Abstract: A correlation tolerance limit setting system using repetitive cross-validation includes: a variable extraction unit randomly classifying data of an initial DB set into training set data and validation set data at a specific rate and then extracting variables for determining a DNBR limit by optimizing coefficients of a selected correlation; a normality test unit testing normality for a variable extraction result; a DNBR limit unit determining whether data sets have a same population or not depending on normality result and determining DNBR limit from a distribution of 95/95 DNBR; and a controller.

Abstract: A correlation tolerance limit setting system using repetitive cross-validation includes: a variable extraction unit randomly classifying data of an initial DB set into training set data and validation set data at a specific rate and then extracting variables for determining a DNBR limit by optimizing coefficients of a selected correlation; a normality test unit testing normality for a variable extraction result; a DNBR limit unit determining whether data sets have a same population or not depending on normality result and determining DNBR limit from a distribution of 95/95 DNBR; and a controller.

Abstract: A system and method are provided for setting a tolerance limit of a correlation by using repetitive cross-validation to prevent intentional or unintentional distortion of data characteristics by human intervention or otherwise, and to prevent risk caused thereby, and to quantify the influence of the distortion of the data characteristics in fitting the correlation and setting the tolerance limit. The system for setting the tolerance limit of the correlation by using repetitive cross-validation according to an embodiment of this presentation includes a variable extraction unit extracting a variable by partitioning a training set and a validation set and by fitting the correlation coefficients; a normality test unit performing a normality test for variable extraction results; and a DNBR limit unit performing a same population test, and determining an allowable DNBR limit for a DNBR value distribution, based upon normality.