• Journal of the Korean earth science society
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• v.40 no.5
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• pp.464-475
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• 2019

We measured night sky brightness (NSB) over the downtown using a Digital Single Lens Reflex (DSLR) camera combined to a small telescope for educational purpose, considering that most secondary schools are located in urban areas and have limitation in the equipment for astronomical observation. Raw format images from DSLR camera are not affected by various camera settings except for the ISO, and the typical photometric uncertainty including filter transformation is about 0.1 mag. Near the zenith, the NSB of the B, V, and r-band is 17.5, 17.1, and $16.9mag\;arcsec^{-2}$, respectively. The approximate limiting magnitude is derived to be 17.5 mag at B-band and 17 mag at V, r-band. A large scale artificial light close to the observation site is the dominant cause for making observing condition worse, increasing the NSB by $0.6mag\;arcseec^{-2}$ regardless of the altitude and filter.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.467-478
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• 2020

Hypervisor virtualize hardware resources to utilize them more effectively. At the same time, hypervisor's characteristics of time and space partitioning improves reliability of flight software by reducing a complexity of the flight software. Korea Aerospace Research Institute chooses one of hypervisors for space, XtratuM, and examine its applicability to the flight software. XtratuM has strong points in performance improvement with high reliability. However, it does not support SMP. Therefore, it has limitation in using it with high performance applications including satellite altitude orbit control systems. This paper proposes RTEMS XM-SMP to support SMP with RTEMS, one of real time operating systems for space. Several components are added as hypercalls, and initialization processes are modified to use several processors with inter processors communication routines. In addition, all components related to processors are updated including context switch and interrupts. The effectiveness of the developed RTEMS XM-SMP is demonstrated with a GR740 board by executing SMP benchmark functions. Performance improvements are reviewed to check the effectiveness of SMP operations.

• Journal of the Korean Institute of Gas
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• v.22 no.2
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• pp.59-66
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• 2018

Using hydrogen fuel is expected to be suitable as a reciprocating internal combustion engine with heightened interest in HALE(High Altitude Long Endurance) UAV(Unmanned Aerial Vehicle). Hydrogen is hightest energy density per mass so it can continue to charge for long periods of time and have positive part of the environmental effects. However, it is estimated that there is less research on hydrogen fuel engine currently applied, and many studies need to be done. Depending on the operation, there are factors that result in supercooling due to reduced radiation or reduce cooling performance due to low air density. Therefore, the experiment was to change the temperature of the cooling water and investigate the effect on engine combustions. The limitation of the stable operation range due to backfire is dominated by the excess air ratio rather than the effect of the cooling water temperature change. When the cooling water temperature increases, the volumetric efficiency decreases and the torque decreases. As the cooling water temperature decreases, the heat loss was increased and consequently the thermal efficiency was decreased.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.554-560
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• 2017

Accurate altimetry is required for the reliable flight control of drones or unmanned air vehicles (UAVs), and the radar altimeter is commonly used owing to its accuracy for the ground level. Due to the limitation for size, weight and power consumption, the frequency modulated continuous wave (FMCW) radar is appropriate for drone because it has lower complexity than that of pulse Doppler (PD) radar. Especially, fast-ramp FMCW radar, which transmits linear FM signal during very short period, is generally utilized, because it is robust for the ego-motion of drone. Therefore, we present the design and implementation results of the radar signal processor (RSP) for fast-ramp FMCW radar system. The proposed RSP was designed with Verilog-HDL and implemented with Altera Cyclone-IV FPGA device. Implementation results show that the proposed RSP includes 27,523 logic elements, 15,798 registers and memory of 138Kbits and can measure the altimeter at the rate of 100Hz with the operating frequency of 50MHz.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.182-194
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• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.90-100
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• 2017

The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.609-625
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• 2022

Validation of satellite data products is critical for subsequent analysis that is based on the data. Particularly, performance of ocean color products in turbid and shallow near-land ocean areas has been questioned for long time for its difficulty that stems from the complex optical environment with varying distribution of water constituents. Furthermore, validation with ship-based or station-based measurements has also exhibited clear limitation in its spatial scale that is not compatible with that of satellite data. This study firstly performed validation of major GOCI-II products such as remote sensing reflectance, chlorophyll-a concentration, suspended particulate matter, and colored dissolved organic matter, using the in-situ measurements collected from ship-based field campaign. Secondly, this study also presents preliminary analysis on the use of drone images for product validation. Multispectral images were acquired from a MicaSense RedEdge camera onboard a UAV to compensate for the significant scale difference between the ship-based measurements and the satellite data. Variation of water radiance in terms of camera altitude was analyzed for future application of drone images for validation. Validation conducted with a limited number of samples showed that GOCI-II remote sensing reflectance at 555 nm is overestimated more than 30%, and chlorophyll-a and colored dissolved organic matter products exhibited little correlation with in-situ measurements. Suspended particulate matter showed moderate correlation with in-situ measurements (R²~0.6), with approximately 20% uncertainty.