• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.75-89
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• 2007

This study has its aim at suggesting new direction of our education to search different ways in housing contents by comparing the necessity perception and demands between teachers and students for housing contents in Technology Home Economics curriculum of middle school. To achieve this aim, I chose middle school teachers in charge of Technology Home Economics and male and female students who are in the first grade in high school in Ulsan. I sent e-mail, mail, and visited researcher to gather the data. I used SPSS +12 statistical package for frequency, percentage, mean, standard deviation, and t-test to analyze the data. Here is the result. First, In the part of application of living place, teachers had necessity perception in use and placement of furniture, and arrangement of objects. Students had necessity perception in the use and placement of furniture, the kind and choice of furniture. Also in the indoor environment and equipment part, both teachers and students had necessity perception in controlling of ventilation, temperature, and humidity. In the part of maintenance repair of housing, teachers had necessity perception in the need for maintenance management but students had necessity perception in house equipments and repair had high necessity perception Second, In housing-related general part, teachers demanded housing for elderly, disabled people, information about future housing and students demanded environmentally friendly living environment, housing for elderly, disabled people. In interior design part, teachers demanded in the expression of interior places through computer, the kind and characteristic of housing material and students demanded the way to reuse old furniture, kind and characteristic of housing material. In the part of housing preparation and occupation, teachers demanded the kind of housing-related occupation and students demanded the housing tax and the process of house purchase or concerned matter. Third, there were some difference of necessity perception and degree of demand between teachers and students. Teachers had higher necessity perception and demand in all part except in demand for housing equipment, maintenance, and environmentally friendly living environment.

• The Korean Society of Law and Medicine
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• v.22 no.3
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• pp.57-95
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• 2021

Korea's period for preservation of embryos is up to five years (the Bioethics Act). However, the study reviewed domestic and foreign laws and drew issues due to the recent demand that the development of related science and technology and the period limitation limit the rights of consent holder for embryo production. the first issue is that preserved embryos are intended for pregnancy, and it is important to ensure that the autonomy of the consent holder is protected through careful consideration based on information such as scientific evidence. the second is that regulations regarding the obligation to manage embryonic preservation institutions are needed. the third is to create a social atmosphere in which embryo creation, preservation, and disposal take place in a minimum range, considering the special status of embryos. based on this issue, the first of the proposals for rational improvement of the regulation and system about embryo preservation is the introduction of an environment in which sufficient explanation and appropriate consent can be exercised and to extend the reasons for the extension of the period, rather than specifying the specific period in law. the second is that institutionalization is necessary considering not only the obligation to manage preservation institutions but also the overall site, such as concerns that may arise as a result. lastly, we propose the introduction of a management method considering the future use of embryos, such as transfer to provide research purposes and donation of pregnancy purposes by others. this process should be a method of sufficient social discussion and consensus, as well as a general consideration of the family relationship with the born child.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.1-15
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• 2021

This study considered Forest Specialists, who are nurtured by the legal system through the analysis of laws and regulations under the jurisdiction of the Korea Forest Service. In particular, the transition process of forest-related laws and laws to train forest specialists were identified. In addition, changes and characteristics regarding the cultivation of professional forestry talents according to forestry policy were investigated. As a result, it was found that Forest Specialist on policy dealt with forestry success for forestry promotion, and forestry engineers dealt with technical skills for forestry industry development. In addition, according to the revision of the laws for the sustainable use of timber, wood-structural engineers, timber grade evaluators, and timber education specialists are trained separately. Forest Specialists concerned with forest welfare policies were found to train forest experts and complete specialized training courses to provide various services for forest cultural and recreation facilities, healing forests, and forest leisure sports facilities. There is an instructor for forest leisure sports. Forest welfare experts are divided into forest education experts and forest healing instructors; forest education specialists are further divided into forest interpreters, forest guides for children, and forest trekking guides. Forest Specialists on forest protection policy were found to train arboretum and garden experts for the efficient management and exhibition of arboretums. Gardens and tree doctors and tree treatment technicians for arboretums wer also trained. A tree doctor and a tree treatment technician were found to have the necessary qualifications to run a tree hospital business, diagnosing and treating tree damage. Therefore, it is thought that the Korea Forest Service is nurturing Forest Specialists with technical capabilities for forestry promotion, forest industry development, and tree treatment; and the Forest Specialists can provide education and welfare services at culture, recreation, treatment, and conservation sites in forests.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.464-477
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• 2013

There is a great need to develop a training program proven to change behavior and improve knowledge. The purpose of this study was to evaluate employee hygiene knowledge, hygiene practice, and cleanliness, before and after HACCP system implementation at one small-size restaurant. The efficiency of the system was analyzed using time-temperature control after implementation of u-HACCP$^{(R)}$. The employee hygiene knowledge and practices showed a significant improvement (p<0.05) after HACCP system implementation. In non-heating processes, such as seasoned lettuce, controlling the sanitation of the cooking facility and the chlorination of raw ingredients were identified as the significant CCP. Sanitizing was an important CCP because total bacteria were reduced 2~4 log CFU/g after implementation of HACCP. In bean sprouts, microbial levels decreased from 4.20 logCFU/g to 3.26 logCFU/g. There were significant correlations between hygiene knowledge, practice, and microbiological contamination. First, personnel hygiene had a significant correlation with 'total food hygiene knowledge' scores (p<0.05). Second, total food hygiene practice scores had a significant correlation (p<0.05) with improved microbiological qualities of lettuce salad. Third, concerning the assessment of microbiological quality after 1 month, there were significant (p<0.05) improvements in times of heating, and the washing and division process. On the other hand, after 2 months, microbiological was maintained, although only two categories (division process and kitchen floor) were improved. This study also investigated time-temperature control by using ubiquitous sensor networks (USN) consisting of an ubi reader (CCP thermometer), an ubi manager (tablet PC), and application software (HACCP monitoring system). The result of the temperature control before and after USN showed better thermal management (accuracy, efficiency, consistency of time control). Based on the results, strict time-temperature control could be an effective method to prevent foodborne illness.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Clinical and Experimental Pediatrics
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• v.48 no.8
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• pp.813-819
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• 2005

Purpose : Medicolegal problems start when the patient asserts the mistake of doctor and doctor does not accept it. The purpose of this study is to assess the actual condition of medicolegal problems and to provide solutions of medicolegal problems in the pediatric field. Methods : There is not official statistical data about medicolegal problems in our country. We gathered data of legal insurance program of Korean Medical Association(KMA) and court cases and other fragmentary data. Results : Between 1981 and 1995, of total 2,338 cases reported to legal problem insurance program of KMA, most common ones were 748 cases of obstetrics and gynecology. Pediatric case was ranked at the 5th, 74 cases(3.1%). According to analysis of 41 medicolegal cases' after 1990, maltreatment of patient had the highest incidence of 14 cases, injection and medication were related to 12 cases, misdiagnosis was 9 cases, patient management were related to 4 cases, and others were 2 cases. The trial result of the medicolegal cases was that 31 cases were compensated, and 8 cases were defeated, and 2 cases were still in the process. Conclusion : The aspect of medical legal problem has the tendency of radicalism and systematization. This brings an economic destitution in the patient and gives damage to a doctor. In order to reduce medicolegal problem, doctor should offer a duty of explanation and efforts to his best to satisfy patient and endeavor to make an intimate doctor-patient relationship.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Energy Engineering
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• v.28 no.3
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• pp.65-79
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• 2019

The study attempted to estimate the optimal facility capacity by combining renewable energy sources that can be connected with gas CHP in industrial complexes. In particular, we reviewed industrial complexes subject to energy use plan from 2013 to 2016. Although the regional designation was excluded, Sejong industrial complex, which has a fuel usage of 38 thousand TOE annually and a high heat density of $92.6Gcal/km^2{\cdot}h$, was selected for research. And we analyzed the optimal operation model of CHP Hybrid System linking fuel cell and photovoltaic power generation using HOMER Pro, a renewable energy hybrid system economic analysis program. In addition, in order to improve the reliability of the research by analyzing not only the heat demand but also the heat demand patterns for the dominant sectors in the thermal energy, the main supply energy source of CHP, the economic benefits were added to compare the relative benefits. As a result, the total indirect heat demand of Sejong industrial complex under construction was 378,282 Gcal per year, of which paper industry accounted for 77.7%, which is 293,754 Gcal per year. For the entire industrial complex indirect heat demand, a single CHP has an optimal capacity of 30,000 kW. In this case, CHP shares 275,707 Gcal and 72.8% of heat production, while peak load boiler PLB shares 103,240 Gcal and 27.2%. In the CHP, fuel cell, and photovoltaic combinations, the optimum capacity is 30,000 kW, 5,000 kW, and 1,980 kW, respectively. At this time, CHP shared 275,940 Gcal, 72.8%, fuel cell 12,390 Gcal, 3.3%, and PLB 90,620 Gcal, 23.9%. The CHP capacity was not reduced because an uneconomical alternative was found that required excessive operation of the PLB for insufficient heat production resulting from the CHP capacity reduction. On the other hand, in terms of indirect heat demand for the paper industry, which is the dominant industry, the optimal capacity of CHP, fuel cell, and photovoltaic combination is 25,000 kW, 5,000 kW, and 2,000 kW. The heat production was analyzed to be CHP 225,053 Gcal, 76.5%, fuel cell 11,215 Gcal, 3.8%, PLB 58,012 Gcal, 19.7%. However, the economic analysis results of the current electricity market and gas market confirm that the return on investment is impossible. However, we confirmed that the CHP Hybrid System, which combines CHP, fuel cell, and solar power, can improve management conditions of about KRW 9.3 billion annually for a single CHP system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.167-185
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• 2019

As most G-20 countries expect medical spending to grow rapidly over the next few decades, the burden of healthcare costs continues to grow globally due to an increase in the elderly population and chronic illnesses, and the ongoing quality improvement of health care services. However, under the rapidly changing technological environment of healthcare and IT convergence, the problem may become even bigger if not properly recognized and not properly prepared. In the context of the paradigm shift and the increasing problem of the medical field, complex responses in technical, institutional and business aspects are urgently needed. The key is to derive a business model that is appropriate for businesses that integrate IT in the medical field. With the arrival of the era of the 4th industrial revolution, new technologies such as Internet of Things have been applied to eHealthcare, and the need for new business models has emerged.In the e-healthcare of the Internet era, it became a traditional firm-based business model. However, due to the characteristics of dynamics and complexity of things Internet in the Internet of things, A business ecosystem-based approach is needed. In this paper, we present and analyze the major success factors of the ecosystem based on the 3 - layer structure of the e - healthcare business ecosystem as a result of research on e - healthcare business ecosystem based on emerging technology such as Internet of things. The three-layer business ecosystem was defined as (1) Infrastructure Layer, (2) Character Layer, and (3) Stakeholder Layer. As the key success factors for the eHealthCare business ecosystem, the following four factors are suggested: (1) introduction of the iHealthcare concept, (2) expansion of the business ecosystem, (3) business ecosystem change process innovation, and (4) business ecosystem leadership innovation.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.