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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wong, Hou-Wa Ieong, Chi-Fong Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350331820

The wireless power transfer (WPT) technology has gained significant attention in recent years due to its potential to provide a convenient and efficient method to charging electronic devices without the need for physical connections. In wireless charging, the inductive coils are significantly important as they are the component that power transfer takes place. In this context, Multi-Objective Particle Swarm Optimization (MOPSO) has emerged as a promising technique for designing WPT coils with improved performance. Aiming to verify the performance of the designed coil, LCC-S compensation topology was utilized as it can provide practical battery charging specifications. © 2023 IEEE.

关键词： Inductive power transmission

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Survey on Machine Learning Techniques Used for Central Serous Retinopathy Detection

Survey on Machine Learning Techniques Used for Central Serou...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Suba, M. Rajini, S. Nirmala Sugirtha Anand, M. Dr. Mgr Educational and Research Institute Department of Computer Science Chennai Tamil Nadu Maduravoyal India Dr. Mgr Educational and Research Institute Department of Computer Applications Chennai Tamil Nadu Maduravoyal India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350344387

Central Serous Retinopathy (CSR) is an important health concern affecting millions of people worldwide, leading to vision loss and blindness. This condition is identified by abnormalities in retinal layers and fluid leakage, frequently occurring in the vicinity of the macula. The disease generates pressure inside the retinal layers, resulting in the detachment of retinal walls and subsequently causing impaired vision. As the macula is home to most of the visual sensory organs, including rods and cones, the eyesight undergoes substantial effects. Thus, early detection of CSR permits preventive actions to be taken to avoid any eye damage. While numerous physical approaches for detecting CSR have been devised previously, they have demonstrated inaccuracies and a lack of reliability. This paper examines the different algorithms, imaging modalities, and the outcomes they yield that have been devised over time for the timely detection of CSR. The research reveals that the SVM classifier has achieved a 97% accuracy rate in recognizing central serous retinopathy ailment based on OCT images. © 2023 IEEE.

关键词： Choroid Neovascularization Fundus Autofluorescence Optical Coherence Tomography image Pigment Epithelium Detachment Retinal Pigment Epithelium Support Vector Machine

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Harmonic Transfer Function Modeling of LCC-S Inductive Power Transfer System

Harmonic Transfer Function Modeling of LCC-S Inductive Power...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Gong, Cheng Ding, Zhaoyi Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China University of Macau Institute of Microelectronics China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350316445

Mathematical model of the inductive power transfer (IPT) systems is essential for stability analysis and control design. Conventional modeling approaches for IPT systems result in high-order models, as each resonant voltage or current is represented by two slowly changing real-valued variables. Alternative modeling methods may decrease the model order, but they are often plagued by drawbacks such as the absence of analytical expressions, reduced generality, and incomplete modeling. Moreover, the harmonic AC components of the IPT system are not well modeled in the existing methods, which affects the analysis accuracy and control optimality. In this paper, a harmonic transfer function (HTF) modeling of the IPT system is proposed, which has good mathematical properties and improved accuracy. First, the circuit configuration and the linear time-varying (LTV) large-signal model are proposed. Then, the derivation of the proposed HTF is comprehensively discussed. Finally, the accuracy of the proposed HTF is verified by simulation and experimental results in comparison with other representative modeling methods, which shows an improved accuracy. © 2023 IEEE.

关键词： harmonics transfer function inductive power transfer system modeling

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Numerical simulation of the nanofluid flow and heat transfer in porous microchannels with different flow path arrangements using single-phase and two-phase models

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International Journal of Thermofluids 2024年 24卷

作者： Sanei, Farnaz M․ Ali, Ali B. Jasim, Dheyaa J. Salahshour, Soheil Akbari, Omid Ali Emami, Nafiseh Department of Mechanical Engineering Khomeinishahr Branch Islamic Azad University Khomeinishahr Iran Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Engineering Al-Amarah University College Maysan Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mechanical Engineering Faculty of Engineering Arak University Arak38156-88349 Iran Department of Chemical Engineering Faculty of Engineering Isfahan University Isfahan Iran

Background: The fluid flow and nanofluid heat transfer are studied in this research through porous microchannels with different flow path arrangements in single-phase and two-phase modes (Mode I and Mode II). In Mode I, the flow inlet is located in the longitudinal direction of the microchannel (single-way path), while in Mode II, the flow inlet is placed in the transverse direction of the microchannel (two-way path). Methods: The finite volume method was utilized to simulate the flow and heat transfer. The porous medium is supposed homogeneous and isotropic with a porosity coefficient of 0.9 and it is assumed that the local thermal equilibrium is established between the fluid and the solid. The Eulerian-Eulerian mixture model is applied for modeling the two-phase flow. As demonstrated, mode II always has a higher heat transfer rate than mode I. However, in contrast, the pressure drop of mode I is lower than in mode II. Besides, using the two-phase model predicts a higher heat transfer rate than the single-phase model in all cases. Significant Findings: The percent increase of pressure in mode II compared to mode I in Re= 100 and 400 is obtained as 11.5 % and 20.8 %, respectively. At Re= 100 in mode I, the heat transfer percentage increases by 52.6 % from Da=1 compared to a case without the porous foam. Whilst, at Re= 400, the rise is found to be 45.5 %. In mode II, at Re=100, the heat transfer percentage increases by 63.9 % from Da= 1 compared to a case without the porous foam. Whilst, at Re= 400, the rise is found to be 43.3 %. Finally, Mode II microchannel has more heat transfer rate and pressure drop than Mode I. © 2024 The Author(s)

关键词： Finite volume method

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Literature Review of Drowsiness Identification and Alert System for Real Time Network

Literature Review of Drowsiness Identification and Alert Sys...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Thirumurugan S. Pradeepan M. L. Sundhar K. R. Dhanapal Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India

Road mishaps are more likely to be caused by tired drivers. Driving for extended periods of time, driving while sleep deprived, and driving while under the influence of drugs, alcohol, or medication are just a few of the factors that contribute to driver fatigue. In developing nations, where the death rate from traffic injuries is above 85% and the disability-adjusted life expectancy is above 90%, traffic injuries and fatalities are a major public health concern. One of the safeguards to avoid accidents is drowsiness detection. Based on the parameters each method uses, drowsiness detection techniques created for defence purposes are divided into four groups. These groups are: Subjective-based, Vehicle-based, Behavioral or Visual-based, Mouth Tracking, Physiological or Non visual-based. The remote healthcare monitoring and drowsiness alert system is a network of interconnected vehicles that communicate with each other and with the surrounding environment using advanced communication technologies. It leverages this network to collect and transmit vital health data from drivers to healthcare professionals who can then use the information's to monitor their health status and provide timely medical interventions when necessary.

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research

A Review of Multi-omics and Machine Learning: Advancements a...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hazem Ayman Madonna Bassem Malak Madkour Mariam Fawzi Nada Amr Youmna Yousry Ziad Elgayar Ashraf Abdelraouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem.

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An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms

An Analytical Approach to Fraudulent Credit Card Transaction...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Yuhes Raajha. M. R Kavin. A Rajkumar. D R. Reshma Dr. R. Santhosh N. Mekala Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India

Technology and the revolution in communication have increased the popularity of digital money usage. Most of the monetary transactions currently take place digitally. It is more convenient and increases the ease for the user. But one major problem in digital money and credit card usage is security. With the increase in credit card usage, security issues increase correspondingly. Many studies and research work are going on to avoid and prevent such practices from taking place. Moreover, various studies on real-international credit scorecard statistics are attributable to confidentiality issues. This paper focuses on current credit card fraud practices and fraud detection methods implemented in real time. Different ML algorithms like fuzzy-based SVM (FSVM), random forest (RF), logistic regression (LR), and support vector machine (SVM) for fraudulent transaction detection on the dataset collected from credit card users have been used to classify legitimate and fraudulent transactions. The comparative analysis of the credit card fraud detection scheme using these classification models was performed with precision, accuracy, sensitivity, and specificity. The comparative analysis outcomes showed that the highest performance was given by the FS VM over other algorithms with an accuracy of 98.61%.

关键词： Support vector machines Renewable energy sources Machine learning algorithms Sensitivity and specificity Credit cards Real-time systems Fraud

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A Survey on Deep Learning and Machine Learning for COVID-19 Detection 5

A Survey on Deep Learning and Machine Learning for COVID-19 ...

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5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： M. Dessouky, Mohamed Sabbeh, Sahar F. Alshehri, Boushra Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Faculty of Computer Sciences and Artificial Intelligence Benha University Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9781450387347

Coronaviruses are a type of virus that can cause a variety of disorders and exist in different types. COVID-19 is derived from a special type of a respiratory illness caused by the SARS-CoV-2 virus, discovered in 2019. Approximately two years ago, COVID-19 was discovered in the Chinese city of Wuhan, and it has since become a worldwide source of concern. A COVID-19 confirmed patient is experiencing symptoms such as fever, fatigue, and a dry cough. Based on the results of laboratory tests and/or chest X-rays, the COVID-19 diagnosis is established. When it comes to research using chest CT scans /X-ray for the diagnosis of COVID-19, which is based on medical imaging, Artificial Intelligence (AI) approaches are increasingly being applied in a variety of ways. Machine learning and deep learning are fields of artificial intelligence that can be used to analyze the data that was acquired in order to better understand the origins of COVID-19. The outcomes of applying such an approach will aid in a better understanding of the nature of the threat and how it might be mitigated. For this reason, this work gives an overview of deep learning and machine learning approaches for the detection of COVID-19. Several COVID-19 detection methods are discussed in detail, as well as the issues, current challenges associated with artificial intelligence and medical researchers' approaches to providing a comprehensive assessment of detecting COVID-19. © 2021 ACM.

关键词： COVID-19

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Cryptographic Ledger for Cyber Security in Smart Grid

Cryptographic Ledger for Cyber Security in Smart Grid

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International Conference on Edge Computing and Applications (ICECAA)

作者： Elanchezhiyan E K. Kalaiselvi D. Banumathy Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu

In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters.

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Hybrid Unsupervised Deformable Image Registration for 4D CT Lung Image Intra-modalities

Hybrid Unsupervised Deformable Image Registration for 4D CT ...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Iqbal, Muhammad Zafar Razzak, Imran Qayyum, Abdul Khan, Tariq Sowmya, Arcot University of New South Wales School of Computer Science and Engineering Sydney Australia Quaid-i-Azam University Department of Computer Sciences Islamabad Pakistan National Heart & Lung Institute Faculty of Medicine Imperial College London United Kingdom

ISBN: (纸本)9798350382204

Deformable image registration is inherently challenging, but when applied to lung images, it becomes even more challenging due to the significant deformations because of extreme inhalation and extreme exhalation phases of breathing. The greatest challenge arises when dealing with the complex, non-linear deformations that occur between the extreme inhale and exhale phases. Recent advancements in deep learning techniques show promise in handling these complex deformation patterns observed in 4D CT lung image modalities. In this study, we proposed an unsupervised hybrid image registration (HIR) framework designed to handle the non-linear deformations in 4DCT lung images without relying on ground truth data. This framework effectively learns the multi-resolution and multi-scale features from 4D CT image volumes to compute more precise displacement vector fields (DVFs). Our evaluation of the proposed HIR framework, conducted on the DIRLAB 4D CT lung image dataset, yielded an average Target Registration Error (TRE) value of $2.05 \pm 1.43$. We compared this performance with state-of-the-art unsupervised image registration methods, demonstrating the effectiveness and accuracy of our approach. © 2023 IEEE.

关键词： Medical imaging

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