• Journal of Korea Foresty Energy
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• v.20 no.2
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• pp.69-80
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• 2001

Larix kaemferi Carr. is one of main timber species in Korea that could be found in plantations and growing stands on all over the country The proper practice of thinning is one of very important techiques in silviculture, which greatly affects the quality and size of timber produced. Proper thinning period is considered to be important for maintaining stand growth before competition initiated. To investigate suitable thinning period, this study investigates the volume and radial growths of Larix kaemferi plantation in Mt. Worak located in the central part of Korea. The main findings and conclusions obtained from this study were as follow ; (1) The tree height curve equation for Larix kaemferi in Mt. Worak was calculated H=4.25783+0.80024D(H=Tree height, D=DBH). (2) To estimate tree volume for Larix kaemferi by DBH and tree height or only by DBH, regression equations were calculated as V=0.00147-0.002095D-0.000211H+0.00015D.H++$0.000744D^2$+$0.000008H^2$(V=Volume(($m^3$), H=Tree height(m), D=DBH(cm)), V=0.0000794-0.000512D+$0.000826D^2$. (3) The criteria of estimating thinning time of Larix kaemferi are the age when maximum tree height-MAI(mean annual increment) obtained and the age when annual DBH increments of dead trees decrease to below average. (4) The age of maximum tree height MAI was not significantly correlated with stocking. Therefore, it can not be used as a criterion for estimating thinning time of Larix kaemferi (5) The estimated thinning time equation of Larix kaemferi was obtained by regression analysis of the disk section collected from dead trees. The obtained equation is Y=0.2825+0.01752X(Y=Desirable thinning age, X=the sum total of nearest 4-trees interval(cm)). (6) General estimated thinning age of Larix kaemferi, which planted 3,086 stocks/ha, is concluded as 12 to 14 year.

• Clinical and Experimental Pediatrics
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• v.51 no.11
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• pp.1158-1164
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• 2008

Purpose : To investigate the discriminative ability of pediatric index of mortality 2 (PIM2) and pediatric risk of mortality III (PRISM III) in predicting mortality in children admitted into the intensive care unit (ICU). Methods : We retrospectively analyzed variables of PIM2 and PRISM III based on medical records with children cared for in a single hospital ICU from January 2003 to December 2007. Exclusions were children who died within 2 h of admission into ICU or hopeless discharge. We used Students t test and ANOVA for general characteristics and for correlation between survivors and non-survivors for variables of PIM2 and PRISM III. In addition, we performed multiple logistic regression analysis for Hosmer-Lemeshow goodness-of-fit, receiver operating characteristic curve (ROC) for discrimination, and calculated standardized mortality ratio (SMR) for estimation of prediction. Results : We collected 193 medical records but analyzed 190 events because three children died within 2 h of ICU admission. The variables of PIM2 correlated with survival, except for the presence of post-procedure and low risk. In PRISM III, there was a significant correlation for cardiovascular/neurologic signs, arterial blood gas analysis but not for biochemical and hematologic data. Discriminatory performance by ROC showed an area under the curve 0.858 (95% confidence interval; 0.779-0.938) for PIM2, 0.798 (95% CI; 0.686-0.891) for PRISM III, respectively. Further, SMR was calculated approximately as 1 for the 2 systems, and multiple logistic regression analysis showed ${\chi}^2(13)=14.986$, P=0.308 for PIM2, ${\chi}^2(13)=12.899$, P=0.456 for PRISM III in Hosmer-Lemeshow goodness-of-fit. However, PIM2 was significant for PRISM III in the likelihood ratio test (${\chi}^2(4)=55.3$, P<0.01). Conclusion : We identified two acceptable scoring systems (PRISM III, PIM2) for the prediction of mortality in children admitted into the ICU. PIM2 was more accurate and had a better fit than PRISM III on the model tested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6630-6638
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• 2014

The concerns for higher returns among investors in Korea are increasing as local interest rates lag behind the global market. The aim of this study was to provide a basis for estimating the precise return on investment to private investors and stakeholders of office buildings by identifying the optimal scale estimation of office building space, derived from managerial expenses. The literature on the economies theories of scales were reviewed, and the average administrative costs on an appropriate scale were assessed by cross-analysis and regression analysis using the U-shaped quadratic and cubic function. The findings suggest that the average office building managerial costs are approximately 6-11% according to the region, 10-13% according to the grade, and 8-9% according to size. Corporate-owned buildings represent the highest in terms of the average managerial costs, and there is an approximately 11.5% difference when it comes to outsourcing. In addition, the elapsed year showed that approximately 5.3 years to meet the lowest U-shaped curve of the average managerial cost. The 'Total floor area' variable shows a ${\bigcap}$-shape as it continue to increase to 72,000-Pyung then decrease gradually. This study presents the fundamental proposition of efficient and practical management of cost, lease and operation for real estate management firms by utilizing LCC.

• Korean journal of applied entomology
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• v.35 no.2
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• pp.119-125
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• 1996

Temperature-dependent development study for overwintered eggs of Pseudococcus comstocki (Kuwana) wasconducted to develop a forecasting model for egg hatch date. Hatch times of overwintered eggs were comparedat five constant temperatures (10, 15, 20, 25, 27$^{\circ}$C) and different collection dates. A nonlinear, four-parameterdevelopmental model with high temperature inhibition accurately described (R2=0.9948) mean developmentalrates of all temperatures. Variation in developmental times was modeled(~~=0.972w9)it h a cumulative Weibullfunction. Least-squares linear regression (rate=O.O06358[Temp.]-0.07566)d escribed development in the linearregion (15-25$^{\circ}$C) of the development curve. The low development threshold temperature was estimated 11.9"Cand 154.14 degree-days were required for complete development. The linear degree-day model (thermal summation)and rate summation model (Wagner et al. 1985) were validated using field phenology data. In degreedaymodels, mean-minus-base method, sine wave method, and rectangle method were used in estimation of dailythermal units. Mean-minus-base method was 18 to 28d late, sine wave method was 11 to 14d late, rectanglemethod was 3 to 5d late, and rate summation model was 2 to 3d late in predicting 50% hatch of overwinteredeggs. hatch of overwintered eggs.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.479-485
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• 2022

At the Korea Institute of Radiological and Medical Sciences, physical human phantoms were developed to evaluate various radiation protection quantities, based on the mesh-type reference computational phantoms of the International Commission on Radiological Protection. The physical human phantoms were fabricated such that a radiophotoluminescent glass dosimeter (RPLGD) with a Tin filter, namely GD-352M, could be inserted into them. A Tin filter is used to eliminate the overestimated signals in low-energy photons below 100 keV. The measurement uncertainty of the RPLGD reader system based on GD-352M should be analyzed for obtaining reliable protection quantities before using it for practical applications. Generally, the measurement uncertainty of RPLGD systems without Tin filters is analyzed for quality assurance of radiotherapy units using a high-energy photon beam. However, in this study, the measurement uncertainty of GD-352M was analyzed for evaluating the protection quantities. The measurement uncertainty factors in the RPLGD include the reference irradiation, regression curve, reproducibility, uniformity, energy dependence, and angular dependence, as described by the International Organization for Standardization (ISO). These factors were calculated using the Guide to the Expression of Uncertainty in Measurement method, applying ISO/ASTM standards 51261(2013), 51707(2015), and SS-ISO 22127(2019). The measurement uncertainties of the RPLGD reader system with a coverage factor of k = 2 were calculated to be 9.26% from 0.005 to 1 Gy and 8.16% from 1 to 10 Gy. A blind test was conducted to validate the RPLGD reader system, which demonstrated that the readout doses included blind doses of 0.1, 1, 2, and 5 Gy. Overall, the E_n values were considered satisfactory.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.235-241
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• 2017

This study was carried out to estimate growth characteristics of Kimchi cabbage cultivated in two different growing seasons and three transplanting dates in the greenhouses, and to create a predicting model for the production of Kimchi cabbage based on the growth parameters and climatic elements. Kimchi cabbages were transplanted three times at intervals of two weeks in spring and autumn growing seasons. Sigmoidal models for the estimation of fresh weight (Fw) was designed with days after transplanting, which were Fw=4451.5/[1+exp{-(DAT-34.1)/3.6}]($R^2=0.992$) and Fw=7182.0/[1+exp{-(DAT-53.8)/11.6}] ($R^2=0.979$), respectively. The relationship between fresh weight of Kimchi cabbage and growing degree days (GDD) was highly correlated, and the regression model represented by Fw=4451.5/[1+exp{-(GDD-34.1)/3.6}] ($R^2=0.992$) in spring growing season. The yield of Kimchi cabbage under spring and autumn growing season were estimated 11348.3kg/10a and 15128.2kg/10a, respectively, which were much different than outdoor culture each growing season, while greenhouse cultivation have shown similar results. To estimate the efficacy of prediction yield in Kimchi cabbage, we will need to supplement a predicting model, which was based on the parameters and climatic elements by the field cultivation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.387-397
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• 2023

Plant height is a growth parameter that provides visible insights into the plant's growth status and has a high correlation with yield, so it is widely used in crop breeding and cultivation research. Investigation of the growth characteristics of crops such as plant height has generally been conducted directly by humans using a ruler, but with the recent development of sensing and image analysis technology, research is being attempted to digitally convert growth measurement technology to efficiently investigate crop growth. In this study, the canopy height of rice grown at various nitrogen fertilization levels was measured using a laser scanner capable of precise measurement over a wide range, and a comparative analysis was performed with the actual plant height. As a result of comparing the point cloud data collected with a laser scanner and the actual plant height, it was confirmed that the estimated plant height measured based on the average height of the top 1% points showed the highest correlation with the actual plant height (R² = 0.93, RMSE = 2.73). Based on this, a linear regression equation was derived and used to convert the canopy height measured with a laser scanner to the actual plant height. The rice growth curve drawn by combining the actual and estimated plant height collected by various nitrogen fertilization conditions and growth period shows that the laser scanner-based canopy height measurement technology can be effectively utilized for assessing the plant height and growth of rice. In the future, 3D images derived from laser scanners are expected to be applicable to crop biomass estimation, plant shape analysis, etc., and can be used as a technology for digital conversion of conventional crop growth assessment methods.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.