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31st International Conference on Electrical engineering, ICEE 2023

作者： Gholizadeh, Iman Raziyan, Haniye Javidan, Reza Isfahan University of Technology Electrical & Computer Engineering Dep Isfahan Iran Sheikh Bahaei University Electrical Engineering Dep Isfahan Iran Shiraz University of Technology Computer Engineering and It Dep Shiraz Iran

ISBN: (纸本)9798350312560

An adaptive neural network-based control method is highly effective for controlling complex systems with various operating situations since it can adapt and train its control strategy to compensate for changes in the system's behavior. The backpropagation algorithm is utilized to optimize the neural controller values and reach the desired system reference. Compared to other neural control methods, the proposed neuro-fuzzy controller's learning algorithm has a quick response time, making it suitable for a wide range of real-world applications that require ANNs. Fuzzy rules can be established to improve the adaptability and performance of a neural controller by optimizing its decisions and taking into account various factors such as data and desired outcomes. Furthermore, these fuzzy rules can help determine the optimal momentum and learning rate values for a given problem. The proposed method has been executed using LabViewprogram and validated in practical through a research lab actuation control structure, demonstrating that optimizing the neuro-fuzzy controller with optimal parameters of the neural network model leads to improved control and faster response times in nonlinear controller systems. © 2023 IEEE.

关键词： Fuzzy rules

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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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 deployment 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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Biological age estimation from ECG using Artificial Intelligence approaches 2

Biological age estimation from ECG using Artificial Intellig...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Sleiman, Jana Daher, Mohamad Khalil, Mohamad Diab, Ahmad Kassem, Abdallah Lebanese University Computer and Communications Engineering Tripoli Lebanon Faculty of Science Faculty of Applied Sciences & Technology Dep. of Computer Science Dep. of Computer Science & Information Technology Tripoli Lebanon Lebanese University Electrical Engineering Tripoli Lebanon Lebanese International University Biomedical Engineering Beirut Lebanon Notre Dame University Faculty Of Engineering ECCE Dept. Zouk Mosbeh Lebanon

ISBN: (纸本)9798331539542

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age. © 2024 IEEE.

关键词： Convolutional neural networks

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Bibliometric Analysis of research trends on Graph Neural Networks 20

Bibliometric Analysis of research trends on Graph Neural Net...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Alavi, Meysam Valiollahi, Arefeh Pakravan, Mansooreh Tarbiat Modares University Information Technology Dept Tehran Iran Tarbiat Modares University Electrical and Computer Engineering Dep Tehran Iran

ISBN: (纸本)9798350383942

Graph Neural Networks (GNNs) are a powerful tool for analyzing complex systems and irregular data structures like graphs, revolutionizing tasks like node classification and link prediction. Therefore, regularly assessing scientific outputs to understand the current landscape is crucial. In this regard, scientometric analysis is utilized to assess, interpret, and anticipate the scientific standing of researchers, research institutions, academic journals, and countries on a global scale. Utilizing bibliometric techniques like distribution analysis, co-word networks, and scientific cooperations has proven valuable in unveiling the production trends of researchers in a scientific field, revealing both explicit and concealed dimensions. Despite existing surveys, a significant gap exists in the literature regarding bibliometric studies on the Web of Science (WoS) database in the emerging field of GNNs. This paper uses bibliometric analysis on WoS to explore the evolving GNN research area, covering contributors, collaborations, top journals, and trends identified through keywords and high citations. The research reveals that China, the United States, and Australia lead in publications on neural graph networks. Additionally, the findings underscore a predominant focus on graph neural networks within the realm of computer science. © 2024 IEEE.

关键词： Graph neural networks

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Towards Unveiling Vulnerabilities and Securing IoT Devices: An Ontology-Based Approach 29

Towards Unveiling Vulnerabilities and Securing IoT Devices: ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Bhole, Mukund Kastner, Wolfgang Sauter, Thilo Institute of Computer Engineering Tu Wien Vienna Austria Institute of Computer Technology Tu Wien Austria Dep. of Integrated Sensor Systems Danube University Krems Austria

ISBN: (纸本)9798350361230

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem. © 2024 IEEE.

关键词： Ontology

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities 29

IT Security Solutions for IT/OT Integration: Identifying Gap...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

ISBN: (纸本)9798350361230

In the contemporary landscape of convergence between Information technology (IT) and Operational technology (OT), maintaining asset visibility within the Industrial Control Systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403-1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries. © 2024 IEEE.

关键词： Data acquisition Industry 4.0 IT Security Solutions IT/OT Integration Operation technology

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Internet of Things (IoT): survey of most important security risks 3

Internet of Things (IoT): survey of most important security ...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Alaskri, Ahmed Salem Ahmed, Nuredin Ali Shaari, Hala Collage of Computer Technology Software Engineering Dep. Tripoli Libya Faculty of Engineering Computer Engineering Department Tripoli Libya Faculty of Information Web Technologies Department Tripoli Libya

ISBN: (纸本)9798350319897

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field. © 2023 IEEE.

关键词： Internet of things

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda 3

Energy Access Assessment Report Focusing on Energy Poverty a...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Khalat, Abdurazag Cape Peninsula University of Technology Dep. of Electrical Electronics and Computer Engineering Cape Town South Africa Sabratha University Dep. of Electrical and Electronics Engineering Sabratha Libya

ISBN: (纸本)9798350319897

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today's Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented. © 2023 IEEE.

关键词： Smart power grids

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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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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

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11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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