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IEEE Industrial Electronics Society Information
Year: 2024 | Volume: 20 | Issue: 4
A Critical Review of Recent Industrial Developments, Trends, and Future Perspectives of Power Electronic Systems: Fuel Cell Electric Vehicles
Publication Year: 2024,Page(s):6060 - 6074
Cited by: Papers (4)
Fuel cell electric vehicles (FCEVs) offer a promising solution for environment-friendly and sustainable future transportation, thanks to their remarkable features (e.g., zero-carbon emission, high-power capacity, long driving range, etc.). This article extensively reviews all power electronic systems in FCEVs with special emphasis on current implementation status, recent breakthroughs, existing in...Show More
Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems
Tohid Behdadnia;Klaas Thoelen;Fairouz Zobiri;Geert Deconinck
Publication Year: 2024,Page(s):6075 - 6088
Cited by: Papers (5)
The stability of modern power grids relies heavily on effective frequency control, mainly achieved through automatic generation control (AGC) systems. However, AGC systems have become increasingly vulnerable to cyber-physical attacks due to their dependence on communication infrastructure and cyber-physical devices. In this article, we present a novel attack strategy that leverages the learning ca...Show More
Heuristically Optimized Features Based Machine Learning Technique for Identification and Classification of Faults in PV Array
Publication Year: 2024,Page(s):6089 - 6098
The faults in photovoltaic (PV) array lead to increased system losses and even fire hazards. The most frequent faults in PV strings are line-to-line (LL) and line-to-ground (LG) faults. Many efforts have been made to develop machine learning-based methods that are capable of detecting faults. However, these methods do not consider low mismatch faults, high impedance faults, active MPPT control, th...Show More
BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation
Publication Year: 2024,Page(s):6099 - 6108
Cited by: Papers (1)
Various kinds of heart diseases pose a serious threat to human health. To effectively treat and prevent these diseases, accurate segmentation of the entire heart structure is crucial for medical research and application. At present, the solution to this problem still needs to rely on a lot of manpower. Not only is this time-consuming, but accuracy is sometimes difficult to guarantee. In the deep l...Show More
Physics-Guided Deep Learning for Plate Permeability Estimation With Single to Multiple Frequency Transformation of Eddy-Current Testing
Zihan Xia;Tian Meng;Ruochen Huang;Adam Fletcher;Yuchun Shao;Mingyang Lu;Anthony Peyton;Wuliang Yin
Publication Year: 2024,Page(s):6109 - 6118
Cited by: Papers (1)
Eddy-current testing technique has been extensively explored for estimating the electromagnetic property of steel plates in various industrial applications. In this article, a physics-guided deep learning (DL) method is proposed to estimate the permeability of plate in high thickness with probe liftoff. A simplified analytical model is derived, which realises the single to multiple frequency induc...Show More
Analytical Expression of the Mirror Coefficient by Joint Analytical Calculation Method
Tianyi Zhang;Guo Wei;Xin Zhi;Lingjun Hao;Jiantao Zhang;Chunbo Zhu
Publication Year: 2024,Page(s):6119 - 6129
Cited by: Papers (1)
To address the universal challenge that the mirror coefficient (MC) cannot be analytically expressed, the joint analytical calculation (JAC) method is initially proposed in this article. Selecting the magnetic vector potential (MVP) as a uniform analytical target, the JAC method constructs a bridge, linking the truncated region eigenfunction expansion (TREE) method and the mirror method. Furthermo...Show More
Nonvolatile CMOS Memristor, Reconfigurable Array, and Its Application in Power Load Forecasting
Quanli Deng;Chunhua Wang;Jingru Sun;Yichuang Sun;Jinguang Jiang;Hairong Lin;Zekun Deng
Publication Year: 2024,Page(s):6130 - 6141
Cited by: Papers (20)
The high cost, low yield, and low stability of nanomaterials significantly hinder the application and development of memristors. To promote the application of memristors, researchers proposed a variety of memristor emulators to simulate memristor functions and apply them in various fields. However, these emulators lack nonvolatile characteristics, limiting their scope of application. This article ...Show More
Water-MBSL: Underwater Movable Binocular Structured Light-Based High-Precision Dense Reconstruction Framework
Publication Year: 2024,Page(s):6142 - 6154
Cited by: Papers (2)
Structured light systems are widely used in underwater dense reconstruction due to their excellent accuracy. However, the current related methods mainly focus on fixed positions. The reconstruction performance in motion is insufficient. Therefore, we propose an underwater movable binocular structured light (MBSL) based high-precision dense reconstruction framework, named WaterMBSL, to realize the ...Show More
FGIP: A Frontier-Guided Informative Planner for UAV Exploration and Reconstruction
Xuetao Zhang;Xiaotian Xu;Yisha Liu;Hanzhang Wang;Xuebo Zhang;Yan Zhuang
Publication Year: 2024,Page(s):6155 - 6166
Cited by: Papers (4)
This article proposes a frontier-guided informative planner for unmanned aerial vehicle volumetric exploration and 3-D reconstruction, which can explore a complex unknown environment and provide the accurate truncated signed distance function reconstruction simultaneously. Different from the existing methods, the key insight of the proposed method is that the hybrid surface frontier is proposed to...Show More
Mutual Active Learning for Engineering Regulated Statistical Digital Twin Models
Publication Year: 2024,Page(s):6167 - 6176
Cited by: Papers (2)
Digital twin (DT) models are computational models that can effectively represent different assets and processes in the manufacturing environment. Moreover, the DT models can support intelligent automation by integrating with the digital foundation and the data analytics provided by the cyber-physical system (CPS) in an industrial environment. To properly model a physical process, a DT model should...Show More
An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions
Publication Year: 2024,Page(s):6177 - 6186
Cited by: Papers (6)
Machinery often operates under time-varying conditions, which can lead to distribution discrepancies in degradation samples. However, most existing domain generalization-based methods for predicting remaining useful life (RUL) are applied to constant operation conditions, and they may demonstrate performance deteriorations across alternating operation conditions. This article proposes an optimal-s...Show More
Minimum Bandwidth Reservation for CBS in TSN With Real-Time QoS Guarantees
Publication Year: 2024,Page(s):6187 - 6198
Cited by: Papers (4)
Time-sensitive networking (TSN) has emerged as a promising standard for real-time domains. Efficiently determining critical configuration parameters for latency guarantees for time-critical flows remains an open question. Current configuration approaches rely on optimization methods with real-time performance analysis feedback to ensure deadline quality-of-service requirements. However, the ex-pos...Show More
Minimum Bandwidth Reservation for CBS in TSN With Real-Time QoS Guarantees
Year: 2024 | Volume: 20 | Issue: 4
Technique of Feature Extraction Based on Interpretation Analysis for Multilabel Learning in Nonintrusive Load Monitoring With Multiappliance Circumstances
Publication Year: 2024,Page(s):6199 - 6208
Cited by: Papers (2)
Nonintrusive load monitoring (NILM) aims to analyze the aggregate information of power consumption and recognize the separate operation states of each individual electrical appliance, in which methods of machine learning are frequently used for efficient and effective computation. This article employs multilabel learning models as the main technique to measure the NILM problems with multiple elect...Show More
Harmonic Sparse Structured Nonnegative Matrix Factorization: A Novel Method for the Separation of Coupled Fault Feature
Publication Year: 2024,Page(s):6209 - 6221
Cited by: Papers (2)
Cyclic spectral coherence (CSCoh) is an effective tool to reveal the cyclostationarity of the fault-induced components (FICs). Integrating CSCoh over the domain of the spectral frequency or decomposing CSCoh by matrix factorization methods can provide an enhanced diagnosis spectrum. However, for compound faults, the integration-based strategy or the factorization-based method will fail once the fe...Show More
Call White Black: Enhanced Image-Scaling Attack in Industrial Artificial Intelligence Systems
Junjian Li;Honglong Chen;Peng Sun;Zhibo Wang;Zhichen Ni;Weifeng Liu
Publication Year: 2024,Page(s):6222 - 6233
Cited by: Papers (1)
The increasing prevalence of deep neural networks (DNNs) in industrial artificial intelligence systems (IAISs) promotes the development of industrial automation. However, the growing employment of DNNs also exposes them to various attacks. Recent studies have shown that the data preprocessing process of DNNs is vulnerable to image-scaling attack. Such attacks can craft an attack image, which looks...Show More
Hybrid Conditional Kernel SVM for Wire Rope Defect Recognition
Shiwei Liu;Yong Liu;Longxiang Shan;Qiaohua Wang;Yanhua Sun;Lingsong He
Publication Year: 2024,Page(s):6234 - 6244
Cited by: Papers (2)
Support vector machine (SVM) has been applied in data classification and defect recognition in various scenes, the common kernels are not always suitable to satisfy the requirement of high accuracy and efficiency inspection. A new hybrid conditional kernel (HCK) and SVM model based on exponential functions is proposed in this article, the validity and feasibility of the basic characterizations are...Show More
Imitating Tool-Based Garment Folding From a Single Visual Observation Using Hand-Object Graph Dynamics
Peng Zhou;Jiaming Qi;Anqing Duan;Shengzeng Huo;Zeyu Wu;David Navarro-Alarcon
Publication Year: 2024,Page(s):6245 - 6256
Cited by: Papers (10)
Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly deformable nature of fabrics. In this article, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual ...Show More
Action-Dependent Heuristic Dynamic Programming With Experience Replay for Wastewater Treatment Processes
Publication Year: 2024,Page(s):6257 - 6265
Cited by: Papers (5)
The wastewater treatment process (WWTP) is beneficial for maintaining sufficient water resources and recycling wastewater. A crucial link of WWTP is to ensure that the dissolved oxygen (DO) concentration is continuously maintained at the predetermined value, which can actually be considered as a tracking problem. In this article, an experience replay-based action-dependent heuristic dynamic progra...Show More
Normal-Operation-Undisturbed Magnet Flux Linkage Monitoring in PMSM Drives via a Mechanical-Model-Based Dual Time-Scale Approach
Chengbo Yang;Bao Song;Juri Jatskevich;Hongchao Zhang;Christopher H. T. Lee
Publication Year: 2024,Page(s):6266 - 6279
Cited by: Papers (1)
Accurate identification of magnet flux linkage is of great importance for the condition monitoring and control optimization of permanent magnet synchronous motor (PMSM) drives. In this work, a normal-operation-undisturbed magnet flux linkage monitoring technique using a mechanical-model-based dual time-scale approach is presented. The proposed technique is composed of two Adaline-type asymptotic o...Show More
A Novel Fused NARX-Driven Digital Twin Model for Aeroengine Gas Path Parameter Prediction
Publication Year: 2024,Page(s):6280 - 6288
In this article, we propose a digital twin model (DTM) based on the nonlinear autoregressive model with exogenous (NARX) inputs model network to predict engine gas path parameters accurately. The DTM is combined by a model-driven model (MDM) and a data-driven model (DDM). To allocate the function of MDM and DDM, a pretreating fusion method is proposed for the first time, which is divided into thre...Show More
Harmonic Suppression for PMSM Applied to MSTMP Based on Virtual Even-Order Fractional Repetitive Controller
Publication Year: 2024,Page(s):6289 - 6299
Cited by: Papers (2)
Current harmonics will produce losses, which make motor heated. The high temperature of the motor applied to turbo molecular pump (TMP) will degrade ultrahigh vacuum performance severely. Current harmonics should be suppressed effectively. The repetitive controller (RC) has been widely used in harmonic suppression. To address key challenges of conventional RC, this article proposes a novel virtual...Show More
Efficient and Secure Blockchain Consensus Algorithm for Heterogeneous Industrial Internet of Things Nodes Based on Double-DAG
Yourong Chen;Yang Zhang;Yubo Zhuang;Kelei Miao;Seyedamin Pouriyeh;Meng Han
Publication Year: 2024,Page(s):6300 - 6312
Cited by: Papers (6)
With the Industrial Internet of Things (IIoT) continuing to expand, lots of data collection, exchange, and authentication generated from an increasing number of access devices is required with heterogeneity, multidimension, and multiobjective networks as its characteristics. However, traditional IIoT systems are vulnerable to security challenges, such as data leakage, theft, and tampering. As one ...Show More
An Efficient Segmentation Model With Multipath Attention Mechanism Enabling Particle Size Characterization of Coal Dust
Publication Year: 2024,Page(s):6313 - 6324
Characterizing the coal particle size distribution is an effective means to reduce potential safety hazards in smart mine construction for disaster prevention and security management. Aiming at the existing approach, pixelwise manual labeling on low-contrast particle images that have irregular contours is challenging and time consuming. Thus, a novel deep learning model based on BlendMask instance...Show More
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