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소셜 빅데이터를 활용한 패럴림픽 인식 분석 : 패럴림픽 개막 전, 중, 후 중심으로
김대경,한동기,이현수 한국특수체육학회 2024 한국특수체육학회지 Vol.32 No.4
The purpose of this study is to understand the public's overall perception through analysis of perceptions of Paralympic keywords using social big data. In order to achieve this research purpose, the analysis data were divided into periods before the opening (July 27 to August 27), during the period (August 28 to September 8), and after the closing (September 9 to October 10), and collected keyword unstructured data on the Paralympics of Naver, Daum Google's news, blogs, and cafes through the Textom collection search engine. And word frequency analysis, TF-IDF, and emotion analysis were conducted. Therefore, the analysis results of this study are as follows. First, frequency analysis and TF-IDF were similar to those of the Paralympics, athletes, Paris, and after the closing of the Paralympics during the opening and during the period, but new keywords such as disability, Boccia, Brunei, challenge, and emotion appeared during the period, and the closing ceremony after the closing ceremony, I am proud, and the president's keywords were newly derived. Second, as a result of the document emotion analysis, the positivity before, during, and after the closing was higher than negative, and overall neutrality was higher. Third, looking at the results of the emotional vocabulary, the positive vocabulary before, during, and after the opening period was generally high, but it was high in proportion to the negative vocabulary such as rejection, sadness, and fear.
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지체장애인의 모바일 스포츠 콘텐츠 이용만족도가 스포츠 직·간접 참여에 미치는 영향: 기술수용모델을 중심으로
김대경,문경민,박진우 한국특수체육학회 2024 한국특수체육학회지 Vol.32 No.3
The purpose of this study is to analyze the impact of satisfaction with mobile sports content on the direct and indirect participation in sports by individuals with physical disabilities, applying the Technology Acceptance Model. The study was conducted via a survey, with data collected from 380 individuals with physical disabilities participating in physical activities through convenience sampling. After excluding 36 incomplete or insincere responses, 344 valid questionnaires were used for the study. The collected data were analyzed using SPSS 26.0 and AMOS 26.0 programs. The results of the confirmatory factor analysis indicated that the research model was appropriate, and the findings from the path analysis are as follows: First, satisfaction with sports content positively influenced perceived usefulness and ease of use. Second, perceived usefulness and ease of use had a positive effect on the intention to use. Third, the intention to use had a positive effect on direct sports participation, but a negative effect on indirect sports participation.
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Cox 비례위험 심층 신경망을 적용한 고장율 추정에 관한 연구
김대경 한국신뢰성학회 2025 신뢰성응용연구 Vol.25 No.2
Purpose: A deep neural network (DNN) is a network that is configured to perform nonlinear operations several times with multiple hidden layers in artificial neural networks[1]. In reliability analysis, the failure rate is an important measure of the reliability of a system or component. This paper reports the use of a DNN to estimate the failure rate for complex outcome data with probabilistic and statistical models such as Cox proportional hazards models. Methods: DNNs are the most appropriate statistical model for estimating failure rates, and artificial neural networks have been applied to failure rate modeling. We estimated the hazard function (failure rate) using DeepSurv, a DNN model. We evaluated the cumulative reference risk function using the Breslow estimator and then employed it to determine the hazard (failure) rate function. Results: We consider the data set provided by task[15] (left truncated and right truncated survival time data sets) and archived at https://github.com/jiaxiang-cheng/Random-Weighted-Bootstrap-with-Weibull. The sample size is 18,000. Conclusion: The risk score and the manufacturer's (average) risk score were calculated. The risk score tends to decrease approximately as the number increases. That is, the risk score gradually decreases with increasing failure time. Upon estimating the hazard (failure rate) functions for 10 manufacturers, high and low values are observed for manufacturers 2 and 8, respectively.
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