• Proceedings of the KSR Conference
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• 2007.05a
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• pp.993-999
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• 2007

With the introduction of the RAMS(Reliability, Availability, Maintainability, Safety), the interest of the system assurance has been increased. First of all, fast-growing electronic circuit requires analyzing the failure rates, by dividing the signalling system more specifically. Since 2005, the K.N.R (Korean National Railway) has incorporated ERP(Enterprise Resource Planning) in order to establish the complete status as the top international comprise, therefore while ordering the project, it has established the classification system and then has been applying to ERP system in 2007. Due to the complex of the classification system, the reliability analysis of the signalling system was assessed with the limit of IXL ATP with On-board and wayside equipment. This paper assumed MTBF(Mean Time Between Failure), MTTR((Mean Time Between Repair) of total signalling system, by using the classification of ERP program.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.250-255
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• 2002

This paper describes the study methods of identifying forest vegetation types, based on this study, forest vegetation classification method based on vegetation index is proposed. According to reflectance data of vegetation canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun, China, many vegetation index are calculated and analyzed. The relationships between vegetation index and vegetation types are that PVI identifies broadleaf forest and conifer forest the most easily, the next is TSAVI and MSAVI, but their calculation is complex. RVI values of different conifer trees vary obviously, so RVI can classify conifer trees. In a word, combination of PVI and RVI is evaluated to classify different vegetation types.

• 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 Korean Academy of Nursing
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• v.34 no.2
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• pp.278-289
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• 2004

Purpose: The purpose of this study was to classify the elderly in long-term care facilities using the Resource Utilization Group(RUG-III) and to examine the feasibility of a payment method based on the RUG-III classification system in Korea. Method: This study measured resident characteristics using a Resident Assessment Instrument-Minimum Data Set(RAI-MDS) and staff time. Data was collected from 530 elderly residents over sixty, residing in long-term care facilities. Resource use for individual patients was measured by a wage-weighted sum of staff time and the total time spent with the patient by nurses, aides, and physiotherapists. Result: The subjects were classified into 4 groups out of 7 major groups. The group of Clinically Complex was the largest (46.3%), and then Reduced Physical Function(27.2%), Behavior Problems (17.0%), and Impaired Cognition (9.4%) followed. Homogeneity of the RUG-III groups was examined by total coefficient of variation of resource use. The results showed homogeneity of resource use within RUG-III groups. Also, the difference in resource use among RUG major groups was statistically significant (p<0.001), and it also showed a hierarchy pattern as resource use increases in the same RUG group with an increase of severity levels(ADL). Conclusion: The results of this study showed that the RUG-Ill classification system differentiates resources provided to elderly in long-term care facilities in Korea.

• Journal of Korean Academy of Nursing
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• v.40 no.6
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• pp.903-912
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• 2010

Purpose: This study was conducted to examine whether the level of classification for long-term care service under longterm care insurance reflects resource utilization level for residents in nursing homes. Methods: From 2 long-term care facilities, the researchers selected 95 participants and identified description and time of care services provided by nurses, certified caregivers, physical therapists and social workers during a 24-hr-period. Results: Resource utilization level was: 281.04 for level 1, 301.05 for level 2 and 270.87 for level 3. Resource utilization was not correlated with level. Differences in resource utilization within the same level were similar with the coefficient of variance, 22.7-27.1%. Physical function was the most influential factor on long-term care scores (r=.88, p<.001). The level for long-term care service did not reflect differences in resource utilization level of residents on long-term care insurance. Conclusion: The results of this study indicate that present grading for long-term care service needs to be reconsidered. Further study is needed to adjust the long-term care classification system to reflect the level of resource utilization for care recipients on the long-term care insurance.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.45-55
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• 2022

Recently, large-scale language models (LPLM) have been shown state-of-the-art performances in various tasks of natural language processing including intent classification. However, fine-tuning LPLM requires much computational cost for training and inference which is not appropriate for dialog system. In this paper, we propose compressed intent classification model for multi-agent in low-resource like CPU. Our method consists of two stages. First, we trained sentence encoder from LPLM then compressed it through knowledge distillation. Second, we trained agent-specific adapter for intent classification. The results of three intent classification datasets show that our method achieved 98% of the accuracy of LPLM with only 21% size of it.

• Water Engineering Research
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• v.3 no.4
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• pp.259-267
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• 2002

South Africa has developed a policy and law that calls and provides for the equitable and sustainable use of water resources. Sustainable resource use is dependent on effective resource protection. Rivers are the most important freshwater resources in the country, and there is a focus on developing and applying methods to quantify what rivers need in terms of flow and water quality. These quantified and descriptive objectives are then related to specified levels of ecological health in a classification system. This paper provides an overview of an integrated and systematic methodology, where, fer each river, and each river reach, the natural condition and the present ecological condition are described, and a level/class of ecosystem health is selected. The class will define long term management goals. This procedure requires each ecosystem component to be quantified, starting with the abiotic template. A modified flow regime is modelled for each ecosystem health class, and the resultant fluvial geomorphology and hydraulic habitats are described. Then the water chemistry is described, and the water quality changes that are likely to occur as a consequence of altered flows are predicted. Finally, the responses to the stress imposed on the biota (fish, invertebrates and vegetation) by modified flow and water quality are predicted. All of the predicted responses are translated into descriptive and/or quantitative management objectives. The paper concludes with the recognition of active method development, and the enormous challenge of applying the methods, implementing the law, and achieving river protection and sustainable resource-use.

• Korean System Dynamics Review
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• v.7 no.1
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• pp.67-90
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• 2006

A series of system dynamics model was developed for forecasting demand and supply of human resource in the electricity industry. To forecast demand of human resource in the electric power industry, BLS (Bureau of Labor Statistics) methodology was used. To forecast supply of human resource in the electric power industry, forecasting on the population of our country and the number of students in the department of electrical engineering were performed. After performing computer simulation with developed system dynamics model, it is discovered that the shortage of human resource in the electric power industry will be 3,000 persons per year from 2006 to 2015, and more than a double of current budget is required to overcome this shortage of human resource.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.169-181
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• 2016

Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.

• Journal of Korean Academy of Nursing
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• v.33 no.2
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• pp.275-283
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• 2003

Purpose: This study was to classify elderly in long-term care hospitals for using Resource Utilization Group(RUG-III) and to consider feasibility of payment method based on RUG-III classification system in Korea. Method: This study designed by measuring resident characteristics using the Resident Assessment Instrument-Minimum Data Set(RAI-MDS) and staff time. The data were collected from 382 elderly over sixty-year old, inpatient in the five long-term care hospitals. Staff time was converted into standard time based on the average wage of nurse and aids. Result: The subjects were classified into 4 groups. The group of Clinically Complex was the largest(46.3%), Reduced Physical Function(27.2%), Behavior Problem(17.0%), and Impaired Cognition(9.4%). The average resource use for one resident in terms of care time(nurses, aids) was 183.7 minutes a day. Relative resource use was expressed as a case mix index(CMI) calculated as a proportion of mean resource use. The CMI of Clinically Complex group was the largest(1.10), and then Reduced Physical Function(0.93), Behavior Problem(0.93), and Impaired Cognition(0.83) followed. The difference of the resource use showed statistical significance between major groups(p<0.0001). Conclusion: The results of this study showed that the RUG-III classification system differentiates resources provided to elderly in long-term care hospitals in Korea.