• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Quality Improvement in Health Care
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• v.9 no.1
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• pp.6-17
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• 2002

Background : This study was conducted to investigate the current situation of medical supply purchasing and stock management at general hospitals having more than 150 beds in Korea and to find methods of effective purchasing and optimal stock management. Methods : Survey was done from staff at the purchasing departments of 229 general hospitals throughout Korea. Data collection was done using a structured questionnaire between January 3 to March 15, 2001. The survey form was returned from 88 hospitals (rate of return: 38.4%). Results : Firstly, 13.6% of the hospitals did not carry the optimal stock of medical supplies, the lead time optimal stock was 3 weeks or longer in 64.4% of the hospitals. Secondly, since 69.8% of the hospitals showed passive attitude toward training on purchasing management and stock management techniques. Thirdly, as for the question on the presence or absence of a deliberation committee for purchasing of new medical supplies, 60% of the hospitals with less than 300 beds did not have one, and 9.4% of the hospitals opened the deliberation committee less than twice a year. Conclusion : At the time of purchasing new medical supplies, purchasing should be done according to the decision by the deliberation committee so that no deduction is made at the time of claiming insurance, and by setting a certain period of time, purchasing of those medical supplies that were not purchased during this period needs to be done according to the decision by the deliberation committee.

• Journal of East Asia Management
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• v.4 no.1
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• pp.53-77
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• 2023

Modern company faces an uncertain future and a competitive environment and are seeking new technologies and creative products to ensure the corporate growth and survival in the market through continuous innovation. "University Industry Cooperation(UIC)" is a point of contact for overcoming the crisis faced by companies and universities in this era and a cooperation platform for mutual prosperity. As a subsidiary of a university, "Technology Holding Company(THC)" is attracting attention as a new window for UIC in Korea. The role of THC is to establish and foster the business opportunities of their subsidiaries and to return investment profits to the university ecosystem again. But recently, the life cycle of technology is getting shorter, and the development cost is steadily increasing. In particular, with the increase of hybrid projects based on convergence and combination, the risk of conducting research(R&D) and new product development(NPD) projects is gradually increasing. A PMO refers to a project management organization that can contribute to improving the success rate of projects with increasing uncertainty by supporting project visibility and appropriate decision-making. The purpose of this study is to raise a research question on whether THC's corporate performance can be improved when "Project Management System(PMO Service)" is introduced into the subsidiary incubation system of THC. This study proposes several research methods to identify the relationship between the introduction of PMO and the corporate performance of THC.

• Journal of Chest Surgery
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• v.56 no.5
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• pp.336-345
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• 2023

Background: The objective of this study was to demonstrate the safety, efficacy, and feasibility of intraoperative monitoring of the recurrent laryngeal nerves during thoracoscopic and robotic 3-field esophagectomy. Methods: This retrospective analysis details our initial experience using intraoperative nerve monitoring (IONM) during minimally invasive 3-field esophagectomy. Data were obtained from a prospectively maintained database and electronic medical records. The study included all patients who underwent minimally invasive (video-assisted thoracic surgery/robotic) transthoracic esophagectomy with neck anastomosis. The patients were divided into those who underwent IONM during the study period and a historical cohort who underwent 3-field esophagectomy without IONM at the same institution. Appropriate statistical tests were used to compare the 2 groups. Results: Twenty-four patients underwent nerve monitoring during minimally invasive 3-field esophagectomy. Of these, 15 patients underwent thoraco-laparoscopic operation, while 9 received a robot-assisted procedure. In the immediate postoperative period, 8 of 24 patients (33.3%) experienced vocal cord paralysis. Relative to a historical cohort from the same institution, who were treated with surgery without nerve monitoring in the preceding 5 years, a 26% reduction was observed in the nerve paralysis rate (p=0.08). On follow-up, 6 of the 8 patients with vocal cord paralysis reported a return to normal vocal function. Additionally, patients who underwent IONM exhibited a higher nodal yield and a decreased frequency of tracheostomy and bronchoscopy. Conclusion: The use of IONM during minimally invasive 3-field esophagectomy is safe and feasible. This technique has the potential to decrease the incidence of recurrent nerve palsy and increase nodal yield.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• Journal of Technology Innovation
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• v.22 no.3
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• pp.287-310
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• 2014

Due to the toughening intelligence-based competitions among nations, it has become important to industrialize R&D results. To industrialize R&D results through technology transfer, the transfer fee between a technology developer and a company should be discussed. While discussing the transfer fee, the results of technology valuation have been used. However, due to the unreliability of its result, the technology transfer market has not been expanded. In this paper, we discuss the improvement of the reliability of the technology valuation method to expand the technology transfer market. The proposed scheme provides graph-type technology valuation results according to various industrial scenarios using objective technology and market data. With the use of the proposed scheme, the technology developer and the consumer (i.e., the company) can determine the appropriate technology transfer fee. Thus, the proposed scheme is expected to contribute to the expansion of the technology transfer market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3919-3925
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• 2015

When a patient with thyroid cancer is released from isolation after I-131 treatment and return to home using a vehicle, travel time should be controlled to reduce the amount of radiation to accompanying person. As the calculation of appropriate travel time is difficult, there is no patient-specific guideline until now. If we assume that there is no excretion and no physical decay during the relatively short travel time, calculation become quite simple; total radiation dose = dose rate ${\times}$ travel time. Results of this simple calculation and conventional calculation were compared using datum from 120 patients. Travel time calculated by simple method was 56% of conventional method in 0.3 m, 91% in 0.5 m and 96% in 1 m. Simple method was safe. It can be applied easily and also can be applied to the patients with hyperthyroidism treated by I-131.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.5
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• pp.241-252
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• 2019

To choose appropriate countermeasures against potential coastal disaster damages caused by a storm surge, it is necessary to estimate the frequency of storm surge heights estimation. As the coastal populations size in the past was small, the tropical cyclone risk model (TCRM) was used to generate 176,689 synthetic typhoons. In simulation, historical paths and central pressures were incorporated as a probability density function. Moreover, to consider the typhoon characteristics that resurfaced or decayed after landfall on the southeast coast of China, incorporated the shift angle of the historical typhoon as a function of the probability density function and applied it as a damping parameter. Thus, the passing rate of typhoons moving from the southeast coast of China to the south coast has improved. The characteristics of the typhoon were analyzed from the historical typhoon information using correlations between the central pressure, maximum wind speed ($V_{max}$) and the maximum wind speed radius ($R_{max}$); it was then applied to synthetic typhoons. The storm surges were calculated using the ADCIRC model, considering both tidal and synthetic typhoons using automated Perl script. The storm surges caused by the probabilistic synthetic typhoons appear similar to the recorded storm surges, therefore this proposed scheme can be applied to the storm surge simulations. Based on these results, extreme values were calculated using the Generalized Extreme Value (GEV) method, and as a result, the 100-year return period storm surge was found to be satisfactory compared with the calculated empirical simulation value. The method proposed in this study can be applied to estimate the frequency of storm surges in coastal areas.

• Korean Journal of Cognitive Science
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• v.22 no.3
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• pp.255-270
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• 2011

The purpose of this study was to examine the effects of nonspecific and specific cue on decision making during situation awareness. Participants were 36 male college students who were randomly assigned to one of three groups: (1) nonspecific situation awareness, (2) specific situation awareness, and (3) a control group. Every participant was in the level 3-4.5 according to American National Tennis Level Program. Participants were asked to watch tennis single defence, single offence, double defence rally and when the screen stops, they were required to push the button(left, middle, or right) appropriate for the ball's direction to return as soon as possible. The experiment was designed to be analyzed for group(3)${\times}$condition(3)${\times}$area(7) using three-way ANOVA. The dependent variables were reaction time, accuracy rate, and amplitude and latency of P300. The result showed that the latency of the nonspecific situation awareness group and the specific situation awareness group was shorter and their amplitudes were higher than the control group. Fz, Cz, Pz were prominent among areas, and the single defence condition was more prominent than the single offence and the double defence condition. As a result of the study, it can be suggested that the information about situation awareness provided beforehand directly affects the brain's information processing. In addition, it shows that ERP can be a useful index for studying situation awareness.