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AQUATIDE Plays a Role as Innovative Skin-Care Vaccine Through The Autophagy Induction
( Chaejin Lim ),( Myungho Kor ),( Seokjeong Yoon ),( Heungjae Kim ),( Kyungho Park ),( Keedon Park ) 한국피부장벽학회 2016 한국피부장벽학회지 Vol.18 No.2
Natural moisturizing factor (NMF) in the stratum corneum has a high moisture retaining efficacy, and plays a major role in the skin barrier function. Pyrrolidone carboxylic acid (PCA) is one of the major NMFs found in human skin. Aquatide, a PCA-mimetic peptide, improves the skin barrier function by stimulating filaggrin expression as well as reducing Trans-Epidermal Water Loss (TEWL) correlated with increased water retention and moisturization. Autophagy is the natural destructive mechanism that allows the orderly degradation and recycling of cellular damaged components. So, Increased autophagy delays ageing process and extends longevity. Sirtuin1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase functioning in the regulation of metabolism, cell survival and organismal lifespan. AQUATIDE alleviates ROS- and heavy metal-mediated senescence and cytotoxicity of cells by SIRT1-dependent autophagy. Furthermore, AQUATIDE reduces pollen-mediated PGE2 release and keeps the skin healthy during an allergic reaction against pollen treatment. In conclusion, AQUATIDE is considered as the first needle-free “skin-care vaccine” for various troubled skins including inflamed and irritated skin as well as aged skin.
MC Dropout을 활용한 CNN 기반 악기 소리 분류의 성능 향상과 Out-of-Distribution 탐지
현준희(Junhee Hyeon),임채진(Chaejin Lim),한동일(Dongil Han) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Convolutional neural networks (CNNs) are widely used in various fields, such as classification, object detection, segmentation, generation, natural language processing, and speech processing. Although CNNs exhibit strong performance on the trained data, they tend to fail on unseen data, leading to unexpected results. Therefore, it is essential to develop and research exception handling methods. In this study, we apply MC-dropout to the CNN model to handle exceptions and compare the performance with the model without MC-dropout. We evaluated the performance using a dataset consisting of instrument sounds, and different sounds. Image classification using CNNs is a wellknown method, but instrument sounds are represented as frequencies rather than images. Therefore, we convert sound into frequency to perform Image classification. We evaluated the ability to handle out-of-distribution data when MC-dropout is applied and examine its impact on the models performance. This study provides insights into improving the performance of instrument sound classification.
배인환,김영후,김태경,오민호,주현수,김슬기,신관준,윤선재,이채진,임용섭,최경호,Bae, Inhwan,Kim, Yeounghoo,Kim, Taekyung,Oh, Minho,Ju, Hyunsu,Kim, Seulki,Shin, Gwanjun,Yoon, Sunjae,Lee, Chaejin,Lim, Yongseob,Choi, Gyeungho 한국자동차안전학회 2019 자동차안전학회지 Vol.11 No.2
This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.