• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.49-63
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• 2021

The exploration of the solar system is carried out through various payloads, and accordingly, many research results are emerging. We tried to apply deep-learning as a method of studying the bodies of solar system. Unlike Earth observation satellite data, the data of solar system differ greatly from celestial bodies to probes and to payloads of each probe. Therefore, it may be difficult to apply it to various data with the deep-learning model, but we expect that it will be able to reduce human errors or compensate for missing parts. We have implemented a model that detects craters on the lunar surface. A model was created using the Lunar Reconnaissance Orbiter Camera (LROC) image and the provided shapefile as input values, and applied to the lunar surface image. Although the result was not satisfactory, it will be applied to the image of the permanently shadow regions of the Moon, which is finally acquired by ShadowCam through image pre-processing and model modification. In addition, by attempting to apply it to Ceres and Mercury, which have similar the lunar surface, it is intended to suggest that deep-learning is another method for the study of the solar system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.123-130
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• 2006

The purpose of the paper is to make the three dimensional road simulation model based on the digital photogrammetry and GIS techniques in the middle of Halla mountain of Jeju island. To do this, we generate DEM (digital elevation model) and digital ortho image using GIS tools such as Arc View, Imagestation and MGE module. In GIS, the overlay map schemes combining of the hill shade, the land slope and aspect were applied. Based on the processes, we can build the best three dimensional road line along the hill side of the island. From the results, we also found that the derived DEM from digital ortho image and the GIS technique were very useful for choosing the best three dimensional road design before the real road construction works in Jeju island.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.5
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• pp.645-652
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• 2010

The aim of this investigation was study on the non-evaporation diesel spray characteristics injected through a common-rail diesel injector under various ambient pressure. The diesel spray was investigated with observation of macroscopic characteristics such as spray tip penetration and spray cone angle by the shadowgraph and the image processing method. The numerical study was conducted using a computational fluid dynamics code, AVL-FIRE. The breakup models used were WAVE model and standard $k-{\varepsilon}$ turbulence model was applied. The numerical study used input data which spray cone angle and fuel injection rate was achieved by Zeuch's method. Comparison with experimental result such as spray tip penetration was good agreement. Distribution of droplet diameter were conducted on four planes where the axial distances were 5, 15, 39 and 49mm respectively downstream from the orifice exit.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.97-104
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• 1999

Digital elevation models(DEMs) represent an important data base for orthophoto generation The quality of a DEM depends on the geometrical accuracy of the original point or line data. This study analyzes the effects of grid space and scanning resolution in DEM creation with image matching method. The less standard deviation of DEM error was introduced when we adopted small grid space, but no effects in scanning resolution. Based on the bias error analysis of the DEM, we found that the error of a large scale of aerial photograph was bigger than that of a small scale case, and that such error mainly came from the closed area in large scale photographs. In order to reduce the closed area, the experiment has been conducted using multi scale and different overlap of aerial photo images. The result shows that the size of closed area and the shaded area has been dramatically decreased due to the adoption of multi scale aerial images instead of a couple of stereo images.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.49-55
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• 2008

The face extraction is very important to provide the images of the roads and road sides without the problem of privacy. For face extraction form roadside images, we detected the skin color area by using HSI and YCrCb color models. Efficient skin color detection was achieved by using these two models. We used a connectivity and intensity difference for grouping, skin color regions further we applied shape conditions (rate, area, number and oval condition) and determined face candidate regions. We applied thresholds to region, and determined the region as the face if black part was over 5% of the whole regions. As the result of the experiment 28 faces has been extracted among 38 faces had problem of privacy. The reasons which the face was not extracted were the effect of shadow of the face, and the background objects. Also objects with the color similar to the face were falsely extracted. For improvement, we need to adjust the threshold.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.291-298
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• 2020

This study proposed a model that can track MPP faster than the existing MPPT algorithm using the particle swarm optimization algorithm (PSO). The proposed model highly sets the acceleration constants of gbest and pbest in the PSO algorithm to quickly track the MPP point and eliminates the power instability problem. In addition, this algorithm was re-executed by detecting the change in power of the solar panel according to the rapid change in solar radiation. As a result of the experiment, MPP time was 0.03 seconds and power was 131.65 for 691.5 W/m2, and MPP was tracked at higher power and speed than the existing P&O and INC algorithms. The proposed model can be applied when a change in the amount of power is detected by partial shading in a Photovoltaic power plant with Photovoltaic connected in parallel. In order to improve the MPPT algorithm, this study needs a comparative study on optimization algorithms such as moth flame optimization (MFO) and whale optimization algorithm (WOA).

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.61-74
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• 2016

The purpose of this paper is to analyze the relationship between the location of the epicenter of a medium-sized earthquake(magnitude 4.8) that occurred on January 20, 2007 in the Odaesan area with lineament features using a shaded relief map(1/25,000 scale) and satellite images from LANDSAT-8 and KOMPSAT-2. Previous studies have analyzed lineament features in tectonic settings primarily by examining two-dimensional satellite images and shaded relief maps. These methods, however, limit the application of the visual interpretation of relief features long considered as the major component of lineament extraction. To overcome some existing limitations of two-dimensional images, this study examined three-dimensional images, produced from a Digital Elevation Model and drainage network map, for lineament extraction. This approach reduces mapping errors introduced by visual interpretation. In addition, spline interpolation was conducted to produce density maps of lineament frequency, intersection, and length required to estimate the density of lineament at the epicenter of the earthquake. An algorithm was developed to compute the Value of the Relative Density(VRD) representing the relative density of lineament from the map. The VRD is the lineament density of each map grid divided by the maximum density value from the map. As such, it is a quantified value that indicates the concentration level of the lineament density across the area impacted by the earthquake. Using this algorithm, the VRD calculated at the earthquake epicenter using the lineament's frequency, intersection, and length density maps ranged from approximately 0.60(min) to 0.90(max). However, because there were differences in mapped images such as those for solar altitude and azimuth, the mean of VRD was used rather than those categorized by the images. The results show that the average frequency of VRD was approximately 0.85, which was 21% higher than the intersection and length of VRD, demonstrating the close relationship that exists between lineament and the epicenter. Therefore, it is concluded that the density map analysis described in this study, based on lineament extraction, is valid and can be used as a primary data analysis tool for earthquake research in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.587-593
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• 2019

The field of geospatial information is rapidly changing due to the development of sensor and data processing technology that can acquire location information. And demand is increasing in various related industries and social activities. The construction and utilization of three dimensional geospatial information that is easy to understand and easy to understand can be an essential element to improve the quality and reliability of related services. In recent years, 3D laser scanners are widely used as 3D geospatial information construction technology. However, 3D laser scanners may cause shadow areas where data acquisition is not possible when objects are large in size or complex in shape. In this study, 3D model of an object has been created by acquiring oblique images using an unmanned aerial vehicle and processing the data. The study area was selected, oblique images were acquired using an unmanned aerial vehicle, and point cloud type 3D model with 0.02 m spacing was created through data processing. The accuracy of the 3D model was 0.19m and the average was 0.11m. In the future, if accuracy is evaluated according to shooting and data processing methods, and 3D model construction and accuracy evaluation and analysis according to camera types are performed, the accuracy of the 3D model will be improved. In the point cloud type 3D model, Cross section generation, drawing of objects, and so on, it is possible to improve work efficiency of spatial information service and related work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.151-159
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• 2011

Due to the Korean government's renewable energy support policy such as the renewable energy utilization building certificate and enlarging the compulsory ratio of investment on the public building, the rooftop photovoltaic projects are expanding rapidly. It is very important for the rooftop photovoltaic projects to analyze the shading effect of the adjacent structures or own facilities. But, the photovoltaic arrangements are planned by the experience of the designers or simple graphic tools. The purpose of this study is to build the process model for optimizing of rooftop photovoltaic arrangement based on three-dimensional geo-spatial information.