• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Journal of the Korean earth science society
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• v.28 no.6
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• pp.655-672
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• 2007

The purpose of this study was to investigate information sources and types of reasoning that non-astronomy major undergraduate students used to build their mental models on the mechanism of the Moon craters formation. In-depth interview was used to collect qualitative data, and the questions for the interview were developed through an analytical induction method. We interviewed four students individually by using Seidman's interview step. The findings revealed that the participants built nonscientific mental models, and yet they held a consistent explanatory framework. The students explained that the crater was made by the fall of a meteorite. They all suggested a similar shape of meteorite even though their drawings about the shape of craters and its related to variables were different from one another. The information sources that the participants used fur their explanatory frameworks were varied, i.e., daily experiences, subject knowledges, and intuition. In addition, they used causal reasoning, intuitional reasoning, knowledge based reasoning, and analogical reasoning.

• 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 Geosynthetics Society
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• v.22 no.4
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• pp.1-8
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• 2023

Space exploration is at the forefront of human scientific endeavors, and planetary exploration rovers play a critical role in studying planetary surfaces. Rover performance is especially vital for safely navigating steep terrain and delicate landscapes found on planets like Mars and the Moon. This paper offers a comprehensive overview of a landing testbed designed to simulate challenging extraterrestrial terrain and loose regolith. The paper briefly outlines lunar crater region topographical features and highlights the importance of these simulations in rover testing. It then explores previous landing testbed developments and describes the design process for a landing testbed to be installed in the dirty thermal vacuum chamber at the Korea Institute of Civil Engineering and Building Technology. Once realized, this proposed landing testbed will enable precise evaluations of rover mobility and exploration capabilities under lunar-like conditions, including high vacuum and extreme temperatures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.741-749
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• 2022

Major space agencies are planning for the rover-based lunar exploration since water-ice was detected in permanently shadowed regions (PSR). Although sunlight does not directly reach the PSRs, it is expected that reflected sunlight sustains a certain level of low-light environment. In this research, the indoor testbed was made to simulate the PSR's lighting and topological conditions, to which low light enhancement methods (CLAHE, Dehaze, RetinexNet, GLADNet) were applied to restore image brightness and color as well as to investigate their influences on the performance of feature extraction and matching methods (SIFT, SURF, ORB, AKAZE). The experiment results show that GLADNet and Dehaze images in order significantly improve image brightness and color. However, the performance of the feature extraction and matching methods were improved by Dehaze and GLADNet images in order, especially for ORB and AKAZE. Thus, in the lunar exploration, Dehaze is appropriate for building 3D topographic map whereas GLADNet is adequate for geological investigation.

• Journal of the Mineralogical Society of Korea
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• v.29 no.3
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• pp.131-139
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• 2016

Detailed knowledge on maskelynite, a glassy phase of plagioclase found in shocked meteorites and impact craters, is essential to understand a shock metamorphism. Here, we explore an inhomogeneous shock metamorphism in the lunar meteorite Mount DeWitt (DEW) 12007 with an aim to understand the formation mechanism of maskelynite. Most plagioclase grains in the DEW 12007 partially amorphized into maskelynite with a unidirectional orientation. Back-scattered electron (BSE) images of maskelynite show a remnant of planar deformation fracture possibly indicating that the maskelynite would be formed by solid-state transformation(i.e., diaplectic glass). Plagioclase with flow texture is also observed along the rim of maskelynite, which would be a result of recrystallization of melted plagioclase. Results of Raman experiments suggest that shock pressure for plagioclase and maskelynite in the DEW 12007 is approximately 5-32 GPa and 26-45 GPa, respectively. The difference in shock pressures between plagioclase and maskelynite can be originated from 1) external factors such as inhomogeneous shock pressure and/or 2) internal factors such as chemical composition and porosity of rock. Unfortunately, Raman spectroscopy has a limitation in revealing the detailed atomic structure of maskelynite such as development of six- or five-coordinated aluminum atom upon various shock pressure. Further studies using nuclear magnetic resonance spectroscopy are necessary to understand the formation mechanism of maskelynite under high pressure.