• KIPS Transactions on Computer and Communication Systems
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• v.12 no.12
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• pp.363-370
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• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.2
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• pp.90-103
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• 2009

The poem 'Kyungjusipiyung(慶州十二詠)' written by Seo, Geo-jeong(徐居正) describes sentiments felt for the ruined historical and cultural landscape of Silla's capital city, Kyungju. It differs from the existing 'Eight Sceneries(八景)' as it conveys the strong metaphorical aesthetics of ruins as the episodes and figures are sung, as well as the myths and stories related to the representative holy places of the Silla culture: Gyelim(鷄林), Banwolseong(半月城), Najeong(蘿井), Oneung(五陵), Geumosan(金鰲山), the scenic beauty of deep placeness, Poseokjeong(鮑石亭), Mooncheon(蚊川), Cheomseongdae(瞻星臺), Boonhwangsa(芬皇寺), Youngmyosa(靈妙寺) and Grave of the General Kim Yu-Sin(金庾信墓). Compared with the former "Eight Sceneries" Poems, including Seo Geojeong's 'Kyungjusipiyung', there is a difference in the content of theme recitation, as well as in structure and form, especially with the deep impression of the classical features of the meanings and acts. The sequence of theme recitation seems to be composed of more than two visual corridors visited during trips that last longer than two days. The dominant emotions expresses in this poem, through written in the spring, are regret and sadness such as 'worn', 'broken and ruined', 'old and sad', without touching on the beauty of nature and the taste for life that is found in most of the Eight Sceneries Poems. Thus, the feelings of the reciter himself, Seo, Geo-jeong, about the described sceneries and their symbolism are more greatly emphasized than the beauty of form. The characteristic aspect of his experiences of ruins expressed from 'Kyungjusipiyung' is that the experiences were, first of all, qualitative of the aura conveyed; that is, the quality omnipresent throughout the culture of Silla as reflected in the twelve historical and cultural landscapes. In this poem, the cultural ruins of the invisible dimension such as the myths and legends are described by repetition, parallelism, juxtaposition, reflection and admiration from the antiphrases, as well as the civilized ruins of the visible dimension such as the various sceneries and features of Kyungju. This seems to be characteristic of the methods by which Seo, Geo-jeong appreciates 'Silla' in the poem 'Kyungjusipiyung'. Ruins as an Aesthetic Object imply the noble pride of Seo, Geo-jeong in identifying himself with the great nature of ruins. In 'Kyungjusipiyung', the images of the ruins of Silla and Kyungju are interspersed in spite of his positive recognition of 'the village of Kyungju' based on his records. However, though the concept of ruins has a pessimistic tone connoting the road of extinction and downfall, the aspect here seems to ambivalently contain the desire to recover and revive Kyungju through the Chosun Dynasty as adominant influence on the earlier Chosun's literary tide. The aesthetics of the scenery found in Seo, Geo-jeong's 'Kyungjusipiyung' contain the strongest of metaphor and symbolism by converting the experiences of the paradoxical ruins into the value of reflective experiences.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.383-392
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• 2007

Karstic features and mining-related cavities not only lead to severe restrictions in land utilizations, but also constitute serious concern about geohazard and groundwater contamination. A microgravity survey was applied for detecting, mapping and monitoring karstic cavities in the test site at Muan prepared by KIGAM. The gravity data were collected using an AutoGrav CG-3 gravimeter at about 800 stations by 5 m interval along paddy paths. The density distribution beneath the profiles was drawn by two dimensional inversion based on the minimum support stabilizing functional, which generated better focused images of density discontinuities. We also imaged three dimensional density distribution by growing body inversion with solution from Euler deconvolution as a priori information. The density image showed that the cavities were dissolved, enlarged and connected into a cavity network system, which was supported by drill hole logs. A time-lapse microgravity was executed on the road in the test site for monitoring the change of the subsurface density distribution before and after grouting. The data were adjusted for reducing the effects due to the different condition of each survey, and inverted to density distributions. They show the change of density structure during the lapsed time, which implies the effects of grouting. This case history at the Muan test site showed that the microgravity with accuracy and precision of ${\mu}Gal$ is an effective and practical tool for detecting, mapping and monitoring the subsurface cavities.

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.275-291
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• 2023

Global warming has made the polar regions more accessible, leading to increased demand for the construction of new resource-development plants in oil-rich permafrost regions. The selection of locations of resource-development plants in permafrost regions should consider the surface displacement resulting from thawing and freezing of the active layer of permafrost. However, few studies have considered surface displacement in the selection of optimal locations of resource-development plants in permafrost region. In this study, Analytic Hierarchy Process (AHP) analysis using a range of geospatial information variables was performed to select optimal locations for the construction of oil-sands development plants in the permafrost region of southern Athabasca, Alberta, Canada, including consideration of surface displacement. The surface displacement velocity was estimated by applying the Small BAseline Subset Interferometric Synthetic Aperture Radar technique to time-series Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar images acquired from February 2007 to March 2011. ERA5 reanalysis data were used to generate geospatial data for air temperature, surface temperature, and soil temperature averaged for the period 2000~2010. Geospatial data for roads and railways provided by Statistics Canada and land cover maps distributed by the North American Commission for Environmental Cooperation were also used in the AHP analysis. The suitability of sites analyzed using land cover, surface displacement, and road accessibility as the three most important geospatial factors was validated using the locations of oil-sand plants built since 2010. The sensitivity of surface displacement to the determination of location suitability was found to be very high. We confirm that surface displacement should be considered in the selection of optimal locations for the construction of new resource-development plants in permafrost regions.