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- 저자 : 이기천(Kichun Lee) (검색결과 3 건)
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Multimodal Emotional State Estimation Model for Implementation of Intelligent Exhibition Services
Kichun Lee(이기천),So Yun Choi(최소윤),Jae Kyeong Kim(김재경),Hyunchul Ahn(안현철) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.1
Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors’ interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor’s emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people’s multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.
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자동차 에어컨용 압축기 사판의 표면 형태에 따른 마찰 마모 거동
권윤기(Yunki Kwon),이건호(Geonho Lee),이기천(Kichun Lee) 한국트라이볼로지학회 2011 한국트라이볼로지학회지 (Tribol. Lubr.) Vol.27 No.2
The tribological characteristics of the swash plate surface of a compressor which is for automobile were investigated. For surface treatments, PTFE and MoS2 are used as a solid lubricant, together with copper alloy. Test condition is set considering actual driving condition. Wear testing is conducted using pin on disk type tester, and the coefficient of friction and the temperature on friction surface are measured. Also, to determine the wear patterns, cross-section of friction surface is analyzed by SEM(scanning electrode microscope). The MoS2, both at dry and lubricated conditions, friction surface and the coefficient of friction maintained rather stable results. But, the PTFE, at oil less condition, sample resulted in rather unstable condition. In case of copper alloy, quite higher friction coefficients(higher than 0.1) were obtained at dry condition. At the temperature of 125oC, seizure has occurred.
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권혁호(Hyuk-Ho Kwon),김승욱(Seung-Wook Kim),최동훈(Dong-Hoon Choi),이기천(Kichun Lee) 대한산업공학회 2016 대한산업공학회지 Vol.42 No.5
The purpose of this study is to challenge a computational regression-type problem, that is handling large-size data, in which conventional metamodeling techniques often fail in a practical sense. To solve such problems, regression-type boosting, one of ensemble model techniques, together with bootstrapping-based re-sampling is a reasonable choice. This study suggests weight updates by the amount of the residual itself and a new error decision criterion which constructs an ensemble model of models selectively chosen by rejection limits. Through these ideas, we propose AdaBoost.RMU.R as a metamodeling technique suitable for handling large-size data. To assess the performance of the proposed method in comparison to some existing methods, we used 6 mathematical problems. For each problem, we computed the average and the standard deviation of residuals between real response values and predicted response values. Results revealed that the average and the standard deviation of AdaBoost.RMU.R were improved than those of other algorithms.
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