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Image-Based Automatic Energy Meter Reading Using Deep Learning

computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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Simulation of a 100 MW Grid-Connected Solar Power Plant and MPPT Control Using the PSO-P&O Technique 11th

Simulation of a 100 MW Grid-Connected Solar Power Plant and ...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Bruk, Betelhem Dereje Shewarega, Fekadu Belay, Birhanu Hailu Yenealem, Mezigebu Getinet Negash, Dereje Shiferaw Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia University Duisburg-Essen Essen Germany Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia School of Electrical and computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia

ISBN: (纸本)9783031641503

Among the most prominent sources of alternative power in recent years is solar energy. Thus, photovoltaic (PV) systems need to be made more efficient and reliable. A PV panel’s conversion capacity is between 23% and 24% when it converts solar energy to electricity. However, the average conversion efficiency for most PV panels was around 15–20%. Identifying the maximum power point (MPP) on PV panels is one way to increase conversion efficiency. MPPT system control is presented and discussed in this paper for a 100 MW system in Metehara, Ethiopia. The aim of this paper is to present a novel hybrid approach for maximum power point tracking (MPPT). A particle swarm optimization (PSO) algorithm and a P&O MPPT technique are combined in this method. In order to achieve the MPP with improved speed and accuracy, the algorithm goes through a series of stages. MATLAB/Simulink was used to model the proposed algorithm under different operating conditions. Through comprehensive simulations across diverse input conditions, the proposed approach’s MPP tracking performance is assessed. When compared with systems lacking MPPT techniques, the hybrid approach demonstrates an average efficiency of 99.76%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Development of a Household Synthetic Load Profile for Rural Electrification 11th

Development of a Household Synthetic Load Profile for Rural ...

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11th EAI International Conference on Advancement of Science and technology, ICAST 2023

作者： Ashetehe, Ahunim Abebe Shewarega, Fekadu Bantyirga, Belachew Biru, Getachew Lakeou, Samuel Faculty of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Electrical and Computer Engineering University Duisburg-Essen Duisburg Germany School of Electrical and Computer Engineering Addis Ababa University Addis Ababa Ethiopia Electrical and Computer Engineering University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9783031641503

Understanding the electrical energy consumption load profile of the rural household is a necessary first step in the economical and reliable sizing of standalone renewable-based power supply systems for rural communities. The electric demand is highly stochastic and varies from time to time, day to day, and season to season. This paper mainly focuses on proposing a new method and constructing a household synthetic load profile based on a stochastic, bottom-up approach while taking into account the household appliances’ technical and operational characteristics without the need for measured historical data. The average energy consumption and stochastic load profile of a single household and a sample rural village in Ethiopia with ten households during weekdays, weekends, and holidays are presented and analyzed for rural electrification. The synthetic load profile is constructed with 1-min high resolution and implemented using MATLAB software. The daily energy demand of a middle-class single family is obtained as 2.029 kWh/day, 2.347 kWh/day, and 2.684 kWh/day on weekdays, weekends, and holidays, respectively. The daily energy usage is increased to 5.084 kWh/day, 5.396 kWh/day, and 6.728 kWh/day for weekdays, weekends, and holidays, respectively, when additional electric stove and blender loads are used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrification

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu 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 Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Deep Learning Approaches for Precision Agriculture: Weed and Cotton Crop Classification Using YOLO and Faster RCNN

Deep Learning Approaches for Precision Agriculture: Weed and...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Majeed, Asad Faridoon, Fatima Irfan, Muhammad Rashid, Ahmar Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331504465

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies. © 2024 IEEE.

关键词： Cotton

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E-URES 2.0: Efficient User-Centric Residual-Echo Suppression with a Lightweight Neural Network

E-URES 2.0: Efficient User-Centric Residual-Echo Suppression...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ivry, Amir Cohen, Israel Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350368741

We recently introduced the Efficient User-centric Residual-Echo Suppression (E-URES) framework, which significantly reduces the floating-point operations per second (FLOPS) required during inference by 90% compared to the URES framework. The E-URES operates based on a user-operating point (UOP) defined by two key metrics: the residual echo suppression level (RESL) and the desired-speech maintained level (DSML) that the user anticipates from the output signal of a residual echo suppression (RES) system. In the first stage, an ensemble of 101 branches is employed, where each branch has two cascaded neural networks: a preliminary RES system with a design parameter, which varies between branches and balances the RESL and DSML of its RES systems' prediction, and a subsequent UOP estimator. In the second stage, a neural network uses available acoustic signals and the UOP to predict which three branches achieve the highest acoustic echo cancellation mean opinion score (AECMOS) within a specified UOP-error tolerance. Then, costly AECMOS calculations are performed only for these selected branches. Despite this efficiency mechanism, the E-URES can apply real-time inference only with dedicated and expensive hardware, limiting its wide adoption. Here, we present E-URES 2.0, which focuses on reducing the computational costs of E-URES in its first stage. A lightweight neural network preprocesses available acoustic signals and the UOP to track a subset of the 101 design parameters that their branches produce the most accurate UOP estimations in their outcomes. Only these branches are calculated during inference and continue to the AECMOS estimation stage. With 60 hours of data, we show that with a negligible performance drop on average, the E-URES 2.0 can reduce 87% of the branches and 61% of the FLOPS of the E-URES and can achieve real-time inference with standard, affordable hardware. © 2025 IEEE.

关键词： AECMOS computational efficiency deep learning Residual-echo suppression user-centric

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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