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김경윤,심기철,김태곤,배세현,이준철,김기도 한국콘텐츠학회 2013 International Journal of Contents Vol.9 No.4
The purpose of this study was to examine the effects of general sling-bridge exercise (GSE) and sling-bridge exercise with rhythmic stabilization technique (SER) on trunk muscle endurance and flexibility in adolescents with low back pain (LBP). 30 adolescents who had complaints of LBP were randomly assigned to one of the two groups: the GSE group (n=15) and SER group (n=15). Subjects performed each exercise programs for 4 weeks with the aim of improving trunk muscle stability; GSE group trained general bridge exercise with sling, SER group trained rhythmic stabilization bridge exercise with sling. The static and dynamic trunk muscle endurance and flexibility were measured before and at the end of the exercise program. The static and dynamic trunk muscle endurance were significantly improved in both groups (p<.05) and the SER group showed significant difference from the GSE group after the exercise (p<.05). The trunk muscle flexibility was significantly improved in both groups (p<.05) and the SER group were significantly different from GSE group post-exercise (p<.05). The results of this study showed that sling bridge exercise with rhythmic stabilization technique may be appropriate for improving trunk muscle stability in adolescents with LBP.
김경윤,양형정,김수형,김정식,Cheah, Wooi-Ping,Kim, Kyoung-Yun,Yang, Hyung-Jeong,Kim, Soo-Hyung,Kim, Jeong-Sik 한국정보처리학회 2008 정보처리학회논문지B Vol.15 No.2
본 논문에서는 인과관계 지식의 표현과 추론에 가장 대표적으로 사용되는 퍼지인식도(FCM, Fuzzy Cognitive Map)와 베이지안 신뢰 네트워크(BBN, Bayesian Belief Network)를 구조적으로 분석한다. 퍼지인식도와 베이지안 신뢰 네트워크는 의사 결정을 지원하는데 중요한 인과관계 지식을 표현하고 추론하는데 사용되는 가장 대표적인 프레임워크이지만 인과관계 지식응용 영역에서 두 프레임워크의 역할에 대한 구조적 비교 연구는 이루어지지 않고 있다. 본 논문에서는 두 프레임워크의 구조적 비교를 통해 퍼지인식도와 베이지안 신뢰 네트워크의 중요한 특징들을 추출하고, 이를 통해 인과 지식 공학에서 어떻게 퍼지 인식도와 베이지안 신뢰 네트워크가 이용되어야 하는지를 보인다. 인과관계 지식의 표현과 추론의 과정을 평가하는데 비교 평가를 위한 항목으로서 본 논문에서는 사용성, 표현력, 추론능력, 정형화와 완결성이 사용되었다. Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.