Ex Situ Plasma Diagnosis by Recognition of X-Ray Photoelectron Spectroscopy Data Using a Neural Network

A New Data Mining Approach for Gear Crack Level Identification Based on Manifold Learning

“…These algorithms include principal component analysis (PCA) and linear discriminate analysis (LDA), etc. However, the linear solutions may lead to losing the nonlinear properties of the original data [10][11][12]. Unlike the linear eigenvector-based feature extraction algorithms, LLE preserves local topology of high-dimensional data in the reduced space.…”

“…The challenge is that it is not easy to determine the most distinguished features. One of the most popular data mining methods, principal component analysis (PCA) and its derivative algorithms, have been proved to be a useful tool for feature reduction and extraction [11]. However, their main limitation lies in their ability to capture the nonlinear properties of the original data [12][13][14].…”

Monitoring of plasma processing chamber using ion energy analyser and time series neural network

“…6 Neural networks have been effectively utilised in characterising material processes, 7 capturing relationships between multiparameterised input patterns and material characteristics, 5,6 and constructing a calibration model of plasma diagnosis. 8 A principal component analysis (PCA) reduced OES was also used for neural network prediction of film properties. 9 An optimised neural network model was reported by combining OES, PCA, neural network and genetic algorithm.…”