Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Genomic instability in Chinese hamster cells after exposure to X rays or alpha particles of different mean linear energy transfer

Evidence has accumulated that radiation induces a transmissible persistent destabilization of the genome, which mag. result in effects arising in the progeny of irradiated but surviving cells. An enhanced death rate among the progeny of cells surviving irradiation persists for many generations in the form of a reduced plating efficiency. Such delayed reproductive death is correlated with an increased occurrence of micronuclei. Since it has been suggested that radiation-induced chromosomal instability might depend on the radiation quality, we investigated the effects of alpha particles of different LET by looking at the frequency of delayed micronuclei in Chinese hamster V79 cells after cytochalasin-induced block of cell division, A dose-dependent increase in the frequency of micronuclei was found in cells assayed 1 week postirradiation or later. Also, there was a persistent increase in the frequency of dicentrics in surviving irradiated cells, Moreover, we found an increased micronucleus frequency in all of the 30 clones isolated from individual cells which had been irradiated with doses equivalent to either one, two or three alpha-particle traversals per cell nucleus, We conclude that the target for genomic instability in Chinese hamster cells must be larger than the cell nucleus. (C) 1997 by Radiation Research Society

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.