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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer Systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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An Innovative IoT-Driven Smart Streetlight Management System Design 2

An Innovative IoT-Driven Smart Streetlight Management System...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shermina Justin, Shekaina Thenmozhi, P. George, Jissy Ann Kumar, T. Ananth Ananth, Christo Muscat College Department of Computing Oman Princess Nourah Bint Abdulrahman University Electrical Engineering Department Saudi Arabia Dept. of Electronics and Communication Engineering Chennai600119 India British University of Bahrain College of Ict Bahrain Ifet College of Engineering Computer Science and Engineering Viluppuram India Samarkand State University Uzbekistan Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350364699

This research introduces a cutting-edge approach to optimizing the management of streetlight systems through automation by using IoT. Traditional methods often suffer from inefficiencies, leading to unnecessary energy consumption and maintenance costs. In this study, we propose a novel automated streetlight management system that leverages advanced technologies such as IoT sensors, machine learning algorithms, and smart scheduling mechanisms. By dynamically adjusting lighting levels based on real-time factors such as traffic density, weather conditions, and time of day, our system aims to achieve significant energy savings while ensuring optimal illumination for pedestrians and vehicles. We present the design architecture of our system, along with experimental results demonstrating its effectiveness in various urban environments. Our findings highlight the potential of automated streetlight management systems to enhance sustainability, improve safety, and reduce operational expenses for municipalities and urban planners. © 2024 IEEE.

关键词： Energy utilization

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Medical Image Synthesis Workbench Supporting Multiple Modalities with Feature-Level Transforms 1

Medical Image Synthesis Workbench Supporting Multiple Modali...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Sharma, Nitin Carlos Cotrina-Aliaga, Juan Philip Bhimavarapu, John Kumar Illa, Pavan Jyotiyana, Priya IMS Engineering College Uttar Pradesh India Universidad Privada San Juan Bautista Faculty of Human Medicine Chincha Peru Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Jaipur Engineering College and Research Centre Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350345957

Multi-modal In recent years, medical image synthesis has arisen as a holistic analytic strategy that typically employs many transformation methods. When it comes to medicine, multi-modal image synthesis refers to the technique of combining images from several imaging modalities to enhance image quality while maintaining detail. Image processing, computer apparition, pattern acknowledgment, machine knowledge, and artificial intellect are just few of the many cutting-edge fields that fall under the umbrella of medical image synthesis. The synthesis of many medicinal imaging modalities has been used extensively in the clinic to help doctors better understand the lesion. This paper presents the firefly algorithm, a multi-learning model workbench that minimises squared errors and maximises synthesis quality via weighted entropy. MATLAB simulations are used to compare the proposed workbench against state-of-the-art methods in terms of peak signal-to-noise ratio, Mean square error, correlation, and precision. The proposed workbench performs well across the board and requires less computing power. © 2023 IEEE.

关键词： Image enhancement

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Infinite coexisting attractors in an autonomous hyperchaotic megastable oscillator and linear quadratic regulator-based control and synchronization

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The European Physical Journal B 2023年第1期96卷 1-13页

作者： Alexander, Prasina Emiroğlu, Selçuk Kanagaraj, Sathiyadevi Akgul, Akif Rajagopal, Karthikeyan Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Electrical and Electronics Engineering Faculty of Engineering Sakarya University Sakarya Turkey Centre for Nonlinear Systems Chennai Institute of Technology Chennai India Department of Computer Engineering Faculty of Engineering Hitit University Çorum Turkey Department of Electronics and Communications Engineering and University Centre for Research and Development Chandigarh University Mohali India

The coexistence of different attractors with fixed parameters affords versatility in the performance of the dynamical system. As a result, recent research has focused on defining nonlinear oscillators with infinite coexisting attractors, the vast majority of which are non-autonomous systems. In this study, we present an infinite number of equilibriums achieved with the simplest autonomous megastable oscillator. To begin, we explore the symmetry property of distinct coexisting attractors, such as periodic, quasi-periodic, and chaotic attractors. The dynamical properties of the proposed system are further investigated using phase portraits, Lyapunov exponents spectrum, and bifurcation diagram using the local maxima of the state variables. The detailed investigation reveals that the proposed system has a wide range of dynamical properties ranging from periodic oscillations to hyperchaos. Linear quadratic regulator (LQR) control-based controllers are designed to control the chaos in the proposed system and also achieve synchronization between proposed systems that have different initial conditions. The effectiveness of the controller designed based on LQR is presented by simulation results. It is shown that the proposed system converges asymptotically to the desired equilibrium point, and the synchronization between the slave and the master systems is achieved by converging the error to zero after controllers are activated.

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Real-time Animal Identification and Alert System using IoT and Deep Learning

Real-time Animal Identification and Alert System using IoT a...

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International Conference on Advances in Information technology (ICAIT)

作者： Manujakshi B C Shashidhar T M Ravikiran H N Asha R Rajeev Bilagi Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Bengaluru Karnataka India Department of Computer Science and Engineering Acharya Institute of Technology Bengaluru Karnataka India Department of Electronics and Communication Engineering Adichunchanagiri University Nagamangala Mandya Karnataka India Department of Electronics and Communication Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The amount of traffic accidents that result in fatalities and injuries is one of the most urgent problems facing the world today. Many tragic incidents are caused by collisions between humans and animals. Worldwide, there are startlingly many automobile accidents—one every few seconds. Our primary objective is to decrease the probability of traffic accidents caused by animals by identifying them and informing cars of their presence. We suggest using IoT technology to create a detection and warning system that can significantly lower animal-related incidents. The purpose of this warning system is to alert cars on the roadway as soon as a detection event takes place by means of a signal. Our solution is based on Internet of Things technology, with the Raspberry Pi3 Model B acting as the main component for car warning and animal identification. Real-time photographs and videos of animal movements are captured by the Pi camera, which is equipped with a Raspberry Pi, through the use of sensors to identify impediments. After that, it alerts any vehicles and pedestrians using the woodland road. Additionally, it marks particular distances at night with lights. In the end, the document's primary goal is to safeguard numerous people from possible harm and to preserve animals from mishaps. In order to do this, the YOLO (You Only Look Once) algorithm was created. It processes approximately 45 frames per second and examines the complete picture in order to determine if objects are present or not.

关键词： Deep learning YOLO Machine learning algorithms Animals Roads Wildlife Forestry

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A Beam-Scanning Reflectarray with the Weighted Average Phase Compensation for the150-160 GHz Communications 6

A Beam-Scanning Reflectarray with the Weighted Average Phase...

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6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Zhou, Zhengdong Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Chung, Kwok L. College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou516007 China

ISBN: (纸本)9798350399059

In this paper, a 15×15 beam scanning reflectarray operating in 150-160 GHz is proposed. The element consists of 3-layer patches conducted with metal vias. The proposed weighted average phase compensation method is used to obtain stable beam and low gain variations during the beam scanning process. Furthermore, the comparisons of radiation between normal phase compensation and weighted average phase are given to verify the effectiveness of the method. Results show that a large scanning angle of ±50° and a maximum gain of 18.7 dBi with gain variations less than 1.8 dB can be achieved, and hence the proposed antenna array is suitable for future 6G communication system. © 2023 IEEE.

关键词： Antenna arrays

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A Comparison of Feature Selection and Feature Extraction in Network Intrusion Detection Systems

A Comparison of Feature Selection and Feature Extraction in ...

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2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022

作者： Vuong, Tuan-Cuong Tran, Hung Trang, Mai Xuan Ngo, Vu-Duc Luong, Thien Van Phenikaa University Faculty of Computer Science Hanoi12116 Viet Nam School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi11657 Viet Nam

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Internet of Things (loT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, loT de-vices are vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for loT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed to machine learning models. This aims to make the detection complexity low enough for real-time operations, which is partic-ularly vital in intrusion detection systems. This paper provides a comprehensive comparison between these two methods in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of UNSW-NB15 dataset. Note that such a comparison between feature selection and feature extraction methods has been overlooked in the literature. Furthermore, based on this comparison, we provide a useful guideline on selecting a suitable intrusion detection type for each specific scenario. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

关键词： Feature extraction

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DataDecon: Data Cleansing Tools for Large Language Model with Efficient Decontamination Techniques

DataDecon: Data Cleansing Tools for Large Language Model wit...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Sumeth Yuenyong Norapat Buppodom Koravich Sangkaew Konthee Boonmeeprakob Prachya Boonkwan Jillaphat Jaroenkantasima Pitikorn Khlaisamniang Anuruth Lertpiya Apivadee Piyatumrong Peerawat Rojratchadakorn Thaweewat Rugsujarit Teerapol Saengsukhiran Kriangkrai Saetan Isada Sukprapa Thanachot Thavornmongkol Nucharee Thongthungwong Patteera Triamamornwooth Chanon Utupon Kobkrit Viriyayudhakorn Phoochit Witchutanon Sadit Wongprayon Thepchai Supnithi Department of Computer Engineering Faculty of Engineering Mahidol University National Electronics and Computer Technology Center Pathumthani Thailand Artificial Intelligence Asssociation of Thailand Big Data Institute (Public Organization) Bangkok Department of Electrical Engineering Faculty of Engineering Kasetsart University Looloo Technology Faculty of Engineering Kasetsart University Kasikorn Labs Kasikorn Business Technology Group Faculty of Science King Mongkut’s Institute of Technology Ladkrabang School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Next AI Solutions Department of Biomedical Engineering Faculty of Engineering Mahidol University Department of Computer Science University of Liverpool Department of Mechanical Engineering King Mongkut’s University of Technology North Bangkok Artificial Intelligence Entrepreneur Association of Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

Large language models (LLMs) play an important role in modern NLP technology as they are versatile for a wide array of NLP tasks. However, constructing an LLM is challenging due to concealed construction pipelines, the lack of cleansed datasets, and hyperparameter settings, making it almost irreproducible. This paper presents an efficient pipeline for constructing an LLM tailored to a low-to-medium-sourced language with a high level of data contamination and tools to cleanse the dataset. Following our pipeline, we constructed OpenThaiGPT, an LLM for Thai, with only open-sourced datasets such as CC100, OSCAR, and mC4, and achieved the state-of-the-art accuracies on our downstream tasks. Here, we disclosed the data statistics and all hyperparameter settings for reproducibility.

关键词： Accuracy Large language models Decontamination Pipelines Data visualization Data models Natural language processing Reproducibility of results Arrays Contamination

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Experimental Analysis of an Efficient Dental Carries Prediction System Based on Improved Convolutional Neural Network (iCNN) Principle

Experimental Analysis of an Efficient Dental Carries Predict...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ranjith, S. Vanitha Lakshmi, M. Sujatha, V. Govindasamy, Suresh Giri, Pallavi Al-Qawasmi, Khaled Jeppiaar Engineering College Department of Ece Chennai India Soe Presidency University Department of ECE. Bengaluru India S.A. Engineering College Department of Master of Computer Applications Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Dental caries is a kind of infectious illness that weakens the structure of teeth, leading to cavities. Because of the discomfort and expense of treatment, studies on dental caries have focused on early diagnosis, as it is one of the most common oral health problems. Due to the high costs and lengthy research times associated with oral healthcare, artificial intelligence has recently been employed to create models that can forecast the likelihood of dental caries. The agony and expensive price of therapy have prompted this investigation toward early detection. Models that may forecast the possibility of dental caries have recently been developed using artificial intelligence. This is in response to the high expenditures and significant time commitments connected with medical research in oral healthcare. In this study, a new method for efficiently identifying dental caries is presented using an Improved Convolutional Neural Network (iCNN). To test how well the suggested scheme works, it is cross-validated with the standard method called Support Vector Machine (SVM). This paper's suggested method, iCNN, demonstrates that dental practitioners may greatly benefit from deep learning to aid in the early diagnosis and treatment of dental caries. © 2024 IEEE.

关键词： Convolutional neural networks

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Turbulence Modeling Through Deep Learning: An In-Depth Study of Wasserstein GANs 4

Turbulence Modeling Through Deep Learning: An In-Depth Study...

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4th International Conference on Smart electronics and Communication, ICOSEC 2023

作者： Alghamdi, Wajdi Mayakannan, S. Sivasankar, G.A. Singh, Jagendra Ravi Naik, B. Venkata Krishna Reddy, Ch. King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah21589 Saudi Arabia Vidyaa Vikas College of Engineering and Technology Tiruchengode Mechanical Engineering Tamilnadu Namakkal India Kit - Kalaignar Karunanidhi Institute of Technology Department of Aeronautical Engineering Coimbatore641402 India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Great Noida India St. Martin's Engineering College Department of Mechanical Engineering Telangana Secunderabad India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350300888

This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. The projected turbulent fields are evaluated quantitatively and statistically. © 2023 IEEE.

关键词： Turbulence

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