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Self-Supervised Pretraining and Quantization for Fault Tolerant Neural Networks: Friend or Foe?

IEEE Access 2025年 13卷 75546-75562页

作者： Milazzo, Rosario Fosson, Sophie M. Morra, Lia Sterpone, Luca Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

Deep neural networks (DNNs) are increasingly being applied in critical domains such as healthcare and autonomous driving. However, their predictive capabilities can degrade in the presence of transient hardware faults, which can lead to potentially catastrophic and unpredictable errors. Consequently, various techniques have been proposed to enhance DNN fault tolerance by modifying the network structure or training procedure, thereby reducing the need for costly hardware redundancy. However, there are design or training choices whose impact on fault propagation has been overlooked in the literature. Specifically, self-supervised learning (SSL) as a pre-training technique has been shown to enhance the robustness of learned features, resulting in improved performance on downstream tasks. This study investigates the error tolerance of different DNN and SSL techniques in image classification and segmentation tasks, including those relevant to Earth Observation. Experimental results suggest that SSL pretraining, whether used alone or in combination with error mitigation techniques, generally enhances DNN fault tolerance. We complement these findings with an in-depth analysis of the fault tolerance of quantized networks. In this context, the use of standard SSL techniques leads to a decrease in accuracy. However, this issue can be partially addressed by employing methods that, during the pretraining phase, incorporate the quantization error into the loss function. © 2013 IEEE.

关键词： Self-supervised learning

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Unsupervised pedestrian intention estimation through deep neural embeddings and spatio-temporal graph convolutional networks

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Pattern Analysis and Applications 2025年第2期28卷 1-14页

作者： Scaccia, Simone Pro, Francesco Amerini, Irene Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

A deep understanding of pedestrian intention and crossing behaviors is crucial in applications like pedestrian attribute recognition and autonomous driving. While vehicles need to predict the movements of pedestrians accurately for safety, the recognition and re-identification systems rely on behavioral cues that help them enhance identity tracking and attribute analysis. Traditional trajectory-based methods for pedestrian intention estimation evaluate the future positions of pedestrians based on their past movements but may fail to capture their true intentions. A more effective approach will anticipate actions by analyzing underlying intent, improving the precision of pedestrian recognition and the motion prediction. Current research on estimating pedestrian intentions primarily depends on supervised learning methods. In contrast, this work introduces an unsupervised learning approach to learn intention representations. This method is based on the idea that similar intentions lead to comparable behaviors among pedestrians, and, therefore, they can be clustered. To achieve this, this paper introduces UnPIE, an unsupervised method for predicting pedestrian intentions. It utilizes Spatio-Temporal Graph Convolutional Networks to encode intentions from videos and map them into a D-dimensional latent space. The training phase incorporates Instance Recognition to increase separation between embeddings from different classes and Local Aggregation to form soft clusters of related embeddings. A supervised non-parametric classifier is used to evaluate the performance of the method. The results demonstrate that UnPIE has comparable performance with respect to supervised approaches and even surpasses them, achieving a higher Precision by about 7% on the Pedestrian Intention Estimation dataset.

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LLCoach: Generating Robot Soccer Plans Using Multi-role Large Language Models

LLCoach: Generating Robot Soccer Plans Using Multi-role Larg...

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27th RoboCup International Symposium, 2024

作者： Brienza, M. Musumeci, E. Suriani, V. Affinita, D. Pennisi, A. Nardi, D. Bloisi, D.D. Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Department of International Humanities and Social Sciences International University of Rome Rome Italy

ISBN: (纸本)9783031858581

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropomorphic robots

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Propofol-Induced Moderate-Deep Sedation Modulates Pediatric Neural Activity:A Functional Connectivity Study

iRADIOLOGY

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iRADIOLOGY 2025年第1期3卷 61-71页

作者： Qiang Zheng Yiyu Zhang Lin Zhang Jian Wang Jungang Liu School of Computer and Control Engineering Yantai UniversityYantaiChina Department of Radiology Xiamen Children's HospitalChildren's Hospital of Fudan University at XiamenXiamenChina

Background:Previous studies have demonstrated the underlying neurophysiologic mechanism during general anesthesia in ***,the mechanism of propofol-induced moderate-deep sedation(PMDS)in modulating pediatric neural activity remains unknown,which therefore was investigated in the present study based on functional magnetic resonance imaging(fMRI).Methods:A total of 41 children(5.10�1.14 years,male/female 21/20)with fMRI were employed to construct the functional connectivity network(FCN).The network communication,graph-theoretic properties,and network hub identification were statistically analyzed(t test and Bonferroni correction)between sedation(21 children)and awake(20 children)*** involved analyses were established on the whole-brain FCN and seven sub-networks,which included the default mode network(DMN),dorsal attentional network(DAN),salience network(SAN),auditory network(AUD),visual network(VIS),subcortical network(SUB),and other networks(Other).Results:Under PMDS,significant decreases in network communication were observed between SUB-VIS,SUB-DAN,and VIS-DAN,and between brain regions from the temporal lobe,limbic system,and subcortical ***,no significant decrease in thalamus-related communication was *** graph-theoretic properties were significantly decreased in the sedation group,and all graphical features of the DMN showed significant group *** superior parietal cortex with different neurological functions was identified as a network hub that was not greatly ***:Although the children had a depressed level of neural activity under PMDS,the crucial thalamus-related communication was maintained,and the network hub superior parietal cortex stayed active,which highlighted clinical prac-tices that the human body under PMDS is still perceptible to external stimuli and can be awakened by sound or touch.

关键词： functional connectivity network moderate-deep sedation neural activity pediatric propofol

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Cepstrum Lifting and Coherent Averaging-based Instantaneous Frequency Estimation of Low-Amplitude Signal Components for Rotating Machinery

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IEEE Sensors Journal 2025年第10期25卷 17479-17490页

作者： Liu, Shaokang Liu, Shuai Wu, Yingjie Liu, Weiliang Liu, Changliang Luo, Zhihong University of North China Electric Power Control and Computer Engineering Department Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China University of Northeast Electric Power Automation Engineering Department Jilin132000 China Inner Mongolia University of Technology Electric Power Department Hohhot010080 China

Instantaneous frequency (IF) serves multiple critical functions in the prognostic and health management of rotating machinery. Current IF estimation methodologies primarily concentrate on enhancing estimation accuracy at the algorithmic level. However, in practical industrial applications, the target signal component is often influenced by varying operating conditions, structural resonances, and background noise, causing it to become submerged or even disappear, ultimately resulting in the failure of conventional IF estimation techniques. To address these challenges, a novel IF estimation approach incorporating signal quality enhancement is developed. First, the complementary multi-source vibration signal is processed by the proposed segmented cepstrum lifting method to overcome the submersion of the resonant frequency band on the target component. Then, processed segmented signals are fused using coherent averaging technology to enhance the amplitude of the target component. Finally, leveraging the first-order approximation capability of the short-time fractional Fourier transform (STFrFT) for nonlinear frequency modulation modes, precise IF estimation is achieved. Verified by field data, the proposed method outperforms mainstream methods in both accuracy and applicability, highlighting its industrial application prospects. © 2025 IEEE.

关键词： Frequency estimation

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Investigating Consumer Behaviour Towards Electric Vehicles: A Systematic Literature Review

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Circular Economy and Sustainability 2025年第2期5卷 1419-1450页

作者： Quaglieri, Luca Mercuri, Francesco Fraccascia, Luca Department of Computer Control and Management Engineering “Antonio Ruberti” - Sapienza University of Rome Rome Italy Department of Mechanical and Aerospace Engineering Sapienza University of Rome Rome Italy Department of Management Sapienza University of Rome Rome Italy Department of High-Tech Business and Entrepreneurship University of Twente Enschede Netherlands

Through the 2030 Agenda, countries are committed to reducing pollutant emissions. In this context, the transport sector plays a relevant role and road transport accounts for a very large proportion of Greenhouse gas emissions (GHGs). Although several papers investigate this issue, none of the previous ones provide a holistic classification of the factors influencing purchase intention towards Electric Vehicles (EVs), as this paper does. Based on a systematic literature review, our research contributes to bridge the gap by setting out a new theoretical framework through which 104 factors were conceptualized with a double level of detail, first grouped into 15 conceptual groups and then clustered into three areas Economic, Personal consumers and Technological and evaluated according to their impact–direct or indirect–on customers' intention to purchase electric vehicles… The factor conceptualization offers theoretical implications by presenting a holistic view of the factors that influence EVs adoption intention. In addition, the study presents managerial implications to support incentive policies for policy makers and marketing strategies for companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Circular economy Consumer behaviour Electric vehicles Intention to adopt Purchase intention Systematic literature review

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Reference Model Design in Multiple Timescale Adaptive control

Reference Model Design in Multiple Timescale Adaptive Contro...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale Systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

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