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Artificial Intelligence Techniques with Digital Twin for Fault Diagnosis in Interconnected systems: A Review

IEEE Access 2025年 13卷 91860-91874页

作者： Nakti, Imen Mansouri, Majdi Al-Hmouz, Rami Khedher, Atef University of Sousse ENISO Laboratory of Advanced Technology and Intelligent systems Sousse4002 Tunisia Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Muscat123 Oman

As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments. © 2013 IEEE.

关键词： Deep learning

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Improvement of dynamic phosphene presentation for a cortical visual prosthesis is task-specific 46

Improvement of dynamic phosphene presentation for a cortical...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Haozhe Zac Wong, Yan Tat Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Monash University Department of Electrical and Computer Systems Engineering Department of Physiology ClaytonVIC3800 Australia

ISBN: (纸本)9798350371499

Cortical visual prostheses can restore vision by directly stimulating the neurons in the visual cortex. The goal of these prostheses is to elicit sufficient light perception, known as phosphenes, to represent complex scenes. However, stimulating a large number of electrodes in cortical visual prostheses can be problematic. This may result in distorted visual perception and failure to present the desired visual stimuli. Previous studies have successfully presented simple patterns, such as letters, by dynamically utilizing a single phosphene. To represent more complex scenes, we propose a new method of information presentation. This method limits the number of phosphenes elicited simultaneously while sequentially presenting the contours of a visual scene. We evaluated its effectiveness with 8 participants across four different tasks. We found that the improvement provided by this method is task-specific. Our findings underscore the importance of considering the temporal features of prosthetic vision in future designs. © 2024 IEEE.

关键词： Prosthetics

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CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2023年第10期42卷 3289-3300页

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical Engineering School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad44000 Pakistan Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging nonvolatile memories (NVMs), with their better scalability, nonvolatility, and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues, such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This article proposes CRAFT, i.e., criticality-aware fault-tolerance enhancement techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of noncritical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on the CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead, i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs aga

关键词： Fault tolerance

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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations 63

A Model-Based Approach for Improving Reinforcement Learning ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ozcan, Erhan Can Giammarino, Vittorio Queeney, James Paschalidis, Ioannis Ch. Boston University Division of Systems Engineering BostonMA02215 United States CambridgeMA02139 United States Boston University Division of Systems Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations. © 2024 IEEE.

关键词： Deep reinforcement learning

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DASA: Delay-Adaptive Multi-Agent Stochastic Approximation 63

DASA: Delay-Adaptive Multi-Agent Stochastic Approximation

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Fabbro, Nicolo Dal Adibi, Arman Poor, H. Vincent Kulkarni, Sanjeev R. Mitra, Aritra Pappas, George J. University of Pennsylvania Electrical and Systems Engineering Department United States Princeton University Department of Electrical and Computer Engineering United States North Carolina State University Electrical and Computer Engineering Department United States

ISBN: (纸本)9798350316339

We consider a setting in which N agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume that the up-link transmissions to the server are subject to asynchronous and potentially unbounded time-varying delays. To mitigate the effect of delays and stragglers while reaping the benefits of distributed computation, we propose DASA, a Delay-Adaptive algorithm for multi-agent Stochastic Approximation. We provide a finite-time analysis of DASA assuming that the agents' stochastic observation processes are independent Markov chains. Significantly advancing existing results, DASA is the first algorithm whose convergence rate depends only on the mixing time τm i x and on the average delay τa v g while jointly achieving an N-fold convergence speedup under Markovian sampling. Our work is relevant for various SA applications, including multi-agent and distributed temporal difference (TD) learning, Q-learning and stochastic optimization with correlated data. © 2024 IEEE.

关键词： Markov chains

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital Communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm 22

Uncovering Spatiotemporal Differences in Cortical Activity C...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mai, Siwei Haddad, Ali E. Najafizadeh, Laleh Rutgers University Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering United States University of Basrah Department of Computer Engineering Iraq

ISBN: (纸本)9798331520526

Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for under-standing how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain's functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high-confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes. © 2025 IEEE.

关键词： Matrix factorization

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Hierarchical Scheduling for Oil Spill SAR Image Detection in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9689-9703页

作者： Son, Seok Bin Park, Soohyun Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of

Oil spills represent significant environmental hazards in ocean ecosystems, requiring rapid and accurate detection and response mechanisms. Due to its efficacy, synthetic aperture radar (SAR) is an important tool for remote monitoring of ocean environments. However, operational constraints such as communication time between low Earth orbit (LEO) satellites and ground stations (GS), as well as finite buffers, pose challenges. To address these issues, this paper proposes a space-air-ground-integrated network (SAGIN) scenario comprising LEO satellites, high altitude platforms (HAPs), and ground stations (GSs), suitable for rapid and accurate oil spill detection using SAR images. In addition, a novel three-step algorithm is proposed to facilitate SAR image transmission effectively. The first step of the proposed algorithm focuses on maximizing the transmission of ocean SAR images from the LEO satellites to the HAPs. This optimization involves the neural Myerson auction (NMA)-based scheduler, a second-price auction mechanism with deep learning methodologies for truthfulness, distributed computation, and low computational complexity. In addition, the pathloss is also considered for effective communication system design. The second step algorithm aims to maximize data transmission efficiency from HAPs to GSs, employing scheduling strategies based on the Markov decision process (MDP). This algorithm enables optimal decision-making over time steps, considering the throughput of each GS and the age of the information (AoI) ratio. Finally, an image compression rate control algorithm based on Lyapunov optimization is employed at the GSs to enhance detection accuracy while ensuring queue stability. The performance of the proposed three-step algorithm is intensively evaluated against various benchmark algorithms, demonstrating its superiority in oil spill detection efficacy. © 1967-2012 IEEE.

关键词： Oil spills

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Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

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CES Transactions on electrical Machines and systems 2024年第1期8卷 51-60页

作者： Beichen Ding Yuting Lu Kaide Huang Guodong Feng Chunyan Lai IEEE the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhen 518107China the Fosan University Foshan 528225China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

关键词： PMSM Magnet temperature estimation Virtual position offset injection Inverter nonlinearity

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