• 제목/요약/키워드: Backing paper

검색결과 62건 처리시간 0.018초

학동기의 스포츠활동과 특기적성활동의 참가가 감성지수 및 성격특성에 미치는 영향 (The influencing effect on E.Q. and personality that both sports activity & speciality aptitude activity in school-childhood can cause)

  • 이한기
    • The Journal of Korean Physical Therapy
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    • 제16권1호
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    • pp.140-156
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    • 2004
  • This study, to find whether a sports activity and a speciality aptitude activity in school-childhood can affect in forming E.Q, has been done in Gyoung-Nam area and Busan wide city with asked 222 of men and women being in their school-childhood and a group of 85 people who had not joined in such activity, using a E.Q testing paper provided by Dae-Gyo Education Corp,. and Seoul National University Educational Research Institute. Following is the results after analyzing the compiled datas. 1. The E.Q. level difference between people who joined, and who not joined in a sports action activity was reported existing, the total E.Q average of those who joined was resulted 212.6, a point 29.6 higher than those not joined of 183.0 ( p< .05). As for the E.Q causing points, it resulted that the points of the joined group is generally up than that of the non-joined group, especially this difference was remarkable in terms of feeling recognition or feeling control, a finding that deserves an attention ( p<.05) 2. Joining periods of sports activity did also have relation to develping E.Q. of school-childhood according to this research, the total E.Q points of a group joined in the activity more than 2 years was 215.5 points, which was 17.4 points higher than those not joined of 186.5 points ( p< .05). Backing again to E.Q causing points in this case, it resulted without exeption of all main causes that those who joined in more than 2 years are generally higher than that of those joined less than 2 years, especially the difference was regarded as big in terms of feeling recognition or feeling control, a finding that deserves an attention ( p<.01). 3. The E.Q. differnce between those joined in a specialty aptitude activity and not joined was studied existing, the total E.Q average points of those joined in a specialty aptitude activity was 207.8, a higher figure by 21.3 points than those not joined group of 186.5 ( p< .05). As for the E.Q causing points, it resulted without exeption of all main causes that those who joined are generally higher than that of those not joined, especially for feeling recognition or feeling control, this difference was more clear, a finding that deserves an attention ( p<.01). 4. It also resulted that E.Q growth depends on the periods to have joined in a speciality aptitude activity, for example, the total E.Q points of those joined in the activity more than 2 years was 217.1, a total more higher by 13.5 points than 203.6 of those not joined ( p< .05). For the E.Q. causing points, it, with the exception of empathy was resulted that those who joined in the speciality aptitude activity more than 2 years are generally higher than those joined less than 2 years, especially the difference is remarkable in terms of feeling recognition or feeling control, a finding that is also remarkable ( p<.05). 5. The E.Q difference between the men and women who joined in both activities of sports & speciality aptitude was found existing, the total E.Q. average for women was resulted 214.2 points, which was 9.2 points higher than men of 205.0. As for the E.Q. causing points, which, without exeption of main causes, women's was reported being high than that of men, in special is more remarkable in terms of feeling control, a finding that deserves an attention. ( p<.05).

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기계학습을 이용한 수출신용보증 사고예측 (The Prediction of Export Credit Guarantee Accident using Machine Learning)

  • 조재영;주지환;한인구
    • 지능정보연구
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    • 제27권1호
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    • pp.83-102
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    • 2021
  • 2020년 8월 정부는 한국판 뉴딜을 뒷받침하기 위한 공공기관의 역할 강화방안으로서 각 공공기관별 역량을 바탕으로 5대 분야에 걸쳐 총 20가지 과제를 선정하였다. 빅데이터(Big Data), 인공지능 등을 활용하여 대국민 서비스를 제고하고 공공기관이 보유한 양질의 데이터를 개방하는 등의 다양한 정책을 통해 한국판 뉴딜(New Deal)의 성과를 조기에 창출하고 이를 극대화하기 위한 다양한 노력을 기울이고 있다. 그중에서 한국무역보험공사(KSURE)는 정책금융 공공기관으로 국내 수출기업들을 지원하기 위해 여러 제도를 운영하고 있는데 아직까지는 본 기관이 가지고 있는 빅데이터를 적극적으로 활용하지 못하고 있는 실정이다. 본 연구는 한국무역보험공사의 수출신용보증 사고 발생을 사전에 예측하고자 공사가 보유한 내부 데이터에 기계학습 모형을 적용하였고 해당 모형 간에 예측성과를 비교하였다. 예측 모형으로는 로지스틱(Logit) 회귀모형, 랜덤 포레스트(Random Forest), XGBoost, LightGBM, 심층신경망을 사용하였고, 평가 기준으로는 전체 표본의 예측 정확도 이외에도 표본별 사고 확률을 구간으로 나누어 높은 확률로 예측된 표본과 낮은 확률로 예측된 경우의 정확도를 서로 비교하였다. 각 모형별 전체 표본의 예측 정확도는 70% 내외로 나타났고 개별 표본을 사고 확률 구간별로 세부 분석한 결과 양 극단의 확률구간(0~20%, 80~100%)에서 90~100%의 예측 정확도를 보여 모형의 현실적 활용 가능성을 보여주었다. 제2종 오류의 중요성 및 전체적 예측 정확도를 종합적으로 고려할 경우, XGBoost와 심층신경망이 가장 우수한 모형으로 평가되었다. 랜덤포레스트와 LightGBM은 그 다음으로 우수하며, 로지스틱 회귀모형은 가장 낮은 성과를 보였다. 본 연구는 한국무역보험공사의 빅데이터를 기계학습모형으로 분석해 업무의 효율성을 높이는 사례로서 향후 기계학습 등을 활용하여 실무 현장에서 빅데이터 분석 및 활용이 활발해지기를 기대한다.