Sangmun Shin

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In many scientific and engineering fields, there are a number of data sets uncontrollable and hard to handle because the nature of measurement of a performance variable may often be destructive or very expensive, which are known as sets of noise factors. Although these noise factors, which may not be controlled by manufacturing and cost reasons, are merged(More)
Data mining (DM) has emerged as one of the key features of many applications on information system. While Data Analysis (DA) represents a significant advance in the type of analytical tools currently available, there are limitations to its capability. In order to address one of the limitations on the DA capabilities of identifying a causal relationship, we(More)
Most data mining (DM) methods reviewed in literature for the factor selection may obtain a number of input factors associated with the interesting response without providing the detailed information, such as relationship between the input factors and response, statistical inferences, and analysis. These DM methods also may not discuss the robustness of(More)
Robust design (RD) has been recognized as one of the most useful approaches to improve the quality of products/processes. Response surface methodology (RSM), a significant tool to perform an RD procedure, is often used to estimate the fitted response functions for the process mean and variance by assuming that experimental errors are normally distributed.(More)
Robust design (RD), implemented in statistical and mathematical procedures to simultaneously minimise the process bias and variability, is widely used in many areas of engineering and technology to represent complex real-world industrial settings. For RD modelling and optimisation, response surface methodology (RSM) is often utilised as an estimation method(More)