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Transformer Load Monitoring and EV Charging Control Function 19

Transformer Load Monitoring and EV Charging Control Function

19th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2022

作者： Kunlasing, Patchimaporn Charbkaew, Noppadol Bunyagul, Teratam Chompusri, Yotaka King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Department of Instrumentation and Electronics Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781665485845

Transformer Load Monitoring (TLM) measures the necessary parameters, electrical metering, ambient temperature, oil temperature. TLM also be used for current of medium voltage feeder monitoring (via CT of load break switch). All measuring parameters can be accessed via online database. Moreover, TLM can control EV charging current. In case of transformer overloading, TLM can send a request to EV charger for reducing the charging current via Zigbee wireless communication. Due to this mechanism, installation of new transformer for supporting of EV energy consuming can be avoided. © 2022 IEEE.

关键词： Smart power grids

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Audiovisual speech recognition based on a deep convolutional neural network

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Data Science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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A Novel Approach to MPPT Photovoltaic Power Systems Using Sliding Mode Control and Iterative & Bisectional Technique

A Novel Approach to MPPT Photovoltaic Power Systems Using Sl...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Nguyen, Nhat Tung Le, Tien Phong Nguyen, Nhat Hai Nguyen, Quang Thuan Thuyloi University Faculty of Electricals and Electronics Engineering Department of Electrical Engineering Hanoi Viet Nam Thai Nguyen University of Technology Electrical Faculty Thai Nguyen Viet Nam School of Information and Communications Technology Hanoi University of Science and Technology Computer Science Department Hanoi Viet Nam Thuyloi University Department of Electrical Engineering Hanoi Viet Nam

ISBN: (纸本)9798350381061

This paper presents an adaptive control method that is combined Sliding Mode Control (SMC) and the Iterative & Bisectional (IB) technique applied in maximum power point trackers (MPPT) of photovoltaic power systems. Mathematical models of photovoltaic power generation and a DC/DC boost converter corresponding to different operating conditions will be introduced to make clear objects and the research approach. A sliding surface is proposed with coefficients that are functions of the solar irradiance power. An equivalent control signal will be determined and the stability analysis will be also mentioned. Received simulation results carried out by MATLAB/SIMULINK software showed that the designed adaptive controller worked with high accuracy, especially in different variations of solar irradiance, maintaining stability and reducing the hysteresis band. The proposed method fixes the disadvantage of sliding mode control in the hysteresis band applied in photovoltaic power generations. The results also present the high accuracy of mathematical models and the feasibility of the proposed control method using real systems. © 2023 IEEE.

关键词： Sliding mode control

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A Cooperative Cognitive Radio Spectrum Sensing Based on Correlation Sum Method with Linear Equalization

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Communications and Network 2023年第1期15卷 1-14页

作者： Entesar Gemeay Ahmed Lebda Department of Computer Engineering Computer and Information Technology College Taif University Taif KSA Department of Electronics and Communication Engineering Faculty of Engineering Tanta University Tanta Egypt

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal Communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

关键词： Spectrum Sensing Cognitive Radio MIMO Equalization

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm 2

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Archana Reddy, R. Reddy, Chetana Nagarjuna, N. Pradeep Kumar, S. Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Telangana India Oxford College of Engineering Department of Electronics and Communication Bangalore India Cvr College of Engineering Department of Csit Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10-2.5, SNR 10-3.4 and PAPR of.10-4.2, underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems. © 2024 IEEE.

关键词： Landscape of optimization MIMO-OFDM system Non-linear weight factor Peak to Average Power Ratio Whale optimization

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EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks

EHG Signal Analysis for Prediction of Term and Preterm using...

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2022 International Conference on Frontiers of Information technology, FIT 2022

作者： Umar Khan, Muhammad Aziz, Sumair Iqtidar, Khushbakht Fernandez Rojas, Raul University of Engineering and Technology Taxila Department of Electronics Engineering Taxila Pakistan National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan University of Canberra Faculty of Science & Technology Canberra Australia

ISBN: (纸本)9798350345933

Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation. © 2022 IEEE.

关键词： Variational mode decomposition

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sivasankar, C. Saravanan, G. Pradeepa, H. Arun, V. Ganesh, E.N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy. © 2023 IEEE.

关键词： Charging Station Dynamic Load Management Electric Vehicle Internet of Things Solar Energy

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Predictive Maintenance Model Using Hybrid Procedure of Improved Quantum Cat Swarm Optimisation for Asset Management in Industry 4.0

Predictive Maintenance Model Using Hybrid Procedure of Impro...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kanchana, S. Rajan, Dhivya Mahaveerakannan, R. Sagar, K. V. Daya Subramanian, P. Rajakumar, B. Department of Computer Science Faculty of Science and Humanities SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of AI & DS JNN Institute of Engineering Chennai Periyapalayam Highway Tamil Nadu Thiruvallur601102 India

ISBN: (纸本)9789819965465

Contemporary manufacturing system design centres on the Industry 4.0 paradigm. A versatile platform secondary the efficient optimisation of manufacturing-related activities, such as predictive keep, is necessary for the successful mixing of cutting-edge technologies like the (IoT), cyber-physical schemes, computing. Predictive maintenance research that already exists often either develops a model without taking maintenance decisions into account or seeks to optimise maintenance in the light of the degradation replicas of the already-known scheme. With the use of predictive maintenance, a company's owner can make decisions like fixing or replacing a failing part of the manufacturing line before it actually fails. Because of this, Industry 4.0 necessitates efficient asset management in order to maximise the distribution of duties and the accuracy of predictive maintenance models. To address the issue of predictive maintenance in fog computing, this paper proposes an enhanced version of the quantum cat swarm optimisation (QCSO) algorithm by combining the quantum bit (Q-bit) and the cat swarm procedure (CSO) and updating the cats based on the quantum rotation angle site. Finally, the (MR) worth is chosen based on the sum of times the procedure is run. FogWorkflowsim uses simulated real-time datasets to evaluate the forecasting performance of QCSO and other existing models. The outcomes prove that the projected method is superior to the currently used predictive maintenance resource allocation methods. In comparison to the approximately 91% to 95% accuracy achieved by previous models, the proposed model has an accuracy of 96%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole for 5G Base Station Antenna Application

Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole f...

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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Santos, Ian Jerome Q. Tangjitjetsada, Maleeya Rakluea, Paitoon Wongsin, Norakamon Rakluea, Chawalit Mahatthanajatuphat, Chatree Chudpooti, Nonchanutt Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Thanyaburi Pathum Thani 12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Wongsawang Bangsue Bangkok10800 Thailand King Mongkut's University of Technology North Bnagkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Wongsawang Bangsue Bangkok10800 Thailand Siam Airport Ground Service Co. Ltd. Don Mueang Bangkok10210 Thailand

ISBN: (纸本)9798350344585

This article provides an explanation of the use of a Twin-Array Quasi Frequency Selective Surface (FSS) Reflector with Dual Band Cross-Dipole for antenna applications in 5G base stations. The base material employed in this study is FR4 sheet, which has a dielectric constant (ϵr) of 4.3. The antenna obtained has dimensions of 100x100 mm2 and functions throughout the frequency spectrum spanning from 730 to 860 MHz, 1700 to 2130 MHz. The primary benefits of this antenna design are its lightweight structural composition, compatibility with the frequency range of 730-860 MHz, and discernible performance attributes. The CST Studio Suite software was used to conduct a simulation of the antenna's performance. The antenna has an operating frequency range of 730-860 MHz and 1700-2130 MHz, enabling it to support a unidirectional wave propagation pattern that is suitable for the implementation of a 5G mobile communication base station. © 2023 IEEE.

关键词： Dipole antennas

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