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Sitting Posture-Based Lighting System to Enhance the Desired Mood
Hyunjoo Bae,Haechan Kim,Hyeon-Jeong Suk 대한인간공학회 2015 大韓人間工學會誌 Vol.34 No.2
Objective: As a cue for desired mood, we attempted to identify types of sitting postures when people are involved in various tasks during their working hours. Background: Physical behaviors in reaction to user contexts were studied, such as automated posture analysis for detecting a subject"s emotion. Sitting postures have high feasibility and can be detected robustly with a sensing chair, especially when it comes to an office. Method: First, we attached seven sensors, including six pressure sensors and one distance sensor, to an office chair. In Part 1, we recorded participants" postures while they took part in four different tasks. From the seven sensors, we gathered five sets of data related to the head, the lumbar, the hip, thigh pressure and the distance between the backrest and the body. We classified them into four postures: leaning forward, upright, upright with the lumbar supporting, and leaning backward. In part 2, we requested the subjects to take suitable poses for the each of the four task types. In this way, we compared the matches between postures and tasks in a natural setting to those in a controlled situation. Results: We derived four types of sitting postures that were mapped onto the different tasks. The comparison yielded no statistical significance between Parts 1 and 2. In addition, there was a significant association between the task types and the posture types. Conclusion: The users" sitting postures were related to different types of tasks. This study demonstrates how human emotion can interact with lighting, as mediated through physical behavior. Application: We developed a posture-based lighting system that manipulates the quality of office lighting and is operated by changes in one"s posture. Facilitated by this system, color temperatures ranging between 3,000K and 7,000K and illuminations ranging between 300lx and 700lx were modulated.
( Hyunjoo Bae ),( Do Hyun Na ),( Ji-yeun Chang ),( Ki Hyun Park ),( Ji Won Min ),( Eun Jeong Ko ),( Hyeyoung Lee ),( Chul Woo Yang ),( Byung Ha Chung ),( Eun-jee Oh ) 대한내과학회 2021 The Korean Journal of Internal Medicine Vol.36 No.1
Background/Aims: To investigate if BK virus (BKV)-specific T cell immunity measured by an interferon-γ enzyme-linked immunospot (ELISPOT) assay can predict the outcome of BK virus infection in kidney transplant recipients (KTRs). Methods: We included 68 KTRs with different viremia status (no viremia [n = 17], BK viremia [n = 27], and cleared viremia [n = 24]) and 44 healthy controls (HCs). The BK viremia group was divided into controller (< 3 months) and noncontroller ( > 3 months) according to sustained duration of BKV infection. We compared BKV-ELISPOT results against five BKV peptides (large tumor antigen [LT], St, VP1-3). Results: BKV-ELISPOT results were higher in three KTRs groups with different BKV infection status than the HCs group (p < 0.05). In KTR groups, they were higher in cleared viremia group than no viremia or BK viremia group. Within the BK viremia group, controller group had higher LT-ELISPOT results compared to noncontroller group (p = 0.032). Also, KTRs without BK virus-associated nephropathy (BKVN) had higher LT, St, VP1, and VP2-ELISPOT results than those with BKVN (p < 0.05). Conclusions: BKV-ELISPOT assay may be effective in predicting clinical outcomes of BKV infection in terms of clearance of BK virus and development of BKVN.
The use of support vector machines in semi-supervised classification
Bae, Hyunjoo,Kim, Hyungwoo,Shin, Seung Jun The Korean Statistical Society 2022 Communications for statistical applications and me Vol.29 No.2
Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.