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IEEE International Conference on Image Processing

作者： Mahshad MahdaviMoghadam Stéphane Coulombe Carlos Vázquez Mohammadreza Jamali Ahmad Vakili Department of Software Engineering and IT École de technologie supérieure Montreal Canada International Laboratory on Learning Systems (ILLS) Department of R&D Summit Tech Multimedia Montreal Canada

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Virtual reality applications possess significant societal potential, capable of revolutionizing user experiences and generating substantial revenue. However, their high demand for bit rates poses significant challenges. The MPEG Immersive Video (MIV) standard, an integral component of MPEG-I, is designed to efficiently compress visual content from multiple cameras by pruning redundant information. This article proposes a new method to enhance the compression efficiency of MIV by grouping and preserving small clusters of non-pruned pixels that would otherwise be discarded in the default configuration of the Test Model for Immersive Video (TMIV). Experimental results demonstrate that the proposed method attains an average Bjøntegaard-delta bitrate (Bd-Br) reduction of $3.35 \%$ across six tested sequences when compared to TMIV with the default configuration. Notably, one of them exhibits a reduction reaching $5.12 \%$.

关键词： Visualization Solid modeling Image coding Bit rate Transform coding Virtual reality Cameras

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Numerical and experimental comparison of the no-load losses in the different grain-oriented electrical steel materials

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： dawood, Kamran Tursun, Semih Senior R&D Specialist Department of Research and Development Astor Enerji Turkey Transformer Design Manager Astor Enerji Turkey

One of the critical pieces of equipment for power transmission and distribution is the transformer. The operation of the transformers is essential for the power supply's continued stability and efficacy. With an increase in rated power, the transformer's manufacturing time, cost, and size all go up. The core loss of the transformer is still a big concern for transformer designers. Minimization of the core loss in the transformer is also important for transformer manufacturers. Accurate evaluation of the core loss is also important for optimizing the magnetic design of the transformer. Evaluating the no-load losses of the new magnetic materials also needs a reliable technique as conventional analytical methods are limited and difficult to implement. In this study, the use of different materials for manufacturing the core of the transformers is used to minimize and compare the no-load losses. In this work, different types of grain-oriented silicon steel and lamination thickness are used for core materials to evaluate and minimize the core losses using numerical analysis. The results of the two different materials are also verified by a no-load experiment test performed on the prototype transformers. Considering the finite element analysis results of the different materials, core losses were calculated as 66.35 % lower in the M-H0 material than in the reference M-6 material. To increase transformer efficiency, a material with low no-load losses can be used. The results of this work will give guidelines to the transformer designers for selecting core materials to improve the efficiency of the transformer. © 2024 The Author(s)

关键词： Finite element analysis Grain-oriented silicon steel M-6 CrGO M-H0 CrGO No-load losses Numerical analysis Optimization

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Fraud detection using recurrent Neural Networks for digital Wallet Security

Fraud Detection using Recurrent Neural Networks for Digital ...

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International Conference on Computer Science and Engineering (UBMK)

作者： Can Iscan Fatma Patlar Akbulut R&D Center of Formica AI Istanbul Türkiye Department of Software Engineering Istanbul Kültür University Istanbul Türkiye

E-wallet use has exploded, bringing in a new era of convenient and secure digital transactions; nevertheless, it has also led to a surge in fraudulent transactions. Using recurrent Neural Networks (rNNs), this research provides an experimental approach for detecting fraudulent use of e-wallets. To detect fraudulent behaviors in e-wallet transactions, we have developed Long-Short Term Memory (LSTM) architecture to make use of the sequential nature of transaction data. Using the United Payment dataset for training and testing, the proposed model successfully classifies fraudulent transactions while reducing the number of false positives. The findings underline the utility of the rNN-based strategy in strengthening the safety and reliability of e-wallet systems, lead off for more secure online financial dealings. This study adds to the growing body of literature on detecting fraudulent use of electronic wallets by developing a model that makes use of deep learning to enhance fraud detection effectiveness in the ever-changing domain of digital payments.

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Application of Hyperledger Blockchain Technology to Logistics Supply Chain with IoT

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Procedia Computer Science 2025年 252卷 814-823页

作者： Ahmet Sayar Muhammet Cüneyd Kurtbaş Çağlayan Sancaktar rana dudu Kabak Şükrü Çakmak Department of Computer Engineering Kocaeli University Kabaoglu Baki Komsuoglu Avenue No:515 Umuttepe İzmit Kocaeli 41010 Turkey Atez Software Technologies R&D Center Skyland B Blok No: 4B İstanbul Turkey

Product movement tracking stands as a pivotal aspect of logistics operations. The ever-expanding application of IoT technologies holds significant potential to enhance product tracking in logistics. However, the rapid expansion of IoT usage has also introduced several challenges, particularly concerning data privacy, security, and transparency. Blockchain technology, through distributed ledger systems, offers a secure and transparent means to manage transactions within a network. In this study, the Hyperledger Fabric blockchain network, chosen for its private and permissioned nature, leverages authentication features to ensure the confidentiality, integrity, and security of data from IoT devices. By integrating the widely adopted MQTT protocol, IoT devices involved in logistics processes communicate effectively with the blockchain network. This study presents a system where IoT devices enable the secure and automated tracking of data through blockchain’s registration and distribution mechanisms, all of which are demonstrated within a simulation environment. developed specifically for the logistics sector, this system holds promising potential to address challenges across various fields.

关键词： Blockchain Hyperledger Fabric Supply Chain Management IoT Security Logistics Tracking distributed Ledger Technology MQTT Protocol for IoT

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Assessing digital Literacy Skills among Farmers to Implement ICT Enabled Soil Health Card recommendation System 14

Assessing Digital Literacy Skills among Farmers to Implement...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Leena, H.U. Panneerselvam, S. Kumar, Latesh K.J. R&D Xiphias Software Technologies Karnataka Bengaluru India Department of MBA Siddaganga Institute of Technology B.H. Road Karnataka Tumakuru572103 India Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States

The present study substantiates the acceptance of Information and Communication Technologies (ICT) among farmers for various agricultural activities and also their exposure to various ICT services. The study is undertaken with the purpose of developing a mobile based application to recommend suitable fertilizer to farmers based on selected crop, variety, soil type and season of their fields. A pilot study was conducted with a well-structured questionnaire which was framed based on literature and experts’ opinion. Subsequently, the questionnaire was reframed and administered to 160 farmers of Kestur village, Tumakuru district, Karnataka, India. The study proved that majority of the farmers are using smartphone but the usage of the Internet and its applications for agriculture purposes is minimal and could be improved by proper training and other interventions. Moreover, we found the computer knowledge and Internet usage knowledge among farmers is independent of their educational level. Consequently, there is vast scope for developing mobile based application to generate Soil Health Card recommendation system. © Grenze Scientific Society, 2023.

关键词： recommender systems

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Harnessing the Power of AI with deep Learning: Innovations and Implications

Harnessing the Power of AI with Deep Learning: Innovations a...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Upender, Talluri Sathiyamoorthy, C. Udayakumar, K. Kirubakaran, d. Purushothaman, V. raja, Kiruba Thangam CMR College of Engineering and Technology Department of CSE Medchal Telangana Hyderabad India Department of Commerce Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India VIT University Department of Software and Systems Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331512965

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (rNNs), are pushing the boundaries of what's possible. Moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues. © 2025 IEEE.

关键词： Convolutional neural networks

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Effect of radial and Axial Magnet Segmentation on PM Eddy Current Losses for Brushless Synchronous Motors

Effect of Radial and Axial Magnet Segmentation on PM Eddy Cu...

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Magnetic Conference - Short Papers (INTErMAG Short Papers), IEEE International

作者： Y. demir M. Onsal M. Aydin R&D Department MDS Motor Design Ltd. Kocaeli Turkey Department of Mechatronics Engr. Kocaeli University Kocaeli Turkey

One of the major issues in the design of high-speed permanent magnet (PM) motors is the magnet losses. Magnet eddy-current losses are one of the significant loss components and it is related to the motor frequency directly. In this paper, a detailed analysis of a high-speed brushless permanent magnet synchronous motor is performed. Magnet losses as well as other loss component such as iron and copper AC and dC losses are also investigated comprehensively for the reference motor selected.

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research on Intelligent Alarm System for Falling rocks on railway

Research on Intelligent Alarm System for Falling Rocks on Ra...

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Pattern recognition and Artificial Intelligence (PrAI), International Conference on

作者： Sha Wen Shengna Zhang Qingmao ren Department of Computer and Software Chengdu Jincheng College Chengdu China R&D Department Chengdu Senchuan Technology Co. Ltd Chengdu China

Along with the rapid development of railway industry in China, the railway network becomes more complex. In order to ensure the train run with higher speed, more safety and stability, the FlowNet2.0 is used in our alarm system to avoid or reduce the damage of natural disasters and dangerous objects on train safety. FlowNet2.0 is based on traditional optical flow method, but it is optimized optical flow algorithm based on deep learning. We use it to develop a new intelligent alarm system for falling rocks along the railway. This system uses advanced video processing technology and the object’s contour, moving speed and angle in optical flow field to process the digital video captured in monitoring place in real time. When accidents, like rocks falling from revetment, landslide displacement, debris flow, foreign object intrusion, etc. happens on the rail track, this system can detect those disasters in real-time accurately, and alarm automatically. This system can replace the traditional watchman with low-cost equipment, detect disasters and alarm in real time. The deployment of this system can not only improve the safety of train significantly, but also save the labor cost from railway Bureau.

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Superplasticity of a friction stir processed overaged WE54 magnesium alloy

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Journal of Magnesium and Alloys 2022年第11期10卷 3156-3166页

作者： Oscar A.ruano Marta Alvarez-Leal Alberto Orozco-Caballero Fernando Carreño Physical Metallurgy Department CENIM-CSICAv.Gregorio del Amo 828040 MadridSpain Department of R&D and programmes Technology Centre of Metal-Mechanical and Transport(CETEMET).Avda.1°de Mayos/n.LinaresJaén2370Spain Department of Mechanical Engineering Chemistry and Industrial DesignPolytechnic University of MadridRonda de Valencia 328012MadridSpain

The coarse-grained WE54 magnesium alloy was heat treated in order to have minimum hardness minimizing the effects of precipitates and solid solution. Friction stir processing(FSP) was applied in severe conditions to obtain fine, equiaxed and highly misoriented grains, with grain sizes even less than 1 μm. The high severity of processing demonstrated to have a strong impact in the microstructure. Consequently,the processed materials exhibited excellent superplasticity at the high strain rate 10^(-2)s^(-1), and temperatures between 300 and 400 ℃. The maximum tensile superplastic elongation of 756% was achieved at 400 ℃ thanks to the operation of grain boundary sliding mechanism(GBS). Besides the new data obtained through tensile testing, the paper deals with a transcendental question regarding the large differences in strain rate values at a given stress in the superplastic regime at maximum elongation compared to other magnesium-based alloys. With this is mind, 19 magnesium alloys from 22 different investigations were analyzed to give some light to this behavior that never was treated before. It is proposed that this behavior has to be attributed to the accommodation process, necessary for GBS to occur, which is hindered by reinforcing solutes.

关键词： WE54 magnesium alloy Friction stir processing Processing severity Superplasticity Grain boundary sliding Solutes

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Automated Breast Cancer diagnosis Using deep Learning CNN Models

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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Emerging research in Computational Science (ICErCS), International Conference on

作者： Saravanan G Jeevanantham S M. Prabin r. Parthasarathy Department of Computer Science and Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Computer Science and Design VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models—Xception, InceptionV3, VGG19, MobileNetV2, and resNet101—all trained for image classification using the ImageNet database, to categorize breast cancer MrI images. The dataset, sourced from Kaggle’s repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients.

关键词： deep learning Accuracy databases Magnetic resonance imaging Computational modeling Transfer learning Training data Generative adversarial networks Breast cancer Tumors

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