Optimizing the AC resistance of litz wire windings is crucial for reducing loss and improving the efficiency of energy transfer in power systems. This study presents a novel method for obtaining the AC resistance of l...
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ISBN:
(纸本)9798350386783;9798350386776
Optimizing the AC resistance of litz wire windings is crucial for reducing loss and improving the efficiency of energy transfer in power systems. This study presents a novel method for obtaining the AC resistance of litz wire windings by using machinelearning algorithms. It simplifies and improves the speed at which AC resistance values are obtained and facilitates the study of AC resistance parameters in litz wire windings. A simulation model is established using an approximate model for optimizing the AC resistance of litz wire windings based on the Dowell equation. Maxwell software is used to generate the training dataset. The frequency, dimensions, number of layers, and conductivity are used as input to the proposed machinelearning model, while the AC resistance of litz wire windings is the output variable. A neural network is coupled with the particle swarm optimization method to optimize the network structure to achieve better results. The proposed machinelearning model has a prediction accuracy rate of 99%.
Banana plants are a crucial agricultural crop worldwide, yet they are vulnerable to various diseases that can drastically impact yield and quality. Traditional methods of disease detection in banana cultivation rely h...
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Digital image processing is a nascent field that emerged alongside computers. Its techniques vary by job. Applications that save, compress, distribute, and visualize images are different from those that analyze images...
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Right now, a lot of subjective human judgment goes into classifying mangoes and other fruits, especially when it comes to poor productivity, which results in less than ideal classification accuracy. Our work suggests ...
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Mixed Martial Arts (MMA) analysis presents unique challenges for predicting fight outcomes and clustering fighter styles. Existing research often falls short of accurately capturing the complexity of fighters' tec...
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ISBN:
(纸本)9798350375084;9798350375077
Mixed Martial Arts (MMA) analysis presents unique challenges for predicting fight outcomes and clustering fighter styles. Existing research often falls short of accurately capturing the complexity of fighters' technical styles and their impact on match results. To bridge these gaps, we propose a novel approach that utilizes machinelearning methods. Specifically, 1) Use factor analysis to derive high-dimensional technical style factors and applies the K-means algorithm to cluster fighters based on these factors. 2) Various machinelearning models, such as Random Forest, Support Vector machine (SVM), Extreme Gradient Boosting (XGBoost), Logistic Regression, Neural Networks are experimentally tested. 3) An ensemble learning model is proposed that employs majority voting among the mentioned models. 4) Based on data from the Ultimate Fighting Championship (UFC), the experimental results demonstrate that the ensemble learning model achieves the highest accuracy of 65.52%, significantly outperforming individual models. An ablation study further validates the importance of these factors in prediction accuracy. These findings underscore the effectiveness of incorporating technical style factors into predictive models, enhancing accuracy and interpretability in the context of MMA.
This document discusses a study focused on developing a machinelearning model to detect extraneous vibrations during the 3D printing process. It highlights the impact of these vibrations on print quality and introduc...
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This document discusses a study focused on developing a machinelearning model to detect extraneous vibrations during the 3D printing process. It highlights the impact of these vibrations on print quality and introduces a system using an Inertial Measurement Unit (IMU) to measure vibrations. Various machinelearning models were evaluated for their ability to distinguish between normal printing vibrations and extraneous vibrations, with the Light Gradient Boosting machine model showing the best performance. The study emphasizes the importance of early detection of defects to save time and cost, contributing to advances in additive manufacturing technologies.
With the help of cognitive radio, users will no longer have to deal with spectrum utilization and allocation *** technology allows them to utilize the idle resources of their system without affecting those who are lic...
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This work looks into machinelearning as a means of enhancing rainfall prediction. A logistic regression model is trained using meteorological data, including meteorological parameters such as temperature, wind speed,...
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Nowadays, diabetes mellitus is a worldwide health concern that is becoming more common. Therefore, for better early detection techniques (machinelearning) is need of hour. Six machinelearning algorithms are applied ...
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Fundamentally, the study's findings show how important machinelearning algorithms are to improving solar power forecasts and system optimization. The study illustrates how well the ANN algorithm predicts solar en...
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