• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.7
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• pp.720-728
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• 2011

In this paper, development of the monopulse secondary surveillance radar antenna which can be used for IFF system is presented. This antenna that is passive linear array is comprised of the row-feeder and several array-elements. The row-feeder provides sum, different and SLS(Sidelobe Supression) channels which are optimized the distribution of the power and phase ratio. The azimuthe sidelobe level of the sum channel beam pattern is -20 dBc or less. The SLS channel covers the sidelobe of the sum-chanel in the whole azimuth angle range. And the difference channel is used to perform the mono-pulse function, improves the detection accuracy in the azimuth direction. Meanwhile, the arrayelement makes shaped beam in the elevation angle, in order to eliminate the clutter and multipath effects from the ground. Performance of the antenna developed is verified by the measurement of S-parameters and far-field beam pattern, and satisfies all of the development specifications well.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.12
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• pp.1177-1183
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• 2012

This study aims to develop a system that maximizes the radiative heat transfer from the heliostat to the receiver by using the configuration factor and a solar tracking device. As the heat transfer from the heliostat to the receiver is delivered by solar radiation, the configuration factor commonly utilized for radiation is applied to control the heliostat. Tracking the sun and calculating its position are possible by using an illuminance sensor (CdS) and Simulink. By applying optimized algorithms programmed using Simulink that maximize the configuration factors among the heliostat, receiver, and sun in real time, the solar absorption efficiency of the receiver can be maximized. Simulations were performed on how to change the angle required to control the elevation and azimuthal angle of the heliostat during the daytime with respect to various distances.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.411-420
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• 2009

To plan a GPS survey, they have to decide if a survey can be conducted at a specific point and time based on the predicted GPS ephemeris. In this study, to predict ephemeris, we used the repeat time of a GPS satellite. The GPS satellite repeat time was determined by analysing correlation among three-dimensional satellite coordinates provided by the 48-hour GPS ephemeris in the ultra-rapid orbits. By using the calculated repeat time and Lagrange interpolation polynomials, we predicted GPS orbits f3r seven days. As a result, the RMS of the maximum errors in the X, Y, and Z coordinates were 39.8 km 39.7 km and 19.6 km, respectively. And the maximum and average three-dimensional positional errors were 119.5 km and 48.9 km, respectively. When the maximum 3-D positioning error of 119.5 km was translated into the view angle error, the azimuth and elevation angle errors were 9.7'and 14.9', respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.395-400
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• 2008

This paper relates to the performance analysis of DoA estimation algorithm in 2-dimensional circular array antenna for the receiving of GPS signal which is used for the performance improvement by elimination of jammer signal. By performing the spatial filtering after the DoA estimation in array antenna, the quality of receiving signal can improve by the nulling of jammer signal from the undesired direction and the forming of beam from the desired direction. In this paper, the MUSIC and MinNorm algorithm used for DoA estimation were applied after fixing the angle and power of jammer signal in 4 element and 7 element circular array antenna. In order to performance analysis, the estimation result and estimation error were computed by computer simulation. As a result, the MUSIC and MinNorm were fairly good in azimuth and elevation angle estimation of DoA in case of good signal to noise ratio and the MUSIC has better performance compared to MinNorm in case of poor signal to noise ratio.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.33-40
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• 2011

This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.593-600
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• 2014

In this paper, Radar cross section (RCS) calculations of advanced naval vessels model with RCS reduction methods are simulated and RCS results are discussed. Especially, this paper are mainly focusing on the facts influencing on RCS, the ways minimizing RCS and material characteristics of RCS changing-rate. RCS analysis results are given for a DDG-1000 type advanced naval vessels, which show that as the elevation angle increased 10 degree, the mean RCS value increased 23.91 dBsm. Also, as the superstructure angle increased 6 degree, the mean RCS value reduced 1.27 dBsm. Finally, the radar absorbing material attachment at the front and back superstructure have been reduced 2.27 dBsm in terms of mean RCS value.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.53-58
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• 2017

This paper describes the design of a simulator for analyzing the accuracy of a correlative interferometer(CI) direction finder. CI direction finder is robust to noise, so it is often used in aircraft or ships where complex antenna installation is required, and the direction finding accuracy is very high. When the radio wave is incident at a specific azimuth angle, the phase difference calculated in a noiseless environment and the phase difference measured in a real environment with noise are fused to estimate the largest correlation coefficient as the azimuth angle of the radio wave. The simulator receives RF frequency, the number of antennas, the antenna coordinates, the transmission signal intensity, the bandwidth of the receiver, the gain and the payload effect, and calculates the direction finding accuracy of 0-360 degrees azimuth and 0-60 degree elevation with 0.5 degree. accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.471-481
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• 2013

Recently, the frequency of debris flow disaster has increased, which is one of the natural disasters during extremely heavy rainfall condition. This paper described the analysis method about deposition characteristics of debris flow using topographic characteristics of debris flow path. First, we observed topographic changes by differencing high- resolution LiDAR DEMs acquired before and after the occurrence of debris flow event. We assumed that deposition on outside of debris flow path was generated by movements due to the inertia of debris flows. Then, we analyzed three topographic characteristics of debris flow path: slope in flow direction, transition angle of flow path, and the net efficiency(L/H) of debris flows defined by the ratio of transport length(L) and elevation difference(H). We applied this method to Umyeon Mountain debris flow event in July 2011. The results showed that deposition on outside of debris flow path due to the inertia of debris flows was significantly related to the transition angle of debris flow path. Also, we figured out that there were more frequent such depositions in locations where the ratio of 'transition angle / (L/H)' is over 8.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.