机译:基于多分类器的融合的电子鼻蒸汽识别
Dept. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB;
aerospace biophysics; air pollution measurement; contamination; electronic noses; feature extraction; inference mechanisms; multilayer perceptrons; occupational health; occupational safety; pattern classification; random noise; sensor fusion; signal classification; signal denoising; support vector machines; Dempster-Shafer classification fusion method; E-nose vapor identification; Parzen classifier; air contaminants monitoring; astronauts health-and-safety; data preprocessing; gas sensor arrays; gas sensor signals; k -nearest neighbors; measurement denoising; multiple classifiers; shuttles; space stations; transient-state feature extraction; wavelet-based denoising method; $k$-nearest neighbor (KNN); formula formulatype="inline"tex$k$/tex/formula-nearest neighbor (KNN); Dempster–Shafer (DS); Dempster??Shafer (DS); electronic nose (e-nose); neural network (NN); support vector machine (SVM); wavelet denoising;
机译:使用Dempster-Shafer证据理论结合多个分类器进行说话人识别
机译:基于Dempster-Shafer证据理论的基于传感器融合和分类器组合的火花塞故障识别
机译:基于Dempster-Shafer证据理论的振动和声学信号分类器融合,用于行星齿轮的故障诊断和分类
机译:基于多分类器的Dempster-Shafer融合的徽标识别
机译:基于Dempster-Shafer理论的不确定性量化和数据融合。
机译:基于登普斯特-谢弗证据理论的时空信息融合识别方法
机译:基于Dempster-Shafer证据理论的基于传感器融合和分类器组合的火花塞故障识别
机译:基于多分类器Dempster-shafer融合的E-Nose蒸汽识别。