• Journal of Korean Academy of Nursing Administration
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• v.8 no.2
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• pp.207-219
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• 2002

The purposes of this research were to: a) define the changing trends of DRGs in comparison to the National Data, b) define the changing trends of Nursing Diagnoses and Nursing Interventions for the 5 most frequently occurring Diagnostic Related Groups (DRGs) across 3 years, and c) define the relationships between nursing diagnoses and nursing Interventions for the 5 most frequently occurring DRGs across the 3 years. This study was a secondary data analysis of medical and nursing data based on the United States Nursing Minimum Data Set and the Uniform Hospital Discharge Data Set retrieved from a Midwestern USA medical center. The results showed interesting comparisons with national statistics as well as practice relevant trends within the nursing data. Additionally, the results showed the possibility that nursing data can be extracted from the medical data, so they can used in the nursing productivity and cost issues etc. In conclusion, this study supports the power of minimum data sets and nursing classifications to begin to describe a more global perspective the inter-relationships and trends of nursing data within the medical diagnosis context.

• Journal of Technologic Dentistry
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• v.34 no.1
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• pp.45-56
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• 2012

Purpose: This study reviews the recent trend of Korean Academy of dental technology research. The data examined are the articles published in the Korean Journal of Korean Academy of dental technology from 1979 to 2011. Methods: The data are retrieved through the internet database Korean Academy of dental technology. The number of paper published is 540, published in 33 volumes of the journal. This study examines research methods, subjects, and author information. Results: Among the 540 articles collected, 422 are Original papers, 106 Clinical Technical Papers, 12case Other papers. 235 are experimental studies. Most experimental studies(235) have examined the ceramic(Porcelain) parts(117). The sole author of 315, co-author of 225 appeared. In turn of dental technicians, and students appeared. Conclusion: The study is to present basic data for research and to indicate the direction of their study in the future.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.5
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• pp.29-45
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• 2016

This study identified vulnerable regions in the national parks of the Republic of Korea (ROK). The potential vulnerable regions were defined as areas showing a decline in forest productivity, low resilience, and high sensitivity to climate variations. Those regions were analyzed with a regression model and trend analysis using the Enhanced Vegetation Index (EVI) data obtained from long-term observed Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded meteorological data. Results showed the area with the highest vulnerability was Naejangsan National Park in the southern part of ROK where 32.5% ($26.0km^2$) of the total area was vulnerable. This result will be useful information for future conservation planning of forest ecosystem in ROK under environmental changes, especially climate change.

• The Korean Journal of Health Service Management
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• v.7 no.3
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• pp.225-235
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• 2013

This study purposed to analyze the productivity of regional medical capacity in attracting medical tourist between 2009 and 2011. Data envelopment analysis (DEA) and Malmquist productivity index (MPI) were applied to test the productivity and changes in study periods. The DEA model included a number of doctors, nurses, and beds as input factors, and a number of inpatient and outpatient, and hospital income as output factors. The result of the study were as follow. Jeju had efficiency value of 1 for three years. Seoul, Busan, Daegu, Daejeon, Ulsan, Gyeonggi, Gangwon, Jeonnam, Gyeongbuk, and Gyeongnam showed an increasing trend of efficiency value over three years. Seoul had the efficiency value, 1, in 2011. But, Incheon, Gwangju showed decreasing efficiency score during the study period. MPI showed overall productivity decrease during the period. Further studies will be needed by collecting more time-series data.

• Journal of Wetlands Research
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• v.1 no.1
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• pp.17-28
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• 1999

The purpose of this study was to analysis water quality monitoring data and to estimate future trends using ARIMA model of time series analysis. Water quality data in Chin yang reservoir were used with monthly monitoring interval during past 7 years. The variations of water quality parameters with periodicity and trend could be estimated by multiplicative ARIMA models and the statistical tests showed a good agreement with the observed data. Therefore, the monthly values of water quality parameters could be forecasted using these models.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.7-16
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• 2009

Because the transport policy of Korea has overemphasized road, the physical distribution function of railroad has dwindled a great deal relatively. Recently, the railway has started to be embossed due to the rise of oil prices and environment problems, in addition the government is investing greatly in railroad. The railway corporation took a big step in its history in changing to a public corporation in 2005, and it has been making every possible endeavor to improve management. This research analyzed the trend and stability of the efficiency of railway container handling goods station in korea from 2002 to 2007 based on time of after being changed to a public corporation in 2005 in order to look into the trend of efficiency. The DEA- AR(Data Envelopment Analysis-Assurance Region) and the DEA-Window, widely used as the estimation techniques of the efficiency, were used. According to the results, the efficiency was a little enhanced in 2003 in comparison with 2002, after which it continuously decreased up to 2006 and again rose in 2007. The efficiency of the railway corporation was 0.6777, but after changing to a public corporation, it showed a trend of better efficiency after some transition period had passed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.513-521
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• 2008

The objective of this study is the evaluation of the impact on the construction condition due to historical observation data and IPCC SRES A2 climate change scenario. For this purpose, daily precipitation and daily mean temperature data which have been observed over the past 30 years by Korea Meteorological Administration are collected and applied. Also, A2 scenarios during 2011~2040 and 2051~2080 are used for this analysis. According to the results of trend analyses on annual precipitation and annual mean temperature, they are on the increase mostly. The available working day and the day occurred an extreme event are used as correlation indices between climate factor and construction condition. For the past observation data, linear regression and Mann-Kendall test are used to analyze the trend on the correlation index. As a result, both working day and extreme event occurrence day are increased. Likewise, for the future, variation analysis showed the similar result to that of the past and the occurrence frequency of extreme events is increased obviously. Therefore, we can project to increase flood damage potential on the construction site by climate change.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.326-334
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• 2023

The purpose of this study is to use long-term water quality monitoring data from tributaries of the Nakdong River system to identify problematic substances in tributaries by examining the rate of exceedance and increase in water quality targets. In the Nakdong River system, monitoring is conducted once a month for 38 tributaries that require intensive management, and this data was used to analyze trends in exceeding and increasing target water quality at each point. The analysis items are eight items that can be evaluated based on river water quality standards: DO, BOD, COD, TOC, SS, total phosphorus, fecal coliform, and total coliform. As a result of the analysis, the target water quality exceedance rate was more than 50%, and the items with an increasing trend were TOC, fecal coliform and total E. coli counts, and the items with an exceedance rate of less than 50% but an increasing trend were SS. TOC is believed to be caused by an increase in non-degradable substances, and the continued increase in Total Coliform will require management of Total ColiformTotal Coliform in effluent water from sewage treatment facilities in the future.