• ETRI Journal
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• v.32 no.3
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• pp.482-485
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• 2010

Feature selection is very important for feature-based relation classification tasks. While most of the existing works on feature selection rely on linguistic information acquired using parsers, this letter proposes new features, including probabilistic and semantic relatedness features, to manifest the relatedness between patterns and certain relation types in an explicit way. The impact of each feature set is evaluated using both a chi-square estimator and a performance evaluation. The experiments show that the impact of relatedness features is superior to existing well-known linguistic features, and the contribution of relatedness features cannot be substituted using other normally used linguistic feature sets.

• Culinary science and hospitality research
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• v.16 no.2
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• pp.367-381
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• 2010

The principal objective of this study was to compare domestic and foreign learning(science) classification systems for foodservice and culinary majors, and to identify any problems with the domestic learning classification system. This study entailed a comparison of domestic and foreign versions of scientific systems addressing hospitality management. This study involved content analysis, which proved to be a useful method for comparing secondary data, and was used to evaluate the science classification systems of the Korea Research Foundation, Korea Science and Engineering Foundation(Korea), National Science Foundation, Oracle Corporation(America), Natural Science and Engineering Research Council(Canada) and the Australian Bureau Of STATISTICS(Australia). As a result, the Korean classification systems were identified as being based on a hierarchical stepwise system, whereas those of other countries were classified on the basis of nominal classifications. The initial research conducted in this study lays the groundwork for effective learning classifications for foodservice and culinary majors in the future.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.229-240
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• 2008

Purpose: To develop the patient classification system based on the resource utilization for reimbursement of long-term care hospitals in Korea. Method: Health Insurance Review & Assessment Service (HIRA) conducted a survey in July 2006 that included 2,899 patients from 35 long-term care hospitals. To calculate resource utilization, we measured care time of direct care staff (physicians, nursing personnel, physical and occupational therapists, social workers). The survey of patient characteristics included ADL, cognitive and behavioral status, diseases and treatments. Major category criteria was developed by modified delphi method from 9 experts. Each category was divided into 2-3 groups by ADL using tree regression. Relative resource use was expressed as a case mix index (CMI) calculated as a proportion of mean resource use. Result: This patient classification system composed of 6 major categories (ultra high medical care, high medical care, medium medical care, behavioral problem, impaired cognition and reduced physical function) and 11 subgroups by ADL score. The differences of CMI between groups were statistically significant (p<.0001). Homogeneity of groups was examined by total coefficient of variation (CV) of CMI. The range of CV was 29.68-40.77%. Conclusions: This patient classification system is feasible for reimbursement of long-term care hospitals.

• Journal of Environmental Science International
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• v.28 no.4
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• pp.455-464
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• 2019

BAT-AEL(Best Available Techniques Associate Emission Level) is the basis for establishing permissible emission standards for the workplace. Therefore, it is necessary to formulate a regulated BAT-AEL setting methodology that is generally applicable to all relevant industries. For the BAT-AEL settings, various factors should be considered such as the pollutants item, whether the workplace is subject to integrated pollution prevention and control, whether BAT is applicable, the basic data type, the emission classification system, and the suitability of the collected data. Among these factors, it is the most important factor to establish the classification system for the emitting facilities such that the emission characteristics of an industrial facility and its pollutants can be effectively reflected. Furthermore the target of the survey workplace should adhere to the BAT guidelines, even if it is a workplace that is subject to an the integrated environmental system. Certified data (SEMS, TMS, cleanSYS, WEMS, etc.) can be used to prioritize the classification system for the emission facility and the emission levels of pollutants. However, the self-measured data, daily logs, and questionnaire data from the workplace can also be used upon agreement of the relevant TWG. The collected data should only be used only when the facility is operating normally. Data that have been determined to be outliers or inappropriate validation methods should also be excluded. The BAT-AEL can be establish by adhering to the following procedure: 1) investigate all relevant workplaces with in the industry, 2)select workplaces for integrated management, 3)Identify BAT application, 4)identify whether BAT is generally applicable, 5)establish a classification system for emitting facilities, 6)collection available data, 7)verify conformity, 8)remove of outliers, 9)prepare the BAT-AEL draft, 10)deliberate, and 11) perform the confirmation procedure.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.168-168
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• 2018

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.336-344
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• 2018

This research proposes a suitable classification and analysis standard for digital exhibition to analyze digital exhibition. Through the previous studies on digital exhibition classification, the necessity of the new standard is suggested and the analysis standard which can be easily applied to the change of concept and form of the newly emerging digital exhibition is established. Digital exhibition should take into account the elements of audience participation that naturally arise from exhibition planning and interactive storytelling format. Classification and analysis of existing digital exhibition spaces are conceptual classification based on keywords. This is because traditional exhibition methodology has been applied in the process of classifying exhibitions and works. However, in digital exhibitions, the interactive aspect between exhibition space, works, and visitors become so important that it is necessary to perform a performative classification between the works and the audience in the digital exhibition. Accordingly, the way of participating directly or indirectly in the exhibition classification should be considered based on what the audience feel. In this research, the interpretation of the classification and composition of the exhibition is based on Benjamin's argument which the classification of the sensory experience of the audience and 'Aktualisierung' closely related to the interaction with the audience. We also present analysis standard for digital exhibition according to the structure of the art exhibition narrative based on the narrative structure of Chatman. This classification methodology will provide the exhibition information in a way that can be easily understood by the visitors and it will be a precedent research that secures the expansion and accessibility of the digital exhibition.

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.279-281
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• 1999

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Spatial Information Research
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• v.18 no.5
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• pp.13-26
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• 2010

In this paper, a dynamic variable window-based topographical classification method is proposed which has the changeable classification units depending on topographical properties. In the proposed scheme, to im prove the classification efficiency, the unit of topographical classification can be changeable dynamically according to the topographical properties and repeated patterns. Also, in this paper, the classification efficiency and accuracy of the proposed method are analyzed in order to find an optimal maximum decision window-size through the experiment. According to the experiment results, the proposed dynamic variable window-based topographical classification method maintains similar accuracy but remarkably reduce computing time than that of a fixed window-size based one, respectively.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.207-215
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• 2015

It seems obvious that the spatial information of existing architectural heritage will be re-structured utilizing BIM technology. In the future to be able to implement such task, a new system of classification of spatial information, which fit to the structural nature of architectural heritage is necessary. This paper intend to suggest the conceptual model that can be the base of establishing new classification system for architectural heritage. For this study we reviewed researches related to classification system of architectural heritage (CS-AH) and BIM based architectural heritage (BIM-AH), first. As a result, we found that CS-AH is focused on building elevation and type, and BIM-AH is biased on the Library and Parametric Modeling. Second, we figured out a relationship between the CS-AH and BIM-AH. From this analysis, we found that BIM-AH is biased on Library and Parametric since the building elevation and type was focused on CS-AH. This review suggests a potential of the 3D CS-AH to expand the range of research for BIM-AH. At last, we suggest the three concept of classification are: 1)horizontality-accumulation relationship, 2)structure-infill relationship, 3)segment-member relationship. These three concept, together as one system of classification, could provide useful framework of new classification system of spatial information for architectural heritage.