• Composites Research
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• v.29 no.1
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• pp.1-6
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• 2016

Low-speed impact was localized on a stiffened composite structure, using 4 FBG sensors with 100 kHz-sampling rate interrogator and devised localization algorithm. The composite specimen consists of a main spar and several stringers, and the overall size of the specimen's surface is about $0.8{\times}1.2m$. Pre-stored reference data for 247 grid locations and 36 stiffener locations are gathered and used as comparison target for a random impact signal. The proposed algorithm uses the normalized cross-correlation method to compare the similarities of the two signals; the correlation results for each sensor's signal are multiplied by others, enabling mutual compensation. 20 verification points were successfully localized with a maximum error of 43.4 mm and an average error of 17.0 mm. For the same experimental setup, the performance of the proposed method is evaluated by reducing the number of sensors. It is revealed that the mutual compensation between the sensors is most effective in the case of a two sensor combination. For the sensor combination of FBG #1 and #2, the maximum localization error was 42.5 mm, with average error of 17.4 mm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.189-196
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• 2005

Recently as map making skills developed and as digital maps prevailed, peoples began to take interest in the realistic 3dimension topography rather than the flat 2 dimension one. The experiment is done by using the topographical information from the digital maps, To analyze the preciseness of this 3dimension topography, analysis of the coordinate-changed standard map image and the location errors of the plane and height from digital values of the map's topography by layers and features, were done. The visual results of locational values differed by every programs of coordinate transformation. Errors of locations also appeared from the methods of correcting the visual sources, when deciding the standard source's datum point. The plan's accuracy of the image data coordinate transformation is about ${\pm}4.1m$. In ground distance, therefore, it is included in the allowed error of the 1:25,000 scale changed map, satisfying the plan's accuracy. Also, by the use of reasonably spaced grid, it satisfied the visual topographical accuracy. Because the 3 dimension topographical map can be produced effectively and rapidly by using various scale's standard map image and the digital map, the further practical use of 3 dimension topographic map made by using the existing topographies and changed maps has high expectations.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.28-38
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• 2004

The purpose of this study is to analyze the urban hydrologic state by the use of GIS, resolution and interpolation. The determination coefficient($R^2$) and Regression Formula were derived from the contour of digital map for the accuracy, and DEM data was made by using TIN interpolation by the size of the grid. By using the observed DEM data, topographical factors were extracted from the small basin, size, the width of a basin and the slope, and were applied in the urban runoff model. Through the model, we tried to find out the most suitable runoff model in a small basin of Yosu-Munsu area. As a result of applying models to the drainage considered, the runoff hydrograph estimated by SWMM model was closer to the observed one than that estimated by ILLUDAS model. The difference between the runoff hydrograph by SWMM and the observed one is maximum error of 19%, minimum error of 5% and average error of 13%. The influence of duration in contrast to pick time is insignificant in a urban small basin. As a conclusion of this study, SWMM model was more suitable and applicable for the urban runoff model than ILLUDAS model due to its accuracy and various abilities.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.165-178
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• 2021

We studied driftwood behaviors including generation and deposition in a tsunami using a numerical simulation. We used an integrated two-dimensional numerical model, which included a driftwood dynamics model. The study area was Sendai, Japan. Observation data collected by Inagaki et al. (2012) were used to verify the simulation results by comparing them with driftwood deposition patterns. A simplified model was developed to consider the threshold of driftwood generation by the drag force of water flows. To consider the volume of driftwood generated, we estimated the total wood number in the study area using Google Earth. Therefore, we simulated more than 13,000 pieces of driftwood that were generated and transported inland from approximately 300,000 trees that were growing in the forest. The final distribution of the driftwood was similar to the observation data. The reproducibility of the generation and deposition patterns of driftwood showed good agreement in terms of longitudinal deposition pattern. In the future, a sensitivity analysis on driftwood parameters, such as the size of the wood, boundary conditions, and grid size, will be implemented to predict the travel patterns of driftwood. Such modeling will be a useful methodology for disaster prediction based on water flow and driftwood.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.857-868
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• 2018

The microphysics and spatio-temporal distribution of atmospheric aerosols are responsible for estimating the optical properties at a given location. Its accurate estimation is essential to plan efficient simulation for radiative transfer. For this sake, synergetic use of reanalysis data with optics database was used as a potential tool to precisely derive the aerosol model on the basis of the major representative particulates exist within a model grid. In detail, mixing of aerosol types weighted by aerosol optical depth (AOD) components has been developed. This synergetic aerosol model (SAM) is spectrally extended up to $40{\mu}m$. For the major aerosol event cases, SAM showed that the mixed aerosol particles were totally different from the typical standard aerosol models provided by the radiative transfer model. The correlation among the derived aerosol optical properties along with ground-based observation data has also been compared. The current results will help to improve the radiative transfer model simulation under the real atmospheric environment.

• Journal of Korea Water Resources Association
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• v.52 no.5
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• pp.337-347
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• 2019

This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM's and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.286-297
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• 2018

In the present study, wave measurements at KOGA-W01 were analyzed and then the numerical wind waves simulations have been conducted to investigate the characteristics of wind waves in the Yellow sea. According to the present analysis, even though the location of the wave stations are close to the coastal region, the deep water waves are prevailed due to the short fetch length. Chun and Ahn's (2017a, b) numerical model has been extended to the Yellow Sea in this study. The effects of tide and tidal currents should be included in the model to accommodate the distinctive effect of large tidal range and tidal current in the Yellow Sea. The wave hindcasting results were compared with the wave measurements collected KOGA-W01 and Kyeockpo. The comparison shows the reasonable agreements between wave hindcastings and measured data, however the model significantly underestimate the wave period of swell waves from the south due to the narrow computational domain. Despite the poorly prediction in the significant wave period of swell waves which usually have small wave heights, the estimation of the extreme wave height and corresponding wave period shows good agreement with the measurement data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.287-298
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• 2020

It is predicted that future climate warming will occur, and the subtropical climate zone currently confined to the south coast of Korea will gradually rise north. The shift of climate zone implies a change in area for cultivating crops. This study aimed to evaluate the current and future status of climate zones based on the high-resolution climate data of South Korea to prepare adaptation measures for cultivating crops under changing agricultural climate conditions. First, the climatic maps of South and North Korea were produced by using the high-resolution monthly maximum and minimum daily temperature and monthly cumulative precipitation produced during the past 30 years (1981-2010) covering South and North Korea. Then the climate zones of the Korean Peninsula were classified based on the Köppen climate classification. Second, the changes in climate zones were predicted by using the corrected monthly climate data of the Korean Peninsula (grid resolution 30-270m) based on the RCP8.5 scenario of the Korea Meteorological Administration. Köppen climate classification was applied based on the RCP8.5 scenario, the temperature and precipitation of the Korean Peninsula would continue to increase and the climate would become simpler. It was predicted that the temperate climate, appearing in the southern region of Korea, would be gradually expanded and the most of the Korean Peninsula, excluding some areas of Hamgkyeong and Pyeongan provinces in North Korea, would be classified as a temperate climate zone between 2071 and 2100. The subarctic climate would retreat to the north and the Korean Peninsula would become warmer and wetter in general.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.