• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.7
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• pp.427-437
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• 2016

This study has the goal of analyzing the techniques and characteristics of urban development, after additionally constructing the high-speed railway in Japan's Kyushu district and building a new railway station to enable the existing traditional stations accommodate with the high-speed railway. Such analysis is made in order to draw the conclusion of its intended (designed) meaning and attributes and to further research on finding an applicable urban development method in the domestic railway station development. The object of this study includes examples of stations renewed within the five years when Shinkansen in the Kyushu district was extended or stations which are in process of development such as Hakata station, Kumamoto station, and Kagoshima-chuo station. From the analysis of this study, the strategies are as follows.; active connecting both geographical location and function of Station, re-establishment of relation with city center and Station, establishment of close linking system for both tourist spot development, methods of Shinkansen line construction and extension a development opposite site of railway, securing the living population from high density & Mixed use development of Station Building.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Smart Media Journal
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• v.12 no.8
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• pp.45-59
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• 2023

Users who want to experience the metaverse for tourism are exposed to strategic planning in space for the purpose of cultural content. In addition, users learn integrated cultural content in the process of proceeding according to the virtual environment. and Along with the meaning of time and space, users will experience space-telling. It is important to induce interest from the beginning of the experience to continue the experience. However, obstacles arise in this process. This is because developers should promote connections with new information to users who do not have sufficient prior knowledge and only have keywords of interest. Therefore, efficient design methods to enhance interest should be studied in advance. But so far, there has been no research on how to systematically design prior organizers to induce interest in virtual space. This study is an interest-inducing design method that occurs in the process of developing the meaning of virtual space and storytelling of cultural content, and can be seen as a basic study using conceptual guidance-based prior organizer education and learning techniques. First, virtual space elements and human behavior theories were considered. Subsequently, five representative examples of previous organizers currently used were explored, and redesigned and proposed based on a conceptual map for information access and delivery purposes. Through this research process, it was possible to confirm that spatial attributes and cognitive interest elements were effectively transmitted to meaningful learning leading to storytelling learning and elements of service design design method through conceptual guidance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.39-50
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• 2024

Color is an essential visual element that has a significant impact on the formation of a city's image and people's perceptions. Quantitative analysis of color in urban environments is a complex process that has been difficult to implement in the past. However, with recent rapid advances in Machine Learning, it has become possible to analyze city colors using photos shared by tourists. This study selected Dali City, a popular tourist destination in China, as a case study. Photos of Dali City shared by tourists were collected, and a method to measure large-scale city colors was explored by combining machine learning techniques. Specifically, the DeepLabv3+ model was first applied to perform a semantic segmentation of tourist sharing photos based on the ADE20k dataset, thereby separating artificial elements in the photos. Next, the K-means clustering algorithm was used to extract colors from the artificial elements in Dali City, and an adjacency matrix was constructed to analyze the correlations between the dominant colors. The research results indicate that the main color of the artificial elements in Dali City has the highest percentage of orange-grey. Furthermore, gray tones are often used in combination with other colors. The results indicated that local ethnic and Buddhist cultures influence the color characteristics of artificial elements in Dali City. This research provides a new method of color analysis, and the results not only help Dali City to shape an urban color image that meets the expectations of tourists but also provide reference materials for future urban color planning in Dali City.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.32-41
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• 2024

In the construction industry, not only safety accidents, but also various complex risks such as construction delays, cost increases, and environmental pollution occur, and management technologies are needed to solve them. Among them, process risk management, which directly affects the project, lacks related information compared to its importance. This study tried to develop a MATM tunnel process risk classification system to solve the difficulty of risk information retrieval due to the use of different classification systems for each project. Risk collection used existing literature review and experience mining techniques, and DB construction utilized the concept of natural language processing. For the structure of the classification system, the existing WBS structure was adopted in consideration of compatibility of data, and an RBS linked to the work species of the WBS was established. As a result of the research, a risk classification system was completed that easily identifies risks by work type and intuitively reveals risk characteristics and risk factors linked to risks. As a result of verifying the usability of the established classification system, it was found that the classification system was effective as risks and risk factors for each work type were easily identified by user input of keywords. Through this study, it is expected to contribute to preventing an increase in cost and construction period by identifying risks according to work types in advance when planning and designing NATM tunnels and establishing countermeasures suitable for those factors.

• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.310-323
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• 2024

The purpose of this study was to understand the vegetation structure and ecological characteristic of Bulgapsan(Mt.) Provincial Park by setting up and surveying 64 plots(100m²). The analysis using the TWINSPAN and DCA techniques found seven community groups: Pinus densiflora-Quercus variabilis community, P. densiflora-P. rigida-Q. serrata community, Q. variabilis-Carpinus tschonoskii community, Q. aliena-Q. variabilis-Cornus controversa community, Q. aliena-Platycarya strobilacea community, Broad-leaved miced community and Q. variabilis community. The result of vegetation community structure analysis showed that P. densiflora community and deciduous Quercus spp. community were in competition, and succession to Quercus spp. community was expected. In the case of other broad-leaved forests, the current status is expected to be maintained. But continuous monitoring is required in areas where Neolitsea sericea and Cephalotaxus appear, which grow naturally in warm temperate forest and southern temperate vegetation zone. Species diversity by communities are confirmed to be highest at 2.6654 in the actively competitive P. densiflora-P. rigida-Q. serrata community, and the lowest in the Deciduous broad-leaved forests community at 1.2548. The results of the tree rings and annual growth analysis showed that dominant trees had an average age of more than 37~87 years. Among them, N. sericea designated as a natural monument was 48~56 years old.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.4
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• pp.45-55
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• 2024

This study aims to distinguish between utilitarian walking and leisure walking activities and analyze the correlation between these types of walking and the walking environment. To measure the walking environment, we utilized Google Street View (GSV) and employed semantic segmentation deep learning techniques to quantitatively assess urban walking environment elements as perceived by pedestrians. A survey was conducted to measure utilitarian walking, leisure walking, and perceived walking environment satisfaction, collecting valid responses from 192 participants. Using the survey data, we visualized utilitarian walking, leisure walking, and perceived walking environment satisfaction, and analyzed the correlation between these variables and the walkability scores. The results indicated that leisure walking had a significant positive correlation with walkability (Pearson's r = 0.121, p-value = 0.012), while there was no significant correlation between utilitarian walking and walkability (Pearson's r = 0.093, p-value = 0.055). These findings suggest that people prioritize mobility efficiency over the walking environment for utilitarian walking, whereas the quality of the walking environment significantly influences the frequency of leisure walking. Based on these results, the study proposes specific strategies to improve the walking environment around residential areas to promote leisure walking. These strategies include creating vertical gardens or various forms of three-dimensional gardens on narrow walkways and improving sidewalk design. The findings of this study can contribute to promoting leisure walking by creating walk-friendly environments, ultimately enhancing urban sustainability and the quality of life for residents.

• Progress in Medical Physics
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• v.20 no.4
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• pp.269-276
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• 2009

Radiation treatment techniques using photon beam such as three-dimensional conformal radiation therapy (3D-CRT) as well as intensity modulated radiotherapy treatment (IMRT) demand accurate dose calculation in order to increase target coverage and spare healthy tissue. Both jaw collimator and multi-leaf collimators (MLCs) for photon beams have been used to achieve such goals. In the Pinnacle3 treatment planning system (TPS), which we are using in our clinics, a set of model parameters like jaw collimator transmission factor (JTF) and MLC transmission factor (MLCTF) are determined from the measured data because it is using a model-based photon dose algorithm. However, model parameters obtained by this auto-modeling process can be different from those by direct measurement, which can have a dosimetric effect on the dose distribution. In this paper we estimated JTF and MLCTF obtained by the auto-modeling process in the Pinnacle3 TPS. At first, we obtained JTF and MLCTF by direct measurement, which were the ratio of the output at the reference depth under the closed jaw collimator (MLCs for MLCTF) to that at the same depth with the field size $10{\times}10\;cm^2$ in the water phantom. And then JTF and MLCTF were also obtained by auto-modeling process. And we evaluated the dose difference through phantom and patient study in the 3D-CRT plan. For direct measurement, JTF was 0.001966 for 6 MV and 0.002971 for 10 MV, and MLCTF was 0.01657 for 6 MV and 0.01925 for 10 MV. On the other hand, for auto-modeling process, JTF was 0.001983 for 6 MV and 0.010431 for 10 MV, and MLCTF was 0.00188 for 6 MV and 0.00453 for 10 MV. JTF and MLCTF by direct measurement were very different from those by auto-modeling process and even more reasonable considering each beam quality of 6 MV and 10 MV. These different parameters affect the dose in the low-dose region. Since the wrong estimation of JTF and MLCTF can lead some dosimetric error, comparison of direct measurement and auto-modeling of JTF and MLCTF would be helpful during the beam commissioning.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.3
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• pp.51-63
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• 2011

The purpose of this study is to create a landscape image that considers the selection of techniques that can enhance landscape reproduction in streetscape evaluation using 3 dimensional simulations and to evaluate ways to verify similarities and the psychological changes on the part of users by season. In the comparison of technique, the Low(apply normal map) technique was selected for the natural representation of trees in a near and middle view and the Plane technique was selected for the distant view. As the result of the verification, all indicators of physical similarity were evaluated over 4.50 points and most indicators of psychological similarity were found to have no difference except for indicators of 'disordered orderly' and 'dirty - clean'. According to the results of analyzing the landscape simulation by season, images of 'bright', 'beautiful', and 'static', etc., were evaluated high for the spring streetscape. The images of 'open', 'refresh', and 'animate' appeared high in summer and images of 'warm' and 'dark' were found to be high in fall. On the other hand, all images were evaluated as low except for the 'orderly' image. In the preference of streetscape by season, summer and spring were highly preferred at 5.01 and 4.98 with winter as the lowest at 3.48. As the results of the analysis of preference factor, the spring streetscape was found to be a major influence in preference by 0.540 in 'aesthetics'. In the case of summer, 'order' was found to be high at 0.417 while influences in preference included 'variety' and 'aesthetics' in fall and 'variety', 'aesthetics', and 'order' in winter. A determination of suitable spatial planning using a comparative analysis of various city streets will be enabled through the methods of this study.