@inproceedings{f2a0143215fa482f9fa765796c11f80c,
title = "Localized model to segmentally estimate miles per gallon (MPG) for equipment engines",
abstract = "In this paper, we built a localized regression model to estimate the miles per gallon (MPG) characteristic for equipment engines based on a serious physical features of this engine. First, we statistically viewed these parameters to build up a basic understanding of the data we collected. Then, with the belief that engines with similar characteristics will perform similarly, we proposed a novel localized model with a novel optimal function based EM algorithm and a novel self-adjusted optimal clustering algorithm to estimate MPG based on the other fully studied engines with similar physical features.",
keywords = "Clustering, Engine parameters, Fuel economy, Machine learning, Regression",
author = "Luo, {Jiu Lin} and Luo, {Hao Jing} and Li, {Ai Min} and Haohan Wang",
year = "2014",
doi = "10.4028/www.scientific.net/AMM.556-562.1069",
language = "English (US)",
isbn = "9783038351153",
series = "Applied Mechanics and Materials",
publisher = "Trans Tech Publications Ltd",
pages = "1069--1074",
booktitle = "Mechatronics Engineering, Computing and Information Technology",
note = "2014 International Conference on Mechatronics Engineering and Computing Technology, ICMECT 2014 ; Conference date: 09-04-2014 Through 10-04-2014",
}