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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Al-Dulaimi, Abdullah Ahmed Hameed, Alaa Ali Guneser, Muhammet Tahir Jamil, Akhtar Department of Electrical and Electronics Engineering Karabuk University Karabuk Turkey Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9783031509193

Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning Detection Electroluminescence image detection Solar panel

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A 25.1-TOPS/W Sparsity-Aware Hybrid CNN-GCN Deep Learning SoC for Mobile Augmented Reality

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IEEE Journal of Solid-State Circuits 2024年第11期59卷 3840-3852页

作者： Huang, Wen-Cong Lin, I-Ting Lin, Ying-Sheng Chen, Wen-Ching Lin, Liang-Yi Chang, Nian-Shyang Lin, Chun-Pin Chen, Chi-Shi Yang, Chia-Hsiang National Taiwan University Graduate Institute of Electronics Engineering Taipei106216 Taiwan MediaTek Hsinchu300096 Taiwan University of California at Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA94720 United States Taiwan Semiconductor Research Institute Hsinchu300091 Taiwan Graduate Institute of Electronics Engineering National Taiwan University Department of Electrical Engineering Taipei106216 Taiwan

Augmented reality (AR) has been applied to various mobile applications. Modern AR algorithms include neural networks, such as convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The high computational complexity of these networks poses challenges for real-time operation on energy-constrained devices. This article presents the first energy-efficient hybrid CNN-GCN system-on-chip (SoC) for mobile AR. A CNN engine exploits the channel-wise structured feature sparsity to eliminate redundant computations and data movements. By utilizing the proposed channel-sparse encoding scheme on a specialized processing element (PE) architecture, up to 8× higher throughput and × higher energy efficiency can be achieved. A reconfigurable convolution PE (CPE) array is deployed for efficient CNN inference. A GCN engine is designed to implement skeleton-based action recognition and gesture recognition. Up to 71% of total operations and 39% of memory footprint can be reduced by leveraging the data and graph properties. A RISC-V MCU is integrated for system control and network deployment. The proposed SoC is implemented in a 28-nm CMOS technology with a core area of 8.28 mm2. By exploiting various sparsity levels across network layers, the chip achieves an up to 3.277-TOPS peak performance and a 25.1-TOPS/W energy efficiency for sparse CNN inference, 2.0× higher energy-efficient than prior arts. It also achieves an up to 72 actions/s recognition throughput, 18× faster than the state of the art. © 2024 IEEE.

关键词： Engines

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RAIN FADE ANALYSIS AND LINK BUDGET PLANNING FOR 14.8–15.3 GHz TERRESTRIAL MICROWAVE LINKS OPERATING IN PENINSULA MALAYSIA

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2022年第8期81卷 43-59页

作者： Abdulrahman, A.Y. Ajiboye, A.T. Department of Electrical & Electronics Engineering University of Ilorin Nigeria Department of Computer Engineering University of Ilorin Nigeria

Most of the reported works on fade duration analyses were mainly based on the International Telecommunication Union-Radio Sector (ITU-R) method, which may not be excellently accurate for link budget analysis of terrestrial microwave links operating in tropical and equatorial climates, hence the need for this study. Others were based on experimental radio links ( © 2022 by Begell House, Inc.

关键词： Forecasting

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Solar and Wind Energy-Based Charging Station Designing for EV with Hybrid Storage Systems Having Power Flow Optimization Using F_MS_GA Algorithm

Solar and Wind Energy-Based Charging Station Designing for E...

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International Conference on Computational Intelligence and Smart Technologies in electrical engineering, CISTEE 2023

作者： Tiwari, Prakash Narayan Tiwari, Kaushal Prasad Dixit, Anshu Bohre, Aashish Kumar Gupta, Sanjeev Kumar Pal, Sitaram Department of Electrical and Electronics Engineering Rabindranath Tagore University Bhopal India Department of Computer Science and Engineering Rabindranath Tagore University Bhopal India Department of Electrical and Electronics Engineering NIT Durgapur West Bengal Durgapur India Department of Electronics Communication Engineering Rabindranath Tagore University Bhopal India

ISBN: (纸本)9789819799152

Renewable energies like solar, wind, etc. have gained a lot of importance in the recent years as they are clean sources that can be brought to use to supply power to charging stations (CS). The growing demand for electric vehicles (EVs) has led to an increasing need for efficient and sustainable charging infrastructure. To optimize the utilization of solar and wind resources, advanced energy management systems are employed in this work. The solar energy system of 25 KW has been integrated with the charging station and its power output and flow across the system has been analyzed that achieves charging of EV clusters. The variable input conditions are studied and power flow management is achieved across the storage systems, grid, and EVs. Wind energy system is further integrated of 11 KW which is operated at its fuel efficiency and then at reduced efficiency to study the impact on the power flow. The control algorithm has been designed with fuzzy interfaced multistage genetic algorithm (F_MS_GA) to achieve efficient energy distribution across the various loading points. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Charging stations

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Reducing peak to average power ratio in optical NOMA based 5G system using advanced SLM method

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Optical and Quantum electronics 2024年第10期56卷 1-13页

作者： Kumar, Arun Chakravarty, Sumit Nanthaamornphong, Aziz Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India Department of Electrical Engineering and Computer Engineering Kennesaw State University KennesawGA United States College of Computing Prince of Songkla University Phuket Thailand

One of the most critical challenges in optical fiber communication systems using non-orthogonal multiple access (NOMA) techniques is reducing the peak-to-average power ratio (PAPR). Optical NOMA often leads to high PAPR, causing non-linear distortion and limiting the system’s gain. The advanced Selected Mapping (SLM) algorithm is introduced in this work to reduce the PAPR of the O-NOMA. In conventional SLM, multiple phase sequences are applied to the input data, which requires numerous Inverse Fast Fourier Transform, increasing computational complexity. The proposed approach simplifies this by selecting a central reference phase sequence, reducing the number of candidate sequences and the number of IFFT operations required. This reduction in complexity does not significantly compromise the PAPR reduction capability, making the method more efficient. The SLM algorithm addresses this issue by applying the phase rotation factor to the transmitted signals, redistributing the power, and reducing the peak levels. This article highlights the benefits and challenges of implementing SLM in optical NOMA systems, including its compatibility with existing NOMA schemes. Matlab 2016 analyzes parameters such as bit error rate (BER), PAPR, and out-of-band radiation. A 66% PAPR reduction can be obtained using the suggested SLM with various phase variables. Furthermore, there is a −2650 reduction in spectrum leakage. The simulation outcomes of the work reveal the potential of SLM to reduce PAPR and enhance the BER and PSD performance of optical NOMA compared with the existing algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： MATLAB

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration

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Journal of engineering and Applied Science 2024年第1期71卷 94页

作者： Kumawat, Manoj Onaolapo, Adeniyi Sharma, Gulshan Barutcu, Ibrahim Cagri Adefarati, Temitope Bansal, Ramesh Department of Electrical Engineering National Institute of Technology Delhi Delhi India Department of Electrical Engineering Technology University of Johannesburg Johannesburg2006 South Africa Department of Electricity and Energy Çölemerik V.H.S. Hakkari University Hakkari30000 Turkey Department of Electrical and Electronics Engineering Federal University Oye Ekiti Oye Ekiti Nigeria Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. © The Author(s) 2024.

关键词： Smart power grids

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Islanding Protection scheme based on IEC61850 GOOSE application for 11kV network with distributed energy resources 33

Islanding Protection scheme based on IEC61850 GOOSE applicat...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Mpaka, Abuyile Krishnamurthy, Senthil Department of Electrical Electronics and Computer Engineering PO BOX 1906 Bellville7535 South Africa

ISBN: (纸本)9798331535162

This study focuses on islanding protection for a microgrid operating at 11kV. The literature provides novel algorithms developed for islanding protection schemes. However, there is a lack of validation and test the IEC61850 GOOSE message application for the islanding protection scheme. The paper's significant contribution is to address the literature gap by comparing legacy islanding protection with the IEC 61850 GOOSE message application. The research utilizes simulation techniques in the DigSilent and Hardware-in-the-Loop (HIL) simulations using RTDS to assess the performance of the Islanding protection scheme. Four test scenarios were conducted, which include undervoltage, overvoltage, underfrequency, and overfrequency protection functionalities. The simulation validation results from these four scenarios confirm that the modeled distribution system meets the required protection criteria for both legacy (hardwired) and modern IEC61850 GOOSE message applications. The simulation results show that the GOOSE message application for the Islanding protection scheme operates faster than the legacy scheme. © 2025 IEEE.

关键词： Islanding

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Modeling of Electrostatics and Currents in a Forward-Biased p-n Junction

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IEEE Transactions on Electron Devices 2024年第8期71卷 4489-4497页

作者： Lin, Kuan-Wun Taur, Yuan National Taiwan University Graduate Institute of Electronics Engineering Taipei10617 Taiwan University of California San Diego Department of Electrical and Computer Engineering La Jolla CA92093 United States

A general model is developed for forward-biased p-n junctions with arbitrary doping concentrations biased at any injection level. It goes beyond the commonly practiced depletion approximation, which is valid only under the low-level injection condition. It is shown that when the applied junction voltage exceeds the built-in potential, the profile of net charge density shrinks to two antisymmetric spikes, one going positive and one going negative, with the same magnitude given by the average of the n-type and p-type doping concentrations. It is also shown that under high-level injection condition, the drift current components become dominant, in some cases yielding an exp(qV/kT) dependence of the total current instead of the commonly perceived exp(qV/2kT) dependence (here, V is the intrinsic junction voltage). By equating the sum of drift and diffusion current densities on both sides of the junction, analytical expressions are derived for the electric fields and, therefore, the total currents. Finally, the decay lengths of excess carriers in the quasi-neutral regions are solved numerically from the current continuity equations coupled to Poisson's equation with no assumptions of charge neutrality. It is shown that the decay lengths in the high-level injection regime are doping-dependent and are longer than the well-known minority-carrier diffusion lengths in the low-level injection regime. © 1963-2012 IEEE.

关键词： Electric fields

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Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

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Multimedia Tools and Applications 2025年第16期84卷 16909-16943页

作者： El-Shafai, Walid El-Mesady, A. Kamal, F.M. Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University PO 35516 Mansoura Egypt

The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t

关键词： Cryptography

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