• Geotechnical Engineering
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• v.14 no.1
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• pp.29-36
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• 1998

The application of a multiple rock classification method, which is a generalization of a binary rock classification, is studied in this paper. In particular, this paper shows how to incorporate qualitative data through a case study. The method suggested in this paper can be effectively used for a systematic multiple rock classification such as RMR system developed by Bieniawski. It will be very useful for rock classifications. In addition, it is known that the expected cost of errors can be atopted to indicate how well a investigation plan is made.

• The korean journal of orthodontics
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• v.22 no.1
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• pp.17-30
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• 1992

Since 1984, many patients have been treated with Multiloop Edgewise Archwire (MEAW) Technique and diagnosed with ODI (Overbite Depth Indicator) and APDI (Anteroposterior Dysplasia Indicator) by the authors. 234 samples of them were selected randomly for the statistical analysis (age, sex, Angle's classification, treatment period, extraction, ODI etc.). Especially, ODI was analysed statistically and its application methods were reviewed. The results and conclusions were as follows: 1. On the 150 patients with normal overbite, the mean values of Class I, II, III malocclusion were $67.5^{\circ}$, $72.2^{\circ}$ and $59.0^{\circ}$. They were significantly different on the level of p < 0.01. 2. In normal overbite samples, ODI decreased with the increase of APDI and the correlation coefficient was -0.54. It seems that this result reflects the characteristics of AB to mandibular plane angle. 3. The regression equation was Y = - 0.57X + 114.64, where X is APDI and Y is ODI. In cases of small or large APDI, it seems to be absurd that the patient's ODI is compared with the mean ODI to differentiate diagnostically the open bite or deep bite tendency from the normal.

• Quality Improvement in Health Care
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• v.22 no.1
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• pp.29-37
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• 2016

Readmission which reflects capacity to manage patients and general level of medical services has been known for one of the causes of medical expenditure due to inefficient service. Compared to disease-specific readmission, hospital wide readmission (HWR) is relatively easy to understand, and has merit to get over limitation of collateral medical services assessment; therefore, a growing interest in development and usage of readmission indicator as quality of care indicator focusing on all-disease is detected. In this study, we investigate current state of risk standardized readmission rate indicator used in the United States, the United Kingdom, and Canada, and examine the considerations when using readmission rate as quality indicator in Korea. Differences in risk-adjustment methods were showed among countries. The United States do not control race not to hide socio-demographic factors on readmission. Canada shows differentiation compared to other countries about reflecting community factors. All three-countries utilize readmission rate as monitoring quality of care rather than incentives or penalty due to the fact that readmission rate could not represent the whole quality of hospital and has a limitation at controlling socio-economic factors. Therefore, for usage readmission rate as quality indicator in Korea, preparing readmission classification standard for Korean medical environment and additional methods for acquiring information by using discharge summary is need. Moreover, continued discussion with clinical specialists is needed for obtain clinical reliability and validity.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.2
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• pp.245-257
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• 2000

The purpose of this study was to validate caregiver outcomes included Nursing Outcomes Classification(NOC) developed by Johnson and Maas at the University of Iowa. A sample of 73 nurse experts working in university affiliated hospitals participated in this study. They were asked to rate indicators that examplified the outcomes on a scale of 1(indicator is not at all characteristic) to 5(indicator is very characteristic). A questionnaire with an adaptation of Fehring's methodology was used to establish the content validity of outcomes. The results were as follow: 1. Eight outcome label were considered to be 'supporting' and three outcome label were considered to be 'nonsupporting'. 2. 'Caregiver-Patient Relationship' attained an OCV score of 0.64 and the highest OCV score among caregiver outcomes.. 3. 'Caregiver Emotional Health' attained an OCV score of 0.54 and the lowest OCV score among caregiver outcomes. Replication study will be needed and outcomes sensitive to Korean culture need to be developed.

• Economic and Environmental Geology
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• v.40 no.3 s.184
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• pp.319-330
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• 2007

The Muposan Tuff is a stratigraphic unit which is distinguished as a cooling unit in the volcanic rocks of the northeastern Kyeongsang Basin. The Muposan Tuff commonly belongs to tuff field according to the granulometric classification and to vitric tuffs according to the constituent classification. The tuffs are mostly densely to partially welded to include very flattened and sometimes stretched pumices and shards, and involve several flow indicator and lateral gradings in maximum diameter and content of their constituents. Movement pattern from flow lineation, lithic and pumice imbrications, asymmetric flow folds, and lateral gradings in maximum diameter and content of their constituents indicate that the Muposan Tuff had a source from the southeastern part.

• Journal of the Korean Institute of Educational Facilities
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• v.23 no.4
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• pp.3-10
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• 2016

This paper presents the investigation results on the classification of the university campuses. For the classification, we selected the spatial index as the evaluation indicator since the environmental factors and maintenance methods vary from university campus to university campus. For the study, we used eight spatial indices of the 30 national universities. This paper provides the spatial characteristics of different campus types, presents campus classification analysis as a future research approach to campus maintenance, and provides the data for the future study of comparison among universities. The results are as follows. 1) The classification investigation categorized the university campuses into three groups. Type 1 is a large-scale type, located near downtown. Type 2 is a medium-scale type, located at a remote site from downtown. Type 3 is a small-scale type, which is located comparatively near downtown. 2) Type 1 is a large-scale mixed area type, and 13 universities belong to this group. Type 2 is a medium-scale suburban area type, and six universities are in this group. Finally, Type 3 is a small-scale downtown area type, and 11 universities belong to this group.

• Tunnel and Underground Space
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• v.19 no.5
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• pp.407-420
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• 2009

In this study geostatistical technique using indicator kriging was performed to evaluate the optimal rock mass classification by integrating the various geophysical information such as borehole data and geophysical data. To get the optimal kriging result, it is necessary to devise the suitable technique to integrate the hard (borehole) and soft (geophysical) data effectively. Also, the model parameters of the variogram must be determined as a priori procedure. Iterative non-linear inversion method was implemented to determine the model parameters of theoretical variogram. To verify the algorithm, behaviour of object function and precision of convergence were investigated, revealing that gradient of the range is extremely small. This algorithm for the field data was applied to a mountainous area planned for a large-scale tunneling construction. As for a soft data, resistivity information from AMT survey is incorporated with RMR information from borehole data, a sort of hard data. Finally, RMR profiles were constructed and attempted to be interpreted at the tunnel elevation and the upper 1D level.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.