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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ovsiannikova, Polina Pakonen, Antti Vyatkin, Valeriy VTT Technical Research Centre of Finland Ltd. Espoo Finland Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleiå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350339918

We present an approach for suggesting possible fixes to an overall I&C nuclear architecture during its design phase. Despite the I&C architecture, in our case, being represented with an ontology, we do not aim to change the properties of an ontology per se. Instead, we focus on the subset of ABox triples that do not contain terminological elements in either subject, predicate, or object parts. Such a subset we call the design artifacts. When the ontology is filled with the design artifacts, the analyst runs the check of non-functional requirements using SPARQL queries. The requirements associated with the queries that returned results do not hold. The goal of the current work is to provide support for the next stage when the analyst has to change the design artifacts so that the queries no longer return results. Our method is based on representing the results of queries as graphs, intersecting them, and finding the minimal changes that prevent the results from being mapped on their (or other) queries. © 2023 IEEE.

关键词： Ontology

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Unveiling anomalies: transformative insights from transformer-based autoencoder models

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International Journal of computers and Applications 2025年第1期47卷 29-44页

作者： Dehkordi, Sajad Balali Nasri, Saeed Dami, Sina Department of Electrical Engineering Najaf Abad Branch Islamic Azad University Najaf Abad Iran Digital Processing and Machine Vision Research Center Najaf Abad Branch Islamic Azad University Najaf Abad Iran Department of Computer Engineering West Tehran Branch Islamic Azad University Tehran Iran

Nowadays, Intrusion Detection Systems (IDS) play a critical role in safeguarding networks by identifying and mitigating unauthorized access and malicious activities, yet face challenges in accurately detecting sophisticated threats while minimizing false positives. In this paper, we present a comprehensive analysis of a Transformer-based Autoencoder model for network intrusion detection using the NSL-KDD dataset. We evaluate the model's anomaly detection performance based on key metrics such as Mean Squared Error (MSE), Precision-Recall Area Under Curve (AUC), and Root Mean Squared Error (RMSE) on the test set. Our findings reveal promising results, with the model achieving low MSE, high AUC, and relatively low RMSE values, indicating its effectiveness in detecting anomalies. Additionally, we provide an in-depth examination of the model architecture, highlighting the role of encoder and decoder layers, dropout regularization, and optimization techniques such as Adam optimizer. Our analysis sheds light on the efficacy of Transformer-based Autoencoder models for network intrusion detection tasks, offering insights into their architectural design and performance evaluation. The novelty of this study lies in the implementation of a Transformer-based Autoencoder architecture for network intrusion detection, showcasing its superior performance in anomaly detection compared to traditional methods. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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Analysis and Control of Frequency Stability in Low-Inertia Power Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2363-2383页

作者： Changjun He Hua Geng Kaushik Rajashekara Ambrish Chandra the Department of Automation Tsinghua Universityand also with Beijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China IEEE the University of Houston HoustonTX 77004 USA the Department of Electrical Engineering Ecole de Technologie SuperieureUniversity of QuebecMontreal QC H3C 1K3Canada

Power electronic-interfaced renewable energy sources(RES)exhibit lower inertia compared to traditional synchronous *** large-scale integration of RES has led to a significant reduction in system inertia,posing significant challenges for maintaining frequency stability in future power *** issue has garnered considerable attention in recent ***,the existing research has not yet achieved a comprehensive understanding of system inertia and frequency stability in the context of low-inertia *** this end,this paper provides a comprehensive review of the definition,modeling,analysis,evaluation,and control for frequency *** commences with an exploration of inertia and frequency characteristics in low-inertia systems,followed by a novel definition of frequency stability.A summary of frequency stability modeling,analysis,and evaluation methods is then provided,along with their respective applicability in various ***,the two critical factors of frequency control—energy sources at the system level and control strategies at the device level—are ***,an outlook on future research in low-inertia power systems is discussed.

关键词： Frequency control frequency stability definition low inertia power system with high penetration of renewable energy sources

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Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller 58

Random suppression of ECG artifacts using a NLSRLMMN-based c...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Faiz, Mohammed Mujahid Ulla Zerguine, Azzedine Kale, Izzet Presidency University Department of Electrical and Computer Engineering Karnataka Bengaluru560064 India Interdisciplinary Research Center for Communication Systems and Sensing Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia Department of Computer Science and Engineering LondonW1W 6UW United Kingdom

ISBN: (纸本)9798350354058

Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages. © 2024 IEEE.

关键词： Mean square error

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Optimal Power Flow Analysis of Power System with Wind Energy and FACTS Devices Using Mountaineering Team-Based Optimizer 6

Optimal Power Flow Analysis of Power System with Wind Energy...

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6th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2024

作者： Mohamed, Amal Amin Kamel, Salah Hassan, Mohamed H. Tolba, Mohamed A. Ahmed Adam, Ahmed Hamed Aswan University Department of Electrical Engineering Aswan Egypt Nuclear Research Center Egyptian Atomic Energy Authority Reactors Department Cairo Egypt School of Automation Chongqing University Chongqing China

ISBN: (纸本)9798350382891

To achieve an environmentally responsible and emission-free society, researchers are working towards replacing fossil fuels with renewable energy sources. In this pursuit, this study proposes the use of Mountaineering Team-Based Optimization (MTBO) to address optimal power flow (OPF) issues associated with unpredictable wind turbines (WT) and Flexible AC Transmission System (FACTS) devices. The proposed approach employs Weibull probabilities to calculate WT's output power, along with other control variables such as the anticipated real electricity production of WT, the production power of thermal power units, and the electrical voltage values of each bus that generates electricity. The primary objective function to be optimized is the total generating cost of the network, which includes the penalty and reserve costs for under-and overestimating WT. The updated IEEE 30 bus test network is used to evaluate the proposed method strategy for OPF difficulties. The success and efficiency of the MTBO technique in resolving the OPF issue are evaluated by comparing the findings with those of other meta-heuristic optimization methodologies using similar testing methodology, control variables, and system conditions. The results show that the MTBO method performs well compared to other methods, making it a viable option for solving OPF issues. © 2024 IEEE.

关键词： Wind power

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Antoniadis, Christos Katsalis, Kostas Tyrovolas, Dimitrios Tegos, Sotiris A. Ioannidis, Sotiris Diamantoulakis, Panagiotis D. Karagiannidis, George K. Liaskos, Christos DOCOMO Euro-Labs Munich Germany Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanon University of Ioannina Computer Science Engineering Department Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (纸本)9798331507428

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future. © 2024 IEEE.

关键词： Resource allocation

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A Comparative Study of Deep Learning Models for Image Super-Resolution Across Various Magnification Levels

A Comparative Study of Deep Learning Models for Image Super-...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Soni, Jayesh Gurappa, Subhash Upadhyay, Himanshu Applied Research Center Florida International University Miami United States Florida International University Department of Electrical and Computer Engineering Miami United States

ISBN: (纸本)9798350391213

Image Super-Resolution (SR) is a vital image processing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imaging, forensic analysis, satellite imagery, etc. Despite its significance, there are still issues with existing SR techniques, such as handling unknown degradation, generalizing across various domains, and recovering the fine details, especially at high scaling factors. Traditional approaches like interpolation and reconstruction-based methods produce poor results, which leads to the use of deep learning models that have demonstrated notable advancements. However, problems like computational complexity, performance drop on real-world data, and dependency on fixed scaling factors remain. In this research, we conduct a comparative analysis of SR models, namely Super-Resolution Convolutional Neural Networks (SRCNN), SwinIR, and Real-ESRGAN, across multiple image domains (e.g., Animals, Cars, Faces, Humans, Landscapes, Ships) using different scaling factors (e.g., x 2, x 3, x 4) and metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), Learned Perceptual Image Patch Similarity (LPIPS), Objective Difference Image (ODI) Score, Tenengrad sharpness and Laplacian Variance. This approach helps us better understand the performance of various deep-learning models, guiding further improvements to ensure they are efficient, robust, and applicable to real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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RescueAid: Smartphone-Aided Situational Awareness For Emergency Response 24

RescueAid: Smartphone-Aided Situational Awareness For Emerge...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Laoudias, Christos Kolios, Panayiotis Ellinas, Georgios University of Cyprus Kios Research and Innovation Center of Excellence Cyprus University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350341010

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform's user interface the location traces and associated images collected during a real field exercise. © 2023 IEEE.

关键词： Smartphones

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Robust Load Frequency Control of Two-Area Interconnected Power System Using Fuzzy-I Controller

Robust Load Frequency Control of Two-Area Interconnected Pow...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Sindi, Hatem F. King Abdulaziz University K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350331790

The paper proposes a Fuzzy-I controller for optimal load frequency control (LFC) in a two-area interconnected power system. The proposed controller is composed of a fuzzy logic controller (FLC) and an integral (I) controller. To achieve a better dynamic response for the LFC, the integral controller and frequency bias parameters of the secondary loop are tuned using the Particle Swarm Optimization (PSO) technique. To boost the efficiency of the optimization, a proposed objective function using the integral of squared error (ISE) and settling time is used. A comparative study and analysis are conducted between the base system controller, optimized I controller, and the proposed controller under three different scenarios of load disturbances. The robustness of the proposed controller is shown by performing a sensitivity analysis for various system parameters and operating conditions. Moreover, the performance of the proposed controller was compared with the previous LFC approaches published in the literature. The simulation outcomes demonstrate that the proposed method has an outstanding performance. The work results are presented as time-domain simulations using MATLAB/SIMULINK tool. © 2023 IEEE.

关键词： Controllers

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