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arXiv 2024年

作者： Zhu, Guangyu Deng, Yiqin Chen, Xianhao Zhang, Haixia Fang, Yuguang Wong, Tan F. Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States School of Control Science and Engineering Shandong Key Laboratory of Wireless Communication Technologies Shandong University Shandong Jinan250061 China Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Computer Science City University of Hong Kong Kowloon Hong Kong

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users’ heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning. © 2024, CC BY-NC-SA.

关键词： Learning algorithms

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Cryptanalysis via Machine Learning Based Information Theoretic Metrics

arXiv

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arXiv 2025年

作者： Kim, Benjamin D. Vasudevan, Vipindev Adat D’Oliveira, Rafael G.L. Cohen, Alejandro Stahlbuhk, Thomas Médard, Muriel Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign ChampaignIL61820 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Mathematical and Statistical Sciences Clemson University ClemsonSC29631 United States Technion Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa32000 Israel MIT Lincoln Laboratory LexingtonMA02421 United States

The fields of machine learning (ML) and cryptanalysis share an interestingly common objective of creating a function, based on a given set of inputs and outputs. However, the approaches and methods in doing so vary vastly between the two fields. In this paper, we explore integrating the knowledge from the ML domain to provide empirical evaluations of cryptosystems. Particularly, we utilize information theoretic metrics to perform ML-based distribution estimation. We propose two novel applications of ML algorithms that can be applied in a known plaintext setting to perform cryptanalysis on any cryptosystem. We use mutual information neural estimation to calculate a cryptosystem’s mutual information leakage, and a binary cross entropy classification to model an indistinguishability under chosen plaintext attack (CPA). These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem and the results can provide a useful empirical bound. We evaluate the efficacy of our methodologies by empirically analyzing several encryption schemes. Furthermore, we extend the analysis to novel network coding-based cryptosystems and provide other use cases for our algorithms. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy. It also identifies the faults in CPA-secure cryptosystems with faulty parameters, such a reduced counter version of AES-CTR. We also conclude that with our algorithms, in most cases a smaller-sized neural network using less computing power can identify vulnerabilities in cryptosystems, providing a quick check of the sanity of the cryptosystem and help to decide whether to spend more resources to deploy larger networks that are able to break the cryptosystem. © 2025, CC BY-NC-ND.

关键词： Information leakage

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Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria

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e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 9卷

作者： Adesina, Lambe Mutalub Ogunbiyi, Olalekan Jimada-Ojuolape, Bilkisu Centre for Sustainable Energy Kwara State University Malete Nigeria Department of Electrical and Computer Engineering Faculty of Engineering and Technology Kwara State University Malete Nigeria

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction. © 2024 The Author(s)

关键词： Comparative assessment ETAP Software Feeders Power distribution systems Reliability assessment System downtime

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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An Image Based Approach of Energy Signal Disaggregation Using Artificial Intelligence 16

An Image Based Approach of Energy Signal Disaggregation Usin...

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16th IEEE International Conference on Industrial and Information Systems, ICIIS 2021

作者： Senarathna, Manuri Herath, Madhawa Thilakanayake, Thakshila D. Liyanage, Migara H. Angammana, Chitral J. Sri Lanka Institute of Information Technology Faculty of Engineering Malabe Sri Lanka University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (纸本)9781665426374

Non-Intrusive Load Monitoring (NILM) is the real-time monitoring of energy consumption data of individual appliances through the decomposition of composite energy signal captured at the household smart energy meter. Most of the existing NILM techniques utilize one-dimensional (1D) time-series signal analysis to predict the individual appliance energy signals. The utilization of image-based methods for the disaggregation of energy signals is a relatively new approach in the NILM domain. This paper presents a study of a novel computer vision-based Artificial Intelligence (AI) approach when compared to the traditional time series-based NILM methods. Gramian Angular Fields (GAF) and Recurrence Plots (RP) have been widely used in recent literature to encode time series signals as images. Novel image classification techniques with the use of Convolutional Neural Networks (CNN) simplify the extraction of nuclear load features from encoded two-dimensional (2D) images. The results considered the indices validation accuracy and validation loss in comparing the performance of different vision-based AI approaches. The results reveal that Gramian Angular Difference Field (GADF) outperforms both Gramian Angular Summation Field (GASF) and RP with a training accuracy of 97.9% and a validation accuracy of 94.2%. A comprehensive analysis and comparison are presented with an in-depth evaluation using multi-state appliances and it was concluded that GADF is the most suitable 1D to 2D conversion method for the representation of time series energy data for disaggregation purposes. © 2021 IEEE.

关键词： Time series

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Joint Computation Offloading and Energy Trading in Electric Vehicular Networks

Joint Computation Offloading and Energy Trading in Electric ...

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Biennial Symposium on Communications, QBSC

作者： Weiyang Feng Xiao Xiao Siyu Lin Ashab Uddin Niloofar Naghdi Pour Ning Zhang School of Electronic and Information Engineering Beijinz Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Windsor ON Canada Traffic Control Technology Beijing China

With the rising number of electric vehicles (EVs), the high computational task and energy management of vehicles bring great challenges to the intelligent transportation system. In this work, we investigate the joint offloading and energy trading strategy in vehicular edge computing (VEC) network. We propose an offloading-trading framework, in which EVs can offload tasks to road side unit (RSU) equipped with edge servers or Energy Fog Center (EFC), i.e, edge nodes and fog nodes, and sell excess power to EFC through Vehicle-to-grid (V2G) technology to improve energy efficiency. We aim to maximize the system utility while satisfying the offloading-trading requirements. Since the original problem is non-convex, we decompose it into two subproblems, i.e., trading energy subproblem and trading-offloading subproblem, and proposed the Farthest and Nearest Comparison Searching (FNC-S) algorithm. Specifically, we derive the closed-form expressions of trading electric energy in the trading energy subproblem. Besides, trading-offloading strategy is obtained at two boundaries of distance based on optimal moving distance searching in the trading-offloading subproblem. Simulation results show that the proposed FNC-S algorithm can significantly improve the utility compared with other baseline schemes.

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Towards an Integrated Framework for Fugitive Methane Source Localization and Monitoring in a Wireless Sensor Network

Towards an Integrated Framework for Fugitive Methane Source ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amira Al Jawhari Yasmine Souissi Jawhar Ghommam Yassine Jamoussi Faisal Mnif Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Department of Engineering GUtech University Muscat Oman Laboratory of Control Energy Management (Cem-Lab) ENIS Tunisa Departement of Computer Science College of Science SQU

Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and detection of methane leakage using a diffusion model based on the gas diffusion theory. Given that centralized Least Square methods are not efficient and robust as they require the gathering and processing of large-scale measurements on a central node. We propose a detection technique which makes use of the distributed (Non-linear) least squares method to overcome this problem. Then, a network of connected methane sensors is used to detect gas leaks. In order to estimate the parameters of the diffusive model for the gas leakage on each sensor node, a distributed recursive estimator of the consensus plus an innovation type technique is used. The characteristics being estimated include the gas source’s distance, which will be effectively triangulated to determine the source’s precise location. The targeted location is subsequently estimated using a location dispersed algorithm-based LS.

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Clothes Changing Person Re-identification via Pose and Style Normalization Network

Clothes Changing Person Re-identification via Pose and Style...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Chunyun Meng Sai Liang Hongli Li Yuki Todo Division of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Marketing Department Zhejiang Kefei Technology Co. Ltd Jiaxing China Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Re-identification involves identifying the same person across multiple non-overlapping cameras. Traditional methods rely heavily on pedestrian appearance, assuming consistent clothing in both query and gallery images, which is unrealistic in real-world scenarios where individuals often change clothes. To address this, we propose the Pose and Style Normalization Network (PSN-Net), which handles clothing and pose variations using adversarial generative networks and feature decoupling. Our model includes a Style Normalization (SN) module to eliminate style differences and a dual-channel feature matching module to enhance pedestrian matching. The results of the comparative and ablation experiments demonstrate that our model can effectively improve pedestrian recognition performance under clothing changes.

关键词： Pedestrians Clothing Cameras Data mining Cognitive systems Identification of persons

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Energy-Based control Approaches for Weakly Coupled Electromechanical Systems

arXiv

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arXiv 2024年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper addresses the regulation and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamiltonian framework. Then, we employ Lyapunov theory and the notion of contractive systems to develop control approaches in the port-Hamiltonian framework. Remarkably, these control methods eliminate the need for solving partial differential equations or implementing any change of coordinates, and are endowed with a physical interpretation. We also investigate the effect of coupled damping on the transient performance and convergence rate of the closed-loop system. Finally, the applicability of the proposed approaches is illustrated in two applications of electromechanical systems via simulations. © 2024, CC BY.

关键词： Damping

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