In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
In light of rapid development of customer requirements, control procedures of quality concept use multivariate analysis. This is because of recent advances in information technology and in recording. The charting proc...In light of rapid development of customer requirements, control procedures of quality concept use multivariate analysis. This is because of recent advances in information technology and in recording. The charting procedures are based on Mahalanobis distance but their performance needs normality and a type-I error rate choice. The DD-diagram is an alternative scheme that uses data depth to avoid these conditions rarely met in practice. For a given error-free sample, the performance of DD-diagram and that of multivariate EWMA control procedures are compared through a real example on individual observations taken from a multivariate quality process.展开更多
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
文摘In light of rapid development of customer requirements, control procedures of quality concept use multivariate analysis. This is because of recent advances in information technology and in recording. The charting procedures are based on Mahalanobis distance but their performance needs normality and a type-I error rate choice. The DD-diagram is an alternative scheme that uses data depth to avoid these conditions rarely met in practice. For a given error-free sample, the performance of DD-diagram and that of multivariate EWMA control procedures are compared through a real example on individual observations taken from a multivariate quality process.
