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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2024年第4期86卷 151-166页

作者： Bogdan, Ioana Corina Simion, Emil Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers "Transilvania" University of Brasov & CiTi Brasov Romania Department of Mathematical Methods and Models National University of Science and Technology POLITEHNICA Bucharest & CiTi Bucharest Romania

Cybersecurity is a field that unifies concepts from various fundamental areas: mathematics, physics, computer science, electronics, sociology, and management. This field is challenging for government authorities, academic institutions, and private entities alike. By pooling the resources of these three actors, we can build secure and resilient systems. In this context, several challenges emerge, such as the exchange of knowledge and expertise, collaborative research projects, the organization of internships and scholarship programs, the exchange of threat intelligence, training, and awareness, and finally, the standardization and regulatory component. The evolving nature of cyber threats and the growing sophistication of attackers necessitate robust and adaptable security frameworks. A "defense-in-depth security model" is mandatory in this context as it offers multiple layers of protection, ensuring that if one defense mechanism fails, others remain in place to prevent breaches. In this paper, we propose a defense-in-depth security model and highlight how key points within the model can be interconnected. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Critical infrastructures

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Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

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IEEE Internet of Things Journal 2025年第12期12卷 19801-19817页

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 IEEE.

关键词： Deep neural networks

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Efficient Scene Reconstruction for Unmanned Aerial Vehicles 5

Efficient Scene Reconstruction for Unmanned Aerial Vehicles

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Bond, Yeana L. Ledwell, Spencer Osornio, Esmeralda Cruz, Alberto C. California State University Bakersfield Department of Computer & Electrical Engineering & Computer Science Bakersfield93311 United States

ISBN: (纸本)9798350358018

There is demand for small, cost-effective Unmanned Aerial Vehicles (U A V s) in industrial applications. These machines are expected to perform autonomous decision-making tasks. Recent work shows promise for systems based on images or video. We are motivated to study Structure From Motion (SFM), a three-dimensional (3D) reconstruction technique from two-dimensional images. It uses low-cost cameras rather than expensive LIDAR such as laser scanning sensors. The goal of this work is to seek an efficient way to improve accuracy and performance of SFM on small UAVs that lack the computation needed to perform costly algorithms such as neural networks. A major step of SFM is to estimate the homography between two views of the same scene by matching pairs of distinct features in an image (also known as geometric verification). Accurate estimation of pairs leads to better 3D models. We study feature-based homography estimation with various Random Sample Consensus (RANSAC) algorithms. Specifically, pre-sampling is one of the most common strategies utilized to improve the original RANSAC algorithm. Inspired by the work of others, we implement a new pre-sampling RANSAC method. Our adaptation, Blur and Contrast RANSAC (BC-RANSAC), has state-of-the-art performance while reducing run-time by a factor of 6.78. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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Unveiling the Power of Sparse Neural Networks for Feature Selection 27

Unveiling the Power of Sparse Neural Networks for Feature Se...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Atashgahi, Zahra Liu, Tennison Pechenizkiy, Mykola Veldhuis, Raymond Mocanu, Decebal Constantin van der Schaar, Mihaela Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Luxembourg Luxembourg

ISBN: (纸本)9781643685489

Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than 50% memory and 55% FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. © 2024 The Authors.

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Identification of nonlinear systems in the frequency domain using two-dimensional Volterra series 10

Identification of nonlinear systems in the frequency domain ...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Soheili, Masoud Shafieirad, Mohsen Morovati, Tahereh Ronaghi, Erfan Kouhi University of Kashan Department of Electrical and Computer Engineering Kashan Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798331509972

This paper presents the identification of second-order two-dimensional kernels using the Volterra series model in the frequency domain. The study focuses on nonlinear systems where the input varies over both time and spatial dimensions. The Volterra series, known for its effectiveness in modeling complex nonlinear systems, is extended to two dimensions. The kernels are computed using the least squares method, with the input defined as a product of cosine functions. By simplifying the expressions and applying the Fourier transform, accurate kernel identification is achieved. The results demonstrate the efficacy of this approach in precisely matching the estimated system with the actual system, as evidenced by the minimal identification error. This work contributes to the development of advanced techniques for nonlinear system identification in industrial and scientific applications. © 2024 IEEE.

关键词： Frequency domain analysis

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Elliptic Localization With Imperfect Clock Synchronization for Known and Unknown Propagation Speed Scenarios

Elliptic Localization With Imperfect Clock Synchronization f...

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作者： Xiao, Yudong Wang, Gang Ho, K.C. Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

In this article, we address the elliptic localization problem in the presence of clock synchronization errors at the receivers and transmitters, when the propagation speed is known as well as not known. For the known propagation speed scenario, we first show that the clock synchronization errors, when modeled as Gaussian random variables, can be treated as part of the composite noise in the measurement model without incurring performance loss in localization. Motivated by this result, we propose a generalized trust region subproblem solution for estimating the object position. For the unknown propagation speed scenario, we first formulate a nonconvex weighted least-squares problem. Such a nonconvex problem is solved by either one of the two proposed methods, semidefinite relaxation or grid search, to jointly estimate the object position and the propagation speed. Furthermore, we conduct the mean square error analysis to show that the proposed methods are able to reach the Cramer-Rao lower bound accuracy under Gaussian noise, which is validated by using simulated and semireal data. © 1965-2011 IEEE.

关键词： Synchronization

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Wheeled Mobile Robot Modeling for Local Navigation Using System Identification 66

Wheeled Mobile Robot Modeling for Local Navigation Using Sys...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Lee, Cheng-Lung Paulik, Mark J. Krishnan, Mohan University of Detroit Mercy Department of Electrical & Computer Engineering and Computer Science DetroitMI48221 United States

ISBN: (纸本)9798350302103

In this work we investigate wheeled mobile robot (WMR) kinodynamic modeling using system identification techniques. The larger context is the development of a novel local navigation algorithm based on long kinodynamic trajectories which account for vehicle drivability. A high-fidelity physics-based differential-drive robot model is used to generate command velocity and ground truth data sets, which are subsequently analyzed using multiple system identification models. Parametric linear ARX and ARMAX models are considered. Experimental results using multiple training and validation data sets corre-sponding to different driving scenarios and vehicle loading are explored. The resulting model-generated WMR paths demonstrate excellent performance. © 2023 IEEE.

关键词： local navigation modeling System identification wheeled mobile robot

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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