• Korean Journal of Health Education and Promotion
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• v.33 no.5
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• pp.83-91
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• 2016

Objectives: This study was aimed to investigate utilization of health promotion indices of the 3rd National Health Plan 2011-2020 (HP2020) in the 6th Korean Community Health Plan. Methods: Health promotion indices were defined as a set of indicators on smoking, alcohol drinking, physical activity, nutrition and obesity used in HP2020. This indices were categorized into essential indicator, accessory indicators and others. Based on chi-square test, we analyzed utilization of health promotion indices in 186 Community Health Plans by regional classifications: four large influence areas (SudoGangwon, Chungcheong, Gyeongsang and HonamJeju) and four regional classification (metropolitan district, city, urban-rural area and rural area) Results: Among total 186 plans, indicator utilization rate were 97.8% in smoking, 71.0% in alcohol drinking, 91.9% in physical activity, 99.5% in nutrition and 72.0% in obesity. Utilization rates of alcohol drinking indicators and essential indicators in alcohol drinking show significantly difference by four large influence areas (p<0.01) and four regional classification (p<0.01). Essential indicators in physical activity show significantly difference by four large influence areas (p<0.01). Conclusions: Central government must provide technical assistance and educate personnel in community health centers and provincial health department about meaning and usefulness of Health Plan 2020 indicators.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.47-56
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• 2020

The stock index is used not only as an economic indicator for a country, but also as an indicator for investment judgment, which is why research into predicting the stock index is ongoing. The task of predicting the stock price index involves technical, basic, and psychological factors, and it is also necessary to consider complex factors for prediction accuracy. Therefore, it is necessary to study the model for predicting the stock price index by selecting and reflecting technical and auxiliary factors that affect the fluctuation of the stock price according to the stock price. Most of the existing studies related to this are forecasting studies that use news information or macroeconomic indicators that create market fluctuations, or reflect only a few combinations of indicators. In this paper, this we propose to present an effective combination of the news information sentiment analysis and various macroeconomic indicators in order to predict the US Dow Jones Index. After Crawling more than 93,000 business news from the New York Times for two years, the sentiment results analyzed using the latest natural language processing techniques BERT and NLTK, along with five macroeconomic indicators, gold prices, oil prices, and five foreign exchange rates affecting the US economy Combination was applied to the prediction algorithm LSTM, which is known to be the most suitable for combining numeric and text information. As a result of experimenting with various combinations, the combination of DJI, NLTK, BERT, OIL, GOLD, and EURUSD in the DJI index prediction yielded the smallest MSE value.

• Environmental and Resource Economics Review
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• v.19 no.4
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• pp.733-752
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• 2010

This study applies the Luenberger indicator approach to estimate productivity of the Korean fossil-fueled power plants. A panel data set of 25 power plants was used. The method incorporates $CO_2$ emission as an undesirable output and shows that ignoring $CO_2$ emission overestimates the productivity change. There are two sources of overestimation. First, the usual method estimates productivity change ignoring the increase in $CO_2$ emission that occurred during the study period. Second, the productivity change estimated by the usual method that does not incorporate $CO_2$ emission is very sensitively affected by the change in operation rate. The paper decomposes the productivity change into the efficiency change and the technical change. The results show that the two sources contribute to the productivity change almost equally. It is also shown that the size and the pattern of productivity change are dependent on the plants' fuel types. Non-LNG power plants which saved their energy consumption and thereby reduced their $CO_2$ emission have achieved relatively high rate of productivity improvement.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.1-15
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• 2017

In 2014, the Financial Services Commission designated the Tech Credit Bureaus(TCB) to issue technical credit evaluation reports. The Five credit rating agencies, KEB Hana Bank and others have issued the technical credit reports since the summer in 2014. Meanwhile, the technology evaluation model of KEB Hana Bank consists of 25 detailed evaluation items. These item classes are weighted and the technology rating is systematically. The technology rating is combined with the credit rating to calculate the technology-credit rating. In this paper, we analyzed the 406 evaluation results issued by KEB Hana Bank. Based on the number of years of work experience, company managerial years, technical personnel score, the possession of R&D department, the amount of R&D investment, the number of certifications, and the number of patents, the Correlation between the above items and the technical grade was analyzed. It was found that quantitative indicators such as the presence of R&D department, patent numbers, and R&D investment expenses had a significant effect on the company's technology grade, and in particular, the presence of R&D department was shown a high correlation with the technology rating.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.48 no.1
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• pp.93-99
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• 2016

The extraction efficiency depending on the extracting methods and the conditions of extraction was investigated. The common steam extraction was compared to the distillation extraction method. The effects of the samples size and the extraction time on the extract yield were also investigated by using UV-Vis spectrophotometer. One of the functional components of pine needle extract as the natural phenol base components were detected by the UV-VIS at around 235 nm wavelength range. The absorbance intensity at around 235 nm wavelength of the pine needle extract was used as the indicator of the extraction efficiency in this experiment. The distillation extraction showed the higher extract yield than the steam extraction. The grinding treatment of pine needles also resulted in the better extract performance, but the severe grinding showed a little decrease in the extract yield especially in case of the distillation extraction method. More than half of the extract was collected at the first stage of the extraction, that was the first 15 minutes in the total 60 minutes extraction.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Structural Engineering and Mechanics
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• v.44 no.1
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• pp.51-60
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• 2012

Concrete sleepers are essential components of the conventional railway. As support elements, sleepers are always subjective to a variety of time-dependent loads attributable to the train operations, either wheel or rail abnormalities. It has been observed that the sleepers may deteriorate due to these loads, inducing the formation of hairline cracks. There are two areas along the sleepers that are more prone to crack: the central and the rail seat sections. Several non-destructive methods have been developed to identify failures in structures. Health monitoring techniques are based on vibration responses measurements, which help engineers to identify the vibration-based damage or remotely monitor the sleeper health. In the present paper, the dynamic effects of the cracks in the vibration signatures of the railway pre-stressed concrete sleepers are investigated. The experimental modal analysis has been used to evaluate the modal bending changes in the vibration characteristics of the sleepers, differentiating between the central and the rail seat locations of the cracks. Modal parameters changes of the 'healthy' and cracked sleepers have been highlighted in terms of natural frequencies and modal damping. The paper concludes with a discussion of the most suitable failure indicator and it defines the vibration signatures of intact, central cracked and rail seat cracked sleepers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.64-71
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• 2016

Recently, nanotechnology has grown as one of the leading science technology along with other converging technologies such as biology, information, medicine etc., bringing the continuous investment of the government in nano-related field. However, it is difficult to measure and evaluate the performance of the national research and development programs because of the multidimensional character of the expected outcomes. This study aims to measuring efficiency of the national nanotechnology research and development programs using DEA model. The decision making units are nine nano-related ministries including the Ministry of Science, ICT and Future Planning. The input variables are total expenditure, number of the programs and average expenditure per program. The output variables are science, technology and economic indicator, and the combination of these outputs are respectively measured as seven different DEA cases. The Ministry of Science, ICT and Future was the first efficient ministry in total technical efficiency. Ministry of Agriculture, Food and Rural Affairs and the Ministry of Food and Drug Safety were efficient in pure technical efficiency, when the Ministry of Commerce Industry and Energy took the first in the scale efficiency. The program efficiency was affected by organizational characteristics such as the institution's scale, the concentration of the research paper or the patent, technology transfer or the commercialization. The result of this study could be utilized in development of the policy in the nanotechnology and the related field. Furthermore, it could be applied for the modification of expenditure management or the adjustment of the research and development programs' input and output scale for each ministry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.111-117
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• 2017

The National Competency Standards (NCS) was designed to implement a competence-based society and solve the problem of inconsistency between the industrial field and education, training, and certification system. This study designed and developed the Korean NCS core vocational competence system, in accordance with the NCS, as an infrastructure to solve fundamental problems such as re-education and the social costs that are incurred in the workplace. Further, this study designed and developed standard indicators to evaluate the technical performance of the system for the global advancement of the Korean NCS core vocational competence system. The NCS core vocational competence system has been developed as an appropriate response type for multiple devices such as computers, tablet PCs, and cellular phones. For the global advancement of the Korean NCS core vocational competence system, this study designed and developed 10 performance evaluation indicators in accordance with 10 global standards, such as linkage-target operating system, interface protocol, packet format, encryption, class component, simultaneous access number, supervisor-to-testtaker response speed, server-to-admin response speed, auto-recovery speed for test answers, and real-time answer transmission speed.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.