC system, which effectively increases the secure calculation capacity of the system by optimizing the resources and trajectory of the UAV server.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
