• Atmosphere
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• v.28 no.4
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• pp.369-382
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• 2018

Planetary boundary layer height (PBLH), produced by the Local Data Assimilation and Prediction System (LDAPS), was verified using RawinSonde (RS) data obtained from observation at Daegwallyeong (DGW) and Sokcho (SCW) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). The PBLH was calculated using RS data by applying the bulk Richardson number and the parcel method. This calculated PBLH was then compared to the values produced by LDAPS. The PBLH simulations for DGW and SCW were generally underestimation. However, the PBLH was an overestimation from surface to 200 m and 450 m at DGW and SCW, respectively; this result of model's failure to correctly simulate the Surface Boundary Layer (SBL) and the Mixing Layer (ML) as the PBLH. When the accuracy of the PBLH simulation is low, large errors are seen in the mid- and low-level humidity. The highest frequencies of Planetary boundary layer (PBL) types, calculated by the LDAPS at DGW and SCW, were presented as types Ι and II, respectively. Analysis of meteorological factors according to the PBL types indicate that the PBLH of the existing stratocumulus were overestimated when the mid- and low-level humidity errors were large. If the instabilities of the surface and vertical mixing into clouds are considered important factors affecting the estimation of PBLH into model, then mid- and low-level humidity should also be considered important factors influencing PBLH simulation performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Journal of the Korean Geosynthetics Society
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• v.11 no.2
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• pp.1-9
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• 2012

In order to review the utility of Lade's single hardening constitutive model, a series of isotropic compression-expansion tests and consolidated drained triaxial tests including as CTC, TC, RTC, and OSP were performed by Baekma river sand with various of stress path. Parameters required in model were determined using these tests. The accuracy of analysis was reviewed by back analysis of test results used to determine the 11 parameters of soil property through the test of each stress path. Also. for verifying the accuracy of prediction for the stress-strain behavior using failure criterion related 9 parameters with correlational equation and constant and yield criterion related parameters h, ${\alpha}$ and ${\eta}_1$, when stress path is different with each other, it has been obtained in the review result of stress path dependent characteristics of the constitutional model through the analyzing results of CTC, TC, RTC, OSP, and fine silica sand tests.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1460-1466
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• 2008

The purpose of this study is to verify how the accounting information of a bankrupt firm which is defined as a dishonor, an impaired total capital, a poor financial performance of a business, a rejection of auditor's opinion and an incongruity of auditor's opinion differs from that of a healthy firm on the basis of the index of financial affairs if the accounting information released by KOSDAQ is valuable. The sampling firms consists of 45 KOSDAQ firms that went bankrupt from 2000 to 2007 and 45 healthy firms which are selected in accordance with the sizes of assets. It has also selected the 30 sampling firms for the confirmation of the model in the same way. According to the result of the in-depth analysis, the variables related to security among the 17 indexes of financial affairs that have been used in this study for 5 years show a noticeable difference between a bankrupt firm and a healthy one. The accuracy of failed firms using this model for confirmation demonstrates 76.7% in 5 years before the bankruptcy, 76.7% in 4 years before that, 65.0% in 3 years before it, 76.7% in 2 years, 88.3% in 1 year. This data shows that the process from a healthy firm to a bankrupt one has progressed gradually and confirms the value of the index of financial affairs, exhibiting the accuracy with 83.8% of a presuming sample and 76.7% of a confirming sample for 5 years.

• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.1-11
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• 2023

This study intended to develop a technique for quantitatively and 3-dimensionally predicting the potential failure zone and impulse that may occur when trees are fall down. The main outcomes of this study are as follows. First, this study established the potential failure zone and impulse calculation formula in order to quantitatively calculate the risks generated when trees are fallen down. When estimating the potential failure zone, the calculation was performed by magnifying the height of trees by 1.5 times, reflecting the likelihood of trees falling down and slipping. With regard to the slope of a tree, the range of 360° centered on the root collar was set in the case of trees that grow upright and the range of 180° from the inclined direction was set in the case of trees that grow inclined. The angular momentum was calculated by reflecting the rotational motion from the root collar when the trees fell down, and the impulse was calculated by converting it into the linear momentum. Second, the program to calculate a potential failure zone and impulse was developed using Rhino3D and Grasshopper. This study created the 3-dimensional models of the shapes for topography, buildings, and trees using the Rhino3D, thereby connecting them to Grasshopper to construct the spatial information. The algorithm was programmed using the calculation formula in the stage of risk calculation. This calculation considered the information on the trees' growth such as the height, inclination, and weight of trees and the surrounding environment including adjacent trees, damage targets, and analysis ranges. In the stage of risk inquiry, the calculation results were visualized into a three-dimensional model by summarizing them. For instance, the risk degrees were classified into various colors to efficiently determine the dangerous trees and dangerous areas.

• Journal of the Korean Institute of Gas
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• v.14 no.1
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• pp.28-36
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• 2010

We have garnered 3,593 data of gas accidents reported for 12 years from 1995, and then analyzed the LPG vaporizer accidents according to their types and causes based on the classified database. According to the results the gas rupture has been the most common accident followed by the release, explosion and then fire accidents, the most frequent accident-occurring sub-cause is LPG check floater faults. In addition, we have applied the Poisson Probability Functions to predict the most-likely probabilities of fire, explosion, release and rupture with the LPG vaporizer in the upcoming 5 years. In compliance with Poisson Probability Functions results, in the item which occurs below 3 "LPG-Vaporizer-Fire", in the item which occurs below 5 "LPG-Vaporizer-Products Faults-Check Floater" and the item which occurs below 10 appeared with "LPG-Vaporizer-Products Faults". From this research we have assured the successive database updating will highly improve the anticipating probability accuracy and thus it will play a key role as a significant safety- securing guideline against the gas disasters.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.3
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• pp.254-263
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• 2012

The survivability of warship is assessed by susceptibility, vulnerability and recoverability. Essentially, a vulnerability assessment is a measure of the effectiveness of a warship to resist hostile weapon effects. Considering the shot line and its penetration effect on the warship, present study introduces the procedural aspects of vulnerability assessments of warship. Present study also considers the prediction of penetration damage to a target caused by the impact of projectiles. It reflects the interaction between the weapon and the target from a perspective of vulnerable area method and COVART model. The shotline and tracing calculation have been directly integrated into the vulnerability assessment method based on the penetration equation empirically obtained. A simplified geometric description of the desired target and specification of a threat type is incorporated with the penetration effect. This study describes how to expand the vulnerable area assessment method to the penetration effect. Finally, an example shows that the proposed method can provide the vulnerability parameters of the warship or its component under threat being hit through tracing the shotline path thereby enabling the vulnerability calculation. In addition, the proposed procedure enabling the calculation of the component's multi-hit vulnerability introduces a propulsion system in dealing with redundant Non-overlapping components.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.369-376
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• 2013

Finance Information System is critical national infrastructure. Therefore it is important to select variables of defect causal factor for the system defect management effectively. We research and analyze detected errors in A Company's Finance Information System for three years. In the result of research and analysis, we have selected 9 variables of defect factor: the trading volume, the fluctuation of KOSDAQ index, and the number of public announcements, etc. Then we have assumed that these variables affect real system errors and analyzed correlation between the hypothesis and the detected system errors. After analyzing, we have extracted the trading volume, the number of orders and fills, changing tasks, and the fluctuations of NASDAQ index as valid variables of defect factor. These variables are proposed for failure prediction model as the variables to manage defects in the finance information system afterward.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.391-402
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• 1994

One of the most important design concept for reinforced concrete structures is to achieve a ductile failure mode, and also moment redistribution for economic design is possible in case that adequate ductility is provided. Flexural ductility index is, therefore, used as a reference for possibility of moment redistribution as well as for prediction of flexural behavior of designed R.C. structures. Ductility index equations, however, provide approximate values due to the linear concrete compressive stress assumption at the tension steel yielding state. Theoretically more exact ductility index is calculated by a numerical analysis with the realistic stress-strain curves for concrete and steel to be compared with the result from tire ductility index equations. Variation of ductility index for the selected variables and the reasonable maximum tension steel ratio for doubly reinforced section are investigated. A moment-curvature curve model is also proposed for future research on moment redistribution.