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检索条件"机构=Dep. of Electrical Engineering and Computer Science"

共 1532 条记录，以下是11-20 订阅

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Detection, Classification, and Location in VSC-HVDC Systems Using Metaheuristic Optimized ANFIS and Hilbert Huang Transform Method 18

Fault Detection, Classification, and Location in VSC-HVDC Sy...

引用

18th International Conference on Protection and Automation in Power Systems, IPAPS 2024

作者： Akbari, Ehsan Shadlu, Milad Samady Mazandaran University of Science and Technology Dep. of Electrical Engineering Babol Iran Azad University Young Researchers and Elite Club Bojnourd Branch Islamic Dep. of Electrical Engineering Bojnourd Iran

ISBN: (纸本)9798350350388

Identifying, classifying, and locating various faults in HVDC networks can significantly reduce maintenance costs in these systems. For this reason, intelligent methods for detecting and locating faults with high accuracy and speed have recently been the focus of system planners and operators. This paper proposes an intelligent fault detection, classification, and location scheme based on the adaptive neuro-fuzzy inference system (ANFIS), whose training and testing data are generated using the Hilbert-Huang (HH) transform-based feature extraction method. DC link current is measured as a fault signal, and its statistical characteristics are extracted employing HH transform. Furthermore, for training and testing ANFIS, a meta-heuristic algorithm with dynamic search capability and high convergence speed called the Modified Cuckoo Search (MCS) Algorithm is utilized. In addition, two conventional training methods, i.e., least square estimation (LSE) and gradient descent (GD), are applied to train ANFIS, and its performance is compared with the MCS algorithm. The numerical analysis results validate the accuracy of 97.37% in classification and 98.39% in location using the proposed framework. Additionally, the comparative study confirms a 12.18% reduction in mean squared error (MSE) value compared with the best result obtained in the literature. © 2024 IEEE.

关键词： Hilbert-Huang transform

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Traffic sign recognition based on CNN vs different transfer learning techniques 5

Traffic sign recognition based on CNN vs different transfer ...

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5th Symposium on Pattern Recognition and Applications, SPRA 2024

作者： Ghouse, Mohammed Farag, Saber Butt, Usman Engineering and Environment Dep. Northumbria University London United Kingdom Computer and Information Science Dep. British University in Dubai Dubai United Arab Emirates

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Traffic sign detection and recognition (TSDR) is a pivotal component of contemporary transportation systems, designed to enhance road safety and operational efficiency. In an era where automation is indispensable, TSDR stands out as a critical tool for optimizing driving experiences. This study presents the development of a TSDR model employing advanced deep learning techniques, with a particular emphasis on convolutional neural networks (CNNs). Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which comprises annotated images of various traffic signs captured under diverse environmental conditions, we achieved remarkable results. Our CNN models demonstrated a 99% detection rate, underscoring their efficacy in traffic sign recognition tasks. Furthermore, transfer learning models yielded impressive accuracies, with VGG19 achieving 99.64%, EfficientNetB7 reaching 99%, and ASNet attaining 99.72%. Despite these promising results, we identified several limitations, including dataset-specific dep.ndencies and the need for further enhancements in model design and training methodologies. Addressing these challenges is necessary for ensuring the robustness and reliability of intelligent transportation systems, thereby significantly improving road traffic management, safety, and efficiency. Copyright © 2025 SPIE.

关键词： Convolutional neural networks

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A lightweight ConvGRU network for Distracted Driving detection 24

A lightweight ConvGRU network for Distracted Driving detecti...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Anagnostou, Pantazis Mitianoudis, Nikolaos Electrical and Computer Engineering Dep. Democritus University of Thrace Xanthi Greece

ISBN: (纸本)9798350339598

In this paper, we explore the problem of automatic detection of dangerous or distracted driving using multi-modal cameras. A deep convolutional model with Gated Recurrent Unit (GRU) layers for classification on the Driver Anomaly Detection (DAD) dataset is proposed. The key features are the limited use of 3D convolutions and the replacement of 2D convolutions with dep.h-wise separable convolutions, which reduce the computational complexity of the model to a small fraction of previous architectures with a small decrease in AUC performance. In addition, the threshold for binary classification between safe and distracted driving is adaptively estimated through the training data. Finally, an ensemble of all multi-modal inputs yields the final classification with favourable performance. © 2023 IEEE.

关键词： Convolution

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A non-contact SpO2 estimation using video magnification and infrared data 48

A non-contact SpO2 estimation using video magnification and ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Stogiannopoulos, Thomas Cheimariotis, Grigorios-Aris Mitianoudis, Nikolaos Democritus University of Thrace Electrical & Computer Engineering Dep. Xanthi Greece

ISBN: (纸本)9781728163277

Peripheral oxygen saturation (SpO2) is one important vital sign to be monitored in individuals, whose health is fragile, such as the elderly. Contactless SpO2 monitoring using RGB cameras has been already developed with satisfactory results. This work explores the case of achieving an acceptable level of performance, when the lightning conditions are not optimal, particularly during night time, by processing solely infrared low-cost camera recordings. The Eulerian Video Magnification (EVM) technique was used to enhance the subtle differences in skin pixel intensity in the facial area. Two approaches were explored for performing regression: one using 12 novel features extracted from the amplified photoplethysmography (PPG) signal and Generalized Additive Models and a second using a 3D Convolution Neural Network (CNN) architecture on the raw amplified forehead video. The root mean square error in the estimated SpO2 levels using both methods is minimal and in the accepted range for these applications. © 2023 IEEE.

关键词： Eulerian Video Magnification (EVM) Generalized Additive Models (GAM) Infrared (IR) peripheral oxygen saturation (SpO2) Regression Neural Networks remote photoplethysmography (rPPG)

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Packet Too Big Detection and its Integration into QUIC 16

Packet Too Big Detection and its Integration into QUIC

引用

16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Völker, Timo Tuxen, Michael FH Münster University of Applied Sciences Dep. of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798350333510

A communication over an Internet Protocol (IP) based network fails if an endpoint sends packets that are too big to reach their destination and if the sender is unable to detect that. The node on the path that drops these packets should respond with a Packet Too Big (PTB) message. However, multiple scenarios exist in which the sender will not receive a PTB message. Even if it does, it refrains from using the information in case it suspects that a potential attacker forged the message. In particular, we are not aware of any implementation of the secure transport protocol QUIC (e.g., used by HTTP/3) that processes PTB messages. In this paper, we present a novel parameterizable PTB detection algorithm for reliable transport protocols that does not dep.nd on PTB messages. We further describe how to integrate our algorithm into QUIC, present results from an evaluation using the algorithm within a QUIC simulation model and, based on these results, suggest concrete parameter values. © 2023 IEEE.

关键词： HTTP

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SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-based models for Dialectal Arabic Identification 1

SANA at NADI 2023 shared task: Ensemble of Layer-Wise BERT-b...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： Almarwani, Nada Aloufi, Samah Dep. of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (纸本)9781959429272

Our system, submitted to the Nuanced Arabic Dialect Identification (NADI-23), tackles the first sub-task: Closed Country-level dialect identification. In this work, we propose a model that is based on an ensemble of layer-wise fine-tuned BERT-based models. The proposed model ranked fourth out of sixteen submissions, with an F1-macro score of 85.43. © 2023 Association for Computational Linguistics.

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Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

引用

2024 International Conference on electrical Machines, ICEM 2024

作者： Ferreira, Fernando J. T. E. Institute of Systems and Robotics University of Coimbra Dep. of Electrical and Computer Engineering Coimbra3030-250 Portugal

ISBN: (纸本)9798350370607

This paper proposes using star-delta and star-series connection modes in three-phase dual-winding squirrelcage induction motors to have available 100% and 55% of the original rated power for a given supply line-to-line voltage. The connection mode can be changed on a nine-terminal block. The experimental results obtained with a commercial 4-kW, 400-V, 50-Hz, 4-pole induction motor whose stator windings were redesigned to provide 4-kW and 2.2-kW output powers are presented and validate the proposed solution. Two-rated-power motors with the proposed output power ratio could be used as spare motors to replace faulty/damaged motors with the same frame size. The proposed solution allows fine-tuning the motor power to the actual maximum power required by the driven application/load and can be considered cost-effective as it may lead to significant gains in the motor efficiency and power factor when the low-power mode is adopted. In many cases, it may avoid the underloaded operation of induction motors, while keeping their original rated power available. © 2024 IEEE.

关键词： Microelectronics

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Performance Analysis of Intelligent Reflecting Surfaces for 5G/6G-Enabled Future Smart Industries with a Focus on Millimeter-Wave Band Communications 59

Performance Analysis of Intelligent Reflecting Surfaces for ...

引用

2024 Annual IEEE International Conference on Communications Workshops, ICC Workshops 2024

作者： Khan, Muhammad Farhan Raza, Ali Iqbal, Adeel Rashid, Adnan Jamshed, Muhammad Ali Pesch, Dirk School of Computer Science and Information Technology University College Cork Cork Ireland Comsats University Islamabad Dep. of Electrical and Computer Engineering Pakistan Yeungnam University Dep. of Info. & Com. Engineering Gyeongsan-si38541 Korea Republic of Politecnico di Bari Dep. of Electrical & Information Engineering Bari Italy James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350304053

Industry 4.0's emergence necessitates reliable communication systems. Millimeter-wave (mmWave) communication is a promising solution, but challenges like limited coverage and atmospheric absorption persist. The Intelligent Reflecting Surfaces (IRS) offers transformative potential by dynamically manipulating wave propagation to improve millimeter-wave (mmWave) communication. This research investigates the impact of the distance of Industrial User Equipment (iUE) from the IRS and Base Station (BS), and the height of the IRS from the ground, along with the impact of sum throughput by increasing the number of IRS elements. The IRS-assisted system exhibits enhanced performance within the mmWave frequency band compared to the sub-6 GHz band. The observed achievable rate gains, exceeding 200% on average, underscore the significant performance enhancement enabled by IRS dep.oyments. Moreover, increasing the number of IRS elements significantly enhances the overall throughput. The decrease in required transmit power with an increasing number of Intelligent Reflecting Surfaces IRS elements can be attributed to the enhanced ability of IRS to efficiently reflect and manipulate electromagnetic waves. As more IRS elements are dep.oyed, they collectively contribute to optimizing signal propagation, allowing for better control and redirection of the transmitted signals. This phenomenon enables the system to achieve the desired communication objectives with less overall transmit power. Additionally, the attainable capacity of the examined IRS-assisted wireless communication system exhibits growth alongside an increase in the elevation of the IRS installation. Results showcase the potential of IRSs to enhance coverage and communication reliability, providing valuable insights for designing efficient wireless systems in smart factories and Industry 4.0 applications. © 2024 IEEE.

关键词： 5G mobile communication systems

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Comparative Analysis of ML Algorithms for Fraud Detection 4

Comparative Analysis of ML Algorithms for Fraud Detection

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Abitova, Gulnara Abalkanov, Miras Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University BinghamtonNY United States Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss. © 2024 IEEE.

关键词： Logistic regression

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