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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Preliminary design of Rapid Response AED Delivery Unmanned Aerial System

Preliminary Design of Rapid Response AED Delivery Unmanned A...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Dziewoński, B. Kaliszuk, K. Kierzkowski, A. Kisiel, T. Kucharski, M. Milewski, M. Napora, Jacek Department of Technical Systems Operation and Maintenance Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350386097

The aim of the article is to present the adopted concept for the construction of an unmanned aircraft delivering a defibrillator to save people with sudden cardiac arrest. The concept is based on an unmanned aircraft in a VTOL flying wing system, achieving a cruising speed of at least 160 km/h. Previous scientific work and commercial solutions in this application are mainly based on classic multi-rotor or VTOL designs but moving at lower speeds. The aim of the work is to build a new structure adapted to the mission of providing a defibrillator for people suffering from cardiac arrest. © 2024 IEEE.

关键词： Defibrillators

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Study on Simulator Sickness and Its Symptoms During Personnel Training in Slow-moving Virtual Reality Tools

Study on Simulator Sickness and Its Symptoms During Personne...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Restel, F.J. Jodejko-Pietruczuk, A. Tubis, A.A. Department of Technical Systems Operation and Maintenance Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350386097

In the Polish railway sector is a visible increasing dynamics of employee turnover. It is a challenge in the current management of railroad personnel. Due to that, new tools are introduced in the teaching process of the employees, also in the railway sector. Such tools are mainly virtual ones, and specifically the immersive virtual reality is interesting. A disadvantage of these tools is the possibility of simulator sickness (SS) occurrence. The paper shows a briefly introduction into this problem combined with a literature review and ended with questions relevant for the research. Then, the methodology of performed research is presented describing the developed virtual reality tool, the actions to be done in the tool, and questions asked to the participants about symptoms of the SS. Noteworthy is that the analysed tool is a slow moving one. The next paragraphs show results of the experiment as well as the conclusions. One of the interesting results was to find the part of people who can or cannot be trained by the VR tool. Interesting is, that for the used slow-motion tool the part of participants unable to finish the training is only about 4%. © 2024 IEEE.

关键词： Virtual environments

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Analysis of the Possibility of Using Eye-tracking in Evaluating the Work of an Airport Security Control Operator

Analysis of the Possibility of Using Eye-tracking in Evaluat...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Kierzkowski, A. Kisiel, T. Mardeusz, E. Ryczyński, J. Department of Technical Systems Operation and Maintenance Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350386097

The security screening system is inextricably linked to the human factor. In the current systems at airports, it is not verified that an operator marks an alarm haphazardly when he evaluates an X-ray scan image of items carried in air transportation. This condition can distort the actual value of screening operators' evaluation indicators. The article presents the preliminary results of ongoing research on the implementation of eye-tracking technology for evaluating the performance of airport screening operators. The research focuses on using eye-tracking technology to track eye focus points. The results of the research indicate that there is an error when pointing the eye to a reference location. The results presented in the article are a prelude to further work on estimating the level of error that should have been considered in the security screening system. © 2024 IEEE.

关键词： Airport security

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Fabrication of PMMA nanocomposite biomaterials reinforced by cellulose nanocrystals extracted from rice husk for dental applications

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Friction 2024年第12期12卷 2808-2825页

作者： Ahmed FOULY Walid M.DAOUSH Hesham I.ELQADY Hany S.ABDO Mechanical Engineering Department College of EngineeringKing Saud UniversityRiyadh 11421Saudi Arabia The King Salman Center for Disability Research Riyadh 11421Saudi Arabia Department of Production Engineering and Mechanical Design Faculty of EngineeringMinia UniversityMinia 61519Egypt Department of Chemistry College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh 11623Saudi Arabia Faculty of Technology and Education Helwan UniversityCairo 11281Egypt Faculty of Engineering Aswan UniversityAswan 81528Egypt Center of Excellence for Research in Engineering Materials(CEREM) King Saud UniversityRiyadh 11421Saudi Arabia Mechanical Design and Materials Department Faculty of Energy EngineeringAswan UniversityAswan 81521Egypt

The primary objective of global studies is to develop the properties and durability of polymers for various *** it comes to dental disability,denture base materials must have sufficient mechanical and tribological performance in order to withstand the forces experienced in the *** work aims to investigate the effects of the addition of low content of cellulose nanocrystals(CNC)on the mechanical and tribological performance of the polymethyl methacrylate(PMMA)*** weight percent of CNC(0,0.2,0.4,0.6,and 0.8 wt%)were added to the PMMA matrix followed by ball milling to evenly distribute the nanoparticles reinforced phase in the matrix *** findings emphasize the significant impact of CNC integration on the performance of PMMA *** increasing the content of the CNC nanoparticles,the mechanical properties of PMMA were *** addition,the tribological outcomes demonstrated a significant reduction in the friction coefficient besides an enhancement in the wear resistance as the weight percentage of nanoparticles *** surface of the worn samples was investigated by utilizing SEM to identify the wear mechanisms corresponding to the different *** addition,a finite elment model(FEM)was developed to ascertain the thickness of the worn layer and the generated stressed on the surfaces of the nanocomposite throughout the friction process.

关键词： polymethyl methacrylate nanocomposite cellulose nanocrystals denture materials rice husk polymethyl methacrylate(PMMA) wear resistance

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年 1-33页

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Development of a Nail-based Application for the Detection of Disease using Image Processing 2

Development of a Nail-based Application for the Detection of...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gote, Pradnyawant M. Kumar, Praveen Dhale, Trishul Mishra, Gaurav Vedprakash Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of a nail-based detection application employing image processing techniques to detect diseases such as systemic disease, fungal disease, Leukonychia (White spots on nails) and Paronychia (Nail bed infection) through analysis of the patterns of the fingernails and other related symptoms. The developed system integrates several techniques, comprising a customized imaging application, a user-friendly interface, and software for real-time image processing and disease classification. A variety of algorithms proposed for image processing, such as Median filtering, Sobel Mask and HOG features, are designed to extract important features from nail images and then use machine learning models that match these features against a database of established markers of various diseases. Comprehensive testing utilizing an annotated dataset of nail images demonstrated the system's accuracy in classifying certain diseases, further showcasing its potential for possible usage as a proficient tool for early disease diagnosis. Thus, this application is easy to use, user-friendly interface, and provides advancements in diagnosing diseases both in clinical settings and in the comfort of one's home. The incorporation of this technology into routine clinical practice might facilitate the possibility of early diagnosis and early intervention, which could improve patient outcomes and reduce healthcare system burdens. © 2024 IEEE.

关键词： Median filters

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Renewable Integration for Green Urban Mobility: A Case Study of Public Transport Depot Electrification 14

Renewable Integration for Green Urban Mobility: A Case Study...

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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Colombo, Cristian Giovanni Saldarini, Alessandro Longo, Michela Ota, Ryosuke Politecnico di Milano Department of Energy Milan Italy Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Faculty of Systems Design Japan

ISBN: (纸本)9781643685199

This paper analyses an electrification strategy for a depot for a public transport company, through an optimized model. The case study analyses both the transition of a depot able to host 10 buses towards e-buses and proposes the integration of a micro-grid, powered by PV panels to power the e-buses in a sustainable way. The study addresses key challenges such as spatial constraints, passenger demand, and financial considerations, through a Genetic Algorithm (GA). The analysis encompasses two scenarios: a mixed fleet of electric and diesel buses and an exclusive focus on e-buses. The study highlights the importance of considering both electric and diesel buses in fleet optimization, and combined with renewables emphasize the advantages of operational flexibility and sustainability. Thanks to the implementation of a micro-grid capable of providing 1123.87 kWh/day to power the e-buses as much as possible. © 2024 The Authors.

关键词： Electric buses

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Product platform design for minimum cost in mass customization manufacturing environment 21

Product platform design for minimum cost in mass customizati...

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21st International Conference on Applied Mechanics and Mechanical engineering, AMME 2024

作者： Ismail, T. Saeed, Ahmed Elkharbotly, A. Zamzam, N. Basic and Applied Sciences Department Faculty of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Egypt Design and Production Engineering Department Ain Shams University Egypt

Nowadays manufacturers are facing several challenges to compete in the marketplace. One of the main challenges can be described by the need to offer a range of products and categories in addition to shortening the time during which the product is brought to the customer while maintaining low costs to achieve maximum profitability. Several approaches emerged during the past couple of decades that enabled designers and manufacturers to offer the product variety within a product family on one hand and allowing flexibility and responsiveness at the manufacturer's side on the other hand. This study addressed the problem of product platform design with the objective of minimizing the manufacturing costs incurred in the platform manufacturing phase and the mass customization phase. A genetic algorithm model that minimizes the total manufacturing cost is developed where optimum product platform design is determined. The developed model is used to study the effect of demand proportion of different product variants within a product family on the platform design and the total manufacturing cost. © Published under licence by IOP Publishing Ltd.

关键词： Smart manufacturing

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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