• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.3
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• pp.427-438
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• 2022

With the recent growing importance over safety management the need for advanced and technical approaches for on-site safety inspection methods has increased. Railway construction is subject to its own particular set of temporal and spatial challenges due to its unique facilities and equipment. This study aimed to investigate the field characteristics of railway infrastructure and improve the conventional field safety management methods by identifying the most appropriate features of AR technology. Group interviews and surveys were conducted with field safety experts to derive the major problems and inspection needs. Subsequently, various features of AR, such as BIM model projection, and remote conferencing, were investigated to determine their applicability to address safety issues. As a result, four problems in the current safety management process, such as 'lack of time due to the conventional inspection method and inspection of areas that are difficult to access', and three major inspection types, such as 'observance of work procedures, status of installation, adequate dimensional spacing', were identified to be improved when adopting AR based techniques. Furthermore, AR technology utilizing plans to solve safety inspection problems and effectively manage major inspection types were proposed, and a follow up survey was conducted with the same field safety experts to derive the priority of technology development.

• Journal of Ecology and Environment
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• v.46 no.3
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• pp.259-272
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• 2022

Background: The Korean Peninsula exhibits a characteristic graded floral distribution, with northern (Manchurian flora) and southern (China-Japan-Korea flora) lineage species coexisting according to climatic and topographical characteristics. However, this distribution has been altered by climate change. To identify ecosystem changes caused by climate change and develop appropriate measures, the current ecological status of the entire Korean Peninsula should first be determined; however, analysis of the current floral distribution in North Korea has been hampered for political reasons. To overcome these limitations, this study constructed a database of floral distributions in both South and North Korea by integrating spatial information from the previously established National Ecological Survey in South Korea and geocoding data from the literature on biological distributions published in North Korea. It was then applied to analyze the current status and distribution characteristics of Manchurian and China-Japan-Korea plant species on the Korean Peninsula. Results: In total, 45,877 cases were included in the Manchurian and China-Japan-Korea floral distribution database. China-Japan-Korea species were densely distributed on Jeju-do and along the southern coast of the Korean Peninsula. The distribution density decreased as the latitude increased, and the distributions reached higher-latitude regions in the coastal areas compared with the inland regions. Manchurian species were distributed throughout North Korea, while they were densely distributed in the refugia formed in the high-elevation mountain regions and the Baekdudaegan in South Korea. In the current distribution of biomes classified according to the Whittaker method, subtropical and endemic species were densely distributed in temperate seasonal forest and woodland/shrubland biomes, whereas boreal species were densely distributed in the boreal forest biome Korean Peninsula, with a characteristic gradation of certain species distributed in the temperate seasonal forest biome. Factor analysis showed that temperature and latitude were the main factors influencing the distribution of flora on the Korean Peninsula. Conclusions: The findings reported herein on the current floral distribution trends across the entire Korean Peninsula will prove valuable got mitigating the ecological disturbances caused by ongoing climate change. Additionally, the gathered flora data will serve as a basis for various follow-up studies on climate change.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.31-41
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• 2023

Directly converting the focal depth and image size of computer-generated-hologram (CGH), which is obtained by calculating the interference pattern of light from the 3D image, is known to be quite difficult because of the less similarity between the CGH and the original image. This paper proposes a method for separately converting the each of focal length of the given CGH, which is composed of multi-depth images. Firstly, the proposed technique converts the 3D image reproduced from the CGH into a Light-Field (LF) image composed of a set of 2D images observed from various angles, and the positions of the moving objects for each observed views are checked using an object detection algorithm YOLOv5 (You-Only-Look-Once-version-5). After that, by adjusting the positions of objects, the depth-transformed LF image and CGH are generated. Numerical simulations and experimental results show that the proposed technique can change the focal length within a range of about 3 cm without significant loss of the image quality when applied to the image which have original depth of 10 cm, with a spatial light modulator which has a pixel size of 3.6 ㎛ and a resolution of 3840⨯2160.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1352-1358
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• 2009

As the demand for a convergence between construction technologies and IT is on the rise, as seen in the visualization of U-City construction, studies on the ways in which IT in should be utilized in the building and construction field have been continuously and actively performed. However, there has been almost no development of standardized technology criteria relating to the life cycle of a building (planning, design, construction, and maintenance). That is, there are almost no examples of efforts made to combine construction technology and IT in a fundamental way, considering the environment, the facility, its spatial characteristics, engineering, materials, and structure, aspects that are commonly required not only for interior spaces but also for exterior construction of U-City. Despite the fact that a state-of-the-art infrastructure has been built, and the competency of users with the cutting-edge technology, composite studies on technologies, facilities, services and spaces are still lacking, and basic research on the composite operation method including compatibility and linkage between facilities and services within a U-City has been insufficient as well. It is generally known that by fusing IT with construction technologies, the total period of construction taken can be reduced and construction expenses can be curtailed, while construction quality can be improved. For this reason, it is vital to prepare a standardized base to connect cutting-edge IT with the construction technologies. In preparing such a base, the most urgent issue is to develop standardized technology criteria. The ultimate objective of this research is to establish the technological criteria system required to apply construction-IT fused technologies to U-Cities, and to develop the technological criteria for the design, construction and maintenance of the U-Cities. This paper, whose objective is to establish development strategies for construction-IT fused technologies by way of analyzing the criteria for conventional construction projects, the necessity of criteria for construction-IT fused technologies, and the current status of U-Cities' development, is the underlying research for this purpose. The strategies established are expected to be utilized in establishing the system of criteria for construction-IT fused technologies, and to contribute to a knowledge base in the construction-IT field. In addition, based on the strategies established, criteria for construction-IT fused technologies, such as design criteria and construction standards, will be developed, and by applying these criteria and standards, the ultimate objectives of U-Cities, which are the enhancement of urban competitiveness and the satisfaction of residents, will be attained

• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.11-22
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• 2023

This study was established to provide basic information necessary for ecological management to restore the naturalness of black locust (Robinia pseudoacacia L.) plantations located in the mountains of Gyeongbuk, Korea. Using vegetation data collected from 200 black locust stands, vegetation types were classified using the TWINSPAN method, the spatial arrangement status according to the environmental gradient was identified through DCA analysis, and a synoptic table of communities was prepared based on the diagnostic species determined by determining community fidelity (Φ) for each vegetation type. The vegetation types were classified into seven types, namely, Quercus mongolica-Polygonatum odoratum var. pluriflorum type, Castanea crenata-Smilax china type, Clematis apiifolia-Lonicera japonica type, Rosa multiflora-Artemisia indica type, Quercus variabilis-Lindera glauca type, Ulmus parvifolia-Celtis sinensis type, and Prunus padus-Celastrus flagellaris type. These types usually reflected differences in complex factors such as altitude, moisture regime, successional stage, and disturbance regime. The mean relative importance value of the constituent species was highest for black locust(39.7), but oaks such as Quercus variabilis, Q. serrata, Q. mongolica, Q. acutissima, and Q. aliena were also identified as important constituent species with high relative importance values, indicating their potential for successional trends. In addition, the total percent cover of constituent species by vegetation type, life form composition, species diversity index, and indicator species were compared.