20 Papers
70 Citations
Yu Gu is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 9, co-authored 20 publications. Previous affiliations of Yu Gu include Monash University & Beijing University of Chemical Technology.
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Papers
Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms
TL;DR: This work uses support vector regression and random forest regression to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets.
167
Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection.
TL;DR: Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.
Application of Random Forest Classifier by Means of a QCM-Based E-Nose in the Identification of Chinese Liquor Flavors
Qiang Li,Yu Gu,Nan-fei Wang +2 more
TL;DR: Taking both the application of the e-nose and the validation of the RF classifier into account, an available method is obtained to identify flavors of Chinese liquors.
91
Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.
TL;DR: A pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) designed and evaluated has reasonable reliability, good fitting and prediction (generalization) performance.
48
Classification and Evaluation of Quality Grades of Organic Green Teas Using an Electronic Nose Based on Machine Learning Algorithms
Huixiang Liu,Dongbing Yu,Yu Gu +2 more
TL;DR: The study shows that the E-nose is effective for the classification and evaluation of organic green teas when an optimal pattern recognition algorithm is selected and achieves good performance both in the tasks of tea grade classification and tea quality evaluation (price regression).