• Tuberculosis and Respiratory Diseases
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• v.61 no.2
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• pp.121-128
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• 2006

Background: The "health-related quality of life" (HRQL) for patients with chronic respiratory disease has been emphasized, because chronic respiratory disease (CRD) is chronic and progressive, and it finally causes disability. HRQL instruments may be useful for monitoring patients' progress or for determining the most appropriate choice of treatment. We describe the adapting St George's Respiratory Questionnaire (SGRQ), which is a self-administered questionnaire developed by Jones et al. (1991), into the Korean version for covering three domains of health for the patients suffering with airways disease. Method: We obtained the original SGRQ from the author after gaining permission. For adaptation, we created an expert panel and translated the original questionnaire into Korean language. The translated questionnaire was then back-translated by bilingual experts and we compared it with the original questionnaire. After correction and feasibility testing, 74 patients with chronic respiratory disease (COPD, asthma, destroyed lung) completed the Korean version of the SGRQ. The clinical status of each patients was evaluated concurrently with measurement of their health status. Result: The Korean version of the SGRQ was acceptable and easy to understand. Cronbach's alpha reliability coefficient was 0.92 for the overall scale and 0.63 for the "Symptoms", subscale, 0.87 for the "Activity", subscale, and 0.89 for the "Impacts" subscales. The correlation coefficients between the overall score and the Borg scale score, oxygen saturation, and forced expiratory volume in one second ($FEV_1$) were 0.52, -0.32 and -0.26, respectively. These results support that the Korean SGRQ was correlated with other measurements. Conclusion: The Korean SGRQ was reliable and valid for patients with chronic respiratory disease, such as COPD, asthma, and destroyed lung. The SGRQ score was well correlated with other respiratory measurements as well. Although further studies should complete the adaptation work, our results suggest that the SGRQ may be used in Korea and also for international studies involving Korean CRD patients.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.63-76
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• 2024

Multi-family housing plays a crucial role as a living and experiencing space, and its environment has a direct impact on the well-being and stability of its residents. Therefore, Crime Prevention Design (CPTED) for multi-family housing is of utmost importance. However, crime-related data in China is not disclosed to the public because of its specificity, making it difficult for researchers to conduct further in-depth studies based on accurate crime data. As a result, the establishment and application of CPTED theory in terms of crime prevention is limited and delayed. This study aims to explore three aspects of CPTED in multi-family housing as perceived by home-buying consumers. It investigated consumer perception of the CPTED, the importance of each element and ways to increase awareness of CPTED in multifamily housing in order to effectively improve multifamily crime prevention design principles and further enhance public safety. This study examined the current state and future trends of CPTED in China by analyzing relevant research reports and literature, aiming to gain insights into the crime prevention awareness of Chinese homeowners. In addition, a survey was conducted on Chinese consumers to unravel the importance of CPTED and increase awareness of its various elements in multifamily-family. This study used a Likert scale and SPSS reliability analysis to determine the cognitive status of multi-family CPTED, the importance of each element, and proposed an improvement plan based on the analysis results. As this study was limited by the difficulty of implementation and the lack of validation of its practical effectiveness, it is recommended that future research needs to validate the effectiveness of crime prevention designs and produce more practical results. Furthermore, it is crucial to utilize this study to inform the implementation of security solutions that are tailored to the unique characteristics of each district. Additionally, it is important to offer guidance on how to enhance community safety by increasing residents' awareness of security through education and information dissemination. The author hopes that the representative multi-family CPTED awareness, the importance of each element, and plans for improvement shall be summarized from this study, and provide foundational data for the future development of CPTED based on the Chinese region.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.4 no.2
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• pp.117-131
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• 2006

A coupled T-H-M(Thermo-Hydro-Mechanical) analysis was carried out for KENTEX (KAERI Engineering-scale T-H-M Experiment for Engineered Barrier System), which is a facility for validating the coupled T-H-M behavior in the engineered barrier system of the Korean reference HLW(high-level waste) disposal system. The changes of temperature, water saturation, and stress were estimated based on the coupled T-H-M analysis, and the influence of the types of mechanical constitutive material laws was investigated by using elastic model, poroelastic model, and poroelastic-plastic model. The analysis was done using ABAQUS, which is a commercial finite element code for general purposes. From the analysis, it was observed that the temperature in the bentonite increased sharply for a couple of days after heating the heater and then slowly increased to a constant value. The temperatures at all locations were nearly at a steady state after about 37.5 days. In the steady state, the temperature was maintained at $90^{\circ}C$ at the interface between the heater and the bentonite and at about $70^{\circ}C$ at the interface between the bentonite and the confining cylinder. The variation of the water saturation with time in bentonite was almost same independent of the material laws used in the coupled T-H-M processes. By comparing the saturation change of T-H-M and that of H-M(Hydro-Mechanical) processes using elastic and poroelastic material mod31 respectively, it was found that the degree of saturation near the heater from T-H-M calculation was higher than that from the coupled H-M calculation mainly because of the thermal flux, which seemed to speed up the saturation. The stresses in three cases with different material laws were increased with time. By comparing the stress change in H-M calculation using poroelasetic and poroelasetic-plastic model, it was possible to conclude that the influence of saturation on the stress change is higher than the influence of temperature. It is, therefore, recommended to use a material law, which can model the elastic-plastic behavior of buffer, since the coupled T-H-M processes in buffer is affected by the variation of void ratio, thermal expansion, as well as swelling pressure.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.613-623
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• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.9 no.1
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• pp.23-30
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• 2006

Purpose: The estimation of fluid deficit is crucial to the proper management of dehydrated children. Without well-documented serial weights on the same scale, the estimation of any given child's fluid deficit is imprecise and dependent largely on subjective clinical criteria. Despite the abundance of literature on clinical and laboratory evaluation of dehydration, few studies have focused on serum uric acid. So, we examined the usefulness of scrum uric acid in gastroenteritis patients with dehydration. Methods: Medical records of 90 gastroenteritis patients were retrospectively reviewed. By the body weight loss, we classified patients with mild, moderate, and severe dehydration groups. We studied the relevance of laboratory data (BUN, creatinine, serum bicarbonate, glucose, urine specific gravity, and uric acid) with dehydration. Results: 54 children (60%) were dehydrated mildly, 24 (26%) dehydrated and moderately, and 12 (14%) dehydrated severely. Statistically significant differences in BUN, creatinine, serum bicarbonate, glucose, and urine specific gravity could not be observed. But there was significant relationship between uric acid and the degree of dehydration. Data analysis suggested that the level of 7.0 mg/dL is the best cut-off value for predicting the development of moderate or severe dehydration. At this cut-off value, the sensitivity and specificity were 66.6% and 87.1%. Conclusion: Our study supports that the measurement of serum uric acid with traditional scale is useful for predicting the development of dehydration. But, in order 10 be used as the indicator for proper treatment at an earlier stage, further validation about serum uric acid is necessary.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.649-663
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• 2019

The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.157-171
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• 2024

The purposes of this study were: (1) to revalidate the revised Systems Thinking Measuring Instrument (Re_STMI) reported by Lee et al. (2024) among Vietnamese high school students and (2) to investigate the differences in systems thinking abilities between Korean and Vietnamese high school students. To achieve this, data from 234 Vietnamese high school students who responded to translated Re_STMI consisting of 20 items and an Scale consisting of 20 items were used. Validity analysis was conducted through item response analysis (Item Reliability, Item Map, Infit and Outfit MNSQ, DIF between male and female) and exploratory factor analysis (principal axis factor analysis using Promax). Furthermore, structural equation modeling was employed with data from 475 Korean high school students to verify the latent mean analysis. The results were as follows: First, in the item response analysis of the 20 translated Re_STMI items in Vietnamese, the Item Reliability was .97, and the Infit MNSQ ranged from .67 to 1.38. The results from the Item Map and DIF analysis align with previous findings. In the exploratory factor analysis, all items were loaded onto intended sub-factors, with sub-factor reliabilities ranging from .662 to .833 and total reliability at .876. Confirmatory factor analysis for latent mean analysis between Korean and Vietnamese students yielded acceptable model fit indices (χ²/df: 2.830, CFI: .931, TLI: .918, SRMR: .043, RMSEA: .051). Lastly, the latent mean analysis between Korean and Vietnamese students revealed a small effect size in systems analysis, mental models, team learning, and shared vision factors, whereas a medium effect size was observed in personal mastery factors, with Vietnamese high school students showing significantly higher results in systems thinking. This study confirmed the reliability and validity of the Re_STMI items. Furthermore, international comparative studies on systems thinking using Re_STMI translated into Vietnamese, English, and other languages are warranted in the context of students' systems thinking analysis.

• Sleep Medicine and Psychophysiology
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• v.21 no.2
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• pp.80-84
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• 2014

Introduction: Sleep has numerous important physiological and cognitive functions that may be particularly important to elite athletes. Sleep deprivation can have significant effects on athletic performance. However, there are few published data related to the amount of sleep obtained by elite athletes. We investigated sleep patterns of Korean women golfers using sleep-related questionnaires. Methods: For this study, 98 Korean university women golfers and 46 age- and sex-matched controls were recruited. All subjects were asked to complete the self-administered sleep questionnaire consisting of questions about habitual sleep patterns (sleep onset time, sleep latency, awakening time in the morning, day time napping time), exercise habits, Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISS), Pittsburgh Sleep Quality Index (PSQI), validation of the Perceived Stress Scale (PSS), and Beck Anxiety Inventory (BAI). Results: The sleep onset time was significantly earlier (pm 23 : $05{\pm}00$ : 52 and 00 : $14{\pm}00$ : 51 ; t = 5.287, p < 0.001), the waking time was later (am 07 : $21{\pm}01$ : 09 and 6 : $35{\pm}00$ : 32; t = -2.715, p = 0.008), the weekday total sleep time was greater ($417.77{\pm}78.18$ minute and $351.52{\pm}77.83$ minute ; t = 4.406, p = 0.001), and the daytime nap time was greater ($77.73{\pm}41.28$ minute and $20.22{\pm}33.03$ minute ; t = 7.623, p < 0.001) in the golf athletes compared to the controls. The PSQI scores were significantly lower, but estimated sleep latency and ESS, ISS, PSS, and BAI scores were not different among the two groups. Conclusion: This study suggests that Korean university women golfers have good sleep patterns resulting in no difference in sleep-related stress compared to age- and sex-matched control students.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.