• Journal of Korean Society for Atmospheric Environment
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• v.24 no.2
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• pp.249-258
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• 2008

Historical emission data for 11 polychlorinated biphenyls (PCBs) and a regional multimedia environmental model, CoZMo-POP 2, were used to predict air concentrations and congener patterns in Korea. The total emission value for South Korea was allocated to sub-provinces and cities based on their population. The spatial distribution of PCB emissions was generally correlated with that of measured atmospheric levels, suggesting that population could be a good surrogate for the intensity of PCB emission in Korea. The simulated time trends of air concentrations well reflected those of emission with a peak in the mid-1970s and insignificant levels in the 2030s. The model predicted that the contribution of volatile PCBs had increased after emission reduction iii the 1970s. This trend would continue until the early 2030s. The measured and modeled PCB levels in the 2000s were in an agreement of an order of magnitude, and their congener patterns were very similar. Consequently, despite of high uncertainty for emission estimates, the emission data for Korea used in this study is considered to be reliable. The results of this study could be compared with simulation data based on a new emission inventory to be developed by measurements in the near future.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.4
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• pp.389-398
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• 2010

Purpose: This study was done to identify the relationship between critical thinking disposition and problem solving ability in nursing students, thereby providing basic data for nursing education. Method: A convenience sample was drawn from 161 nursing students. Data were collected from June 2008 to October 2008. Instruments used in the study were the California Critical Thinking Disposition Inventory (CCTDI) developed by Facione & Facione (1992) and the Problem Solving Inventory (PSI) developed by Heppner & Petersen (1982). The collected data were analyzed using t-test, ANOVA, Scheffe' test, Pearson correlation coefficients and multiple regression with SPSS 12.0. Results: The total mean score for CCTDI was 278.41 and PSI was 119.23. For general characteristics, there were statistically significant differences in CCTDI according to satisfaction with nursing majors (F=6.29, p=.00) and PSI according to academic achievement (F=3.45, p=.02) and marital status (t=2.43, p=.02). A statistically significant negative correlation was found between CCTDI and PSI. Critical thinking self-confidence, Analyticity and Inquisitiveness were significant predictors of PSI. Conclusion: The findings of this study indicate that critical thinking disposition influences problem solving ability. Therefore, the findings provide significant basic data for nursing education and nursing practice.

• Journal of Information Processing Systems
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• v.4 no.3
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• pp.113-120
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• 2008

Most enterprises have controlled claim data related to marketing, production, trade and delivery. They can extract the engineering information needed to the reliability of unit from the claim data, and also detect critical and latent reliability problems. Existing method which could detect abnormal quality unit lists in early stage from claim database has three problems: the exclusion of fallacy probability in claim, the false occurrence of claim fallacy alarm caused by not reflecting inventory information and too many excessive considerations of claim change factors. In this paper, we propose a process and methods extracting abnormal quality unit lists to solve three problems of existing method. Proposed one includes data extraction process for reliability measurement, the calculation method of claim fallacy alarm probability, the method for reflecting inventory time in calculating claim reliability and the method for identification of abnormal quality unit lists. This paper also shows that proposed mechanism could be effectively used after analyzing improved effects taken from automotive company's claim data adaptation for two years.

• Journal of Oriental Neuropsychiatry
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• v.32 no.4
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• pp.303-320
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• 2021

Objectives: Although medical typologies based on indigenous biopsychological ideas have been described, their integrity has been questioned due to its theory-driven nature in categorization. Therefore, studies on the Sasang typology, a temperament-based traditional Korean medicine, are needed to examine whether it is possible to classify types of specific biopsychological profiles using data-driven analysis. Methods: Psychological measures of the Eastern Sasang Personality Questionnaire (SPQ) and Western NEO-Personality Inventory (NEO-PI) along with physical measures and Sasang types were acquired from 2,049 participants. Latent groups based on the SPQ and NEO-PI subscale scores were extracted using Latent Profile Analysis. Their psychosomatic features were then compared with those of Sasang types. Results: Three SPQ-based latent groups showed distinctive psychological and physical features consistent with those of Sasang types. However, four NEOPI-based latent groups presented only psychological features. Furthermore, SPQ-High and SPQ-Low latent groups demonstrated similar psychosomatic profiles to those of So-Yang and So-Eum Sasang types, respectively. Conclusions: This study illustrates that biopsychological profiles of Sasang types are supported by psychosomatic features of latent groups based on SPQ of Eastern psychology, signifying that the categorization of Sasang typology have acceptable validity and reliability.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.149-160
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• 2006

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Korean Journal of Environmental Biology
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• v.34 no.2
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• pp.73-78
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• 2016

Bhutanese forests have been well preserved and can sequester the atmospheric carbon (C). In spite of its importance, understanding Bhutanese forest C dynamics was very limited due to the lack of available data. However, forest C model can simulate forest C dynamics with comparatively limited data and references. In this study, we aimed to simulate Bhutanese forest C dynamics at 6 plots with the Forest Biomass and Dead organic matter Carbon (FBDC) model, which can simulate forest C cycles with small amount of input data. The total forest C stock ($Mg\;C\;ha^{-1}$) ranged from 118.35 to 200.04 with an average of 168.41. The C stocks ($Mg\;C\;ha^{-1}$) in biomass, litter, dead wood, and mineral soil were 3.40-88.13, 4.24-24.95, 1.99-20.31, 91.45-97.90, respectively. On average, the biomass, litter, dead wood, and mineral soil accounted for 36.0, 5.5, 2.5, and 56.0% of the total C stocks, respectively. Although our modeling approach was applied at a small pilot scale, it exhibited a potential to report Bhutanese forest C inventory with reliable methodology. In order to report the national forest C inventory, field work for major tree species and forest types in Bhutan are required.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.825-833
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• 2013

An estimation of confidence intervals is essential to calculate uncertainty for greenhouse gases inventory. It is generally assumed that the population has a normal distribution for the confidence interval of parameters. However, in case data distribution is asymmetric, like nonnormal distribution or positively skewness distribution, the traditional estimation method of confidence intervals is not adequate. This study compares two estimation methods of confidence interval; parametric and non-parametric method for exponential distribution as an asymmetric distribution. In simulation study, coverage probability, confidence interval length, and relative bias for the evaluation of the computed confidence intervals. As a result, the chi-square method and the standardized t-bootstrap method are better methods in parametric methods and non-parametric methods respectively.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.185-191
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• 2015

This study presents an estimate of on-site surface fuel loadings composed of coarse woody debris (CWD) using $5^{th}$ National Forest Inventory (NFI) data in South Korea. We classified CWD data into forest type, region and decay class, and used conversion factors by decay class and tonne of oil equivalent developed in the country. In 2010, the total wildfire fuel load of CWD was estimated as 8.9 million TOE; those of coniferous, deciduous and mixed forests were 3.5 million TOE, 2.8 million TOE and 2.6 million TOE, respectively. Gangwon Province had the highest wildfire fuel load of CWD (2.3 million TOE), whereas Seoul exhibited the lowest wildfire fuel load of CWD (0.02 million TOE). Wildfire fuel loads of CWD were estimated as 2.9 million TOE, 1.9 million TOE, 2.4 million TOE and 1.7 million TOE for decay classes I, II, III and IV, respectively. The total wildfire fuel load of CWD corresponded to the calorific value of 8.2 million tons crude oil, 2.46% of that of living trees. Proportionate to the growing stock, total wildfire fuel load of CWD was in a broad distinction by region, while its TOE $ha^{-1}$ was not. This implies that there is no need to establish different guidelines by region for management of CWD. The results of this work provide a baseline study for scientific policy guidelines on preventing wildfires by proposing CWD as wildfire fuel load.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.177-188
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• 2005

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.