• Spatial Information Research
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• v.14 no.1 s.36
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• pp.129-150
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• 2006

Mineral potential mapping is an important procedure in mineral resource assessment. The purpose of this study is to analyze mineral potential using weight of evidence model and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential mineral in the Gangreung area, Korea. for this, a spatial database considering mineral deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The used mineral deposits were non-metallic(Kaolin, Porcelainstone, Silicastone, Mica, Nephrite, Limestone and Pyrophyllite) deposits of sedimentary type. The factors relating to mineral deposits were the geological data such as lithology and fault structure, geochemical data, including the abundance of Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, Zn, $Cl^-,\;F^-,\;{PO_4}^{3-},\;{NO_2}^-,\;{NO_3}^-,\;SO_{42-}$, Eh, PH and conductivity and geophysical data, including the Bouguer and magnetic anomalies. These factors were used with weight of evidence model to analyze mineral potential. Probability models using the weight of evidence were applied to extract the relationship between mineral deposits and related factors, and the ratio were calculated. Then the potential indices were calculated by summation of the likelihood ratio and mineral potential maps were constructed from Geographic Information System (GIS). The mineral potential maps were then verified by comparison with the known mineral deposit areas. The result showed the 85.66% in prediction accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.5
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• pp.371-380
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• 2017

The risk-targeted seismic design concept was first included in ASCE/SEI 7-10 to address problems related to the uniform-hazard based seismic concept that has been constructed without explicitly considering probabilistic uncertainties in the collapse capacities of structures. However, this concept is not yet reflected to the current Korean building code(KBC) because of insufficient strong earthquake data occurred at the Korean peninsula and little information on the collapse capacities of structures. This study evaluates the risk-targeted seismic performance of steel ordinary concentrically braced frames(OCBFs). To do this, the collapse capacities of prototype steel OCBFs are assessed with various analysis parameters including building locations, building heights and soil conditions. The seismic hazard curves are developed using an empirical spectral shape prediction model that is capable of reflecting the characteristics of earthquake records. The collapse probabilities of the prototype steel OCBFs located at the Korean major cities are then evaluated using the risk integral concept. As a result, analysis parameters considerably influence the collapse probabilities of steel OCBFs. The collapse probabilities of taller steel OCBFs exceed the target seismic risk of 1 percent in 50 years, which the introduction of the height limitation of steel OCBFs into the future KBC should be considered.

• Current Optics and Photonics
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• v.2 no.3
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• pp.269-279
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• 2018

We are currently investigating the feasibility of a 1.6 m telescope with a laser-guide star adaptive optics (AO) system. The telescope, if successfully commissioned, would be the first dedicated adaptive optics observatory in South Korea. The 1.6 m telescope is an f/13.6 Cassegrain telescope with a focal length of 21.7 m. This paper first reviews atmospheric seeing conditions measured over a year in 2014~2015 at the Bohyun Observatory, South Korea, which corresponds to an area from 11.6 to 21.6 cm within 95% probability with regard to the Fried parameter of 880 nm at a telescope pupil plane. We then derive principal seeing conditions such as the Fried parameter and Greenwood frequency for eight astronomical spectral bands (V/R/I/J/H/K/L/M centered at 0.55, 0.64, 0.79, 1.22, 1.65, 2.20, 3.55, and $4.77{\mu}m$). Then we propose an AO system with a laser guide star for the 1.6 m telescope based on the seeing conditions. The proposed AO system consists of a fast tip/tilt secondary mirror, a $17{\times}17$ deformable mirror, a $16{\times}16$ Shack-Hartmann sensor, and a sodium laser guide star (589.2 nm). The high order AO system is close-looped with 2 KHz sampling frequency while the tip/tilt mirror is independently close-looped with 63 Hz sampling frequency. The AO system has three operational concepts: 1) bright target observation with its own wavefront sensing, 2) less bright star observation with wavefront sensing from another bright natural guide star (NGS), and 3) faint target observation with tip/tilt sensing from a bright natural guide star and wavefront sensing from a laser guide star. We name these three concepts 'None', 'NGS only', and 'LGS + NGS', respectively. Following a thorough investigation into the error sources of the AO system, we predict the root mean square (RMS) wavefront error of the system and its corresponding Strehl ratio over nine analysis cases over the worst ($2{\sigma}$) seeing conditions. From the analysis, we expect Strehl ratio >0.3 in most seeing conditions with guide stars.

• Journal of Distribution Science
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• v.4 no.2
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• pp.5-20
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• 2006

Retail is called location business because it is one of the most important factors to estimate management of stores for retailers who are going to sell products directly to customers. Retailers' management achievements are shown in sale in general. Therefore, retailers tend to focus on ways to increase the numbers of customers in order to raise sales. First of all, in this research, I am going to examine the most fundamental models such as Reilly's retail gravitation, converse model, huff probability model and multiful losit model in selecting stores. Secondly, I am going to provide the process and analyzing ways to predict estimated sales amount with the previous theory model. Also I am going to predict estimated sales amount of the department store L which is located in D metorpolitan city. Lastly, I am going to argue about the problem of this research and the next research subject. Our main goal is to provide ways to complement and inspect sales estimation models, which can be used in fields after taking characters of high class structure of Korea into consideration on the base of previous researches. According to the result of the research, my conclusion is that if the process of analysis and changing factors are complemented, revise model, which can reflect reality of Korea, will be provided. Therefore, in the future study, we have to build up theory models to suit for our retail market through critic reviews about the existing high class structure of Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.6
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• pp.677-684
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• 2014

In response to possible HNS (Hazardous and Noxious Substance) spill accident, HNS spill accident scenario and response scenario were developed. The accident area listed in scenarios is the coastal area of Busan, and scenario for possible accident in the designated area and strategies to respond the accident were developed, respectively. The scenario for accident was developed by designating HNS spill according to risk evaluation of HNS and analysis of HNS spill probability along the coastal area of Busan, and then estimating possible and potential impact from the accident. The scenario for response has been suggested as a systematical responding operations in order to effectively reduce the estimated impact from the accident. The possible HNS spill accident on the seas around Busan, has been designated by the spillage of 1,000ton of xylene due to collision accident in Gamcheon Port, and the possible impacts occurred by the accident has been simulated with the help of the atmospheric and oceanic dispersion model of xylene. In the responding scenario for the accident, a phased strategies regarding emergency rescue of peoples, protection and recovery of xylene, protective measures for the responders, and post management of the accident have been suggested.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Journal of Platform Technology
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• v.8 no.3
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• pp.10-21
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• 2020

Partial discharge occurs a lot in high-voltage power equipment such as switchgear, transformers, and switch gears. Partial discharge shortens the life of the insulator and causes insulation breakdown, resulting in large-scale damage such as a power outage. There are several types of partial discharge that occur inside the product and the surface. In this paper, we design a predictive model that can predict the pattern and probability of occurrence of partial discharge. In order to analyze the designed model, learning data for each type of partial discharge was collected through the UHF sensor by using a simulator that generates partial discharge. The predictive model designed in this paper was designed based on CNN during deep learning, and the model was verified through learning. To learn about the designed model, 5000 training data were created, and the form of training data was used as input data for the model by pre-processing the 3D raw data input from the UHF sensor as 2D data. As a result of the experiment, it was found that the accuracy of the model designed through learning has an accuracy of 0.9972. It was found that the accuracy of the proposed model was higher in the case of learning by making the data into a two-dimensional image and learning it in the form of a grayscale image.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.460-467
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• 2022

In this study, we collected snake roadkill data from 2006 to 2017 and developed a species distribution model to identify the pattern of snake roadkill and predict the potential hotspot of snake roadkill in the Odaesan National Park of South Korea. During the study period, snake roadkills occurred most frequently on the road, which passes through between forest and stream at an altitude of about 600 m. The modeling result showed that the occurrence probability of snake roadkill was high on a road with a gentle slope at a distance of 25 m from the stream and an altitude of 600 m. The most susceptible regions for snake roadkill in the Odaesan National Park were located on National Route 6, about 2.2 km and 11.7 km away from the southern border of the park, and on Local Road 446, 3.44 km away from the southern border of the park. The results of this study suggest that providing alternative basking places and eco-corridors and installing protection fences that block the inflow of snakes into roads, preferentially around roads and streams at an altitude lower than 700 m would be an effective way of reducing snake roadkill in the Odaesan National Park.