首页|Reports Outline Machine Learning Findings from Department of Electronic and Elec trical Engineering (A Rail Wheel Contact Temperature Prediction Model Using Fibe r Bragg Grating Sensor On Test Rig)
Reports Outline Machine Learning Findings from Department of Electronic and Elec trical Engineering (A Rail Wheel Contact Temperature Prediction Model Using Fibe r Bragg Grating Sensor On Test Rig)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Beng aluru, India, by NewsRx correspondents, research stated, "The focus of this rese arch is the development of predictive models for temperature forecasting of rail wheel contact temperature through data collection from experimental setup with Single wheel test rig. Fibre sensing technology and the implementation of machin e learning techniques are used." Financial support for this research came from AICTE RPS. Our news editors obtained a quote from the research from the Department of Elect ronic and Electrical Engineering, "Our approach involves utilizing a dataset con taining crucial variables such as time, speed, weight, and sensor readings in or der to accurately predict temperature changes. To achieve this, we employ a thor ough preprocessing methodology that includes data cleansing, normalization, and feature selection, followed by the implementation of various machine learning al gorithms for regression tasks. The effectiveness of each model is evaluated usin g metrics like Mean Squared Error and R-squared. Experimental results reveal sig nificant findings, including a Linear Regression model with an R-squared value o f 0.9176, indicating it accounts for 91.76% of temperature variati on. Furthermore, Decision Tree and Random Forest models exhibit remarkable accur acy, achieving R-squared values of 0.999997 and 0.999995 respectively. Through e xtensive analysis and discussion, we gain insights into the strengths and limita tions of different models, ultimately identifying the most optimal approach for temperature prediction."
BengaluruIndiaAsiaCyborgsEmergin g TechnologiesMachine LearningDepartment of Electronic and Electrical Engine ering
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