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Guest Editorial: Advanced Intelligent Manufacturing System: Theory, Algorithms, and Industrial Applications IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-05-24 Qiang Liu, Jialu Fan, Jin-Xi Zhang, Yaochu Jin
Intelligent manufacturing has promoted the development of Industry 4.0 and enabled the manufacturing industry to gradually move into the stage of intelligence with the rapid development of the Internet of Things and the Industrial Internet. An intelligent manufacturing system is a manufacturing system that can automatically adapt to changing environments and varying process requirements with minimal
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Guest Editorial: Cyber-Physical Threats and Solutions for Autonomous Transportation Systems IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-05-24 A. Brighente, M. Conti, R. Poovendran, J. Zhou
The rapid evolution of technology has radically changed our everyday lives from multiple points of view. Systems and devices are nowadays more interconnected and capable of taking autonomous decisions without or with limited human intervention. Among the others, transportation systems are populated by smart and interconnected vehicles that need to communicate with each other and with critical infrastructures
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Combined Electrical and Heat Load Restoration Based on Bi-Objective Distributionally Robust Optimization IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-05-01 Shuai Lu, Yuan Li, Shixing Ding, Wei Gu, Yijun Xu, Meng Song
As extreme events such as natural disasters and cyberattacks become more frequent, the resilience of energy systems has become increasingly important. However, due to the growing interdependence between natural gas, district heating, and power systems, the resilience of energy systems is becoming more and more complicated. Typical challenges include conflicts in the restoration of heterogeneous energy
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LightGBM-Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-17 Zhipeng Jiao, Hongda Wang, Jianchun Xing, Qiliang Yang, Man Yang, Yutao Zhou, Jiubin Zhao
The remaining useful life (RUL) degradation under driving conditions is complex. The features from incremental capacity-differential voltage curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL. This article proposes a light gradient boosting machine (LightGBM)-based framework with electrochemical theory to achieve RUL
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PoseNormNet: Identity-Preserved Posture Normalization of 3-D Body Scans in Arbitrary Postures IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Ran Zhao, Xinxin Dai, Pengpeng Hu, Adrian Munteanu
Three-dimensional (3-D) human models accurately represent the shape of the subjects, which is key to many human-centric industrial applications, including fashion design, body biometrics extraction, and computer animation. These tasks usually require a high-fidelity human body mesh in a canonical posture (e.g., “A” pose or “T” pose). Although 3-D scanning technology is fast and popular for acquiring
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To Transmit or Predict: An Efficient Industrial Data Transmission Scheme With Deep Learning and Cloud-Edge Collaboration IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Yu Wu, Bo Yang, Dafeng Zhu, Qi Liu, Cheng Li, Cailian Chen, Xinping Guan
Many computation-intensive industrial applications need to be run in the cloud, which relies on a lot of sharply varying data transmitted from the industrial field. To save the communication bandwidth and ensure data with required accuracy obtained by the cloud, we design a data transmission architecture based on dual prediction scheme and cloud-edge collaboration and a dual-mode algorithm based on
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A Comprehensively Improved Interval Type-2 Fuzzy Neural Network for NOx Emissions Prediction in MSWI Process IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Junfei Qiao, Zijian Sun, Xi Meng
The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This article proposes a comprehensively improved interval type-2 fuzzy neural network (CI-IT2FNN) for NOx
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Electric Power Fuse Identification With Deep Learning IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Simon Giard-Leroux, Guillaume Cléroux, Shreyas Sunil Kulkarni, François Bouffard, Martin Vallières
As part of arc flash studies, survey pictures of electrical installations need to be manually analyzed. A challenging task is to identify fuse types, which can be determined from physical characteristics, such as shape, color, and size. To automate this process using deep learning techniques, a new dataset of fuse pictures from past arc flash projects and data from the web was created. Multiple experiments
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RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Alessio Caporali, Kevin Galassi, Bare Luka Žagar, Riccardo Zanella, Gianluca Palli, Alois C Knoll
Deformable linear objects (DLOs), such as cables, wires, ropes, and elastic tubes, are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically
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Multiobjective Bayesian Optimization for Aeroengine Using Multiple Information Sources IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-16 Ran Chen, Jingjiang Yu, Zhengen Zhao, Yuzhe Li, Jun Fu, Tianyou Chai
Aeroengine performance optimization rem- ains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation
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Resilient Coordination of Nonlinear Uncertain Lagrangian Systems With Adversarial Agents: A Norm-Based Approach IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Xiaoyuan Luo, Yuliang Fu, Jiange Wang, Xiaolei Li, Xinping Guan, Yuyan Zhang
In this article, the resilient coordination problem of networked Lagrangian systems with adversarial agents is considered. A novel algorithm called norm-based resilient decision algorithm is proposed to exclude the impact of adversarial agents. To ensure the coordination of networked Lagrangian systems, the maximum number of adversarial agents related to the robustness of the communication network
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Exploiting Spike-and-Slab Prior for Variational Estimation of Nonlinear Systems IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Xinpeng Liu, Xianqiang Yang
Identification of nonlinear dynamic systems remains challenging nowadays. Although the nonlinear autoregressive with exogenous input (NARX) model is flexible to describe complex nonlinear behaviors, it is critical to select appropriate model terms to obtain a parsimonious description of the system. In this article, a variational Bayesian (VB) approach to the estimation of NARX systems is developed
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Periodic Event-Triggered Robust Distributed Model Predictive Control for Multiagent Systems With Input and Communication Delays IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Mengzhi Wang, Chengcheng Zhao, Jinhui Xia, Jian Sun
This article investigates the problem of event-triggered distributed model predictive control (DMPC) for continuous-time nonlinear multiagent systems (MASs) subject to bounded disturbances, input and communication delays simultaneously. The compensation schemes for input and communication delays are proposed, respectively. A new optimal control problem (OCP) to achieve consensus among all agents is
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Vibration-Signal-Based Deep Noisy Filtering Model for Online Transformer Diagnosis IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Zhikai Xing, Yigang He, Xiao Wang, Jianfei Chen, Bolun Du, Liulu He, Xiaoyu Liu
Machine learning methods are effective for the diagnosis of power transformer faults. However, influenced by uncertainty and noise in data, machine-learning-based diagnostic methods are still in the initial phase of certain assets in power systems. To mitigate this gap, a deep noisy filtering diagnostic model is proposed for accurate and rapid evaluations of power transformer faults using noisy vibration
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WPFSAD: Wind Power Forecasting System Integrating Dual-Stage Attention and Deep Learning IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Tong Niu, Jianzhou Wang, Pei Du, Wendong Yang
Wind power forecasting with high accuracy and stability concretely contributes to efficient scheduling and risk management of wind power systems. However, current studies remain limited due to these two shortcomings: 1) overemphasizing model combination strategy and data preprocessing, thus ignoring the improvement of the model's internal computing mechanism; and 2) much attention was paid to the single-stage
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PID-Like Model Free Adaptive Control With Discrete Extended State Observer and Its Application on an Unmanned Helicopter IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-15 Chao Wang, Xin Huo, Kemao Ma, Ruihang Ji
Aiming at the problem that how to design a controller without using model information for an unmanned helicopter (UH), a novel data-driven method based on model free adaptive control (MFAC) is proposed in this article. A new form of dynamic linearization (DL) equation composed of pseudo-partial derivative (PPD) and external disturbance is built in order to reduce the influence of disturbance on PPD
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ECS-Grid: Data-Oriented Real-Time Simulation Platform for Cyber-Physical Power Systems IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Tianshi Cheng, Tong Duan, Venkata Dinavahi
ECS-Grid is the first data-oriented real-time electromagnetic transient simulation platform for cyber-physical power systems (CPPS). Traditional simulation tools are constrained by object-oriented programming (OOP) architecture, which is now a significant obstruction to creating a comprehensive cyber-physical simulation. Therefore, the proposed ECS-Grid platform follows a new data-oriented paradigm
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Robotic Continuous Grasping System by Shape Transformer-Guided Multiobject Category-Level 6-D Pose Estimation IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Jian Liu, Wei Sun, Chongpei Liu, Xing Zhang, Qiang Fu
Robotic grasping is one of the key functions for realizing industrial automation and human–machine interaction. However, current robotic grasping methods for unknown objects mainly focus on generating the 6-D grasp poses, which cannot obtain rich object pose information and are not robust in challenging scenes. Based on this, in this article, we propose a robotic continuous grasping system that achieves
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Incrementally Contrastive Learning of Homologous and Interclass Features for the Fault Diagnosis of Rolling Element Bearings IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Chuan Li, Xiaotong Lei, Yunwei Huang, Faisal Nazeer, Jianyu Long, Zhe Yang
Bearing condition is a non-negligible part of mechanical equipment health monitoring. Most of the existing bearing fault diagnosis methods are based on the premise that all data classes are known and lack the capability of incremental diagnosis of fault modes. However, in engineering practice, the initial monitoring data only provide normal condition, and the subsequent data of different classes of
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Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Hao Ran Chi, Rui Silva, David Santos, José Quevedo, Daniel Corujo, Osama Abboud, Ayman Radwan, Artur Hecker, Rui L. Aguiar
Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for
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Restricted Sparse Networks for Rolling Bearing Fault Diagnosis IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Huaxiang Pu, Ke Zhang, Yiyao An
The application of deep learning-based rolling bearing fault diagnosis methods in high reliability scenarios is limited due to low transparency. In addition, the scaling up of the deep learning models, in order to improve the performance of rolling bearing fault diagnosis (RBFD), has led to difficulties in its application in low-resource scenarios. Based on these facts, a new neural network, restricted
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Automated Brain Tumor Segmentation for MR Brain Images Using Artificial Bee Colony Combined With Interval Type-II Fuzzy Technique IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Saravanan Alagarsamy, Vishnuvarthanan Govindaraj, Senthilkumar A
Accurate prediction of brain tumors is vital while getting to the forum of medical image analysis, where precision in decision-making is of paramount importance, and the problems are to be addressed forthwith. For over a decade, innumerable medical imaging techniques using artificial intelligence and machine learning have been promulgated. This article is intended to develop an algorithm that forges
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Reinforcement Learning of Optimal Input Excitation for Parameter Estimation With Application to Li-Ion Battery IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Rui Huang, Jackson Fogelquist, Xinfan Lin
Estimation and diagnosis of system states and parameters is ubiquitous in industrial applications. Estimation is often performed using input and output data, and the quality of input excitation has critical impact on the accuracy of the results. Therefore, optimal input excitation design has been receiving increasing research attention. Previously, input design is formulated as an optimization problem
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Improved Duty Compensation Control-Based Bidirectional Resonant DC−DC Converter With Reduced Input-Current Ripple for Battery Energy Storage Systems IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Bilal Abdul Basit, Minsung Kim, Jin-Woo Jung
This article proposes an improved duty control-based resonant bidirectional dc–dc converter (BDC) to remarkably minimize input-current ripple for battery energy storage systems. The proposed converter offers a resonant-boost operation using a pulsewidth-modulation (PWM) full-bridge circuit with a push-pull transformer under the forward mode. Whereas, in the backward mode, a resonant-buck operation
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HPCchain: A Consortium Blockchain System Based on CPU-FPGA Hybrid-PUF for Industrial Internet of Things IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-13 Kai Qian, Yinqiu Liu, Xiaoming He, Miao Du, Suofei Zhang, Kun Wang
Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forward higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, i.e., physical unclonable function (PUF) and blockchain. In detail, PUF can generate multiple challenge–response pairs (CRPs) for IIoT
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LTE RSSI Based Vehicular Localization System in Long Tunnel Environment IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-09 Beomju Shin, Jung Ho Lee, Chongwon Kim, Sanghoon Jeon, Taikjin Lee
Current navigation systems rely heavily on global navigation satellite systems, which provide stable positioning results in open-sky environments but exhibit severe performance degradation in areas where the signal is compromised, such as dense urban environments, underground parking lots, and tunnels. Current navigation systems use an initial entry velocity to estimate a vehicle's position in a tunnel
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Secure MPC-Based Path Following for UAS in Adverse Network Environment IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-09 Zhaowen Feng, Guoyan Cao, Karolos M. Grigoriadis, Quan Pan
This article considers the path-following problem for an unmanned aerial system (UAS), in which an online remote control station computes and sends control input signals to the vehicle over an adverse communication network. In that network configuration, the cyberattackers and malicious eavesdroppers are prone to erode the UAS's safety properties such as operational security and information privacy
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Bipartite Time-Varying Output Formation Tracking for Multiagent Systems With Multiple Heterogeneous Leaders Under Signed Digraph IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-07 Weihua Li, Huaguang Zhang, Yunfei Mu, Yingchun Wang
This article investigates the bipartite time-varying output formation tracking issue for heterogeneous linear multiagent systems with multiple leaders under signed digraph. Different from previous related works, the leaders are considered to be heterogeneous, which greatly increases the difficulty of designing distributed control strategies. To address this issue, we develop a novel fully distributed
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Efficient Control Representation in Digital Twins: An Imperative Challenge for Declarative Languages IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-07 Chiara Cimino, Federico Terraneo, Gianni Ferretti, Alberto Leva
Digital twins (DTs) are enablers for the fast optimization processes required in the Industry 4.0 context. Declarative equation-based modeling languages, in turn, enable the creation of large-scale simulation-based DTs, as they relieve the analyst from creating the solution code. However, most industrial assets are cyber-physical systems, the cyber part being their digital controls. With the available
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Reciprocal of Exponential Varying-Parameter RNN Solving Repetitive Tracking Control Problems With Tolerance of Random Initial Error Compounded With Noise Perturbation IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Yong Wang, Yu Han, Zhaojia Tang, Wanquan Liu, Ping Wang
Positioning and posture of the robotic joints and end effector could probably introduce random initial errors. Those errors could exponentially deteriorate with compounded of common noise perturbation to cause the final failure of repetitive tracking control. To better improve the tolerance of those complex errors, a novel reciprocal of the exponential varying-parameter recurrent neural network (RE-VP-RNN)
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A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Farbod Parvin, Jawad Faiz, Yuan Qi, Ahmad Kalhor, Bilal Akin
This article proposes a novel deep learning (DL)-based interturn short-circuit fault (ISCF) severity diagnosis method using the transformer neural network (TNN). The input features are the currents in alpha–beta reference frame while the outputs are the number of shorted turns and short-circuit (SC) current amplitude. By only monitoring the stator currents, this method provides a comprehensive overview
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Graph-Augmented Social Translation Model for Next-Item Recommendation IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Bin Wu, Lihong Zhong, Yangdong Ye
Next-item recommendation has been a hot research topic in academia and industry, which aims to help users discover the next interesting item. In this article, we propose a novel solution, namely graph-augmented social translation model (GAST), which investigates the utility of dynamic social influence for the task of next-item recommendation. Specifically, we introduce a gated graph convolution module
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Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Mo Qu, Yongjing Wang, Duc Truong Pham
This article proposes a platform for robots to learn disassembly tasks based on reinforcement learning (RL) techniques. The platform is demonstrated by a robot learning the skill of removing a bolt along a door-chain groove in a data-driven way, where the clearance between the bolt and the groove is less than 1 mm. Furthermore, the relationship between the performance of the learned skills and the
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Few-Shot Fault Diagnosis Method of Rotating Machinery Using Novel MCGM Based CNN IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Gongye Yu, Peng Wu, Zhe Lv, Jijie Hou, Bo Ma, Yongming Han
The existing fault diagnosis methods can achieve good results when various status fault data are available. However, the construction of the diagnosis model is often unachievable in the actual application because only normal data are available, which is actually a few-shot fault diagnosis problem. Therefore, a novel intelligent few-shot fault diagnosis method of rotating machinery based on the convolutional
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Abnormal Condition Detection Method of Industrial Processes Based on the Cascaded Bagging-PCA and CNN Classification Network IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Shizeng Lu, Huijun Dong, Hongliang Yu
The accurate detection of abnormal working conditions is very important for the safe and stable operation of production process in process industry. Considering that normal data can be easily obtained in industry, unsupervised learning is one of the important methods of anomaly detection. Different from the experience setting of unsupervised anomaly detection index, supervised learning can set anomaly
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Integrated Optimal Energy Management and Sizing of Hybrid Battery/Flywheel Energy Storage for Electric Vehicles IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Abbas Mehraban, Ebrahim Farjah, Teymoor Ghanbari, Lauric Garbuio
This article presents an integrated optimal energy management strategy (EMS) and sizing of a high-speed flywheel energy storage system (FESS) in a battery electric vehicle. The methodology aims at extending the battery cycle life and drive range by relegating fast dynamics of the power demand to the FESS. For the EMS, the battery power and FESS energy are considered as weighted objectives of an optimization
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Attribute-Based Data Sharing Scheme With Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Hu Xiong, Hanxiao Wang, Weizhi Meng, Kuo-Hui Yeh
The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors
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A Novel Distributed CVRAE-Based Spatio-Temporal Process Monitoring Method With Its Application IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Peng Tang, Kaixiang Peng, Zhiwen Chen, Jie Dong
Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy, and sewage treatment. In this article, a novel spatio-temporal monitoring method is proposed for the
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Distributed Coordination LFC Approach for Interconnected Power Systems Under Detection and Compensation Mechanism Targeting DoS Attacks IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Jin Yang, Qishui Zhong, Kaibo Shi, Shouming Zhong
A distributed coordination load frequency control (LFC) approach is proposed for interconnected power systems, and a detection and compensation mechanism targeting denial-of-service (DoS) attacks is designed in this work. Firstly, different from traditional LFC, a hybrid-type distributed control strategy is proposed, which takes full advantage of local control information and coordination control information
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Early Warning of Loss and Kick for Drilling Process Based on Sparse Autoencoder With Multivariate Time Series IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Zheng Zhang, Xuzhi Lai, Sheng Du, Wanke Yu, Min Wu
Complicated geological environments lead to a high risk of drilling incidents. Early warning of loss and kick for the drilling process is essential to ensure process safety. On account of the nonlinear and temporal correlation of drilling parameters, an early warning method for loss and kick based on sparse autoencoder with multivariate time series is proposed. The sparse autoencoder is utilized for
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Energy and Performance-Efficient Dynamic Consolidate VMs Using Deep-Q Neural Network IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Zhao Tong, Jiake Wang, Yuqiong Wang, Bilan Liu, Qiang Li
With cloud computing facing higher levels of Big Data than ever, the processor scale is rapidly expanding. Large clusters place a heavy burden on cloud service providers and the environment. High energy consumption decreases the economic benefits of cloud service providers while enormous power demands pressure on the environment. The dynamic consolidation of virtual machines (VMs), which uses live
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Novel Diagnosis Method for GIS Mechanical Defects Based on an Improved Lightweight CNN Model With Load Adaptive Matching IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Yao Zhong, Jian Hao, Qingsong Lliu, Ying Li, Xu Li, Ruijin Liao, Xiping Jiang
Mechanical defects of GAS-insulated metal-enclosed switchgear (GIS) equipment seriously threaten power grid security, but on -site complex operating conditions create great challenges for defect diagnosis. Therefore, this article proposes a novel diagnosis and state assessment method for GIS mechanical defects under varying currents. First, a time-frequency analysis method of GIS vibration signals
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Multiobjective Energy Management Strategy for Multienergy Communities Based on Optimal Consumer Clustering With Multiagent System IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-06 Linyun Xiong, Donglin He, Yalan He, Penghan Li, Sunhua Huang, Shaobo Yang, Jie Wang
This article aims to investigate the issue of energy management for multienergy communities with diversified energy consumer profiles. Since the energy consumers have their points of parity and differences in their demographic, psychological, and behavioral traits, it is assumed that the energy management scheme should be capable of meeting their actual needs instead of just reducing the energy bills
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New Algorithm Applied to Transformers' Failures Detection Based on Karhunen–Loève Transform IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-03 Bruno Albuquerque de Castro, Amanda Binotto, Jorge Alfredo Ardila-Rey, José Renato Castro Pompéia Fraga, Colin Smith, André Luiz Andreoli
Industry and science have been growing attention to developing systems that ensure the integrity of high voltage devices like power transformers. The goal is to avoid unexpected stoppages by detecting incipient failures before they become a major problem. In this context, the detection of discharge activity is an effective way to assess the condition operation of power transformers since this type
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Resilient Path Planning of Unmanned Aerial Vehicles Against Covert Attacks on Ultrawideband Sensors IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-03 Jiayi He, Xin Gong
In this article, a resilient path planning scheme is proposed to navigate an unmanned aerial vehicle (UAV) to its planned (nominal) destination with minimum energy consumption in the presence of a strategic attacker. The UAV is equipped with the following two sensors: 1) a GPS sensor, which is vulnerable to spoofing attacks; 2) a well-functioning ultrawideband (UWB) sensor, which is possible to be
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Hierarchical Free Gait Motion Planning for Hexapod Robots Using Deep Reinforcement Learning IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-03 Xinpeng Wang, Huiqiao Fu, Guizhou Deng, Canghai Liu, Kaiqiang Tang, Chunlin Chen
This article addresses the problem of legged locomotion in unstructured environments, and a novel hierarchical multicontact motion planning method for hexapod robots is proposed by combining free gait motion planning and deep reinforcement learning. We structurally decompose the complex free gait multicontact motion planning task into path planning in discrete state space and gait planning in a continuous
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Highly Accurate Manipulator Calibration via Extended Kalman Filter-Incorporated Residual Neural Network IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Weiyi Yang, Shuai Li, Zhibin Li, Xin Luo
With the rapid development and wide applications of industrial manipulators, a vital concern rises regarding a manipulator's absolute positioning accuracy. The manipulator calibration models have proven to be highly efficient in improving the absolute positioning accuracy of an industrial manipulator. However, existing calibration models commonly suffer from the low calibration accuracy caused by the
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Hybrid Policy-Based Reinforcement Learning of Adaptive Energy Management for the Energy Transmission-Constrained Island Group IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Lingxiao Yang, Xiaofeng Li, Mengwei Sun, Changyin Sun
This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy management to realize the optimal operation for the island group energy system with energy transmission-constrained environment. An island energy hub (IEH) model that can realize the energy cascade utilization is proposed. Compared with the traditional model, the IEH can satisfy the special energy demand of island
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Physics-Based Model Predictive Control for Power Capability Estimation of Lithium-Ion Batteries IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Yang Li, Zhongbao Wei, Changjun Xie, D. Mahinda Vilathgamuwa
The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for
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BRAS: Bidirectional Reflectance Adjustment Strategy for 3-D Reconstruction of Mirror-Like Surface IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Zheng Sun, Ben Wang, Yabing Zheng, Minghui Duan, Xin Fan, Yi Jin, Jinjin Zheng
A mirror-like surface (MLS) reflects highlight, aggravating the image saturation in structured light 3-D reconstruction systems and precluding defect detection based on reconstructed 3-D profiles. Previous studies have focused on a strategy of limiting the luminous flux entering the camera. However, the high intensity of the reflected highlight forces the limitation to be strengthened, which heavily
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Nonconvex Activation Noise-Suppressing Neural Network for Time-Varying Quadratic Programming: Application to Omnidirectional Mobile Manipulator IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Zhongbo Sun, Shijun Tang, Long Jin, Jiliang Zhang, Junzhi Yu
This article proposes an improved general zeroing neural network model to suppress noise and to enhance the real-time performance of solving TVQP problems. The proposed model allows nonconvex activation functions and has noise suppression characteristics, i.e., the NCNSZNN model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem
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A Novel Traffic Classifier With Attention Mechanism for Industrial Internet of Things IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Ruijie Zhao, Yiteng Huang, Xianwen Deng, Yong Shi, Jiabin Li, Zijing Huang, Yijun Wang, Zhi Xue
With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow sequences for traffic analysis. However, existing models
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Delay Safety-Aware Digital Twin Empowered Industrial Sensing-Actuation Systems Using Transferable and Reinforced Learning IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Hansong Xu, Jun Wu, Heng Pan, Jia Gu, Xinping Guan
The industrial visual sensing-actuation system is an implementation approach to construct the loop between the digital twin and physical systems, which is facing the following challenges. First, the cross-digital-physical information exchanges bring a high end-to-end delay that threatens the functional safety of industrial systems. Second, industrial scenarios are diverse, such as manufacturing, chemical
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BE-TRDSS: Blockchain-Enabled Secure and Efficient Traceable-Revocable Data-Sharing Scheme in Industrial Internet of Things IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Ruonan Ma, Leyou Zhang, Qing Wu, Yi Mu, Fatemeh Rezaeibagha
As an important component of the Industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange the production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semitrusted and has structural limitations and vulnerability, which makes it necessary to realize data
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Trustworthy Fault Diagnosis With Uncertainty Estimation Through Evidential Convolutional Neural Networks IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Hanting Zhou, Wenhe Chen, Longsheng Cheng, Jing Liu, Min Xia
Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under the closed-world assumption that any testing data are within classes of the training data. However, in reality, out-of-distribution (OOD) cases, such as new fault conditions, can happen after the original trained model is deployed. Most of the current DNNs are deterministic, which can misclassify with high confidence
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GelStereo Palm: A Novel Curved Visuotactile Sensor for 3-D Geometry Sensing IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Jingyi Hu, Shaowei Cui, Shuo Wang, Chaofan Zhang, Rui Wang, Lipeng Chen, Yuhao Li
Recently, visuotactile sensors have shown promising potential in robotics due to their high-resolution sensing ability. Unfortunately, the majority of available visuotactile sensors are limited to flat shapes, which severely limits their application possibilities. In this article, we propose a novel curved visuotactile sensor, the GelStereo Palm, which senses the 3-D contact geometry on a curved surface
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Plant-Wide Process Fine-Scale Monitoring via Distributed Static Magnitude-Dynamic Difference IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Bing Song, Yimeng Song, Yuting Jin, Hongbo Shi, Yang Tao, Shuai Tan
To monitor the plant-wide process finely, a novel distributed static magnitude-dynamic difference (DSM-DD) method is proposed in this article. First, given the high dimension of the collected data in the plant-wide process, the entire data space is divided into four orthogonal subspaces according to whether the data obey Gaussian distribution and whether it has serial correlation. Second, both the
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Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-02 Rahul Dubey, Rishi Raj Sharma, Abhay Upadhyay, Ram Bilas Pachori
The variational nonlinear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This article proposes an automated method for NM selection and IF initialization, which works on the scale-space representation-based automated boundary detection in a magnitude spectrum. The proposed automated VNCMD (AVNCMD) method is applied for bearing fault
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Modeling and Optimization of Parallel Disassembly Line Balancing Problem With Parallel Workstations IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-01 Wei Liang, Zeqiang Zhang, Yanqing Zeng, Tao Yin, Tengfei Wu
To reasonably arrange disassembly facilities and plan enterprise space, we propose a parallel disassembly line balancing problem (PW-PDLBP) with parallel workstations. Additionally, a mixed-integer nonlinear programming (MINLP) model that minimizes the line length, number of workstations, idle time balancing index, and energy consumption is established based on the problem characteristics and is solved
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A Robust High-Quality Current Control With Fast Convergence for Three-Level NPC Converters in Microenergy Systems IEEE Trans. Ind. Inform. (IF 12.3) Pub Date : 2023-02-01 Lei Liu, Zhenbin Zhang, Yunfei Yin, Yu Li, Haotian Xie, Mingyuan Zhang, Yuxin Zhao, Ralph Kennel
Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of the converter. This article proposes a robust high-quality current control strategy for the 3L-NPC power