• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.126-135
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• 2015

This study was conducted to develop a large scale map of species-specific plantation sites based on selected environmental variables such as topography, soil, and climatic factors in Namwon city. Site index equations by tree species were first regressed to 27 environmental variables that could influence the productivity of forest sites using digital forest site maps, digital climate maps, and the 5th National Forest Inventory data. Site index equations by tree species were all evaluated to estimate site productivity using 4-5 environmental variables, and the models' reliability was confirmed based on evaluation statistics. The determination coefficients of site index equations by species ranged from 0.42 to 0.76. With the site index equations, the site conditions appropriate for productive sites by species were considered to assess spatial distribution of productive areas for each species. The final map for optimal plantation in Namwon city was produced based on both site index equations and site conditions appropriate for productive sites by each species using GIS technique. Field survey was conducted to evaluate the suitability of selected species on the map of species-specific plantation sites. Results showed that the plantation map provides relatively reasonable spatial distribution of productive areas for selected species. It was revealed, however, that the sites evaluated as 'not suitable' for any tree species should be revised and complemented with additional information, especially with the site conditions appropriate for productive sites by species of interest. The outcomes of this study are expected to provide information for making customized species-specific plantation maps.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.41
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• pp.6.1-6.10
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• 2019

Background: Osteomyelitis is an intraosseous inflammatory disease characterized by progressive inflammatory osteoclasia and ossification. The use of quantitative analysis to assist interpretation of osteomyelitis is increasingly being considered. The objective of this study was to perform early diagnosis of osteomyelitis on digital panoramic radiographs using basic functions provided by picture archiving and communication system (PACS), a program used to show radiographic images. Methods: This study targeted a total of 95 patients whose symptoms were confirmed as osteomyelitis under clinical, radiologic, pathological diagnosis over 11 years from 2008 to 2017. Five categorized patients were osteoradionecrosis, bisphosphonate-related osteonecrosis of jaw (BRONJ, suppurative and sclerosing type), and bacterial osteomyelitis (suppurative and sclerosing type), and the control group was 117 randomly sampled. The photographic density in a certain area of the digital panoramic radiograph was determined and compared using the "measure area rectangle," one of the basic PACS functions in INFINITT PACS^® (INFINITT Healthcare, Seoul, South Korea). A conditional inference tree, one type of decision making tree, was generated with the program R for statistical analysis with SPSS^®. Results: In the conditional inference tree generated from the obtained data, cases where the difference in average value exceeded 54.49 and the difference in minimum value was less than 54.49 and greater than 12.81 and the difference in minimum value exceeded 39 were considered suspicious of osteomyelitis. From these results, the disease could be correctly classified with a probability of 88.1%. There was no difference in photographic density value of BRONJ and bacterial osteomyelitis; therefore, it was not possible to classify BRONJ and bacterial osteomyelitis by quantitative analysis of panoramic radiographs based on existing research. Conclusions: This study demonstrates that it is feasible to measure photographic density using a basic function in PACS and apply the data to assist in the diagnosis of osteomyelitis.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.353-366
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• 2015

The purpose of this study was to examine how region-specific characteristics affect the occurrence of cardiac arrest. To analyze, we combined a unique data set including key indicators of health condition and cardiac arrest occurrence at the 244 small administrative districts. Our data came from two main sources in Korea Center For Disease Control and Prevention (KCDC): 2010 Out-of-Hospital Cardiac Arrest Surveillance and Community Health Survey. We analyzed data by using multiple regression, geographically weighted regression and decision tree. Decision tree model is selected as the final model to explain regional variations of cardiac arrest. Factors of regional variations of cardiac arrest occurrence are population density, diagnosis rates of hypertension, stress level, participating screening level, high drinking rate, and smoking rate. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors are important when regionally customized health policy is implemented to decrease the cardiac arrest occurrence.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.229-238
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• 2013

Out-of-hospital cardiac arrest is a major public health problem in Korea. The survival rate to discharge remains at approximately 3.5% and only 1% have good neurological function. To increase the survival rate, prehospital care should restore spontaneous circulation. The purpose of this study was to analyze the factors associated with return of spontaneous circulation(ROSC) after out-of-hospital cardiac arrest. Data used for this study were collected from KCDC Out-of-Hospital Cardiac Arrest Surveillance 2009. As for the results of decision tree analysis, it is clear that prehospital CPR, cardiac arrest witness, activity, past history(cancer/heart disease/stroke), place, bystander CPR, response time, age, etc are significant contributing factors in ROSC. Among 16 cardiac arrest types from decision tree classification, the ROSC rate of type 1 is the highest(29.6%). Also notable is the fact that bystander CPR was strongly correlated with ROSC of patents with cardiac arrest occurring in non-public places. Community resources should be concentrated on increasing bystander CPR and early prehospital emergency care.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.63-69
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• 2018

This study is to examine the determinants of the elderly poverty by using the Decision-tree analysis. In line with this perspective, this study includes individual characteristics, family characteristics, working characteristics, and periodic income characteristics after retirement as determinants for senior poverty. The study uses data from the Korean Retirement and Income Study based on panel survey and employs the Decision-tree analysis to explain the causes of the elderly poverty. As the result of analysis, earned wage has the greatest effect on the elderly poverty. Depending on status of the earned wage, there are 2 different variable groups. One with no earned wage includes public pension, education, and residence, paid employee and gender in the other with earned wage. Based on the analytical results, the study suggests measures to address the elderly poverty.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.11-20
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• 2010

In this study, the effects of stand height growth on GIS-based viewshed analysis were investigated. DSM was created by combining stand height layers on DEM using map algebra functions. In developing the tree height layers, the digital forest-type maps, forest site maps and stand yield tables of Korea Forest Research Institute were used. The time horizon for viewshed analysis were 40 years. Two viewpoints in crossings of downtown for viewshed analyses were chosen using a projective mapping technique. The effects of tree height growth over time on visibility were measured in terms of the depth of blind areas and the area of visible regions. The results of viewshed analyses show that 17% of visible regions is reduced when we use DSM instead of DEM. As the tree height grows, the visibility gets worse and worse and the depth of blind area increases.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.55-61
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• 2012

The primary factors of the LOS(Line-of-Sight) analysis process are terrain height, camera capacity, and canopy cover. The canopy cover rate differs depending on the changing season, and its value is influenced by the tree density, tree height, and etc. This study generated the canopy cover value based on relationship between NDVI(Normalized Difference Vegetation Index) and DMT(Density Measure % of Tree/Canopy Cover), which is a digital map attribute, and then performed the LOS analysis on six station of test sites. As results, It was found that NDVI and DMT are correlated with each other through the experiments. Based on this finding, new DMT map can be generated using NDVI. Also, There is a difference between the result of visibility analysis using the present DMT and one using a new DMT. Especially, the spatial distributions of the detected visible areas are significantly different between the two visibility analysis results.