• The Journal of Korean Academy of Prosthodontics
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• v.60 no.4
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• pp.395-403
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• 2022

In the fracture operation of pan facial fracture, there are many cases where the shape and position of the occlusal and oral structures are abnormally changed because the index for repositioning to the original position is insufficient. And trismus and gag reflex in patients with pan facial fractures increase the difficulty of dental treatment, and the difference in the position of the jaw bone makes it difficult to restore aesthetically and functionally. In this case, digital workflows for minimal intraoral work could be selected to reduce patient discomfort and the difficulty of dental treatment. This case is using a digital workflow from implant planning to final prosthesis production in a patient with acquired skeletal grade III, trismus, and gag reflex due to pan facial fracture 15 years ago. In this case report, the use of digital workflow in a patient who has difficulty in dental treatment was able to minimize patient discomfort and obtain esthetic and functionally appropriate results.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Analyses & Alternatives
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• v.6 no.2
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• pp.185-226
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• 2022

This study examines the crisis of trust of French journalism in the context of a global decline of media credibility. First of all, in the process of a huge social movement called the 'yellow vest' movement that started in 2018, distrust of the French journalism was expressed in an extreme form. This study examines some external factors in terms of the historical development of the French journalism and the public's long-standing 'criticism of journalism'. Specifically, this study first examines the quantitative indicators of trust of French journalism which were shown in Digital News Report published by the Reuters Institute for the Study of Journalism. Next, it examines the historical and institutional formation process of French journalism and public distrust that emerged along with it. And specifically, the structural crisis-economic crisis, digital transformation and intensification of competition, and deterioration of quality problems etc.-of the French journalism exposed in media coverage on social movement in 2018 is review in relation with the working process or 'routine' of actual news production. In conclusion, this study asserts that the various aspects of internal rifts in French journalism system, as well as external shocks (the influence of social movements), are a key factor in explaining the recent decline of trust in French journalism.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korean Geotechnical Society
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• v.25 no.4
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• pp.19-29
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• 2009

In this study, a numerical analysis was performed to predict the sequential behavior of anchored retaining wall where the failure accident took place, and verified accuracy of prediction through the comparisons between prediction and field measurement. The emphasis was given to the wall behaviors and the variation of sliding surface based on the two different methods of elasto-plastic and finite element (shear strength reduction technique). Through the comparison study, it is shown that the bending moment and the soil pressure at construction stages produce quite similar results in both the elasto-plastic and finite element method. However, predicted wall deflections using elasto-plastic method show underestimate results compared with measured deflections. This demonstrates that the elasto-plastic method does not clearly consider the influence of soil-wall-reinforcement interaction, so that the tension force (anchor force and earth pressure) on the wall is overestimated. Based on the results obtained, it is found that finite element method using shear strength reduction method can be effectively used to perform the back calculation analysis in the anchored retaining wall, whereas elasto-plastic method can be applicable to the preliminary design of retaining wall with suitable safety factor.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.795-806
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• 2017

In this paper, we propose a user experience evaluation method for virtual reality living space contents and apply it to prototype simulations to measure user experience. The user evaluation method proposed in this paper is based on the existing user evaluation methodology, but it is established by changing the experience and evaluation factors to match the characteristics of the virtual reality living space. The generated virtual reality living space simulation is configured similar to real space and is placed real objects that the user actually uses in each space, so that users can feel reality. The subjects performed the tasks according to the given scenarios and then evaluated the simulation. As a result of the evaluation, the main goal of the simulation, the items that make the users feel realistic, were lower than our expectations. This means that future simulations need to be modified in a way that adds or expands elements that enhance the realism. In this way, the UX evaluation method proposed in this paper is expected to provide a more objective indicator to the content developer or designer in order to grasp the user's demand in the virtual reality living space.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.79-89
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• 2023

The inventory of many materials requires a large storage space, and the longer the storage period, the higher the potential maintenance cost. When materials are stored on a construction site, there are also concerns about safety due to the reduction of room for movement and working. On the other hand, construction sites that do not store materials have insufficient inventory, making it difficult to respond to demands such as sudden design changes. Ordering materials is then subject to delays and extra costs. Although securing an appropriate amount of inventory is important, in many cases, material management on a construction site depends on the experience of the site manager, so a reasonable material inventory management plan that reflects the construction conditions of a site is required. This study proposes an economical material management method by reflecting variables such as the status of the preceding and following activities, site size, material delivery cost, timing of an order, and quantity of orders. To this end, we set the appropriate inventory amount while adjusting related activities in the activity network, using float time for each activity, the size of the yard, and the order quantity as the main variables, and applied a genetic algorithm to this process to suggest the optimal order timing and order quantity. The material delivery cost derived from the results is set as a fitness index and the efficiency of inventory management was verified through a case application.

• Journal of the Korean Geographical Society
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• v.40 no.1 s.106
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• pp.126-152
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• 2005

In recent years, there are some confusions related to the definition and existence of mountain ranges, which have been described in current geography text books. We contend that these confusions came from the lack of understanding on the geomorphological processes that form the mountain system in Korea. This research attempts to clarify the definition of mountain ranges and offer geological and geomorphological explanations about the formation of them. Based on the analyses of the social and cultural causes underlying the recent debates on the existence of mountain ranges, we tried to identify the relationships among the definition of mountain ranges, geological structure of Korea, and the forming processes of mountain ranges. The current and past mountain range maps were compared with geological structures, geological maps, surface curvature, and hill shade maps. The latter two maps were derived from a Digital Elevation Model of the Korean Peninsular. The results show that we are able to prove the existence of most mountain ranges, which provides a useful framework to understand the geological evolution of Korean peninsular and formation of mountainous landscape of Korea. In terms of their morphological continuity and genesis, however, we identified five different categories of mountain ranges: 1) Uplift mountain ranges(Hamkyeong Sanmaek, Nangrim Sanmaek, Taebaek Sanmaek), which were formed by the uplift processes of the Korean Peninsular during the Tertiary; 2) Falut mountain ranges(Macheonryeong Sanmaek, Sobaek Sanmaek, Buksubaek Sanmaek), whirh were directly related to the uplift processes of the Korean Peninsular during the Tertiary; 3) Trust mountain ranges(Jekyouryeong Sanmaek, Kwangju Sanmaek, Charyeong Sanmaek, Noryeong Sanmaek), which were formed by the intrusion of granite and consequent orogenic processes during the Mesozoic era; 4) Drainage divide type mountain ranges, which were formed by the erosion processes after the uplift of Korean Peninsular; 5) Cross-drainage basin type mountain ranges (Kangnam Sanmaek, Eunjin Sanmaek, Myelak sanmaek), which were also formed by the erosion processes, but the mountain ranges cross several drainage basins as connecting mountains laterally We believe that the current social confusions related to the existence of mountain ranges has partly been caused by the vague definition of mountain ranges and the diversity of the forming processes. In order to overcome theses confusions, it is necessary to characterize the types of them according the genesis, the purpose of usages and also the scale of maps which will explains the mountain systems. It is also necessary to provide appropriate educational materials to increase the general public's awareness and understanding of geomorphological processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.82-92
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• 2018

The purpose of this study is to develop a model for the density management of planting sites and an additional model for new planting sites. In the Dongtan New Town of Hwaseong, there are buffer green spaces, with widths between 8m and 15m, between roads and apartment complexes. A total 38 survey plots were set to examine the planting patterns and the density of landscape trees. The Crown Overlapping Index (COI) was developed to assess the level of overcrowding as far as tree growth and development effectively. Pinus strobus recorded the most serious level of overcrowding growth and development. Its average density and average COI were very high at $0.3trees/m^2$ and 35.6%, respectively. There were many areas in which its COI was above 45%. The criteria for density management were set by standardizing the COI into three levels, which were above 45% (Type A), 30~45% (Type B), and under 30% (Type C). A model was proposed to manage poorly growing trees and to develop a model to select and manage trees of similar specification based on the planting patterns. The trees of density management areas were reviewed in terms of tree types and the ease of transplanting to establish an application system for the management plans according to the possibility of transplanting, thinning, and pruning. In new buffer green spaces, the planting density of Pinus strobus was lowered to $0.20{\sim}0.25trees/m^2$, with that of shrubs being reduced to $1.5{\sim}2.0trees/m^2$, leading to a planting design model to cover the lower parts in at least 30~40%.