• Korean Journal of Social Welfare
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• v.36
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• pp.199-228
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• 1998

This study is designed to find out the determinants of dual-earner wives' needs for family-supportive services. For this purpose, a hypothetical model which explains the relationships among 6 stressors, role overload, stress and needs for 4 family-supportive services is developed. Using the data collected by purposive sampling from 234 professional women and 208 blue-collar women living in Chinju and Sacheon, the hypothetical model developed in this study was tested. In order to examine occupational class differences, a model for professionals and another model for blue-collars were developed separately and compared. For data analysis, a covariance structure analysis was used. The best-fitting model for professional women (df=141, GFI=0.928, CFI=0.965) and the model for blue collar women (df=141, GFI=0.902, CFI=0.912) were found. As a result of comparing two models, 9 common relationships were found:l)Greater dissatisfaction with child care service increases role overload; 2)Longer work hours increases role overload; 3) Higher level of role overload increases stress; 4)Higher level of stress increase needs for leaves; 5)Older child increases needs for flexible work pattern; 6)Younger child increases needs for finalcial assistance for child care fee; 7)needs for financial assistance for child care increases needs for on-site child care services; 8)needs for on-site child care services increases needs for leaves; 9)needs for leaves increases needs for flexible work pattern. With the exception of these 9 common relationships, the analyses revealed substantial differences between professional and blue-collar dual-earner wives. Based on the common and differential needs between 2 groups of wives, the effective ways to provide family-supportive services according to the needs of individual dual-earner wives who are in different familial, financial, and work conditions were suggested.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• The Korean Journal of Nuclear Medicine
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• v.28 no.1
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• pp.75-84
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• 1994

The need for assessment of ureteric function in the patient with an obviousely dilated ureter has increased particularly with the added spectrum of asymptomatic patients presenting with hydrone-phrosis and hydroureter on antenatal and perinatal ultrasound. To assess the influence of ureteral status on kidney washout during $^{99m}Tc$-DTPA diuretic renography, ureteral images were reviewed in 80 children referred for hydronephrosis. A scintigraphically abnormal ureter was defined as an intense and continuous image of > 10 min during diuretic renography. Out of them, a total of 16 nephroureteral systems in 12 children with scintigraphically abnormal ureter were analyzed. A diuretic washout index using response half time (t1/2) by linear fitting after lasix injection, was determined on renal (Kt1/2) and ureteral (Ut1/2) curves (diuretic renogram vs. diuretic ureterogram). Diuretic ureterogram curve patterns corresponding to normal (type I), obstructive (II) and non-obstructive (III) cases were described. Compared with X-ray data, diuretic renography was highly sensitive (88%) and specific (99%) for detecting any ureteral abnormality. Despite an obstructive Kt1/2 (>20 min), no patient with an abnormal ureter underwent therapy at the ureteropelvic junction because the hydronephrosis regressed after surgery at the lower level. Our data indicate that the abnormal ureter findings during diuretic renography have to be recognized before therapy for children with hydeonephrosis.

• Journal of Radiation Protection and Research
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• v.17 no.1
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• pp.21-30
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• 1992

The frequencies of KCCH cyclotron neutron (30 cGy/min) or $^{60}Co\;{\gamma}-rays$ (210 cGy/min)-induced asymmetrical interchanges (dicentrics and centric rings) and acentric fragments (deletion) at several doses were measured in the normal human peripheral blood lymphocytes Chromosome aberrations were scored at the first nitosis after stimulation with phytohemagglutinin. The neutron and y-ray data were analysed on linear, power-law, quadratic and linear-quadratic model . When the dicentrics and centric rings of ${\gamma}-rays$ datas were pooled and fitted to these model, good fits were obtained to power-law $[Y=(5.81{\pm}1.96){\times}10^6D^{1.93+0.06},\; P=0.931]$, quadratic $[Y=(3.91{\pm}0.09){\times}10^{-6}D^2,\;P=0.972]$ an linear-Quadrati model $[Y=(6.55{\pm}6.83){\times}10^{-5}D+(3.72{\pm}0.22){\times}10^{-6}D^2\; P=0.922]$, except for linear model (P=0.067) As in the case of neutron data, the best fit was obtained to the linear model $(Y=(6.12{\pm}0.17){\times}10^{-3}\;D-0.22,\;P=0.987]$ and good fits were obtained to power-law$[Y=(5.36{\pm}3.02) {\times}10^{-4}D^{1.42+0.11},\; P=0.601]$ and linear-quadratic model$[Y=(2.43{\pm}0.70){\times}10^{-3}D+(1.21{\pm}0.39){\times}10^{-7}D^2$, \;P=0.415], except for quadratic model (P<0.005). The relative biological effectiveness (RBE) of neutron compared with y-ray was estimated by best fitting model. In the asymmetrical interchanges range between 0.1 and 1.5 per cell, the RBE was found to be $2.714{\pm}0.408$.

• Korean Journal of Social Welfare
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• v.51
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• pp.29-61
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• 2002

This study was designed to find out the determinants of types of bullying behavior, and the effects of types of bullying behavior on the bullies. For this purpose, a hypothetical model which explains the relationships among 6 environmental factors, 5 types of bullying behavior, and 5 outcome variables for the bullies was developed. Using the data collected from 177 junior and high school students who have bullied the other students, the hypothetical model was tested. For data analysis, a path analysis was used, and the best-fitting model was found (df=78, GFI=0.953, CFI=1.00). As a result of analyzing the model, types of bullying behavior were found to be determined by the different environmental factors: Isolation was determined by 2 factors (feeling of isolation from friends, exposure to bullying), social bullying by 2 factors (lack of support from parents, exposure to bullying), verbal bullying by conflicts with parents, physical bullying by 3 factors (lack of support from parents, exposure to isolation and exposure to bullying), and instrumental bullying by lack of support from parents. On the other hand, the pleasure that the bullies feel after bullying behavior was increased by isolation, verbal bullying and physical bullying, while decreased by instrumental bullying. Guilt feeling was decreased by isolation and instrumental bullying, while increased by physical bullying. Isolation increased the tendency of blaming the victim. Isolation and instrumental bullying increased bullies' self-esteem, while social bullying decreased self-esteem. Verbal bullying increased the extent of bullying, while instrumental bullying decreased the extent of bullying. Based on the findings, the intervention strategies to change the bullies' attitudes toward victim, and to increase social support from the significant others as well as the effective ways to reorganize the school environment in order to reduce and prevent bullying behavior were suggested.

• Publications of The Korean Astronomical Society
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• v.36 no.3
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• pp.47-63
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• 2021

Variability is one of the major characteristics of Active Galactic Nuclei (AGN), and it is used for understanding the energy generation mechanism in the center of AGN and/or related physical phenomena. It it known that there exists a time lag between AGN light curves simultaneously observed at different wavelengths, which can be used as a tool to estimate the size of the area that produce the radiation. In this paper, We present long term near-infrared variability of optically bright type 1 AGN using the Wide-field Infrared Survey Explorer data. From the Milliquas catalogue v6.4, 73 type 1 QSOs/AGN and 140 quasar candidates are selected that are brighter than 18 mag in optical and located within 5 degree around the ecliptic poles. Light curves in the W1 band (3.4 ㎛) and W2 band (4.6 ㎛) during the period of 2010-2019 were constructed for these objects by extracting multi-epoch photometry data from WISE and NEOWISE all sky survey database. Variability was analyzed based on the excess variance and the probability P_var. Applying both criteria, the numbers of variable objects are 19 (i.e., 26%) for confirmed AGN and 12 (i.e., 9%) for AGN candidates. The characteristic time scale of the variability (τ) and the variability amplitude (σ) were derived by fitting the DRW model to W1 and W2 light curves. No significant correlation is found between the W1/W2 magnitude and the derived variability parameters. Based on the subsample that are identified in the X-ray source catalog, there exists little correlation between the X-ray luminosity and the variability parameters. We also found four AGN with changing W1-W2 color.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.189-198
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• 2022

In this present study, we investigated the effect of the offset correction factor calculation method on the sulfur dioxide (SO₂) column density in the SO₂ retrieval algorithm of the Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020. The GEMS operational SO₂ retrieval algorithm is the Differential Optical Absorption Spectroscopy (DOAS) - Principal Component Analysis (PCA) Hybrid algorithm. In the GEMS Hybrid algorithm, the offset correction process is essential to correct the absorption effect of ozone appearing in the SO₂ slant column density (SCD) obtained after spectral fitting using DOAS. Since the SO₂ column density may depend on the conditions for calculating the offset correction factor, it is necessary to apply an appropriate offset correction value. In this present study, the offset correction values were calculated for days with many cloud pixels and few cloud pixels, respectively. And a comparison of the SO₂ column density retrieved by applying each offset correction factor to the GEMS operational SO₂ retrieval algorithm was performed. When the offset correction value was calculated using radiance data of GEMS on a day with many cloud pixels was used, the standard deviation of the SO₂ column density around India and the Korean Peninsula, which are the edges of the GEMS observation area, was 1.27 DU, and 0.58 DU, respectively. And around Hong Kong, where there were many cloud pixels, the SO₂ standard deviation was 0.77 DU. On the other hand, when the offset correction value calculated using the GEMS data on the day with few cloud pixels was used, the standard deviation of the SO₂ column density slightly decreased around India (0.72 DU), Korean Peninsula (0.38 DU), and Hong Kong (0.44 DU). We found that the SO₂ retrieval was relatively stable compared to the SO₂ retrieval case using the offset correction value on the day with many cloud pixels. Accordingly, to minimize the uncertainty of the GEMS SO₂ retrieval algorithm and to obtain a stable retrieval, it is necessary to calculate the offset correction factor under appropriate conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.12 s.138
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• pp.1596-1604
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• 2004

Quality Function Deployment(QFD) is a product development tool which ensures that the voice of the customer needs is heard and translated into products. To develop a sensible brassiere for middle-aged women QFD was adopted. In this study the applicability and usefulness of QFD was examined through the engineering design process for a sensible brassiere for middle-aged women. The customer needs for the wear comfort of brassiere was made by one-on-one survey of 100 women who aged 30-40. The customer competitive assessment was generated by wearing tests of 10 commercial brassieres. The subjective assessment was conducted in the enviornmental chamber that was controlled at $28{\pm}1^{\circ}C,\;65{\pm}3\%RH.$ As a results, we developed twenty-one customer needs and corresponding HOWs for the wear comfort of brassiere. The Customer Competitive Assessment was generated by wearing tests of commercial brassiere. The subjective measurement scale and dimension for the evaluation of sensible brassiere were extracted from factor analysis. Four factors were fitting, aesthetic property, pressure sensation, displacement of brassiere due to movement. The most critical design parameter was wire-related property and second one was stretchability of main material of brassiere. Also, wearing comfort of brassiere was affected by the interaction of initial stretchability of wing and support of strap. Engineering design process, QFD was applicable to the development of technical and aesthetic brassieres.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.3 s.151
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• pp.378-385
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• 2006

To release fashion trends in an efficient way, many of the apparel business and fashion educational institutes in land adopt fashion shows employing fashion models. Modeling rather than flat pattern making realizes the majority of the complicated design works for the fashion shows. However, for the different measurements between the dress form and the real human model, problems often occur during the modeling and fitting processes. Researches on the standard dress form development representing professional fashion models' features are therefore in urgent need to enable the related apparel business and fashion institutes to make appropriate use of the dress form in their jobs. The study has been conducted as a preliminary study using a questionnaire method ultimately to develop the female dress form. A questionnaire in the research aimed at an investigation into the actual conditions of and satisfaction with the usage and the body measurements of existed dress forms. Approximately 30 fashion-related educational institutes and 10 apparel companies responded to the survey. Data derived from the survey was analyzed using SPSS version 10.1, the statistics tool. The results throughout the research were discussed in terms of largely three categories that are; (1) the general conditions of the usage of the dress form to prepare fashion shows: e.g. the frequency of holding the fashion show in an annual term, the proportion of professional and amateur models employed for the fashion show, the methods to construct garments, types and number of dress forms utilized and etc.; (2) factors considered to purchase the dress form e.g. its functionality, shapes, sizes, duration, price, A/S condition and etc.; and(3) satisfaction with the similarity between the dress form and the human body in the relation to the body measurements. Measurements in length wise, front and back waist lengths, neck to bust point on the dress forms were apparently differed from the ones of the actual body. In particular, differed torso length measurements cause the problem to have to alter the whole silhouette, consequently, the resultant patterns as well. In girth measurements, in order of bust and waist girths, the satisfaction was low.