• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.298-299
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• 2018

In this study, the discriminant power of the DEA models which do not require the prior information of decision makers was evaluated. Entropy model, Bootstrap model, Benevolent Cross Efficiency model, Aggressive Cross Efficiency model and Game Cross Efficiency model were selected as the DEA model for discriminant power evaluation. The discriminant power of five DEA models was evaluated using coefficient of variation and degree of importance. According to the evaluation results, the rank of discrimination power was evaluated in the order of Entropy model, Aggressive CE model, Benevolent CE model, Game CE model, and Bootstrap model in both evaluation indexes.

• Acute and Critical Care
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• v.33 no.4
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• pp.216-221
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• 2018

Background: In this study, we analyze the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS) 3, and Mortality Probability Model $(MPM)_0$ III in order to determine which system best implements data related to the severity of medical intensive care unit (ICU) patients. Methods: The present study was a retrospective investigation analyzing the discrimination and calibration of APACHE II, APACHE IV, SAPS 3, and $MPM_0$ III when used to evaluate medical ICU patients. Data were collected for 788 patients admitted to the ICU from January 1, 2015 to December 31, 2015. All patients were aged 18 years or older with ICU stays of at least 24 hours. The discrimination abilities of the three systems were evaluated using c-statistics, while calibration was evaluated by the Hosmer-Lemeshow test. A severity correction model was created using logistics regression analysis. Results: For the APACHE IV, SAPS 3, $MPM_0$ III, and APACHE II systems, the area under the receiver operating characteristic curves was 0.745 for APACHE IV, resulting in the highest discrimination among all four scoring systems. The value was 0.729 for APACHE II, 0.700 for SAP 3, and 0.670 for $MPM_0$ III. All severity scoring systems showed good calibrations: APACHE II (chi-square, 12.540; P=0.129), APACHE IV (chi-square, 6.959; P=0.541), SAPS 3 (chi-square, 9.290; P=0.318), and $MPM_0$ III (chi-square, 11.128; P=0.133). Conclusions: APACHE IV provided the best discrimination and calibration abilities and was useful for quality assessment and predicting mortality in medical ICU patients.

• Fashion & Textile Research Journal
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• v.3 no.5
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• pp.415-422
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• 2001

Focusing on working with fashion show as fashion model in South Korea, this study was develop a fashion model typology based on specific model characteristics related demographics and body, job satisfaction/dissatisfaction factors. The survey was done through questionnaire and 194 fashion models were used in the data analysis. The statistical analysis used in this study were frequency, ${\chi}^2$-test, cluster Analysis, MANOVA. ANOVA and Duncan multiple range test. The results of this study were as follows: 1) The majority of sample were as follows: unmarried, college graduate and undergraduate, resident in the Seoul, 20 to 24 yrs old female with 175-177 cm, 52-54 kg, B-W-H (33-24-35 inch). 2) The types of fashion model were classified into 4 types: showing type, lack of professionalism type, matured professionalism type, dissatisfaction with job-environment type. The model segments were profiled on levels of various job related attitude factors, job satisfaction/dissatisfaction, model education, working condition, human relation, regulation discrimination, fashion show stage level, the job of model, body & clothing, and promising job. 3) There were significant differences found between their sex, academic background, guarantee, and in all factors in their job related attitude factors, job satisfaction/dissatisfaction, model education, working condition, human relation, regulation discrimination, fashion show stage level, the job of model, body & clothing, style promising job.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.401-418
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• 2023

Recently, the socio-economic interest in Large Language Models (LLMs) has been growing due to the emergence of ChatGPT. As a type of generative AI, LLMs have reached the level of script creation. In this regard, it is important to address the issue of discrimination (sexism, racism, religious discrimination, ageism, etc.) in the performing arts in general or in specific performing arts works or organizations in a large language model that will be widely used by the general public and professionals. However, there has not yet been a full-scale investigation and discussion on the issue of discrimination in the performing arts in large-scale language models. Therefore, the purpose of this study is to textually analyze the perceptions of discrimination issues in the performing arts from LMMs and to derive implications for the performing arts field and the development of LMMs. First, BBQ (Bias Benchmark for QA) questions and measures for nine discrimination issues were used to measure the sensitivity to discrimination of the giant language models, and the answers derived from the representative giant language models were verified by performing arts experts to see if there were any parts of the giant language models' misperceptions, and then the giant language models' perceptions of the ethics of discriminatory views in the performing arts field were analyzed through the content analysis method. As a result of the analysis, implications for the performing arts field and points to be noted in the development of large-scale linguistic models were derived and discussed.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1255-1258
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• 1998

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. This method doesn't take account of discrimination to other words. To complement this problem, this paper proposes a word verification method by re-recognition of the recognized word and its similar word using the discriminative function between two words. The similar word is selected by calculating the probability of other words to each HMM. The recognizer haveing discrimination to each word is realized using the weighting to each state and the weighting is calculated by genetic algorithm.

• Journal of Labour Economics
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• v.24 no.2
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• pp.63-94
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• 2001

Based on the human capital theory, the wage differentials among laborers are generated from the discrepancy of human capital stock which depends on individual laborer's decision. Hence, the wage differentials among laborers or between male and female are not the results of discrimination, but the results of individual choice. But, if the individual choice for human capital stock would be affected by the male-female discrimination, the explanation for male-female wage differentials base on the human capital stock has a bias. Actually, women have experienced in the discrimination on labor market participation due to gravity, parturition, infant rearing. Also, it is a fact that women have been discriminated against men in labor market owing to social, traditional, and cultural discriminations. If woman or her parent will less invest on human capital than man owing to the existence of discrimination in labor market, the 'expected human capital stock' instead of human capital stock will explain male-female wage differentials better. Therefore, in this study, we set up three models; first model includes working hours, industry, occupation, etc which are in general used as explanatory variables for wage decision, second model includes the variables which reflect the traditional human capital stock together with the first model's explanatory variables, third model employes the 'expected human capital stock' instead of traditional human capital stock. From the empirical test, the estimates of discrimination in three models are .93, .60, and, .48 respectively. This result implies that the male-female wage differentials in Korea can be explained by the discrepancy of 'expected human capital stock'. Since the discrepancy in expected human capital stock depend on the disparity in life-cycle labor force participation, male-female wage differentials can largely be attributed to male-female disparity in expected lifetime labor force participation.

• Journal of the Korean Magnetic Resonance Society
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• v.22 no.4
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• pp.149-157
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• 2018

Diabetes is known to be one of common causes for several types of peripheral nerve damage. Diabetic neuropathy (DN) is a significant complication lowering the quality of life that can be frequently found in diabetes patients. In this study, the metabolomic characteristic of DN and Diabetes was investigated with NMR spectroscopy. The sera samples were collected from DN patients, Diabetes patients, and healthy volunteers. Based on the pair-wise comparison, three metabolites were found to be noticeable: glucose, obviously, was upregulated both in DN patients (DNP) and Diabetes. Citrate is also increased in both diseases. However, the dietary nutrient and biosynthesized metabolite from glucose, ascorbate, was elevated only in DNP, compared to healthy control. The multivariate model of OPLS-DA clearly showed the group separation between healthy control-DNP and healthy control-Diabetes. The most significant metabolites that contributed the group separation included glucose, citrate, ascorbate, and lactate. Lactate did not show the statistical significance of change in t-test while it tends to down-regulated both in DNP and Diabetes. We also conducted the ROC curve analysis to make a multivariate model for discrimination of healthy control and diseases with the identified three metabolites. As a result, the discrimination model between healthy control and DNP (or Diabetes) was successful while the model between DNP and Diabetes was not satisfactory for discrimination. In addition, multiple combinations of lactate and citrate in the OPLS-DA model of healthy control and diabetes group (DNP + Diabetes patients) gave good ROC value of 0.952, which imply these two metabolites could be used for diagnosis of Diabetes without glucose information.

• Journal of Ginseng Research
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• v.37 no.3
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• pp.341-348
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• 2013

Identification of the origins of Panax ginseng has been issued in Korea scientifically and economically. We describe a metabolomics approach used for discrimination and prediction of ginseng roots from different origins in Korea. The fresh ginseng roots from six ginseng cooperative associations (Gangwon, Gaeseong, Punggi, Chungbuk, Jeonbuk, and Anseong) were analyzed by UPLC-MS-based approach combined with orthogonal projections to latent structure-discriminant analysis multivariate analysis. The ginsengs from Gangwon and Gaeseong were easily differentiated. We further analyzed the metabolomics results in subgroups. Punggi, Chungbuk, Jeonbuk, and Anseong ginseng could be easily differentiated by the first two orthogonal components. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model predicted their geographical origins as 99.7% probability. The robust discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of P. ginseng samples.

• Water Engineering Research
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• v.6 no.2
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• pp.73-89
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• 2005

Two kinds of high resolution GCMs with the same spatial resolutions but with different schemes run by domestic and foreign agencies are used to clarify the usefulness and sensitivity of GCM for water resources applications for Korea. One is AMIP-II (Atmospheric Model Intercomparison Project-II) type GCM simulation results done by ECMWF (European Centre for Medium-Range Weather Forecasts) and the other one is AMIP-I type GCM simulation results done by METRI (Korean Meteorological Research Institute). Observed mean areal precipitation, temperature, and discharge values on 7 major river basins were used for target variables. Monte Carlo simulation was used to establish the significance of the estimator values. Sensitivity analyses were done in accordance with the proposed ways. Through the various tests, discrimination condition is sensitive for the distribution of the data. Window size is sensitive for the data variation and the area of the basins. Discrimination abilities of each nodal value affects on the correct association. In addition to theses sensitivity analyses results, we also noticed some characteristics of each GCM. For Korean water resources, monthly and small window setting analyses are recommended using GCMs.

• Asia Marketing Journal
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• v.12 no.2
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• pp.135-154
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• 2010

This paper considers a duopoly where switching costs exist. The analysis proves that temporal price reductions can be pure strategy equilibrium where firms earn more profit than in a regular price strategy. Greater profits result from price discrimination in temporal price reductions. The equilibrium is contrasted with previous studies, which explain temporal price reductions as a result of mixed strategy. In a given model with an assumption about forming switching cost, firms can control their range of loyal consumers by properly setting their regular and promotional prices. The model shows that temporal price reduction tends to raise the regular price and decrease the range of loyal consumers.