• The Korean Journal of Archival Studies
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• no.33
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• pp.155-206
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• 2012

Interests in archives management are getting expanded from the public sector into the cultural and artistic field for the ten years after legislation of "Act on the Management of Public Archives" in 1999. However, due to lack of recognition on the importance of archives in the cultural and artistic field, it is rather frequent that information is kept scattered or archives are lost. As an example, absence of precise contract documents or notes of bestowal keeps people from locating great amount of cultural properties, and because of it these creative properties are in the risk of thefts, the closed-door auctioning, or trades in unofficial channels. As how a nation manages cultural and artistic creation inside the nation reflects its cultural level, it can be said that one of the indexes to notice the extent of a nation's cultural level is to take a look at how they are circulated. This study started from this point. Growing economy and rising interests in culture and art made the society more cognizant of the importance and value that visual artworks have, but the archives and information which are showing the context of these artworks and are produced in the course of social interaction are relatively disregarded because too much emphasis lies on the work itself. It is harder to find archives or documentations in Korea than in other advanced countries about the artists themselves or philosophical discourse on the background of the artworks. There is not so much interest to preserve the archives and information produced after the exhibition also, and they are used for no more than promotion or reference. Hereupon, the researcher recognized the importance of visual arts archives and believed that systemic management on them are high in need. And metadata is an essential way for the systemic management, as recently management on artworks or their archives are conducted using the system of the agencies even though they are not produced electronically. The objective of this study is to manage visual arts archives systematically by designing a data model reflecting traits of visual arts archives. Metadata are needed in the every course of archives from acquisition to management, preservation and application. Visual arts archives find its rich value only when a systemic relationship is established among information on artist, artwork and events including exhibition. By establishing a Multiple Entity Data Model, in which artworks, artists and events (exhibitions) make relationship all together, metadata for management on visual arts archive gets more efficiency and at the same time explanatory trait of the archive gets higher. For this reason we, in the study, tried to design a data model by setting each as an independent entities and designating relations between them, in order to find a way to manage visual arts archives more systematically.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.2
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• pp.84-87
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• 2018

Purpose The radiopharmaceutical used in the nuclear medicine department is used only for the specific patient according to the prescription or instruction of the doctor without selling, so it is dispensed and it is distributed and used for the examination. Radiopharmaceuticals administered to patients should be managed appropriately as well as radiation safety management during dispensation. The purpose of this study is to investigate microbial contamination during dispensation of radiopharmaceuticals Materials and Methods This study distinguished between general workbench and clean workbench and performed three tests. First, microbial cultivation test of radiopharmaceutical prepared and dispensed in general workbenches and sterile workbenches were carried out five times, respectively. The second test was performed settle plate method three times before and after the use of the exhaust filter. Finally, Adenosine Triphosphate (ATP) measurement was performed in each workbench to measure bacterial counts. In addition, ATP measurement were carried out by designating locations and items that may be contaminated during dispensation. Results In the microbial culture test, no microorganisms were detected in both samples. In the settle plate method, it was detected without using of the exhaust filter in a general workbench once. In the ATP measurement test, it was measured at the level of 400 RLU or less, which is the standard value of contamination, in both workbenches surface. In additional ATP measurement test, the refrigerator handle in the distribution room was measured above the reference value of 1217 RLU, the vacuum vial shield of the Tech Generator at 435 RLU, and the syringe holder at 1357 RLU. After environmental disinfection, the results were reduced to 311 RLU, 136 RLU, and 291 RLU. Conclusion No contamination by bacteria was found in both workbenches. However, microbial contamination may occur if the use of an exhaust filter or proper hand hygiene is not achieved. Regular inspections and management for aseptic processing themselves will be necessary.

• Journal of Nutrition and Health
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• v.54 no.5
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• pp.560-572
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• 2021

Purpose: In this study, the serving size of home meal replacement (HMR)-soups (Guk, Tang) and stews (Jjigae) available in the Korean market was investigated, and an evaluation of the nutrition per serving was conducted based on the nutrition labeling. Methods: The market research was conducted from March to August 2021 on products sold on the internet, convenience stores, supermarkets, and hypermarkets. A total of 370 products were investigated and classified into 3 types: Guk (n = 129), Tang (n = 132), and Jjigae (n = 109). Results: An analysis of the survey revealed that 72.9% of Guk, 71.2% of Tang, and 79.8% of Jjigae had labels with servings per container, and 89.2% of Guk, 91.7% of Tang, and 99.1% of Jjigae had labels with nutrition facts. The nutritional evaluation per serving of Guk, Tang, and Jjigae was conducted for 259 products (87 Guk, 86 Tang, and 86 Jjigae) having labels containing both the servings per container and nutrition facts. The average serving size of Tang was 367.6 g, which was significantly higher than Guk (325.3 g) and Jjigae (305.1 g) (p < 0.001). The calorie content of Jjigae (171.4 kcal) and Tang (162.3 kcal) was significantly higher than Guk (90.8 kcal) (p < 0.001), and the protein content was the highest in Tang (16.3 g) (p < 0.001). The sodium content per serving of Jjigae (1,479.0 mg) was significantly higher than Guk (1,073.3 mg) and Tang (959.8 mg) (p < 0.001). The percent daily value per serving of all three types was less than 10% on average for calories and 15-30% for protein, whereas for sodium showed an average of around 50% (48-74%). Conclusion: The serving size and nutritional value per serving of the HMR-soups and stews found in this study can be used as basic data to establish the reference serving size.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.528-536
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• 2023

In Korea, from January 2023, the Act on Labeling and Advertising of Food was revised to reflect the use-by-date rather than the sell-by-date. Hence, the purpose of this study was to establish a system for calculating the safety factor and determining the recommended use-by-date for each food type, thereby providing a scientific basis for the recommended use-by-date labels. A safety factor calculation technique based on scientific principles was designed through literature review and simulation, and opinions were collected by conducting surveys and discussions including industry and academia, among others. The main considerations in this study were pH, Aw, sterilization, preservatives, packaging for storage improvement, storage temperature, and other external factors. A safety factor of 0.97 was exceptionally applied for frozen products and 1.0 for sterilized products. In addition, a between-sample error value of 0.08 was applied to factors related to product and experimental design. This study suggests that clearly providing a safe use-by-date will help reduce food waste and contribute to carbon neutrality.

• Journal of Preventive Medicine and Public Health
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• v.24 no.3 s.35
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• pp.441-458
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• 1991

The objective of this study was to provide basic reference data for stabilization scheme of medical insurance benefits through forecasting of the medical care utilization rate, the medical expence per case, and the treatment days per case in medical insurance program for government employees & private school teachers and for industrial workers. For the achievement of above objective, this study was carried out by Box-Jenkins time series analysis (ARIMA Model), using monthly statistical data from Jan. 1979 to Dec. 1989, of medical insurance program for government employees & private school teachers and for industrial workers. The results are as follows ; ARIMA model of the medical care utilization rate in medical insurance program for government employees & private school teachers was ARIMA (1, 1, 1) and it for outpatient in medical insurance program for industrial workers was ARIMA (1, 1, 1), while it for inpatient in medical insurance program for industrial workers was ARIMA (1, 0, 1). ARIMA model of the medical expense per case in medical insurance program for government employees & private school teachers and for outpatient in medical insurance program for industrial workers were ARIMA (1, 1, 0), while it for inpatient in medical insurance program for industrial workers was ARIMA (1, 0, 1). ARIMA model of the treatment days per case of both medical insurance program for government employees & private school teachers and industrial workers were ARIMA (1, 1, 1). Forecasting value of the medical care utilzation rate for inpatient in medical insurance program for government employees & private school teachers was 0.0061 at dec. 1989, 0.0066 at dec. 1994 and it for outpatient was 0.280 at dec. 1989, 0.294 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 0.0052 at dec. 1989, 0.0056 at dec. 1994 and it for outpatient was 0.203 at dec. 1989, 0.215 at 1994. Forecasting value of the medical expense per case for inpatient in medical insurance program for government employees & private school teachers was 332,751 at dec. 1989, 354,511 at dec. 1994 and it for outpatient was 11,925 at dec. 1989, 12,904 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 281,835 at dec. 1989, 293,973 at dec. 1994 and it for outpatient was 11,599 at dec. 1989, 11,585 at 1994. Forecasting value of the treatment days per case for inpatient in medical insurance program for government employees & private school teachers was 13.79 at dec. 1989,13.85 at an. 1994 and in for outpatient was 5.03 at dec. 1989, 5.00 at dec. 1994, while it for inpatient in medical insurance program for industrial workers was 12.23 at dec. 1989, 12.85 at dec. 1994 and it for outpatient was 4.61 at dec. 1989, 4.60 at 1994.

• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.35-39
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• 2019

Purpose Hepatitis B virus (hepatitis B virus, HBV) infection is a worldwide major public health problem and it is known as a major cause of chronic hepatitis, liver cirrhosis and liver cancer. And serologic tests of hepatitis B virus is essential for diagnosing and treating these diseases. In addition, with the development of molecular diagnostics, the detection of HBV DNA in serum diagnoses HBV infection and is recognized as an important indicator for the antiviral agent treatment response assessment. We performed HBeAg assay using Immunoradiometric assay (IRMA) and Chemiluminescent Microparticle Immunoassay (CMIA) in hepatitis B patients treated with antiviral agents. The detection rate of HBV DNA in serum was measured and compared by RT-PCR (Real Time - Polymerase Chain Reaction) method Materials and Methods HBeAg serum examination and HBV DNA quantification test were conducted on 270 hepatitis B patients undergoing anti-virus treatment after diagnosis of hepatitis B virus infection. Two serologic tests (IRMA, CMIA) with different detection principles were applied for the HBeAg serum test. Serum HBV DNA was quantitatively measured by real-time polymerase chain reaction (RT-PCR) using the Abbott m2000 System. Results The detection rate of HBeAg was 24.1% (65/270) for IRMA and 82.2% (222/270) for CMIA. Detection rate of serum HBV DNA by real-time RT-PCR is 29.3% (79/270). The measured amount of serum HBV DNA concentration is $4.8{\times}10^7{\pm}1.9{\times}10^8IU/mL$($mean{\pm}SD$). The minimum value is 16IU/mL, the maximum value is $1.0{\times}10^9IU/mL$, and the reference value for quantitative detection limit is 15IU/mL. The detection rates and concentrations of HBV DNA by group according to the results of HBeAg serological (IRMA, CMIA)tests were as follows. 1) Group I (IRMA negative, CMIA positive, N = 169), HBV DNA detection rate of 17.7% (30/169), $6.8{\times}10^5{\pm}1.9{\times}10^6IU/mL$ 2) Group II (IRMA positive, CMIA positive, N = 53), HBV DNA detection rate 62.3% (33/53), $1.1{\times}10^8{\pm}2.8{\times}10^8IU/mL$ 3) Group III (IRMA negative, CMIA negative, N = 36), HBV DNA detection rate 36.1% (13/36), $3.0{\times}10^5{\pm}1.1{\times}10^6IU/mL$ 4) Group IV(IRMA positive, CMIA negative, N = 12), HBV DNA detection rate 25% (3/12), $1.3{\times}10^3{\pm}1.1{\times}10^3IU/mL$ Conclusion HBeAg detection rate according to the serological test showed a large difference. This difference is considered for a number of reasons such as characteristics of the Ab used for assay kit and epitope, HBV of genotype. Detection rate and the concentration of the group-specific HBV DNA classified serologic results confirmed the high detection rate and the concentration in Group II (IRMA-positive, CMIA positive, N = 53).

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.67-75
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• 2009

Purpose: As to the analytical method of data, the various programs in which it is used for the quantitative rating of the Tl-201 myocardial perfusion SPECT are reported that there is a difference. Therefore, the measured value error of the mutual program is expected to be generated even if the quantitative analysis is made against data of the same patient. Using quantitative index that able to represent myocardial perfusion defect level, we aimed to determine correlation among three myocardial perfusion analysis programs 4DM (4DMSPECT), QPS (Quantitative Perfusion SPECT), ECT (Emory Cardiac Toolbox) that be used generally in most departments of Nuclear Medicine. Materials and Methods: We analyzed the 145 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Mediacal Center from December 1th 2008 to February 14th 2008. We sorted as normal group and abnormal group. Normal group consist of 80 patients (Male/Female=38/42, age:$65.1{\pm}9.9$) who have low possibility of cardiovascular disease. And abnormal group consist of 65 patients (Male/Female=45/20, age:$63.0{\pm}8.7$) who were diagnosed cardiovascular disease with reversible perfusion defect or fixed perfusion defect through myocardial perfusion SPECT results. Using the 4DM, QPS, and ECT programs, the total defect extent (TDE) such as LAD, LCX, RCA and the summed stress score (SSS) have been analysed for their correlations and statistical comparison with the paried t-test for the quantitative indices analysed from each group. Results: The correlation of 4DM:QPS, QPS:ECT, ECT:4DM each group result from 145 patients is 0.84, 0.86, 0.82 at SSS, 0.87, 0.84, 0.87 at TDE, and both index showed good correlation. In paired t-test and Bland-Altman analysis results showed no statistically significant difference in the comparison of QPS:ECT at the mean SSS and TDE, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index. The correlation of 4DM:QPS, QPS:ECT, ECT:4DM program results from abnormal group (65 patients) is 0.72, 0.72, 0.70 at SSS and 0.77, 0.70, 0.77 at TDE and TDE and SSS has a good correlation. In abnormal group, paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.89) and TDE (p=0.23) comparison, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). In normal group (80 patients), paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.95) and TDE (p=0.73) comparison. And 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). Conclusions: The perfusion defect of the Tl-201 myocardial perfusion SPECT was analyzed in not only the patient in whom it has the cardiovascular disease but also the patient in whom the possibility of the cardiovascular disease is few. In the comparison of the all group research, the mean of the TDE and SSS, 4DM was lower than QPS and ECT progrms. Each program showed good correlation and the results showed statistically significant difference. However, in this way, it is determined to be compatible about the analysis value in which the large-scale side between the programs uses each program a difference in a clinical in the Bland-Altman analyzed result in spite of the good correlation and cannot use. but, this analyzed result will be able to be usefully used as the reference material for the clinical read and is expected.

• Tuberculosis and Respiratory Diseases
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• v.61 no.2
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• pp.129-136
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• 2006

Background: To interpret lung function tests, it is necessary to determine the lower limits of normal (LLN) and to derive a consensus on the interpretative algorithm. '0.7 of LLN for the $FEV_1$/FVC' was suggested by the COPD International Guideline (GOLD) for defining obstructive disease. A consensus on a new interpretative algorithm was recently achieved by ATS/ERS in 2005. We evaluated the accuracy of '0.7 of LLN for the $FEV_1$/FVC' for diagnosing obstructive diseases, and we also determined the effect of the new algorithm on diagnosing ventilatory defects. Methods: We obtained the age, gender, height, weight, $FEV_1$, FVC, and $FEV_1$/FVC from 7362 subjects who underwent spirometry in 2005 at the Asan Medical Center, Korea. For diagnosing obstructive diseases, the accuracy of '0.7 of LLN for the $FEV_1$/FVC' was evaluated in reference to the $5^{th}$ percentile of the LLN. By applying the new algorithm, we determined how many more subjects should have lung volumes testing performed. Evaluation of 1611 patients who had lung volumes testing performed as well as spirometry during the period showed how many more subjects were diagnosed with obstructive diseases according to the new algorithm. Results: 1) The sensitivity of '0.7 of LLN for the $FEV_1$/FVC' for diagnosing obstructive diseases increased according to age, but the specificity was decreased according to age; the positive predictive value decreased, but the negative predictive value increased. 2) By applying the new algorithm, 34.5% (2540/7362) more subjects should have lung volumes testing performed. 3) By applying the new algorithm, 13% (205/1611) more subjects were diagnosed with obstructive diseases; these subjects corresponded to 30% (205/681) of the subjects who had been diagnosed with restrictive diseases by the old interpretative algorithm. Conclusion: The sensitivity and specificity of '0.7 of LLN for the $FEV_1$/FVC' for diagnosing obstructive diseases changes according to age. By applying the new interpretative algorithm, it was shown that more subjects should have lung volumes testing performed, and there was a higher probability of being diagnosed with obstructive diseases.