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( Seoyoung Park ),( Joo Hyun Kim ),( Younglim Son ),( Sung Ho Goh ),( Sangtaek Oh ) 한국미생물 · 생명공학회 2016 Journal of microbiology and biotechnology Vol.26 No.6
Longan (Dimocarpus longan Lour.) has been used as a traditional oriental medicine and possesses a number of physiological activities. In this study, we used cell-based herbal extract screening to identify longan fruit extract (LFE) as an activator of osteoblast differentiation. LFE up-regulated alkaline phosphatase (ALP) activity, induced mineralization, and activated Runx2 gene expression in MC3T3-E1 cells. Furthermore, treatment of MC3T3-E1 cells with LFE promoted the phosphorylation of extracellular signal-regulated kinase1/2 (Erk1/2); however, abrogation of Erk1/2 activation with PD98059 resulted in down-regulation of the phospho- SMAD1/5/8 and Runx2 levels, which in turn reduced the ALP activity. Our findings suggest that LFE exerts its osteogenic activity through activation of the ERK signaling pathway and may have potential as an herbal therapeutic or a preventive agent for the treatment of osteoporosis.
Real-time Air Conditioner Zone Temperature Control by Cascaded Intelligent PI
Seoyoung Nam,Jung E. Son,Nakhoon Kim 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
In this paper, we control an air conditioner system thought the application of a newly developed Intelligent Proportional Integral (i-PI) Controller that regulate a zone temperature to desired setpoint temperature. This method approximates the unknown nonlinear system function and is applied to the problem of controlling compressor for a vapor compression cycle system where evaporating pressure target is automatically determined by the i-PI so that zone temperature is met to desired setpoint.
정서중심 집단상담 프로그램이 시민단체 활동가의 정서조절, 스트레스 대처방식 및 심리적 안녕감에 미치는 효과
손혜선(Son Hyeseon),고서영(Ko Seoyoung) 부경대학교 인문사회과학연구소 2021 인문사회과학연구 Vol.22 No.1
본 연구의 목적은 정서중심 집단상담 프로그램이 시민단체 활동가의 정서 조절, 스트레스 대처 전략 및 심리적 안녕감에 미치는 효과를 검증하는 것이다. 이를 위해 연구대상은 시민단체 활동가 18명으로 실험집단과 통제집단에 각각 9명씩 구성하여 총 10회기를 주 2회 100분씩 5주 동안 실시되었다. 프로그램 효과성 검증을 위해 실험-통제집단 사전 동질성 검증 및 공분산분석(ANCOVA)을 실시하였다. 본 연구의 결과는 다음과 같이 요약될 수 있다. 첫째, 정서중심 집단상담 프로그램에 참여한 실험집단은 프로그램에 참여하지 않은 통제집단에 비해 프로그램 참여 후 정서조절이 증가되었다. 둘째, 정서중심 집단상담 프로그램에 참여한 실험집단은 프로그램에 참여하지 않은 대조군보다 프로그램 참여 후 대처 전략을 향상시켰다. 셋째, 정서중심 집단상담 프로그램에 참여한 실험집단은 프로그램에 참여하지 않은 통제집단보다 프로그램 참여 후 심리적 안녕감을 향상시켰다. 이러한 연구를 바탕으로 시민단체 활동가의 정서적 조절 능력을 향상시키고 효과적인 스트레스 대처 전략을 활용하여 심리적 안녕감을 향상시키는 데 효과적인 프로그램이라는 것을 밝혀냈다는 데 의의가 있다. 마지막으로 본 연구결과를 토대로 후속 연구에 대한 제언과 시사점을 논의하였다. The purpose of this study is to verify the effects of Emotional focused group counseling program on the emotion regulation, stress coping strategies, and psychological well-being with NGO activists. To this end, the study subjects consisted of 18 NGO activists, 9 people each in the experimental group and the control group, and a total of 10 sessions were conducted twice a week for 100 minutes for 5 weeks. To verify the effectiveness of the program, the experiment-control group pre-identity verification and covariance analysis (ANCOVA) were conducted. The results of this study can be summarized as follows. First, the experimental group that participated in the Emotional focused group counseling program showed increased emotional regulation after participating in the program compared to the control group that did not participate in the program. Second, the experimental group that participated in the Emotional focused group counseling program improved coping strategies after participating in the program than the control group who did not participate in the program. Third, the experimental group that participated in the Emotional focused group counseling program improved psychological well-being after participating in the program than the control group who did not participate in the program. Based on these studies, it is meaningful that it is an effective program to improve the emotional regulation ability of NGO activists and to improve psychological well-being by utilizing effective stress coping strategies. Finally, based on the results of this study, suggestions and implications for subsequent research were discussed.
Jung E. Son,Seoyoung Nam,Kyungwon Kang,Joongbeom Lee 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
This paper deals with an fault detection and diagnosis (FDD) of appropriate refrigerant charge amount (RCA) using a feed-forward backpropagation neural network (FBNN) for multi-split variable refrigerant flow (VRF) systems. Faulty RCA operations of the VRF systems result in thermal discomfort for the occupants, lower coefficient of performance (COP), and equipment damage. Typical data driven neural network based methods give rise to computation complexity caused by data dimensionality and redundant data. Moreover, critical weakness of the BPNN results in deficient model generalization and over-fitting. This paper presents a fault detection scheme that uses the reliefF feature selection algorithm as a preprocessing technique to avoid the explosion of complexity while extraction critical feature information. Then, using a BPNN, it is shown that the proposed FDD algorithm renders the RCA of VRF systems classified. As a result the proposed technique can help to maintain the healthy VRF systems, provide thermal comfort, and save energy consumption.
Jung E. Son,Seoyoung Nam,Nakhoon Kim 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10
This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover, the combined techniques usually result in only uniformly ultimately bounded (UUB) stability caused by the inherent NN approximation error. This paper presents a control scheme that uses an integral sliding mode control as a feedback term and an adaptive neural controller as a feedforward term based on the desired compensation adaptive law (DCAL) technique. First, we develop a new DCAL formulation which avoids the explosion of complexity caused by the general NN backstepping scheme to compensate for nonlinear system uncertainties, bounded system disturbances, and unknown state time delays. Then, using a Lyapunov-Krasovskii (LK) functional, it is shown that the proposed controller renders the class of uncertain nonlinear time-delay systems asymptotically stable.
Lipid Bilayer Control of Nascent Adhesion Formation
Peter J. Butler,Seoyoung Son 대한의용생체공학회 2015 Biomedical Engineering Letters (BMEL) Vol.5 No.3
The adhesion of cells to an extracellular matrix is a dynamicprocess involving structural and signaling proteins, that, inturn, regulate key cellular processes, including migration,gene expression, differentiation and signaling. Integrins playimportant roles as primary adhesion receptors, and integrinmediatedcell adhesion sites can be differentiated, based onsize and location, into nascent adhesions, focal complexes,focal adhesions, and fibrillar adhesions. The formation ofnascent adhesions to a surface requires the bending of themembrane toward a surface, diffusion of integrins to the areaof close contact, and molecular adhesion. Each of theseprocesses is sensitive to the lipid make up of the membrane. Therefore, the lipid bilayer may exert significant control overthe dynamics of nascent adhesion formation. In this review,we consider the structure and components of cellularadhesions and lipid bilayers. We then review membraneproperties of bending rigidity, viscosity, and thickness thatare thought to have a central role in the formation of nascentcell adhesions via membrane curvature, redistribution ofintegrins, and bond formation.
박은총(Eunchong Park),김수연(Sooyeon Kim),손승우(Seungwoo Son),박서영(Seoyoung Park),이두희(Duehee Lee) 대한전기학회 2019 전기학회논문지 Vol.68 No.12
We propose a new method that improves the prediction accuracy of wind power generation by using two machine learning algorithms, support vector machine (SVM) and gradient boosting machine (GBM). We participate in the wind power forecasting competition held by KPX to verify the performance. First, we construct individual models in parallel using the data only at the corresponding target time since the data quality of weather data decreases as the target time increases. Second, we use the ensemble method by using two machine learning algorithms, SVM and GBM. Third, we extend the wind power generation data by interpolation to reduce the variation and estimate actual wind power generation. Fourth, we reconstruct the extended wind power generation data to prevent from converging to the average value and. We describe characteristics of stepwise model and present each result with normalized mean absolute error.