首页|Researchers from Federal University of Santa Catarina Publish New Studies and Fi ndings in the Area of Machine Learning (Machine Learning for Real-Time Fuel Cons umption Prediction and Driving Profile Classification Based on ECU Data)
Researchers from Federal University of Santa Catarina Publish New Studies and Fi ndings in the Area of Machine Learning (Machine Learning for Real-Time Fuel Cons umption Prediction and Driving Profile Classification Based on ECU Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting originating from Florianopoli s, Brazil, by NewsRx correspondents, research stated, "Data extracted directly f rom a vehicle's electronic control unit (ECU) play a crucial role in the automot ive industry because they contain valuable information from the engine and elect ronic parts." Financial supporters for this research include Fundacao De Desenvolvimento Da Pe squisa-fundep Rota 2030/LINHA V. The news reporters obtained a quote from the research from Federal University of Santa Catarina: "These data have the potential to enable compliance analysis, d etect faults and errors, and guarantee driver and car safety as well as product quality. Among the possible uses of the data from the ECUs, driving profile anal ysis and fuel consumption prediction stand out, which enable analyses for insure rs and transportation companies, and help to reduce fuel consumption and greenho use gases, in addition to providing feedback to the driver. In this work, we app ly machine learning algorithms to real data from an engine ECU to examine the dr iver's driving behavior and accurately classify their fuel efficiency. Moreover, we develop regression models that predict fuel consumption for vehicles in oper ation. To ensure the effectiveness of our models, we carefully select variables strongly correlated with fuel consumption using a feature selection process. Com pared to related works, both our profile classification results in precision, re call, and accuracy metrics, and our regression models result in the metrics of m ean square errors, mean absolute error, and coefficient of determination, which are superior or similar."
Federal University of Santa CatarinaFl orianopolisBrazilSouth AmericaCyborgsEmerging TechnologiesMachine Lear ning
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