[논문]딥러닝을 이용한 핸드크림의 마찰 시계열 데이터 분류

김지원; 이유민; 한상헌; 김경택

doi:10.11627/jkise.2021.44.3.098

[국내논문] 딥러닝을 이용한 핸드크림의 마찰 시계열 데이터 분류
Deep Learning-based Approach for Classification of Tribological Time Series Data for Hand Creams 원문보기

Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.44 no.3, 2021년, pp.98 - 105

김지원 (한남대학교 산업경영공학과) , 이유민 (한남대학교 산업경영공학과) , 한상헌 ((주)테라리더) , 김경택 (한남대학교 산업경영공학과)

Abstract ▼ AI-Helper

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

Keyword

참고문헌 (27)

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상세보기
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