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청소년 태권도 선수들의 운동열정과 자기관리 및 스포츠 자신감의 관계
류호윤(Yoo, Ho-Yoon),양대승(Yang, Dae-Seung) 한국체육과학회 2017 한국체육과학회지 Vol.26 No.3
This study aims to analyze the impact of Passion and self-management strategy on sport confidence in Youth Taekwondo players and provide basic data helpful for improving their ability to play Taekwondo, and resultingly, our conclusions can be drawn as follows. For this purpose, the subjects were selected via purposive sampling method of nonprobability sampling with 502 Youth Taekwondo athletes. The collected data were analyzed with reliability analysis, correlation analysis, confirmatory factor analysis, and structural equation analysis through SPSS 21.0, SPSS PROCESS Macro, AMOS 21.0 program. Results are summarized as below: First of all, Passion of the players had positive effect on Sport confidence. Secondly, passion of the players had positive effect on Self-Management, and Self- Management had positively affect Sport confidence. Lastly, Self-Management of the players mediated the relationship between Passion and Sport confidence.
인공지능 모델에 의한 지하수위 모의결과의 적절성 판단을 위한 허용가능한 예측오차 범위의 추정
신문주,문수형,문덕철,류호윤,강경구,Shin, Mun-Ju,Moon, Soo-Hyoung,Moon, Duk-Chul,Ryu, Ho-Yoon,Kang, Kyung Goo 한국수자원학회 2021 한국수자원학회논문집 Vol.54 No.7
Groundwater is an important water resource that can be used along with surface water. In particular, in the case of island regions, research on groundwater level variability is essential for stable groundwater use because the ratio of groundwater use is relatively high. Researches using artificial intelligence models (AIs) for the prediction and analysis of groundwater level variability are continuously increasing. However, there are insufficient studies presenting evaluation criteria to judge the appropriateness of groundwater level prediction. This study comprehensively analyzed the research results that predicted the groundwater level using AIs for various regions around the world over the past 20 years to present the range of allowable groundwater level prediction errors. As a result, the groundwater level prediction error increased as the observed groundwater level variability increased. Therefore, the criteria for evaluating the adequacy of the groundwater level prediction by an AI is presented as follows: less than or equal to the root mean square error or maximum error calculated using the linear regression equations presented in this study, or NSE ≥ 0.849 or R<sup>2</sup> ≥ 0.880. This allowable prediction error range can be used as a reference for determining the appropriateness of the groundwater level prediction using an AI.