• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.86-88
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• 2009

This article suggests an online-based remote fault current mode discrimination method in order to identify the causes of the power line faults with various causes. For that, it refers to existing cause identification methods and categorizes modes by fault causes based on statistical techniques beforehand and performs the pre-treatment process of fault currents by each cause acquired from the fault recorder into a phase space in order to extract the characteristics of fault currents by each cause. After that, for the fault mode categorization, it discriminates modes by each cause using data by each cause as leaning data through utilizing RBF network. And then it tests the validity of the suggested method as applying it to the data of the actual fault currents acquired from the fault recorder in the electric power transmission center.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.5
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• pp.242-250
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• 2005

This paper proposes a new protective relaying algorithm using Neuro-Fuzzy and wavelet transform. To organize advanced nuero-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of Dl coefficient and RSM value within half cycle after fault occurrence. Subsequently, advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within 1/2 after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.293-296
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• 2004

This paper proposes a new protective relaying algorithm using ACI(Advanced Computational Intelligence) and wavelet transform. To organize the advanced neuro-fuzzy algorithm, it is important to select target data reflecting various transformer transient states. These data are made of changing-rates of D1 coefficient and RSM value within half cycle after fault occurrence. Subsequently, the advanced neuro-fuzzy algorithm is obtained by converging the target data. As a result of applying the advanced neuro-fuzzy algorithm, discrimination between internal fault and inrush is correctly distinguished within half cycle after fault occurrence. Accordingly, it is evaluated that the proposed algorithm can effectively protect a transformer by correcting discrimination between winding fault and inrushing state.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1529-1529
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• 2001

The watercore in apple is very important factor in storage and sorting of fruit. Most consumers tend to prefer the apple included watercore in immediately after harvest, however the watercore causes fruit flesh to brown during storage and lose the worth after all. But it is practically impossible to judge to the naked eye whether an apple has watercore or not. Therefore, the rapid, accurate and non-destructive analysis method for discrimination of watercore should be settled without delay. In this study we attempted the discrimination and quantitative analysis of watercore in apple fruit using near-infrared transmittance spectroscopy ‘Fuji’ apple fruits produced in Kyungpook of Korea was used in this experiment. The watercore content in apple was evaluated by graphic treatment of culled slice sections(10 mm). NIR transmittance spectra were collected over the 500 to 1000 nm spectral region with a spectrometer (Sentronic Co., Germany). The calibration models were carried out by partial least squares (PLS) analysis between NIR spectra data of apples and chemical data of watercore content. The spectra were different in absorbance between apple included watercore and not included one. Apple included watercore had higher absorption band than sample not included one at 732 and 820 nm. The calibration model seems to be accurate to predict the watercore content in apple fruit, the correlation coefficient (R) and root mean square error of prediction (RMSEP) were 0.99 and 0.93%, respectively. This result indicates that the PLSR calibration model by using NIR transmittance spectroscopy could be used for discrimination of watercore in apple fruit.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.1-14
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• 2020

IT businesses in Korea have relatively strong regulations. While providing the same service, domestic businesses are in a situation of 'reverse discrimination of regulations' as they are less competitive than global IT companies in accordance with the application of the personal information protection legislation in Korea. In this paper, Personal Information Protection legislation was classified and laws of major countries were analyzed in comparative ways. It also compared and analyzed the "private policy" presented by representative Internet sites (Naver, Daum, Google, Facebook) that provide services to users in Korea. We also proposed three aspects of legislation improvement to address reverse discrimination.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.317-326
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• 2018

The study was design to examine the degree of disability and the moderating effect of discrimination experiences on the job satisfaction of the disabled workers' disability acceptance. The 8th year data from the Panel Survey of Employment for the Disabled was used to extracting 2270 disable wage-workers who met the study purpose as a sample. The moderate effect was verified by using regression analysis. As a result, first, the disability acceptance of disabled wage workers presented a positive effect on job satisfaction. Second, the effect of disability acceptance on job satisfaction differed by the degree of disability. Third, the effect of disability acceptance on job satisfaction differed by the discrimination experiences. Which refers that the study identify the cushion of degree of disability and the discrimination experiences, which play an important role in the job satisfaction of the disabled wage workers. Therefore, this study suggests that it can be used as a practical implication and basic data for the program developments improving the job satisfaction of disabled wage workers.

• 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.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.249-256
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• 2019

The purpose of this study was to analyze the association between discrimination and job satisfaction among non-regular office workers. The data was collected from 128 employees of 7 university hospitals using a standardized questionnaire. In research methodology, the data was analyzed with descriptive statistics, t-test, ANOVA, pearson's correlation, multiple regression analysis. The results of the study are as follows. Unjust working environment, position unfairness, employment instability were found to have a negative(-) effect on job satisfaction. Gender, education, income in general characteristics had a significant negative(-) effect on job satisfaction. The discrimination of non-regular office woman workers was higher and job satisfaction was lower than that man workers. These results showed that hospitals need active efforts to create friendly work environment for non-regular woman workers.

• Journal of the Korean Society of Tobacco Science
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• v.27 no.1 s.53
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• pp.134-140
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• 2005

The purpose of this study was the discrimination of different tobacco types by the E-Nose/tongue and the analysis of what human sensory attributes are correlated with e-instrument's sensors. Samples were made from five groups of Korean domestic tobacco leaves, aged burley and not aged, aged flue-cured and not aged and blending types of the four. Instrumental tests were conducted to discriminate characteristics among different tobacco samples by the E-Nose and the E-Tongue. Sensory attributes of tobacco tastes were impact, irritation, bitterness, hay-like, tobacco taste, smoke volume, smoke pungent and mouth cleanness. STATISTICA software was used to analyze correlation between the human sensory data and the raw data of e-instruments. Discrimination analysis can be achieved using principal components analysis (PCA) and discriminant factorial analysis(DFA). As a result, impact, bitterness, irritation, smoke volume and smoke pungent of human sensory attributes were correlated with data from the several clustered E-Nose sensors(p < 0.10). And bitterness, irritation, and smoke pungent of human sensory attributes were correlated with data from the E-Tongue sensors(p < 0.10). PCA plot by the E-Nose shows that aged tobacco and not aged were discriminated and DFA plot shows that three groups(aged burley, not aged burley and flue-cured) were discriminated. PCA plot by the E- Tongue shows that flue-cured tobacco was separated from burley. Our results indicated that the e-instruments are sensitive enough to distinguish among tobacco types and their several sensors are reacted to the human sensory attributes.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.128-136
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• 2003

In today's design trend of vehicle structure, crush zone is fiequently reinforced by adding a box-shaped sub-frame in order to avoid an excessive deformation against a high-speed offset barrier such as EU Directive 96/97 EC, IIHS offset test. That kind of vehicle structure design results in a relatively monotonic crash pulse for airbag ECU(Electronic Control Unit) located at non-crush zone. As for an angular crash event, the measured crash signal using a single-axis accelerometer in a longitudinal direction is usually weaker than that of frontal barrier crash. Therefore, it is not so easy task to achieve a satisfactory crash discrimination performance for offset and angular crash events. In this paper, we introduce a new crash discrimination algorithm using an electronic X-Y 2-axis accelerometer in order to improve crash discrimination performance especially for those crash events. The proposed method uses a crash signal in lateral direction(Y-axis) as well as in longitudinal direction(X-axis). A crash severity measure obtained from Y-axis acceleration is used to improve the discrimination between fire and no-fire events. The result obtained by the proposed measure is logically ORed with an existing algorithm block using X-axis crash signal. Simulation and pulse injection test have been conducted to verify the performance of proposed algorithm by using real crash data of a 2,000cc passenger vehicle.