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Optimal Location to Use Solar Energy in an Urban Situation

Computers, Materials & Continua 2023年第4期75卷 815-829页

作者： Ngakan Ketut Acwin Dwijendra Indrajit Patra N.Bharath Kumar Iskandar Muda Elsayed M.Tag El Din Faculty of Engineering Udayana UniversityBali80361Indonesia An Independent Researcher NIT DurgapurWest Bengal700027India Department of Electrical and Electronics Engineering Vignan’s Foundation for Science Technology and ResearchGuntur522001India Department of Doctoral Program Faculty Economic and BusinessUniversitas Sumatera UtaraMedan20222Indonesia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

关键词： Solar energy renewable energy machine learning

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Characteristics of Switched Reluctance Motor Using Operating Area Expandable Drive Circuit in Unexpanded Operating Area 27

Characteristics of Switched Reluctance Motor Using Operating...

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27th International Conference on electrical Machines and Systems, ICEMS 2024

作者： Ishizawa, Taisei Sugai, Ryuya Goto, Hiroki Funato, Hirohito Utsunomiya University Graduate program in Information Electrical and Electronic Systems Engineering Japan Utsunomiya University Japan Utsunomiya University Faculty of Engineering Japan

ISBN: (纸本)9784886864406

In driving switched reluctance (SR) motors at high speeds, it is necessary to apply a high voltage during excitation and demagnetization. To clarify the usefulness of the proposed drive circuit, this paper focuses on the reduction of copper loss in motors and confirms the effect of copper loss reduction by comparing it with the conventional drive circuit. We also focused on capacitor voltage fluctuations caused by resonance phenomena and examined the effect of increasing torque by actively utilizing resonance phenomena. © 2024 The Institute of electrical Engineers of Japan.

关键词： Circuit resonance

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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Coordinated-axis Weight for Real-Time Trace Transform

Coordinated-axis Weight for Real-Time Trace Transform

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2023 International electrical engineering Congress, iEECON 2023

作者： Jundang, Nattapong Sitjongsataporn, Suchada Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Mahanakorn University of Technology Faculty of Engineering and Technology Department of Electronic Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

This paper presents a method for building Coordinated-axis Weight (CW) that has the advantage can be used to increase the speed for converting image data into the domain of real-time trace transforms. The main idea for this method is to use a feature reduction method for image data that must be used in cooperation with supervised machine learning outcome predictions. But the obtained result still retains the distinctive features of the data image in the form of a trace transform. Start by reducing the number of features from the image data, which will be used to reduce the size of the image to the appropriate image size, which in this article will reduce the image size to 45x45 pixels. The next step is to train the data using a Decision Tree (DT) method by aligning the reduced image data in a 1D model. In addition to the image data, two more features are added, i.e. the φ and ρ positions, to inform the prediction that each position has a different value. And the last step is to bring the data to create a model for predicting the answer. The weights obtained from the model are called Coordinated-axis Weights (CW). The experiments within this paper were experimented using the Caltech-256 database, which was able to measure the MSE, RMSE, and PNSR of the images compared to the original trace transform response images at 4.1, 2.02, and 42.00, respectively, including the highest predictive speed of 0.2 ms per image. © 2023 IEEE.

关键词： Decision trees

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Reinforced Deep Learning By Discriminant Feature Trace Transform

Reinforced Deep Learning By Discriminant Feature Trace Trans...

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2023 International electrical engineering Congress, iEECON 2023

ISBN: (纸本)9798350332360

Classification of objects using YOLO deep learning is currently very popular. When the discovered object is rotated, however, the pre-trained YOLO model frequently generates incorrect answer predictions. In order to improve the accuracy of the YOLO deep learning pre-trained model, we present in this paper a method for rotating an object's image back to its initial gain using the DFTF algorithm. The DFTF algorithm is designed to take the results of the trace transform and accumulate the values for each column in order to predict the object's degree of rotation. The results of this calculation are then used to rotate the object back to its normal axis. The experiment is therefore a collaboration between YOLO and DFTF with the aim of more accurately predicting the object's responses. Five groups of data will be chosen for experimentation, each of which can be used to determine the precision of the experimental image with rotational issues. The average of accuracy is 98.56%. © 2023 IEEE.

关键词： Forecasting

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm 10

Performance Evaluation of Coverage Prediction in 4G Networks...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Yuliana, Hajiar Iskandar Hendrawan Ridwan, Azwar Mudzakkir Charisma, Atik Somantri, Nivika Tiffany Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Indonesia Institut Teknologi Bandung Doctoral Program of School of Elecrtical Engineering and Informatics Indonesia Bandung Indonesia UIN Sunan Gunung Djati Bandung Department of Electrical Engineering Indonesia

ISBN: (纸本)9798350377057

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. The data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. The model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. The experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. The conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability. © 2024 IEEE.

关键词： Adversarial machine learning

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The Study of Materials for Electric Field Protection of Power Transformer Testing 9

The Study of Materials for Electric Field Protection of Powe...

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9th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2023

作者： Meesuk, Peerawat Bunpakdee, Prajak Sorn-In, Sirirat Bansomdejchaopraya Rajabhat Univrtsity Electrical Engineering and Automation Control System Program Faculty of Engineering and Industrial Technology Bangkok Thailand

ISBN: (纸本)9798350322040

This paper proposes the analysis of the material can be protected from transformer testing and presents a mathematical model of the power transformer. In this research, transformers were study in four protective materials, wood, rubber, glass and acrylic that are used in between the electric field measurement and the transformer. By simulating the test results of a transformer with a voltage of 230 kV. Computer-based simulation utilizing the finite element method (FEM) is exploited as a tool for visualizing electric fields distribution volume a power transformer. The simulation results showed that the material with the best protection against the electric field was wood insulation and the best installation location was a distance of 5 m from the transformer. © 2023 IEEE.

关键词： Finite element method

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Filho, Jose Oniram De A. Limaverde Oliveira, Luiz Henrique Bautista, Jaider Fortaleza, Eugenio Munoz, Daniel M. Department of Mechanical Engineering Brasília Brazil University of Brasília Department of Electrical Engineering Brasília Brazil University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Colombia

ISBN: (纸本)9798350374575

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Compact Complementary Split Ring Resonator Based Sensor for Non-Invasive Glucose monitoring

Compact Complementary Split Ring Resonator Based Sensor for ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Zahran, Adnan Aly, Youssif Ayman Eldamak, Angie R. Elsheakh, Dalia N. Ain Shams University Electronics and Communication Systems Engineering Program Faculty of engineering Cairo Egypt Ain Shams University Faculty of engineering Electronics and Communication Systems Engineering Dept. Cairo Egypt Badr University in Cairo Faculty of Engineering and Technology Electrical Dept. Badr City Cairo11829 Egypt

ISBN: (纸本)9798350351408

This paper presents a novel approach for noninvasive glucose monitoring using a Compact Complementary Split Ring Resonator (CCSRR) based sensor. The sensor design is initially simulated using High-Frequency Structure Simulator (HFSS) software, and then experimental validation is done. We carried out simulations with and without a finger phantom model to closely resemble conditions in real situations. The sensor exhibits high sensitivity and accuracy in detecting glucose concentrations within blood samples. Vector Network Analyzer was used along with the sensor to validate the simulation results. The results demonstrate close agreement between simulation and experimental data, affirming the effectiveness of the proposed sensor design for non-invasive glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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