• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5214-5218
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• 2011

In this paper, bandwidth improvement of a series-fed two dipole array(STDA) antenna applicable for mobile communication base station antennas is studied. The proposed STDA antenna consists of two strip dipole antennas with different lengths which are connected directly trough a coplanar stripline(CPS). By adjusting the spacing between the two dipoles and the length of the second dipole, the bandwidth of the STDA can be enhanced. In addition, an integrated balun composed of a short-circuited microstrip line and a slot line is utilized to minimize the area required for a feeding part, and a broadband impedance matching is obtained by adjusting the feeding point. Based on the proposed antenna structure, an STDA antenna covering the frequency band ranging from 1.75 GHz to 2.7 GHz, which includes almost all the existing mobile communication frequency bands, with more than 5 dBi gain is designed and fabricated on an FR4 substrate with dielectric constant of 4.4 and thickness of 1.6mm, and experimentally tested. The fabricated antenna shows impedance bandwidth of 49%(1.7-2.8 GHz) for VSWR<2, a gain higher than 5.5 dBi, and a front-back ratio better than 12 dB.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.175-183
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• 2015

The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we first collect various high-resolution (HR)-low-resolution (LR) text image pairs, and quantize the LR images, and extract HR-LR block pairs. Based on quantized LR blocks, the LR-HR block pairs are clustered into a pre-determined number of classes. For each class, an optimal 2D-FIR filter is computed, and it is stored into a dictionary with the corresponding LR block for indexing. At the synthesis stage, each quantized LR block in an input LR image is compared with every LR block in the dictionary, and the FIR filter of the best-matched LR block is selected. Finally, a HR block is synthesized with the chosen filter, and a final HR image is produced. Also, in order to cope with noisy environment, we generate multiple dictionaries according to noise level at the learning stage. So, the dictionary corresponding to the noise level of the input image is chosen, and a final HR image is produced using the selected dictionary. Experimental results show that the proposed algorithm outperforms the previous works for noisy images as well as noise-free images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.520-525
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• 2019

Mines are an important infrastructure for securing resources, but safety problems can arise in the course of operation. Recently, the mining process is very complicated due to the large scale and mechanization. Therefore, it is necessary to construct accurate geospatial information on mine for systematic and safe mine operation. The geospatial information construction using the existing total station has a disadvantage that a lot of work time is required because the target must be collimated and measured. In this study, the data of the mines were acquired with the total station and the 3D laser scanner, and the mine spatial information was constructed by using the shape based registration method. By using the static scanner data of some area applying the reference point surveying result of the total station, it was possible to construct the accurate result on the wide area acquired by the mobile scanner effectively. Also, the accuracy of the constructed geospatial information was evaluated and the deviation of mean 0.083m was shown. Point cloud products constructed through the research can contribute to the efficiency improvement of mine management by enabling quantitative analysis such as visualization of mine shape, distance, area and slope, and automation of drawing creation for cross section shape.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.265-278
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• 2019

Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.100-100
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• 2017

하천에 유입된 오염물질의 2차원 혼합거동은 하천 주흐름에 의한 이송현상과 유속 성분의 수심평균 값에 대한 공간적 편차로부터 야기되는 분산현상으로 설명 할 수 있다. 이는 3차원 이송확산 방정식으로부터 수심 적분된 2차원 이송-분산 방정식으로 수학적 유도가 가능하며, 수심방향으로 적분하는 과정에서 발생되는 농도의 분산항은 Taylor Dispersion 개념에 기초하여 종방향 및 횡방향의 2차원 분산계수로 표현된다. Fischer(1978)는 연직방향 유속분포로부터 2차원 분산계수를 추정하는 해석해를 수학적으로 유도하였으나, 실제 하천에서 정밀한 연직방향 유속분포를 계측하는 것은 많은 비용 및 노동력을 초래한다. 따라서 선행 연구자들은 2차원 혼합모형의 분산계수를 산정하고자 실험적 방법으로써 추적자실험을 수행하였다. 추적자실험은 추적자 물질을 수체에 주입한 후 농도의 변화를 관측함으로써 추적자물질이 하천에서 이송 및 분산되는 과정을 이해하는데 유용하다. 기존의 추적자실험은 고정된 위치에서 농도를 계측하여 시계열적인 농도의 변화를 관측한 후, 오염운 동결가정을 통해 종,횡방향 분산계수의 산정이 가능하지만, 오염물질 농도의 공간적 분포를 얻기에는 한계가 있다. 본 연구에서는 기존의 추적자실험법의 한계를 극복하고자 형광물질을 이용한 추적자실험을 수행함과 동시에 드론에 장착된 디지털카메라를 이용하여 항공영상을 취득 및 분석하여, 하천에 주입된 형광물질의 농도분포를 시공간적으로 추출하는 기법을 개발하고, 이를 바탕으로 오염물질의 2차원 혼합거동을 분석하였다. 본 실험은 한국건설기술연구원의 안동하천실험센터의 A3실험수로에서 수행되었으며, 실험수로는 평균 하폭 5 m, 평균 수심 0.44 m, 유량 $0.96m^3/s$의 실제 소규모 하천과 유사한 축척을 가지고 있다. 추적자물질은 Rhodamine WT 용액이 사용되었으며, 실험수로 내 설치된 15개의 형광광도계(YSI-600OMS)를 이용하여 농도를 측정하였다. 항공영상의 취득을 위해 이용된 드론은 DJI-Phantom 3 Professional 이며, 3840x2160의 해상도로 초당 30 frame의 동영상으로 취득되었다. 영상의 정합 및 좌표화를 위해 RTK-GPS를 이용하여 12개의 지상 기준점의 좌표를 취득한 후, 사영변환을 통해 영상좌표를 지상좌표로 변환하였다. 영상의 픽셀값을 농도장으로 변환하기 위해 각 RGB 밴드의 픽셀값을 통계적으로 분석하여 농도장으로 변환하였으며, 영상으로부터 얻은 농도장은 형광광도계에 의해 실측된 농도와 결정계수 0.9이상의 수준으로 정확도를 나타냈다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1105-1114
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• 2022

A study on a method to overcome the limitations of the topographical and hydrological observation environment for estimating the QPE with high consistency with the ground rainfall by utilizing the spatiotemporal observation advantages of the rainfall radar for use in flood forecasting, and quantitative observations of localized rainfall due to these limiting conditions Uncertainty should be identified in terms of flood analysis. Against this background, in this study, 22 major heavy rain events in 2016 were analyzed for each of Mt. Biseul (BSL), Mt. Sobaek (SBS), Mt. Gari (GRS), Mt. Mohu (MHS), and Mt. Seodae (SDS) to determine the observation distance and altitude. The uncertainty of observation was quantified and an error map was derived. As a result of the analysis, it was found that, on average, the rainfall radar exceeded 10% up to 100 km and 30% over 150 km. Based on the average radar operating altitude angle, it was found that the error for the altitude was approximately 10% or less up to the second altitude angle, 20% at the third or higher altitude angle, and more than 50% at the fourth altitude angle or higher.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.