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Research on New Media Art Generation by Usingthe Empirical Research Method
Cai-xian Ye,Tsenguun Ganbat,Lijun Xu 한국인터넷전자상거래학회 2023 인터넷전자상거래연구 Vol.23 No.6
With the rapid development of new computer technology, the field of new media art is facing new ways of creation and expression.This paper aims to use MR technology, big model AIGC and decision tree CART algorithm to reuse circular space, generate scenes in real time, provide rich and personalized experience for new media art trainer, and improve the replay rate of customers. This study uses empirical research method to test teenagers under 15 years old as users to study their physiological and psychological responses when using new media art works. Then through questionnaire surveys, in-depth interviews and other methods to obtain users ' experience feedback on new media art works to understand the user's cognitive, emotional, behavioral and other experiences of art works.Then data preprocessing and data mining are used to process the data to ensure the reliability and effectiveness of the experimental samples.Use machine learning model to train and use generative artificial intelligence to generate content and then generate new media art based on user experience in real time.
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Pin-Chao Liao,Bingsheng Liu,Yanqing Wang,Xiaoyun Wang,Tsenguun Ganbat 대한토목학회 2017 KSCE Journal of Civil Engineering Vol.21 No.7
Since human behavior has been argued as one of the most critical leading indicators of accidents, effective intervention in workers’ behavior may foster improved safety performance. Given that human behavior is directed by cognition, researchers have proposed various cognitive models of human behavior in order to provide a clearer understanding of how to allocate management resources. However, the influence of human cognition on behavior remains an overarching concept, rather than providing an understanding of the heterogeneity among various trades. Therefore, cognition research provides no guidance regarding how to strategically allocate management resources such as training. This study employed structural equation modeling to identify the cognitive structures of two trades (reinforcing steel bar and elevator workers). We find that the cognitive structures of workers in these sample trades are significantly different, indicating that management strategies should vary accordingly. Training of mechanical workers should focus on crew leads, who can further influence self-efficacy, risk comparisons, and workers’ perception of external conditions. For rebar crews, safety training should focus on self-supporting defense capability and elucidating dangerous behaviors. External conditions have a significant role in ensuring worker safety; safe facilities engender safe behaviors. This study lays a foundation of strategic resource allocation for behavioral management on construction jobsites.
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