The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process i...The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.展开更多
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 this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in ...In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.展开更多
The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatu...The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatural patterns for the control chart by using back-propagation neural network is also presented.展开更多
文摘The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.
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 this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.
基金Supported bv the Commission of Science,Technology and Industry for National Defence of China.
文摘The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatural patterns for the control chart by using back-propagation neural network is also presented.
