• Journal of the Korean Society of Environmental Restoration Technology
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• v.2 no.1
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• pp.66-71
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• 1999

Main concern is to figure out the growth response of Pinus densiflora to hydrologic conditions in the central Korea. Continuous measurements were carried out with six trees with dendrometers in the Chungbuk National University experimental forest (Wolak-san) during 1995~1996. Surrounding hydrological conditions reflected by the solar radiation, air temperature, precipitation, soil water were included in measurements. Their effects on the biological response of trees was investigated and expressed as response functions. With these response functions, tree growth model was developed. Soil water availability was more related to the tree growth than air temperature. Limited number of biological measurements with dendrometer could permit determination of dynamics of radial tree growth to the hydrological conditions. Tree growth model could be used to check and revise the statistical transfer function of dendrohydrology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1149-1157
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• 1994

In this case of the mobile communication of vehicles with satellite, the signal at attenuation is due to roadside trees. To analyze this signal attenuation, a roadside tree was modeled as different obstacles of rectangular type and then using Fresnel and Kirchhoff diffraction theory, a formula was derived for signal intensity variation caused by the roadside tree model. The signal attenuation of a roadside tree model was obtained by numerical analysis with variation of the elevation angle, the position and distance between a receiver and a transmitter, and these were compared with experimental results. The results of comparison between theoretical and experimental values show, as expected, the good agreement of the signal attenuation trend.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.181-184
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• 2008

The quality of orthoimages mainly depends on the elevation information and exterior orientation (EO) parameters. Since LiDAR data directly provides the elevation information over the earth's surface including buildings and trees, the concept of true orthorectification has been rapidly developed and implemented. If a LiDAR-driven digital surface model (DSM) is used for orthorectification, the displacements caused by trees and buildings are effectively removed when compared with the conventional orthoimages processed with a digital elevation model (DEM). This study sequentially utilized LiDAR data to generate orthorectified digital aerial images. Experimental orthoimages were produced using DTM and DSM. For the preparation of orthorectification, EO components, one of the inputs for orthorectification, were adjusted with the ground control points (GCPs) collected from the LiDAR point data, and the ground points were extracted by a filtering method. The orthoimage generated by DSM corresponded more closely to non-ground LiDAR points than the orthoimage produced by DTM.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.39.2-39.2
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• 2010

We have investigated the evolution of the galaxy morphology in the hierarchical universe taking advantage of Semi-Analytic Model (SAM). It is well known that the galaxy morphology is related to the dynamical and the chemical evolution. This implies that we need to understand overall physical processes in the galaxy to reproduce its morphology. Thus we implemented gradual hot gas stripping of satellite galaxies in a galaxy cluster and recycling of stellar mass losses into our model in order to describe star formation rate of galaxies accurately. To morphologically classify galaxies, the evolution of disc and bulge components is traced carefully. We compute our models based on the dark matter halo merger trees generated by N-body simulations as well as the Extended Press-Schechter (EPS) formalism. We present morphological differences caused by the use of different merger trees.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.184-192
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• 2018

In this study was implemented to develop tree stem weight prediction equation of Larix kaempferi in central region by selecting a standard site, taking into account of diameter and position of the local trees. Fifty five sample trees were selected in total. By utilizing actual data of the sample trees, 11 models were compared and analyzed in order to estimate four different kinds of weights which include fresh weight, ovendry outside bark weight, ovendry inside bark weight and merchantable weight. As to estimate its weight, the study has classified its model according to three parameters: DBH, DBH and height, and volume. The optimal model was chosen by comparing the performance of model using the fit index and standard error of estimate and residual distribution. As a result, the formula utilizing DBH (Variable 1) is $W=a+bD+cD^2$ (3) and its fit index was 90~92%. The formula for DBH and height (Variable 2) is $W=aD^bH^C$ (8) and its fit index was 97~98%. In summation, Variable 2 model showed higher fitness than Variable 1 model. Moreover, fit index of formula for total volume and merchantable volume (W=aV) showed high rate of 98~99%, as well as resulting 7.7-17.5 with SEE and 8.0-10.0 with CV(%) which lead to predominately high fitness in conclusion. This study is expected to provide information on weights for single trees and furthermore, to be used as a basic study for weight of stand unit and biomass estimation equations.

• Proceedings of the Korean Quaternary Association Conference
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• 2002.12a
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• pp.91-91
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• 2002

• Korean Journal of Environment and Ecology
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• v.27 no.5
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• pp.571-578
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• 2013

This study generated regression models through a direct harvesting method to estimate carbon storage and uptake by Pinus densiflora and Pinus koraiensis, the major evergreen tree species in urban landscape, and established essential information to quantify carbon reduction by urban trees. Open-grown landscape tree individuals for each species were sampled reflecting various diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute the total carbon storage per tree. Annual carbon uptake per tree was quantified by analyzing radial growth rates of stem samples at breast height. The study then derived a regression model easily applicable in estimating carbon storage and uptake per tree for the two species by using diameter at breast height (DBH) as an independent variable. All the regression models showed high fitness with $r^2$ values of higher than 0.98. While carbon storage and uptake by young trees tended to be greater for P. densiflora than for P. koraiensis in the same diameter sizes, those by mature trees with DBH sizes of larger than 20 cm showed results to the contrary due to a difference in growth rates. A tree of P. densiflora and P. koraiensis with DBH of 25 cm stored 115.6 kg and 130.0 kg of carbon, respectively, and annually sequestered 9.4 kg and 14.6 kg. The study has broken new grounds to overcome limitations of the past studies which quantified carbon reduction of the study species by substituting, due to a difficulty in direct cutting and root digging of landscape trees, coefficients from forest trees such as biomass expansion factors, ratios of below ground/above ground biomass, and diameter growth rates.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• The Journal of Information Systems
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• v.30 no.4
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• pp.21-42
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• 2021

Purpose In order for companies to continue to grow, they should properly manage human resources, which are the core of corporate competitiveness. Employee turnover means the loss of talent in the workforce. When an employee voluntarily leaves his or her company, it will lose hiring and training cost and lead to the withdrawal of key personnel and new costs to train a new employee. From an employee's viewpoint, moving to another company is also risky because it can be time consuming and costly. Therefore, in order to reduce the social and economic costs caused by employee turnover, it is necessary to accurately predict employee turnover intention, identify the factors affecting employee turnover, and manage them appropriately in the company. Design/methodology/approach Prior studies have mainly used logistic regression and decision trees, which have explanatory power but poor predictive accuracy. In order to develop a more accurate prediction model, XGBoost is proposed as the classification technique. Then, to compensate for the lack of explainability, SHAP, one of the XAI techniques, is applied. As a result, the prediction accuracy of the proposed model is improved compared to the conventional methods such as LOGIT and Decision Trees. By applying SHAP to the proposed model, the factors affecting the overall employee turnover intention as well as a specific sample's turnover intention are identified. Findings Experimental results show that the prediction accuracy of XGBoost is superior to that of logistic regression and decision trees. Using SHAP, we find that jobseeking, annuity, eng_test, comm_temp, seti_dev, seti_money, equl_ablt, and sati_safe significantly affect overall employee turnover intention. In addition, it is confirmed that the factors affecting an individual's turnover intention are more diverse. Our research findings imply that companies should adopt a personalized approach for each employee in order to effectively prevent his or her turnover.