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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss dep.oyment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Singh, Amandeep Margaria, Tiziana Demrozi, Florenc University of Limerick Dep. of Computer Science and Information Systems Ireland University of Stavanger Dep. of Electrical Engineering and Computer Science Norway

ISBN: (纸本)9798350346473

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results (\geq 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory. © 2023 IEEE.

关键词： Edge computing

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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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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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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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Study on the Robotic Arm Trajectory Compensation System with Deviation Sensing 3

Study on the Robotic Arm Trajectory Compensation System with...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science Zhaoqing University Zhaoqing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

Robotic arms play an important role in the automation industry. Robotic arms plan processing paths based on the shape of the workpiece. However, the processing path often deviates due to inaccurate placement of the workpiece. To solve the issue of deviations, this paper develops a trajectory compensation system for a robotic arm with offset perception. This system uses the concept of workpiece boundary projection and designs an offset calculation algorithm by comparing the dimensions of the workpiece drawings, correcting errors caused by missing corners of the workpiece. In terms of operation, the system uses photoelectric sensors to quickly scan the workpiece boundary and immediately calculate the displacement and rotational offset of the workpiece. Subsequently, when deviations occur due to different placement positions of the workpiece, the robotic arm's motion path and action must be corrected and compensated to maintain precision. Tests on selected workpieces show that the corrected rotational angle error is less than 0.4 degrees, and the translational error is less than 0.7 millimeters. Since these correction error values are lower than the 1.0 mm error threshold for the robotic arm's trajectory tracking control. The offset-sensing robotic arm trajectory compensation system developed in this study can satisfy the requirements of smart automation production lines and arrive at the manufacturer's application standards. © 2024 The Authors.

关键词： Robotic arms

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Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

Detecting 5G Signal Jammers with Autoencoders Based on Loose...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varotto, Matteo Valentin, Stefan Tomasin, Stefano University of Padova Dep. of Information Engineering Padova Italy Darmstadt University of Applied Sciences Dep. of Computer Science Darmstadt Germany University of Padova Dep. of Information Engineering Dep. of Mathematics Padova Italy

ISBN: (纸本)9798350370218

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed. © 2023 IEEE.

关键词： 5G Convolutional Autoencoder IQ Signals Jamming Detection New Radio Wireless Intrusion Prevention

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Investigation of a 7-Axis Suspended Robotic Arm for Automated Spraying System 3

Investigation of a 7-Axis Suspended Robotic Arm for Automate...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Liu, Chao-Chung Liu, Jun-Chi Liu, Chao-Shu Dep. of Computer Science ZhaoQing University ZhaoQing526061 China Dep. of Industrial Design National United University Miaoli360301 Taiwan Dep. of Mechanical Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan

ISBN: (纸本)9781643685649

This study successfully develops a 7-axis suspended robotic arm for an automated spraying system, considering factors such as flexible operation modes, spray area dimensions, workpiece geometry, and the degrees of freedom required for effective operation. By using SolidWorks software for verification, the endpoints of the robotic arm can reach all positions within the workspace. The design and fabrication of the arm are completed according to specifications for structural strength, load capacity, rated torque output, and rotational speed. Kinematic analyses, both forward and inverse, are applied to determine the posture and position of the 7-axis robotic arm, facilitating the planning and design of the spraying trajectory. The control system architecture utilizes a multi-axis servo control board, integrated with a manifold deformation control method for trajectory tracking. Simulations and experiments are conducted to use a car shell workpiece provided by the manufacturer. The results from the simulations of MATLAB analyses show that the maximum tracking error is less than 0.4 mm. In experiments with the robotic arm, the maximum error is less than 0.7 mm. The results of the simulation and experiment are quite close, indicating that the deformation control method effectively operates the posture and movement of the 7-axis suspended robotic arm. Therefore, the 7-axis suspended robotic arm developed in this study will be effectively utilized in the production line of the automated spraying system. © 2024 The Authors.

关键词： MATLAB

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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...

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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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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions 16

A Review of Grid-Connected Inverters and Control Methods Und...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Marangalu, Milad Ghavipanjeh Rehman, Fazal Ur Kurkdjian, Naser Vosoughi Computer Queen's University Dep. Of Electrical and Computer Engineering KingstonON Canada University of L'Aquila Dep. Information Engineering Computer Science and Mathematics L'Aquila Italy San Diego State University Faculty of Electrical and Computer Engineering San DiegoCA92182-0001 United States

ISBN: (纸本)9798331533946

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration. © 2025 IEEE.

关键词： control strategy fault conditions grid-connected inverters Photovoltaic systems

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