• Journal of Astronomy and Space Sciences
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• v.26 no.3
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• pp.305-316
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• 2009

In this study, we suggest an empirical forecast of CIR (Corotating Interaction Regions) and geomagnetic storm based on the information of coronal holes (CH). For this we used CH data obtained from He I $10830{\AA}$ maps at National Solar Observatory-Kitt Peak from January 1996 to November 2003 and the CIR and storm data that Choi et al. (2009) identified. Considering the relationship among coronal holes, CIRs, and geomagnetic storms (Choi et al. 2009), we propose the criteria for geoeffective coronal holes; the center of CH is located between $N40^{\circ}$ and $S40^{\circ}$ and between $E40^{\circ}$ and $W20^{\circ}$, and its area in percentage of solar hemispheric area is larger than the following areas: (1) case 1: 0.36%, (2) case 2: 0.66%, (3) case 3: 0.36% for 1996-2000, and 0.66% for 2001-2003. Then we present contingency tables between prediction and observation for three cases and their dependence on solar cycle phase. From the contingency tables, we determined several statistical parameters for forecast evaluation such as PODy (the probability of detection yes), FAR (the false alarm ratio), Bias (the ratio of "yes" predictions to "yes" observations) and CSI (critical success index). Considering the importance of PODy and CSI, we found that the best criterion is case 3; CH-CIR: PODy=0.77, FAR=0.66, Bias=2.28, CSI=0.30. CH-storm: PODy=0.81, FAR=0.84, Bias=5.00, CSI=0.16. It is also found that the parameters after the solar maximum are much better than those before the solar maximum. Our results show that the forecasting of CIR based on coronal hole information is meaningful but the forecast of goemagnetic storm is challenging.

• Journal of muscle and joint health
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• v.8 no.1
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• pp.122-140
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• 2001

The aims of this study were to understand and to predict the determinent factors affecting the exercise behaviors and physical fitness by testing the Pender's revised health promotion model, and to help the patients with rheumatoid arthritis and osteoarthritis perform the continous exercise program, and to help them maximize the physical effect such as muscle strength, endurance, and functional status and mental effects including self efficacy and quality of life, and improve the physical and mental well being, and to provide a basis for the nursing intervention strategies. Of the selected variables in this study, the endogenous variables included the physical fitness, exercise score, exercise participation, perceived benefits of action, perceived barriers of action to exercise, activity-related affect(depression) and perceived self-efficacy, interpersonal influences(family support), situational factors(duration of arthritis, fatigue) and the exogenous variables included personal sociocultural factor(education level), personal biologic factor(body mass index), personal psychologic factor(perceived health status) and prior related behavior factors(previous participation in exercise, life-style). We analyzed the clinical records of 208 patients with rheumatoid arthritis and degenerative arthritis who visited the outpatient clinics at H university hospital in Seoul. Data were composed of self reported qustionnaire and good of fitness score which were obtained by padalling the ergometer of bicycle for 9 minutes. SPSS Win 8.0 and Window LISREL 8.12a were used for statistical analysis. Of 75 hypothetical paths that influence on physical fitness, exercise participation, exercise score, perceived benefits of action, perceived barriers of action to exercise, activity-related affect(depression) and perceived self-efficacy, interpersonal influences(family support), situational factors(duration of arthritis, fatigue), 40 were supported. The physical fitness was directly influenced by life-style, perceived health status, education level, family support, fatigue, which explained 12% of physical fitness. The exercise participation were directly influenced by life-style, education level, past exercise behavior, perceived benefits of action, perceived barriers of action, depression and duration of arthritis, which explained 47% of exercise participation. Exercise score were directly affected by perceived self efficacy. BMI, life-style, past exercise behavior, perceived benefits of action, family support, perceived health status. perceived barriers of action, and fatigue, which explained 70%. Perceived benefits of action was directly influenced by BMI, life-style, which explained 39%. Perceived barriers of action were directly influeced by past exercise behavior, perceived health status, which explained 7%. Perceived self efficacy were directly influeced by level of education, perceived health status, life-style, which explained 57%. Depression were directly influeced by past exercise behavior, BMI, life-style, which explained 27%. Family support were directly influeced by life-style, perceived health status, which explained 29%. Fatigue were directly influeced by BMI, life-style, perceived health status. which explained 41%. Duration of arthritis were directly influeced by life-style, past exercise behavior, BMI, which explained 6%. In conclusion, important variables for physical fitness were life-style, and variable affecting exercise participation were life-style. Perceived self-efficacy of exercise was a significant predictor of exercise score. BMI, Life-style, perceived benefits of action, family support, past exercise behavior showed direct effects on perceived self-efficacy. Therefore, disease related factor should be minimized for physical performance and well being in nursing intervention for patients with rheumatoid arthritis, and plans to promote and continue exercise should be seeked to reduce disability. In addition, Exercise program should be planned and performed by the exact evaluation of exercise according to the ability of the patients and the contents to improve the importance of exercise and self efficacy in self control program, dedicated educational program should be involved. This study suggest that the methods to reduce the disease related factors, the importance of daily life-style, recognition of benefit of exercise, and educational program to promote self efficacy should be considered in the exercise behavior promotion and nursing intervention for continous performance. The significance of this study is also thought to provide patients with chronic arthritis the specific data for maximal physical and mental well being through exercise, chronic therapeutic procedure, daily adaptation and confrontation in nursing intervention.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Clinical and Experimental Pediatrics
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• v.50 no.4
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• pp.369-375
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• 2007

Purpose : There have been numerous researches on urine ${\beta}_2$-microglobulin (${\beta}_2$-M) concerned with primary nephrotic syndrome and other glomerular diseases, but not much has been done in relation to pediatric age groups. Thus, our hospital decided to study the relations between the analysis of the test results we have conducted on pediatric patients and renal functions. Methods : Retrospective data analysis was done to 102 patients of ages 0 to 4 with renal diseases with symptoms such as hematuria, edema, and proteinuria who were admitted to Chung-Ang Yongsan Hospital and who participated in 24-hour urine and urine ${\beta}_2$-M excretion test between January of 2003 and January of 2006. Each disease was differentiated as independent variables, and the statistical difference of the results of urine ${\beta}_2$-M excretion of several groups of renal diseases was analyzed with student T-test by using test results as dependent variables. Results : Levels of urine ${\beta}_2$-M excretion of the 102 patients were as follows : 52 had primary nephrotic syndrome [MCNS (n=45, $72{\pm}45{\mu}g/g$ creatinine, ${\mu}g/g-Cr$), MPGN (n=3, $154{\pm}415{\mu}g/g-Cr$), FSGS (n=4, $188{\pm}46{\mu}g/-Cr$], six had APSGN ($93{\pm}404{\mu}g/g-Cr$), seven had IgA nephropathy ($3,414{\pm}106{\mu}g/g-Cr$), 9 had APN ($742{\pm}160{\mu}g/g-Cr$), 16 had cystitis ($179{\pm}168{\mu}g/g-Cr$), and 12 had HSP nephritis ($109{\pm}898{\mu}g/g-Cr$). IgA nephropathy (P<0.05) and APN (P<0.05) were significantly higher than in other renal diseases. Among primary nephrotic syndrome, FSGS with higher results of ${\beta}_2$-microglobulin test had longer treatment period (P<0.01) when compared to the lower groups, but no significant differences in Ccr, BUN, or Cr were observed. Conclusion : IgA nephropathy and APN groups showed significantly higher level of ${\beta}_2$-M excretion value than other groups. Although ${\beta}_2$-microglobulin value is not appropriate as an indicator of general renal function and pathology, it seems to be sufficient in the differential diagnosis of the UTI and in the prediction of the treat-ment period of nephrotic syndrome patients.

• Korean Journal of Animal Reproduction
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• v.9 no.1
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• pp.9-30
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• 1985

The study was carried out to find out the changes of the sex hormone levels in the milk of Holstein cows during the reproductive stages such as the estrous cycle, pregnancy and periparturient period. The FSH, LH, estradiol-17$\beta$ and progesterone from the milk samples were assayed by radioimmunoassay methods. The results of this study were summarized as follows: 1. The levels of progesterone and estradiol-17$\beta$ were similar among inter-quarters, but they were higher in after milking than before milking times, with no statistical significance. 2. The milk progesterone levels during the estrous cycles reached a peak mean level of 3.55$\pm$0.26ng/$m\ell$ at 15 days after estrus and they did not show any differences among the length of estrous cycles. The estradiol-17$\beta$ levels during the estrous cycles showed a peak level of 36.40$\pm$2.38pg/$m\ell$ at estrus, and decreased(17.20$\pm$0.46 pg/$m\ell$ to 18.65$\pm$1.26pg/$m\ell$) at luteal phase. 3. The FSH levels during the estrous cycles ranged from 2.25$\pm$0.23mIU/$m\ell$ to 4.35$\pm$0.24mIU/$m\ell$ showing significant changes. The LH levels during the estrous cycles gradually increased and remained a peak level of 10.90$\pm$0.36mIU/$m\ell$ from 20 to 25 days after estrus. 4. The progesterone levels during the pregnancy were decreased from 30 to 60 days after artificial insemination, and therafter continuously increased until 240 days. The estradiol-17$\beta$ levels during the pregnancy were 24.56$\pm$1.19pg/$m\ell$ at day 30 after artificial inseminaton, and increased rapidly until 180 days. The levles were agagin decreased by 26.17$\pm$3.03pg/$m\ell$ until 210 days and markedly increased by 68.00$\pm$8.70pg/$m\ell$ until 240 days. 5. The prolactin levels during the pregnancy were 31.27$\pm$2.31ng/$m\ell$ and 42.60$\pm$2.37ng/$m\ell$ at day 150 and 240 after artificial insemination respectively. The LH levels during the pregnancy reached a peak of 27.47$\pm$7.90mIU/$m\ell$ at day 30 after artificial insemination, and thereafter gradually decreased. 6. The progesterone levels during the periparturient period reached a peak of 4.61$\pm$0.34ng/$m\ell$ at day 3 prepartum, and thereafter gradually decreased, and showed 2.05$\pm$0.60ng/$m\ell$ at day 7 postpartum. The estradiol-17$\beta$ levels during the periparturient period showed high level from 207.23$\pm$6.04pg/$m\ell$ at day 1 prepartum to 239.90$\pm$13.90pg/$m\ell$ at day 2 prepartum, and thereafter began to decline and reached 51.87$\pm$1.72pg/$m\ell$ at by 7 postpartum. 7. The prolactin levels during the periparturient period showed relatively higher level at the time of parturition. The LH levels during the periparturient period rnage from 6.32$\pm$0.32mIU/$m\ell$ to 13.90$\pm$1.37mIU/$m\ell$ showing significant changes. 8. The progesterone levels(4.6$\pm$0.8ng/$m\ell$) of the pregnant cows were significantly higher than those (1.84$\pm$1.4ng/$m\ell$) of nonpregnant cows. The cows of artificial insemination from 61 to 90 days after parturition showed higher progesterone levels. 9. During 20 to 25 days after artificial insemination, the accuracy of pregnancy diagnosis from milk progesterone levels were 94.4% for nonpregnant cows(<2.3ng/$m\ell$), and 75.0% for pregnant cows( 3.2ng/$m\ell$). The average overall accuracy of pregnancy prediction for nonpregnant and pregnant cows 83.3% 10. The results obtained this study suggest that the understanding of the endocrinological mechanisms by means of milk hormone analysis during the estrous cycle, pregnancy and parturition would give the basic information needed for increasing efficiency of reproduction. This study would not only provide an accurate method of the early pregnancy diagnosis by milk progesterone levels but also contribute to the research of providing the method of detecting of FSH levels in milk, which was difficult in blood serum.

• Tuberculosis and Respiratory Diseases
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• v.60 no.1
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• pp.76-82
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• 2006

Background : The objective of this study was to understand sleep-related problems, and to determine whether the sleep questionnaires is a clinically useful method in patients who need polysomnography. Methods : Subjects were patients who performed polysomnography and who asked to answer a sleep questionnaires at the Sleep Disorders Clinic of St. Paul's Hospital, Catholic University of Korea. Baseline characteristics, past medical illness, behaviors during sleep-wake cycle, snoring, sleep-disordered breathing and symptoms of daytime sleepiness were analyzed to compare with data of polysomnography. Results : The study population included 1081 patients(849 men, 232 female), and their mean age was $44.2{\pm}12.8years$. Among these patients, 38.9% had an apnea-hypopnea index(AHI)<5, 27.9% had $5{\leq}AHI<20$, 13.2% had $20{\leq}AHI<40$, and 20.0% had $40{\leq}AHI$. The main problems for visiting our clinic were snoring(91.7%), sleep apnea(74.5%), excessive daytime sleepiness(8.0%), insomnia(4.3%), bruxism(1.1%) and attention deficit(0.5%). The mean value of frequency of interruptions of sleep was 1.6 and the most common reason was urination(46.3%). Epworth Sleepiness Scale(ESS) had a weak correlation with AHI(r=0.209, p<0.01). When we performed analysis of sleep questionnaires, there were significant differences in the mean values of AHI according to the severity of symptoms including snoring, daytime sleepiness, taking a nap and arousal state after wake(p<0.05). Conclusion : On the basis of statistical analysis of sleep questionnaires, the severity of subjective symptoms such as ESS, snoring, daytime sleepiness and arousal state after wake correlated with the AHI significantly. Therefore the sleep questionnaires can be useful instruments for prediction of the severity of sleep disorder, especially sleep-disordered breathing.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.573-579
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• 2019

The purpose of this study is predicted easily the entrance surface dose (ESD) in chest digital radiography. We used two detector type such as flat-panel detector (FP) and IP (Imaging plate detector). ESD was measured at each exposure condition combined tube voltage with tube current using dosimeter, after attaching on human phantom, it was repeated 3 times. Phantom images were evaluated independently by three chest radiologists after blinding image. Dose-area product (DAP) or exposure index (EI) was checked by Digital Imaging and Communications in Medicine (DICOM) header on phantom images. Statistical analysis was performed by the linear regression using SPSS ver. 19.0. ESD was significant difference between FP and IP($85.7{\mu}Gy$ vs. $124.6{\mu}Gy$, p=0.017). ESD was positively correlated with image quality in FP as well as IP. In FP, adjusted R square was 0.978 (97.8%) and linear regression model was $ESD=0.407+68.810{\times}DAP$. DAP was 4.781 by calculating the $DAP=0.021+0.014{\times}340{\mu}Gy$. In IP, adjusted R square was 0.645 (64.5%) and linear regression model was $ESD=-63.339+0.188{\times}EI$. EI was 1748.97 by calculating the $EI=565.431+3.481{\times}340{\mu}Gy$. In chest digital radiography, the ESD can be easily predicted by the DICOM header information.