• Clinical and Experimental Reproductive Medicine
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• v.35 no.4
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• pp.309-317
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• 2008

Objectives: The aim of this study was to evaluate the usefulness of Anti-mullerian hormone (AMH) as a predictive marker for ovarian response and cycle outcome in IVF cycles. Methods: From Jan., to Aug., 2007, 111 patients undergoing IVF/ICSI stimulated by short or antagonist protocol were selected. On cycle day 3, basal serum AMH level and FSH level were measured. The correlation between basal serum AMH or FSH, and COH outcome was analyzed and IVF outcome was compared according to the AMH levels. To determine the threshold value of AMH for poor- and hyper-response, ROC curve was analyzed. Results: Serum AMH showed higher correlation coefficient (r=0.792, p<0.001) with the number of retrieved mature oocyte than serum FSH (r=-0.477, p<0.001). According to ovarian response, FSH and AMH leves showed significant differences among poor, normal, and hyperresponder. For predicting poor (${\leq}2$ oocytes) and hyperresponse (${\geq}17$ oocyets), AMH cut-off values were 0.5 ng/ml (the sensitivity 88.9% and the specificity 89.5%) and 2.5 ng/ml (sensitivity 85.7%, specificity 87.0%), respectively. According to the AMH level, patients were divided into 3 groups: low (${\leq}0.60\;ng/ml$), normal ($0.60{\sim}2.60\;ng/ml$), and high AMH (${\geq}2.60\;ng/ml$). The number of retrieved mature oocytes was significantly higher ($2.7{\pm}2.2$, $8.1{\pm}4.8$, $16.5{\pm}5.7$) and total gonadotropin dose was lower ($3530.5{\pm}1251.0$, $2957.1{\pm}1057.6$, and $2219.2{\pm}751.9\;IU$) in high AMH group (p<0.001). There was no significant difference in fertilization rates and pregnancy rates (23.8%, 34.0%, 37.5%) among the groups. Conclusions: Basal serum AMH level correlated better with the number of retrieved mature oocytes than FSH level, suggesting its usefulness for predicting ovarian response. However, IVF outcome was not significantly different according to the AMH levels. Serum AMH level presented good cut-off value for poor- or hyper-responders, therefore it could be useful in prediction of cycle cancellation, gonadotropin dose, and OHSS risk in IVF cycles.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.97-106
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• 2015

Purpose : This study evaluate the thermal changes caused by use of the chemoport for drug administration and blood sampling during radiofrequency hyperthermia. Materials and Methods : 20cm size of the electrode radio frequency hyperthermia (EHY-2000, Oncotherm KFT, Hungary) was used. The materials of the chemoport in our hospital from currently being used therapy are plastics, metal-containing epoxy and titanium that were made of the diameter 20 cm, height 20 cm insertion of the self-made cylindrical Agar phantom to measure the temperature. Thermoscope(TM-100, Oncotherm Kft, Hungary) and Sim4Life (Ver2.0, Zurich, Switzerland) was compared to the actual measured temperature. Each of the electrode measurement position is the central axis and the central axis side 1.5 cm, 0 cm(surface), 0.5 cm, 1.8 cm, 2.8 cm in depth was respectively measured. The measured temperature is $24.5{\sim}25.5^{\circ}C$, humidity is 30% ~ 32%. In five-minute intervals to measure the output power of 100W, 60 min. Results : In the electrode central axis 2.8 cm depth, the maximum temperature of the case with the unused of the chemoport, plastic, epoxy and titanium were respectively $39.51^{\circ}C$, $39.11^{\circ}C$, $38.81^{\circ}C$, $40.64^{\circ}C$, simulated experimental data were $42.20^{\circ}C$, $41.50^{\circ}C$, $40.70^{\circ}C$, $42.50^{\circ}C$. And in the central axis electrode side 1.5 cm depth 2.8 cm, mesured data were $39.37^{\circ}C$, $39.32^{\circ}C$, $39.20^{\circ}C$, $39.46^{\circ}C$, the simulated experimental data were $42.00^{\circ}C$, $41.80^{\circ}C$, $41.20^{\circ}C$, $42.30^{\circ}C$. Conclusion : The thermal variations were caused by radiofrequency electromagnetic field surrounding the chemoport showed lower than in the case of unused in non-conductive plastic material and epoxy material, the titanum chemoport that made of conductor materials showed a slight differences. This is due to the metal contents in the chemoport and the geometry of the chemoport. And because it uses a low radio frequency bandwidth of the used equipment. That is, although use of the chemoport in this study do not significantly affect the surrounding tissue. That is, because the thermal change is insignificant, it is suggested that the hazard of the chemoport used in this study doesn't need to be considered.

• The Journal of Korean Society for Radiation Therapy
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• v.23 no.1
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• pp.31-39
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• 2011

Purpose: Helical Tomotherapy allows only coplanar beam delivery because it does not allow couch rotation. We investigated a method to introduce non-coplanar beam by tilting a patient's head for Tomotherapy. The aim of this study was to compare intrafractional movement during Tomotherapy between coplanar and non-coplanar patient's setup. Materials and Methods: Helical Tomotherapy was used for treating eight patients with intracranial tumor. The subjects were divided into three groups: one group (coplanar) of 2 patients who lay on S-plate with supine position and wore thermoplastic mask for immobilizing the head, second group (non-coplanar) of 3 patients who lay on S-plate with supine position and whose head was tilted with Variable Axis Baseplate and wore thermoplastic mask, and third group (non-coplanar plus mouthpiece) of 3 patients whose head was tilted and wore a mouthpiece immobilization device and thermoplastic mask. The patients were treated with Tomotherapy after treatment planning with Tomotherapy Planning System. Megavoltage computed tomography (MVCT) was performed before and after treatment, and the intrafractional error was measured with lateral(X), longitudinal(Y), vertical(Z) direction movements and vector ($\sqrt{x^2+y^2+z^2}$) value for assessing overall movement. Results: Intrafractional error was compared among three groups by taking the error of MVCT taken after the treatment. As the correction values (X, Y, Z) between MVCT image taken after treatment and CT-simulation image are close to zero, the patient movement is small. When the mean values of movement of each direction for non-coplanar setup were compared with coplanar setup group, X-axis movement was decreased by 13%, but Y-axis and Z-axis movement were increased by 109% and 88%, respectively. Movements of Y-axis and Z-axis with non-coplanar setup were relatively greater than that of X-axis since a tilted head tended to slip down. The mean of X-axis movement of the group who used a mouthpiece was greater by 9.4% than the group who did not use, but the mean of Y-axis movement was lower by at least 64%, and the mean of Z-axis was lower by at least 67%, and the mean of Z-axis was lower by at least 67%, and the vector was lower by at least 59% with the use of a mouthpiece. Among these 8 patients, one patient whose tumor was located on left frontal lobe and left basal ganglia received reduced radiation dose of 38% in right eye, 23% in left eye, 30% in optic chiasm, 27% in brain stem, and 8% in normal brain with non-coplanar method. Conclusion: Tomotherapy only allows coplanar delivery of IMRT treatment. To complement this shortcoming, Tomotherapy can be used with non-coplanar method by artificially tilting the patient's head and using an oral immobilization instrument to minimize the movement of patient, when intracranial tumor locates near critical organs or has to be treated with high dose radiation.

• Journal of Nutrition and Health
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• v.48 no.3
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• pp.277-288
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• 2015

Purpose: As the rate of senior citizens living alone increases in the current aging society, there is much concern regarding the health and nutritional intake of solitary senior citizens. Therefore, this study compared the nutritional intake of senior citizens according to their family type. Methods: In July and August of 2011, two senior citizen welfare centers in Seoul were visited to survey 267 elderly women. Excluding 54 subjects for which the data were incomplete, information from 213 subjects was analyzed. The subjects were divided into three family types, living alone (LA, n = 74), living with spouse (LS, n = 78), and living with children (LC, n = 61). Results: The mean age of the LA group was the highest, while the mean age of the LS group was the lowest (p < 0.001), and WHR of the LC group was the highest (p = 0.049). Income was the highest in the LS group (p < 0.001). Frequency of eating out was the lowest in the LA group (p = 0.031). By Duncan's multiple analysis, the amounts of energy intake, vegetable protein, fat, calcium, phosphorus, potassium, selenium, Vit D, Vit E, $Vit\;B_2$, niacin, $Vit\;B_6$, $Vit\;B_{12}$, and cholesterol were significantly higher in the LS group compared with the LA or LC group (p < 0.05). The intakes of calcium, Vit D, $Vit\;B_{12}$, and cholesterol were still significantly different among the three groups, even after adjustment for age and monthly income. The LA group ate less fruit and fish than the LS or LC group (p < 0.05). The LA group showed the lowest dietary diversity and the LS group showed the highest diversity (p = 0.014), however, the significance of dietary diversity score among the three groups disappeared after adjustment for age and monthly income. Conclusion: Elderly women living with spouse were receiving better nutrition than elderly women living alone or living with children. Therefore, solitary elderly women who do not live with their spouse or children should be offered greater opportunities to receive a balanced meal at a congregational kitchen or welfare center. To ensure their healthy diet, it is essential to provide continuous nutrition education with these groups in mind.

• Journal of Nutrition and Health
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• v.42 no.5
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• pp.423-433
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• 2009

Folate and vitamin $B_{12}$ are essential cofactors for homocysteine (Hcy) metabolism. Homocysteinemia has been related with cardiovascular and neurodegenerative disease. We examined the effect of folate and/or vitamin $B_{12}$ deficiency on biomarkers of one carbon metabolism in blood, liver and brain, and analyzed the correlation between vitamin biomarkers in mild and moderate homocysteinemia. In this study, Sprague-Dawley male rats (5 groups, n = 10) were fed folatesufficient diet (FS), folate-deficient diet (FD) with 0 or 3 g homocystine (FSH and FDH), and folate-/vitamin $B_{12}$-deficient diet with 3 g homocystine (FDHCD) for 8 weeks. The FDH diet induced mild homocysteinemia (plasma Hcy 17.41 ${\pm}$ 1.94 nmol/mL) and the FDHCD diet induced moderate homocysteinemia (plasma Hcy 44.13 ${\pm}$ 2.65 nmol/mL), respectively. Although liver and brain folate levels were significantly lower compared with those values of rats fed FS or FSH (p < 0.001, p < 0.01 respectively), there were no significant differences in folate levels in liver and brain among the rats fed FD, FDH and FDHCD diet. However, rats fed FDHCD showed higher plasma folate levels (126.5 ${\pm}$ 9.6 nmol/L) compared with rats fed FD and FDH (21.1 ${\pm}$ 1.4 nmol/L, 22.0 ${\pm}$ 2.2 nmol/L)(p < 0.001), which is the feature of "ethyl-folate trap"by vitamin $B_{12}$ deficiency. Plasma Hcy was correlated with hepatic folate (r = -0.641, p < 0.01) but not with plasma folate or brain folate in this experimental condition. However, as we eliminated FDHCD group during correlation test, plasma Hcy was correlated with plasma folate (r = -0.581, p < 0.01), hepatic folate (r = -0.684, p < 0.01) and brain folate (r = -0.321, p < 0.05). Hepatic S-adenosylmethionine (SAM) level was lower in rats fed FD, FDH and FDHCD than in rats fed FS and FSH (p < 0.001, p < 0.001 respectively) and hepatic S-adenosylhomocysteine (SAH) level was significantly higher in those groups. The SAH level in brain was also significantly increased in rats fed FDHCD (p < 0.05). However, brain SAM level was not affected by folate and/or vitamin $B_{12}$ deficiency. This result suggests that dietary folate- and vitamin B12-deficiency may inhibit methylation in brain by increasing SAH rather than decreasing SAM level, which may be closely associated with impaired cognitive function in nutritional homocysteinemia.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Nutrition and Health
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• v.42 no.8
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• pp.770-783
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• 2009

The school foodservice was quantitatively extended by policy of government all the while. There was carried out the survey of customer satisfaction about school foodservice by the ministry of education, science, and technology since 2006 years. Therefore, the purpose of this study was to grasp an improvement of the scores of school foodservice' quality attributes and satisfaction as compared with the preceding year by respondents and school type (elementary school, middle school, and high school). An annual survey was practiced to respondents (students, parents, and faculty) on september 2007 years and 2008 years in 16 cities and provinces. The statistics was analyzed to descriptive analysis and t-test for SPSS 12.0. The scores of school foodservice' quality attributes and overall customer satisfaction were almost increased to students, parents, and faculty and especially, big elevation in middle school. There was big increased the quality attributes such as 'providing information on foodservice', 'pleasant foodservice environment', 'kindness offered by employee' in elementary school, middle school, and high school to total respondents. An overall satisfaction in school foodservice was improved from 69.2 score to 71.9 score. On students, scores of overall satisfaction was increased from 72.9 to 74.0 as students of elementary school and from 61.5 to 65.8 as students of middle school (p < .001). Therefore, for improvement and development of school foodservice, there should be a necessary for an operator of school foodservice and an office of education to make an effort.