• Title/Summary/Keyword: obstacle recognition

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The Evolution of Innovation Cluster : Focusing on the Daedeok Innopolis (혁신클러스터의 진화 : 대덕연구개발특구를 중심으로)

  • Hwang, Doohee;Cheong, Young Chul;Chung, Sunyang
    • Journal of Korea Technology Innovation Society
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    • v.21 no.4
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    • pp.1207-1236
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    • 2018
  • This paper explores the life cycle of innovation cluster, especially focussing on the Korean representative innovation cluster, Daedeok Innopolis. For this purpose, we review theoretically how an innovation cluster has been growing up. In particular, we discuss how a cluster has been formed and activated by governmental innovation policies from an evolutionary perspective. By doing so, the study identifies the typical features of an innovation cluster according to each dimensions of the cluster life cycle. The results of this study are as follows: First, in this study, Daedeok Innopolis has characteristics of latency, emergence, growth, and maturity from evolutionary perspective. Second, the governmental structure of the Daedeok Innopolis is a strong government-led and top-down structure, which has features of inclusiveness and flexibility such as umbrella policy. Third, the Daedeok Innopolis can be seen that adaptive or renewal development, as while, it can be applied fine adjustment the innovation cluster policy towards the recognition of innovation obstacle at each dimensions of the life cycle. Therefore, these discussions expose what kind of policy interventions should be addressed to form and develop the innovation cluster according to the cluster life cycle, as while, the development of adaptive policies during the risk and take-off period. Ultimately, the study provides that a different kind of policy instruments and tools should be implemented according to innovation cluster development and its distinctive characteristic per each dimensions of the cluster life cycle.

A Survey on the Knowledge and Attitude of Workers Concerning Occupational Health (근로자의 산업보건 지식과 태도에 관한 조사연구)

  • 박영식;조수열;남철현
    • Journal of Environmental Health Sciences
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    • v.18 no.2
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    • pp.3-18
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    • 1992
  • This research was carried out on 1,017 production workers for four months from May to August, 1991, to search more effective management method of their health by grasping their knowledge and attitude on industrial health. The results of this study can be summarized as follows: 1. As for general characteristics, 74.2% were male and 25.8% were female among the 1,017 workers. The two largest age groups were 30~39, 38.7%. As for education level, graduation from high school was 58.6%, 61.2% were married, 35.9% owned their house, and workers who worked more than 1 year less than 5 years was 52.9%, workers who worked 8 hours a day was 46.7%, the largest group income level was 60~69 thousand won 21.2%, and the degree of satisfaction with work was ordinary, 45.6%. 2. The degree of recognition concerning occupational diseases was 92.5% at a very high rate. Causes of occupational diseases under the present work field were in order of noise, dust, heavy metal. The largest group of the counterplan for prevention was an improvement of working environment, 62.0%. 3. The major cause that threatens worker's health was poor working environment, 31.4%. As the best method for workers' health management, working environment management was pointed. 4. As for health examination result, the response that it is of use to health management was 53.8%. As for examination method and result, 42.7% responded that they are formal. The practice period was more than once every six months as the largest group, and the highest desire for improvement was that they wants an exact information of the result. 5. 49.3% of the respondents know about the measurement of working environment an the response that the measurement is necessary to improve working environment was 57.9%, and that the results from the measurement were reflected on improvement an management 57.5%. Appropriate period to take a measurement was more than once per six months, 40.2% and per three months, 29.1%. 6. As for safety and halth instruction, 34.5% were educated for both, 38.2% for only safety education and just 4.6% for only health education. 51.9% responded that they had never been educated out of work place. The period of its practice was more than once a month, 39.5% and every three months, 21.3%. 7. The importance of safety and health showed that the one is equal to the other, 59.8%, that the one is more important, 29.6%, and that other is more important, 7.6%. 67.7% said the necessity of a safety and health manager. 8. In spite of more or less health obstacle of work environment, 14.9% of the respondents wanted to overwork to gain an allowance for over-time work, 39.9% didn't, and 40.2% according to condition and state. 9. As the most important cause of industrial accident, 40.2% indicated unsafe behavior. As for the individual protective instrument, 66.1% of all the respondents said they have worn it to protect industrial diseases. 10. As for the degree of understanding of the contents in Industrial Safety and Health Law and Industrial Law of Accident Insurance, an affirmative response was respectively 49.3% and 50.8% and the sources of safety-health information were televisions and radios, 28.0%. Therefore, it is necessary that we do positive working environmental improvement, continuous management and health education's inforcement to increase their health and prevent occupational diseases.

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Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence (인공지능의 사회적 수용도에 따른 키워드 검색량 기반 주가예측모형 비교연구)

  • Cho, Yujung;Sohn, Kwonsang;Kwon, Ohbyung
    • Journal of Intelligence and Information Systems
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    • v.27 no.1
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    • pp.103-128
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    • 2021
  • Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.