• Journal of Home Health Care Nursing
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• v.6
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• pp.19-32
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• 1999

The purpose of this study was to develop of home nursing care classification and home health care costs of the free-standing home nursing care agency. This study was done through 3 steps The First stage, home nursing care classification was identified and classified by literature, review-committee and expert meeting. The second stage, cost elements for home nursing care visit were identified and accounted. That were divided into direct nursing care cost, indirect nursing care cost, management cost and transportation cost. Third stage, total cost of per visit was produced. Data were collected from 810 visits of 120 patients received home dare and from January. 1999 to November, 1999, and analysed with EXCEL program. The obtained results are as follows : 1. Home nursing care classification was consisted of 6 high level classification domain and 10 low level classification domain and 163 home nursing care behavior. 2. The cost of home nursing care per visit was 30,638 won which were direct and indirect nursing care cost(16.305won), management cost(5,255won) and transportation cost (9,098won). In conclusion. Home nursing behavior care classification developed in this study would be used as home health care standard. And the home nursing care costs can be used as a fundamental data for the further development of home health care costs in Korea.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.317-322
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• 2010

This paper proposes how to improve performance of the Korean document sentiment-classification system using semantic properties of the sentiment words. A sentiment word means a word with sentiment, and sentiment features are defined by a set of the sentiment words which are important lexical resource for the sentiment classification. Sentiment feature represents different sentiment intensity in general field and in specific domain. In general field, we can estimate the sentiment intensity using a snippet from a search engine, while in specific domain, training data can be used for this estimation. When the sentiment intensity of the sentiment features are estimated, it is called semantic orientation and is used to estimate the sentiment intensity of the sentences in the text documents. After estimating sentiment intensity of the sentences, we apply that to the weights of sentiment features. In this paper, we evaluate our system in three different cases such as general, domain-specific, and general/domain-specific semantic orientation using support vector machine. Our experimental results show the improved performance in all cases, and, especially in general/domain-specific semantic orientation, our proposed method performs 3.1% better than a baseline system indexed by only content words.

• Journal of Korean Academy of Nursing Administration
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• v.13 no.4
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• pp.431-444
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• 2007

Purpose: The purpose of this study was to identify nursing interventions performed by nurses working in small-medium sized hospitals. Method: Using NIC(3rd revision), the data were collected from 241 staff nurses working in 6 general hospitals. Result: Fourteen interventions were performed at least once a day. The physiological: basic and complex domain was most frequently performed. The most frequently performed intervention was medication: IV, and followed by medication-related interventions such as medication: IM, analgesic administration, IV therapy, and IV insertion. Fifty-five interventions were rarely performed. Most of them were in the family domain. ICU nurses performed interventions most often, while OBGY-pediatric units nurses performed them least. The ICU nurses performed the physiological: basic and complex, and the safety domain more often than medical and OBGY-pediatric units nurses. For the OBGY-pediatric units, the nurses were performed rarely the family domain, especially child bearing and rearing care. Conclusion: This survey has helped to figure out the nursing care in small-medium sized hospitals. These findings will help in building and improving of the standardized interventions for small-medium sized hospitals in located local provinces.

• Genomics & Informatics
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• v.16 no.4
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• pp.23.1-23.5
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• 2018

Virus taxonomy was initially determined by clinical experiments based on phenotype. However, with the development of sequence analysis methods, genotype-based classification was also applied. With the development of genome sequence analysis technology, there is an increasing demand for virus taxonomy to be extended from in vivo and in vitro to in silico. In this study, we verified the consistency of the current International Committee on Taxonomy of Viruses taxonomy using an in silico approach, aiming to identify the specific sequence for each virus. We applied this approach to norovirus in Caliciviridae, which causes 90% of gastroenteritis cases worldwide. First, based on the dogma "protein structure determines its function," we hypothesized that the specific sequence can be identified by the specific structure. Firstly, we extracted the coding region (CDS). Secondly, the CDS protein sequences of each genus were annotated by the conserved domain database (CDD) search. Finally, the conserved domains of each genus in Caliciviridae are classified by RPS-BLAST with CDD. The analysis result is that Caliciviridae has sequences including RNA helicase in common. In case of Norovirus, Calicivirus coat protein C terminal and viral polyprotein N-terminal appears as a specific domain in Caliciviridae. It does not include in the other genera in Caliciviridae. If this method is utilized to detect specific conserved domains, it can be used as classification keywords based on protein functional structure. After determining the specific protein domains, the specific protein domain sequences would be converted to gene sequences. This sequences would be re-used one of viral bio-marks.

• Physical Therapy Korea
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• v.30 no.2
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• pp.136-143
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• 2023

Background: The International Classification of Functioning, Disability and Health (ICF) model, created by the World Health Organization, provides a theoretical framework that can be applied in the diagnosis and treatment of various disorders. Objects: Our research purposed to ascertain the relationship between structure/function, activity, and participation domain variables of the ICF and pain, pain-associated disability, activities of daily living (ADL), and quality of life in patients with chronic low back pain (LBP). Methods: Two-hundred patients with chronic LBP (mean age: 35.5 ± 8.8 years, females, n = 40) were recruited from hospital and community settings. We evaluated the body structure/function domain variable using the Numeric Pain Rating Scale (NPRS) and Roland-Morris disability (RMD) questionnaire. To evaluate the activity domain variable, we used the Oswestry Disability Index (ODI) and Quebec Back Pain Disability Scale (QBDS). For clinical outcome measures, we used Short-form 12 (SF-12). Pearson's correlation coefficient was used to ascertain the relationships among the variables (p < 0.05). All the participants with LBP received 30 minutes of conventional physical therapy 3 days/week for 4 weeks. Results: There were significant correlations between the body structure/function domain (NPRS and RMD questionnaire), activity domain (ODI and QBDS), and participation domain variables (SF-12), rending from pre-intervention (r = -0.723 to 0.783) and postintervention (r = -0.742 to 0.757, p < 0.05). Conclusion: The identification of a significant difference between these domain variables point to important relationships between pain, disability, performance of ADL, and quality in participants with LBP.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.59-62
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• 2000

A software application builder has designed and partially implemented a E-Business software system using several reusable in-house software components. The builder finds an externally available third-party software components that satisfies solve desired functionality or behavior. We need systematic classification of the component from the domain. We propose a reference architecture of E-Business domain. It is used to search and reuse requiring components.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.27-44
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• 2001

A fast Poisson solver on irregular domains, based on bound-ary methods, is presented. The harmonic polynomial approximation of the solution of the associated homogeneous problem provides a good practical boundary method which allows a trivial parallel processing for solution evaluation or straightfoward computations of the interface values for domain decomposition/embedding. AMS Mathematics Subject Classification : 65N35, 65N55, 65Y05.

• Smart Structures and Systems
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• v.23 no.1
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• pp.71-79
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• 2019

In general, it may be advantageous to measure the pressure pulsation near a valve to detect a valve defect in a linear compressor. However, the acceleration signals are more advantageous for rapid classification in a mass-production line. This paper deals with the performance improvement of fault classification using only the compressor-shell acceleration signal based on the relation between the refrigerant pressure pulsation and the shell acceleration of the compressor. A transfer function was estimated experimentally to take into account the signal noise ratio between the pressure pulsation of the refrigerant in the suction pipe and the shell acceleration. The shell acceleration signal of the compressor was modified using this transfer function to improve the defect classification performance. The defect classification of the modified signal was evaluated in the acceleration signal in the frequency domain using Fisher's discriminant ratio (FDR). The defect classification method was validated by experimental data. By using the method presented, the classification of valve defects can be performed rapidly and efficiently during mass production.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.8-14
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• 2017

Deinterlacing is a method to convert interlaced video, including two fields (even and odd), to progressive video. It can be divided into spatial and temporal methods. The deinterlacing method in the spatial domain can easily be hardware-implemented, but yields image degradation if information about the deinterlaced pixel does not exist in the same field. On the other hand, the method in the temporal domain yields a deinterlaced image with higher quality but uses more memory, and hardware implementation is more difficult. Furthermore, the deinterlacing method in the temporal domain degrades image quality when motion is not estimated properly. The proposed method is for deinterlacing in the spatial domain. It uses several deinterlacing methods according to statistical characteristics in neighboring pixel locations. In this procedure, the proposed method uses the C4.5 algorithm, a typical classification algorithm based on entropy for choosing optimal methods from among the candidates. The simulation results show that the proposed algorithm outperforms previous deinterlacing methods in terms of objective and subjective image quality.