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알코올 의존 환자 가족의 공동의존에 영향을 미치는 요인과 이에 따른 가족의 대처방식
선지영(Jee-Young Sun),이계성(Kye-Seong Lee),신상은(Sang-Eun Shin),박주언(Ju-Eon Park) 한국중독정신의학회 2006 중독정신의학 Vol.10 No.2
Objectives:The purpose of this study is to investigate the level and affecting factors of codependence & relationship with how to cope with patient in the family of alcoholics. Method:The subject were 60 family members of patients with alcohol dependence. All subjects were assessed by questionnaire about the demographic data, Children of Alcoholics Screening Test (CAST), Korean Version of Alcohol Use Disorders Identification Test (AUDIT-K), the Korean Version of Checklist from Codependents Anonymous (CCA-K), Spouse Sobriety Influence Inventory (SSII). Alcohol related characteristics of patients were surveyed through chart review and interview with family members. Results:First, the level of codependence among family with alcohol dependence was 93.3%. Among the characteristics of family, only the number of male brothers had significant positive relation with codependence. The presence of job among family members related with codependence & had significant positive relationship with how to cope with patients. Second, among the characteristics of patients, many factors that reflects severity of alcohol dependence had significant positive relationship with codependence & how to cope with patients. Conclusion:Our result support that codependence was the stress response came from in the relation with alcohol dependent patients because alcohol related characteristics of patients highly related with codependence compared to the characteristics of family members such as family history of alcohol. In the aspect of treatment, codependence should be considered, we have to help the family members to increase capacity how to cope with patients.
RefineDet과 Deformable convolution을 활용한 다중 도로 객체 검출 및 교통 신호, 표지 인식 네트워크
이소열(So-Yeol Lee),선지영(Jee-Young Sun),고성제(Sung-Jea Ko) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
In this paper, we propose a deep convolutional neural network that detects multiple on—road objects and recognizes the traffic lights and signs for autonomous driving. The proposed network first detects on—road objects by using RefineDet with deformable convolution, and the color of traffic lights and subtype of traffic signs are then classified by a simple convolutional neural network—based recognition module. Experimental results demonstrate that the proposed network can effectively detect on—road objects and recognize traffic lights and signs.
한규범 ( Gyu-beom Han ),선지영 ( Jee Young Sun ),임경선 ( Kyung-sun Lim ),김종국 ( Jong-kook Kim ) 한국정보처리학회 2012 한국정보처리학회 학술대회논문집 Vol.19 No.2
사람이 근육을 움직여 활동을 하면 근골격근에서 50μV~5mV의 미세한 전압이 측정된다. 이 신호를 증폭하고 적절한 주파수를 여과시키면 근육의 수축·이완의 정도를 알아내어 움직임이나 동작을 유추해 낼 수 있다. 본 논문에서는 의수 또는 Power-Assist 로봇 등을 사람의 손가락 움직임과 동일하고 더 정밀하게 제어하기 위해 상완 상단부분에서 손가락의 근전도를 측정하는 방식을 연구한다.