Publications by authors named "Eugene Yujun Fu"
This paper presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time-dependent sensor signals were obtained from 60 normal/ignition cooking experiments. A total of 200 000 data instances are documented and analyzed.
View Article and Find Full Text PDFResearch was conducted to examine the use of Support Vector Regression (SVR) to build a model to forecast the potential occurrence of flashover in a single-floor, multi-room compartment fire. Synthetic temperature data for heat detectors in different rooms were generated, 1000 simulation cases are considered, and a total of 8 million data points are utilized for model development. An operating temperature limitation is placed on heat detectors where they fail at a fixed exposure temperature of 150 °C and no longer provide data to more closely follow actual performance.
View Article and Find Full Text PDFArticle Synopsis
- * An automated tool called CData is being developed to simplify creating input files and summarizing simulation results, and it generates synthetic data for diverse fire situations.
- * Three machine learning algorithms—support vector machine, decision tree, and random forest—are applied to predict fire locations based on temperature data, with decision tree and random forest showing high accuracy (93%-96%) in predictions.