Abstract: Disclosed is a degradation prediction device predicting a lifetime of a target material, including: at least one processor, and the at least one processor is configured to train a degradation index prediction model based on the representative value of a degradation index of a material by environmental conditions and the environmental information of a material subjected to a destructive testing, train a LMP (Larson-Miller Parameter) value prediction model based on the representative value of a degradation index of a material by environmental conditions and a theoretical value of LMP at a destructive testing, predict a degradation index for a target material using the degradation index prediction model for which training is completed based on environmental information of the target material and predict a LMP value of the target material using the LMP value prediction model for which training is completed based on the predicted degradation index.

Abstract: Disclosed is an annotation device including at least one processor. The at least one processor generates a plurality of superpixels in an annotation target image based on a predetermined non-parametric segmentation method, recommends segmentation regions based on outlines of the plurality of superpixels, respectively, and performs labeling for each of the recommended segmentation regions based on a user input for labeling.