• Journal of muscle and joint health
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• v.20 no.1
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• pp.52-61
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• 2013

Purpose: This study was conducted to evaluate the effects of education about pressure ulcer classification and incontinence-associated dermatitis on knowledge and self-efficacy in pressure ulcer among nurses. Methods: One group pretest-posttest design was used. Participants were 41 nurses in a tertiary hospital in Seoul. A 90 minutes lecture was delivered. The self-reported-questionnaire on knowledge and self-efficacy and photograph test for knowledge were done at pre- and post-education. Results: After education, there was significant increase in the score of knowledge measured by questionnaire (mean 1.76, p<.001), in the score of knowledge measured by photographs (mean 2.00, p<.001) and in the score of self-efficacy (mean 5.17, p<.001). Conclusion: This study showed that knowledge and self-efficacy in pressure ulcer were improved by the education about pressure ulcer classification and incontinence-associated dermatitis. This program may be used for enhancing nurses' abilities of caring pressure ulcer.

• Journal of the Korean Society of Safety
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• v.15 no.4
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• pp.139-149
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• 2000

This study aims to review and assess the existing postural classification schemes used for evaluating postural loads in industry. The schemes can be classified into three categories: self-report, observational and instrument-based techniques depending upon how to record working postures. Of the three techniques, this study was mainly focused on the observational methods. The observational technique is most widely used in the industrial sites because it does not interfere with work, and is easy and simple to use and cost-effective without requiring the use of expensive equipment for estimating the angular deviation of a body segment from the neutral position. In spite of the usefulness and applicability, the techniques have some problems: 1) The existing observational techniques lack the consistency in the class limits of the motion categories in each body segment; 2) Most of them do not provide the post-analysis criteria needed to judge whether or not any posture is acceptable in view point of the postural load; and 3) They can not precisely evaluate the postural load for a given posture because the external loads and dynamic factors including acceleration, moment and force were not taken into consideration.

• Korean Journal of Occupational Health Nursing
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• v.28 no.2
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• pp.75-82
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• 2019

Purpose: This study aimed to determine the influence of injury and/or injured area classification on depression in patients with industrial injuries. Methods: The participants comprised438 patients who consented to participate and completed self-reported questionnaires. Data were analyzed using SPSS/WIN version 22.0 for descriptive statistics, $x^2$ test, fisher's exact test, ANOVA, and post-hoc $Scheff{\acute{e}}$ test. A stepwise multiple regression analysis was used to identify factors influencing depression. Results: The results indicated that the effect of disease classification and injured areas on depression were significantly different in patients with industrial injuries. The results further showed that severe depression was significantly higher in cardiovascular patients and patients with an injured area of the head and waist. The most powerful predictor was age (50~59 years), return to work (reemployment), disease classification (cardiovascular), and injured area (head, including vascular disease). Conclusion: This study showed that the most influential variable of depression in patients with industrial injuries were cardiovascular issues, injury areas of the head and waist, being aged 50~59 years, and reemployment. To reduce depression in these patients, it is important to develop and implement a psychiatric rehabilitation program that helps patients to formulate a concrete plan and goal for recovery, enabling patients to actively engage in their rehabilitation.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.105-108
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• 2005

In damage investigation of building structures suffering from earthquake, estimation of residual seismic capacity is essential in order to access the safety of the building against aftershocks and to judge the necessity of repair and restoration. It has been proposed that an evaluation method for post-earthquake seismic capacity of reinforced concrete buildings based. on the residual energy dissipation capacity (the residual seismic capacity ratio )in lateral force-displacement curve of structural members. The proposed method was adopted in the Japanese 'Damage Level Classification Standard' revised in 200l. To evaluate the residual seismic capacity of RC column, experimental tests with positive and negative cyclic loading was carried out using RC building column specimen. Parameters used by the experiment are deformability and member proportion. From the test results, it is appropriated that the residual seismic capacity of RC buildings damaged by earthquakes is evaluated using the method in the Guideline.

• Journal of the Korean Society of Physical Medicine
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• v.4 no.2
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• pp.101-106
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• 2009

Purpose：The purpose of this study was to find the effect of intensive therapy on gross motor function measure(GMFM) score in cerebral palsy. Methods：Twenty eight cerebral palsy children were recruited in this study. Gross motor Function Measure(GMFM) score and Gross motor Function Classification System(GMFCS) were used to evaluate as functional change and functional level. Intensive therapy period for cerebral palsy children was 3, 4, and 5 weeks. Statistical analysis was used paired T test and one way ANOVA to know change between pre and post therapy was used. Results：GMFM Score of pre- and post- intensive therapy showed the statistically significant difference. Intensive therapy period indicated the statistically significant difference in GMFM score. GMFCS level did not reveal statistically significant difference in GMFM score. Conclusion：Intensive therapy was effective on gross motor function measure(GMFM) score in cerebral palsy.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1512-1523
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• 2022

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.667-685
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• 2003

To address the requirements of gap analysis for species protection, as well as the needs of state and federal agencies for detailed digital land cover, a 43-class map at the vegetation alliance level was created for the state of Kansas using multi-temporal Thematic Mapper imagery. The mapping approach included the use of three-date multi-seasonal imagery, a two-stage classification approach that first masked out cropland areas using unsupervised classification and then mapped natural vegetation with supervised classification, visualization techniques utilizing a map of small multiples and field experts, and extensive use of ancillary data in post-hoc processing. Accuracy assessment was conducted at three levels of generalization (Anderson Level I, vegetation formation, and vegetation alliance) and three cross-tabulation approaches. Overall accuracy ranged from 51.7% to 89.4%, depending on level of generalization, while accuracy figures for individual alliance classes varied by area covered and level of sampling.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.96-102
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• 2012

An advanced form of the Partitioned Feature-based Classifier with Expertise Table(PFC-ET) is proposed in this paper. As is the case with the PFC-ET, the proposed classifier model, called Classifier Integration Model(CIM), does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. The proposed CIM utilizes a proportion of selected cluster members instead of the expertise table in PFC-ET to minimize the error in confusion table. The proposed CIM is applied to the classification problem on two data sets, Caltech data set and collected terrain data sets. When compared with PFC model and PFC-ET model. the proposed CIM shows improvements in terms of classification accuracy and post processing efforts.

• Journal of KIISE
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• v.43 no.1
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• pp.80-86
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• 2016

A speech-act is a behavior intended by users in an utterance. Speech-act classification is important in a dialogue system. The machine learning and rule-based methods have mainly been used for speech-act classification. In this paper, we propose a speech-act classification method based on the combination of support vector machine (SVM) and transformation-based learning (TBL). The user's utterance is first classified by SVM that is preferentially applied to categories with a low utterance rate in training data. Next, when an utterance has negative scores throughout the whole of the categories, the utterance is applied to the correction phase by rules. The results from our method were higher performance over the baseline system long with error-reduction.