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김지원(Ji Won Kim),이유민(You Min Lee),한상헌(Shawn Han),김경택(Kyeongtaek Kim) 한국산업경영시스템학회 2021 한국산업경영시스템학회지 Vol.44 No.3
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.
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Chengyu Yang,Xuesong Cai,Gaojie Dong,Andreas Schellenberg,Shawn You 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.7
This paper recounts a real-time hybrid simulation (RTHS) that employed a shake table coupled with an actuator to evaluate the seismic performance of a single-span girder bridge. The test specimen for this RTHS was a scaled single-span girder bridge, which was subjected to Loma Prieta excitation in the test. In the RTHS, the bridge specimen, which lacked mass on top of the bridge deck, was physically tested by a shake table and an actuator, whilst the mass on the bridge, as well as the pile foundation and the soil were modeled numerically. OpenSEES was used for numerical modeling and OpenFresco was used for the communication between the test system and numerical model. An adaptive time series (ATS) technique was employed to compensate for time delay in the RTHS. In this way, a novel bridge engineering RTHS application case composed of shake table and actuator control was presented. To validate the results of the RTHS, a shake table test (STT) of the bridge structure with full mass on the bridge deck was conducted correspondingly. Comparing the RTHS and STT results, it can be concluded that acceleration histories, bearing deformation histories, and strain histories of the selected points matched well. The application of RTHS to assess the seismic behavior of the single-span girder bridge was reasonably verified in this paper.
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