首页|Data on Machine Learning Discussed by Researchers at Tsinghua University (Predicting the Explosion Limits of Hydrogen-oxygendiluent Mixtures Using Machine Learning Approach)
Data on Machine Learning Discussed by Researchers at Tsinghua University (Predicting the Explosion Limits of Hydrogen-oxygendiluent Mixtures Using Machine Learning Approach)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, we present a new methodology for predicting the explosion limits of hydrogen-oxygen-diluent mixtures by using machine learning approach. Results show that the explosion limits are accurately predicted with the logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and feedforward neural network (FNN) algorithms when using the optimal hyperparameters.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Tsinghua University, “In terms of computational cost, the LR and DT require the lower costs, the RF requires the high training and prediction costs and the training cost of the FNN is higher due to the large number of hyperparameters. In terms of prediction accuracy, the FNN predicts the explosive/non-explosive boundary more accurately with different amounts of training data. Furthermore, the receiver operating characteristic (ROC) curve and area under curve (AUC) values further prove the superiority of the five classifiers.”
BeijingPeople’s Republic of ChinaAsiaChalcogensCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningTsinghua University
NETL
NSTL
Elsevier
