Yufeng Wang
7 Papers
Yufeng Wang is an academic researcher. The author has contributed to research in topics: Chemistry & Energy (signal processing). The author has an hindex of 1, co-authored 2 publications.
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Papers
Determination of soil source using Laser induced breakdown spectroscopy combined with feature selection
Yu Ding,Shu Yan,Ao Hu,Meiling Zhao,Jing Chen,Linyu Yang,Wenjie Chen,Yufeng Wang +7 more
TL;DR: This study combines Laser Induced Breakdown Spectroscopy (LIBS) with feature selection and machine learning to accurately predict soil sources, with applications in agricultural planning, forensic analysis, and archaeological research.
2
A comparative study of classification models for laser-induced breakdown spectroscopy of Astragalus origin
TL;DR: The results show that the classification effect of GWO-RF-ELM is the best, and its macro-precision, macro-recall and macro-F1 score were 92%, 100%, 92.04% and 95.86%, respectively, which provides an effective method for the identification of Astragalus.
2
Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil
Yu Ding,Yufeng Wang,Jing Chen,Wenjie Chen,Ao Hu,Yan Shu,Meiling Zhao +6 more
TL;DR: The GA-Boruta-ELM model exhibits excellent prediction capability for quantifying fenthion in soybean oil samples using LIBS.
2
Rapid classification of whole milk powder and skimmed milk powder by laser-induced breakdown spectroscopy combined with feature processing method and logistic regression.
Yu Ding,Wenjie Chen,Jing Chen,Lin-Yu Yang,Yufeng Wang,Xing-Qiang Zhao,Ao Hu,Yan Shu,Meiling Zhao +8 more
TL;DR: A novel LIBS-based method combined with feature processing and logistic regression achieves high accuracy (99.33-99.67%) in classifying whole milk powder and skimmed milk powder, with improved modeling efficiency using PCA and MI feature processing methods.
Energy value measurement of milk powder using laser-induced breakdown spectroscopy (LIBS) combined with long short-term memory (LSTM).
Yu Ding,Meiling Zhao,Shu Yan,Ao Hu,Jian Chen,Wenjie Chen,Yufeng Wang,Linyu Yang +7 more
TL;DR: The experimental results demonstrate that the LSTM model has superior predictive performance compared to the other models and can accurately measure the energy value of milk powder and provide an effective and feasible means for its commercial measurement.