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대학스포츠 지도자의 카리스마 리더십이 신뢰 및 팀 응집력에 미치는 영향
안시후(An, Si-Hoo),전병관(Chun, Byung-Kwyan) 한국체육과학회 2014 한국체육과학회지 Vol.23 No.1
The purpose of this study was to examine the effect of charismatic leadership for college sports on trust and team cohesion of college athletes. To achieve this purpose of the study, purposive sampling method among nonprobability sampling methods was employed and college athletes in Gyeonggi area were chosen as the subject of the study. Out of total 380 questionnaires collected, 354 questionnaires were used for final analysis excluding 26 questionnaires that were not answered properly. The method of statistical analysis was Factorial Analysis, Reliability Analysis, T-test, One-way ANOVA, Correlation Analysis and Multiple Regression Analysis through statistic SPSS version 21.0. The result acquired from study methods and procedure above are as followings: First, in terms of demographic characteristic, charismatic leadership, trust and team cohesion had statistically significant differences. Second, concerning the effect of charismatic leadership for college sports on trust, expected trust and expression had an effects on trust and tendency for athletes to identify themselves with their leaders. Third, charismatic leadership for college sports showed statistically significant effects on team cohesion. Fourth, charismatic leadership for college sports on trust showed statistically significant effects on team cohesion.
전장 상황 인지 보고서 생성을 위한 Text-to-Text 멀티 태스크 학습
허종국,임새린,안시후,박진혁,이영재,조용원,목충협,조준호,김성범 대한산업공학회 2022 대한산업공학회지 Vol.48 No.6
Advances in new communication technologies enable commanders to collect various information in battlefield situations. However, it is difficult to make quick and accurate decisions on the battlefield because of vast amount of information. To address this problem, several studies attempt to change tabular data into an easy-to-understand text format. Existing table-to-text studies are not suitable for battlefield situations because they use specific domain data such as WIKIBIO and WIKITABLETEXT. In this study, we propose a table-to-text transfer transformer (TaT4) that uses special tokens to transform log table data into a single sequence to preserve table information. Moreover, the proposed TaT4 uses multi-task learning that can leverage cross-task data of types in a single model to improve generalization performance. We conduct experiments on eight datasets generated from three Korean defense modeling and simulations (M&S) of battlefield situations in the Army, Air Force, and Navy. The proposed TaT4 outperforms the existing table-to-text models.