• Explosives and Blasting
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• v.34 no.3
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• pp.10-16
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• 2016

Recently, transition from open pit to underground mining in limestone mines is an increasing trend in Korea due to environmental issues such as noise, dust and vibrations caused by crushers and equipment. The severe damages in the surrounding rock mass of underground opening caused by explosive blasting may lead to rock fall hazards or casualties. It is well known that variables which mainly affect blast-induced rock falls in underground mining are: blast vibration level, joint orientation and distribution and shape of the cross sections of underground structures. In this study, UDEC program, which is a DEM code, is used to simulate blast vibration-induced rock fall in underground openings. Variation of joint space, joint angle and joint normal stiffness was considered to investigate the effect of joint characteristics on the blast vibration-induced rock fall in underground opening. Finally, jointed rock mass models considering blast-induced damage zone were examined to simulate the critical blast vibration value which may cause rock falls in underground opening.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.54-62
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• 2011

The pressurant mass required for propellant tank pressurization with operating condition variation was estimated by using the numerical model already developed for this purpose. The model was applied to the concept design results of KSLV-II first stage oxygen tank. The supplied pressurant temperature, oxygen volumetric flow rate, and the ratio of length to diameter of the tank were selected as variables. The required pressurant mass and mass flow rate, collapse factor, ullage temperature distribution were predicted, and the results showed that the pressurant temperature had the largest effect on the amount of the required pressurant mass. The pressurizing efficiency of the propellant tank was calculated through analyzing energy distribution in the ullage. It was found that the gas-to-wall heat transfer in the ullage was dominant, and much of the pressurant energy was lost to tank wall heating.

• Korea Real Estate Review
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• v.28 no.3
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• pp.21-35
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• 2018

The recent performance of residential mortgages demonstrated how default risk operated separately from prepayment risk. In this study, we investigated the determinants of the borrowers' decisions pertaining to early termination through default from the mortgage performance data released by Freddie Mac, involving securitized mortgage loans from January 2011 to September 2013. We estimated a Cox-type, proportional hazard model with a single risk on fundamental factors associated with default options for individual mortgages. We proposed a mortgage default model that included two specifications of delinquency: one using a delinquency binary variable, while the other using a delinquency probability. We also compared the results obtained from two specifications with respect to goodness-of-fit proposed in the spirit of Vuong (1989) in both overlapping and nested models' cases. We found that a model with our proposed delinquency probability variable showed a statistically significant advantage compared to a benchmark model with delinquency dummy variables. We performed a default prediction power test based on the method proposed in Shumway (2001), and found a much stronger performance from the proposed model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.915-922
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• 2008

Impervious surface is an important index for the estimation of urbanization and the assessment of environmental change. In addition, impervious surface influences on short-term rainfall-runoff model during rainy season in hydrology. Recently, the necessity of impervious surface estimation is increased because the effect of impervious surface is increased by rapid urbanization. In this study, impervious surface estimation is performed by using remote sensing image such as Landsat-7 ETM+image with $30m{\times}30m$ spatial resolution and satellite image with $1m{\times}1m$ spatial resolution based on Jungnangcheon basin. A tasseled cap transformation and NDVI(normalized difference vegetation index) transformation are applied to Landsat-7 ETM+ image to collect various predict variables. Moreover, the training data sets are collected by overlaying between Landsat-7 ETM+ image and satellite image, and CART(classification and regression tree) is applied to the training data sets. As a result, impervious surface prediction model is consisted and the impervious surface map is generated for Jungnangcheon basin.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.131-135
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• 2024

Baseball attracts the largest audience among professional sports in Korea. In particular, attendance is the primary source of income in baseball. Previous studies have limitations in reflecting the characteristics of individual stadium. For instance, the KIA Tigers exhibit the highest away game revenue among domestic teams, but they show lower home game earnings. Therefore, we aim to predict the daily attendance at the Gwangju-KIA Champions Field of the KIA Tigers using deep learning. We collected and preprocessed daily attendance, dates, weather, and team-related variables for Gwangju-KIA Champions Field from 2018 to 2023. We propose a deep learning-based linear regression model to predict the daily attendance. We expect that the proposed deep learning model will be used as basic information to increase the club's revenue.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.239-256
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• 2020

Health is an important issue due to increased life expectancy. As a result, the demand for industry and services associated with individual health, health-related programs and services will be facilitated by a method to evaluate and classify the health level of an individual based on various factors. This study suggests a methodology to measure and score an individual health level. A credit scoring model was introduced to implement the categorization of variables, construct a prediction model, and to score individual health level. Cohort DB provided by National Health Insurance Service was used to illustrate overall procedures. It is expected that the suggested model can be utilized in designing and managing health care services as well as other health-related programs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.11-21
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• 2005

A three-dimensional parallel Euler flow solver has been developed for the simulation of unsteady rotor-fuselage interaction aerodynamics on unstructured meshes. In order to handle the relative motion between the rotor and the fuselage, the flow field was divided into two zones, a moving zone rotating with the blades and a stationary zone containing the fuselage. A sliding mesh algorithm was developed for the convection of the flow variables across the cutting boundary between the two zones. A quasi-unsteady mesh adaptation technique was adopted to enhance the spatial accuracy of the solution and to better resolve the wake. A low Mach number pre-conditioning method was implemented to relieve the numerical difficulty associated with the low-speed forward flight. Validations were made by simulating the flows around the Georgia Tech configuration and the ROBIN fuselage. It was shown that the present method is efficient and robust for the prediction of complicated unsteady rotor-fuselage aerodynamic interaction phenomena.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.218-225
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• 2021

The purpose of this study is to present a rational analytical method for predicting the behavioral characteristics from crack occurrence to fracture for a one-way CFRP grid reinforced concrete slab specimen. A total of four specimens were selected by Zhang et al.(2004) as the main experimental variables for CFRP grid amount, material properties and loading method. Analysis was performed through the Nonlinear Finite Element analysis program(RCAHEST), which applied the newly modified constitutive relational equations by the author. The mean and coefficient of variation for maximum moment from the experiment and analysis results was predicted 1.38 and 7 %. The mean and coefficient of variation for displacement corresponding maximum moment from the experiment and analysis results was predicted 1.41and 9.8 %. The prediction results for the behavioral characteristics from crack occurrence to fracture were verified and evaluated. It is judged that additional research is needed to secure various experimental results and to develop a more reliable analytical method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.11
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• pp.763-770
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• 2022

In an environment where multiple drones are operated, collisions can occur when path points overlap, and collision avoidance in preparation for this is essential. When multiple drones perform multiple tasks, it is not appropriate to use a method to generate a collision-avoiding path in the path planning phase because the path of the drone is complex and there are too many collision prediction points. In this paper, we generate a path through a commonly used path generation algorithm and propose a collision avoidance method using speed profile optimization from that path segment. The safe distance between drones was considered at the expected point of collision between paths of drones, and it was designed to assign a speed profile to the path segment. The optimization problem was defined by setting the distance between drones as variables in the flight time equation. We constructed the constraints through linearize and convexification, and compared the computation time of SQP and convex optimization method in multiple drone operating environments. Finally, we confirmed whether the results of performing convex optimization in the 20 drone operating environments were suitable for the multiple drone operating system proposed in this study.