• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.65-71
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• 2019

With regard to current Neck CT, Bismuth shielding boards are often being used to reduce exposure to superficial organs such as the thyroid. However, beam hardening often occurs near superficial organs with Bismuth shielding boards and variations in CT Number, Noise, and Uniformity values occur severely. This study looked into the usefulness of shielding boards made from aluminum and silicone that can be easily obtained and have good machinability by comparing them to the existing Bismuth shielding board. An Aluminum 7.3mm and a Silicone 21.5mm were made with shielding ratios similar to that of the Bismuth(0.06 mmPb). TLD (TLD-100) was placed on the thyroid area of the Phantom (RS-108T) and 5 doses were measured for each. To compare image quality, CT Number and Noise variations in axial images of the thyroid area in Neck CT images were compared. Also, variations in CT Number, Noise, and Uniformity were measured in the AAPM phantom images and compared. In the results, when thyroid doses for each shielding board were compared, the Bismuth shielding board showed a 14% reduction, the Silicone 21.5mm showed a 15% reduction, and the Aluminum 7.3mm showed a 13% reduction compared to the Non-Shield. Statistically, there were no significant differences in comparison with the Bismuth shielding board. In CT Number variations of thyroid area images, variations were largest for the Bismuth shielding board. With Uniformity evaluations of the AAPM phantom, the Bismuth shielding board was found unsuitable and the Aluminum 7.3mm and Silicone 21.5mm satisfied the acceptance criteria. Research results show that the Aluminum 7.3mm and Silicone 21.5mm have a similar shielding ratio to the high-priced Bismuth shielding board that is currently being used clinically and in comparison tests of CT Number attenuation coefficient variations, Noise, and Uniformity which are phantom image evaluation items, they proved to be better than Bismuth shielding boards. If various shielding boards are made using aluminum and silicone, sized appropriately for superficial organs, it would be useful in decreasing patient doses.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of Life Science
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• v.33 no.2
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• pp.169-175
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• 2023

As a protective defensive mechanism against ultraviolet (UV) light exposure in skin tissue, melanocytes produce the pigment melanin. Tyrosinase plays a key role in melanin production in melanocytes. However, the overproduction of melanin can lead to lesions, such as freckles and dark spots. Thus, it is clinically important to find a modulating molecule to control melanogenesis by regulating tyrosinase expression and/or activity. It is known that catechin, a plant flavonoid, can reduce melano- genesis through the downregulation of tyrosinase expression. Here, we tested whether catechin derivatives isolated from the stem bark of Ulmus parvifolia have an effect on melanin production by regulating tyrosinase in mouse melanoma cells and in vitro mushroom tyrosinase. The catechin derivatives used in this study included C5A, C7A, C7G, and C7X. Treatments using these catechin derivatives reduced melanin production in mouse melanoma B16F10 cells in which melanogenesis was stimulated by α-MSH. Notably, the anti-melanogenic effects of catechin derivatives were similar to those of kojic acid, a well-known anti-melanogenic molecule. Both C5A and C7A directly inhibited the activity of tyrosinase isolated from mushrooms in vitro. Furthermore, our in silico computational simulation showed that these two compounds were expected to bind to the active site of tyrosinase, which is similar to kojic acid. In addition, all four catechin derivatives reduced tyrosinase protein expression. In summary, our results showed that catechin derivatives can reduce melanogenesis by regulating tyrosinase activity or expression. Thus, this study suggests that catechin derivatives isolated from U. parvifolia can be novel modulators of melanin production.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.82-108
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• 2024

With the transition of archaeological recording method's transition from analog to digital, the 3D scanning technology has been actively adopted within the field. Research on the digital archaeological digital data gathered from 3D scanning and photogrammetry is continuously being conducted. However, due to cost and manpower issues, most buried cultural heritage organizations are hesitating to adopt such digital technology. This paper aims to present a digital recording method of relics utilizing open-source software and photogrammetry technology, which is believed to be the most efficient method among 3D scanning methods. The digital recording process of relics consists of three stages: acquiring a 3D model, creating a joining map with the edited 3D model, and creating an digital drawing. In order to enhance the accessibility, this method only utilizes open-source software throughout the entire process. The results of this study confirms that in terms of quantitative evaluation, the deviation of numerical measurement between the actual artifact and the 3D model was minimal. In addition, the results of quantitative quality analysis from the open-source software and the commercial software showed high similarity. However, the data processing time was overwhelmingly fast for commercial software, which is believed to be a result of high computational speed from the improved algorithm. In qualitative evaluation, some differences in mesh and texture quality occurred. In the 3D model generated by opensource software, following problems occurred: noise on the mesh surface, harsh surface of the mesh, and difficulty in confirming the production marks of relics and the expression of patterns. However, some of the open source software did generate the quality comparable to that of commercial software in quantitative and qualitative evaluations. Open-source software for editing 3D models was able to not only post-process, match, and merge the 3D model, but also scale adjustment, join surface production, and render image necessary for the actual measurement of relics. The final completed drawing was tracked by the CAD program, which is also an open-source software. In archaeological research, photogrammetry is very applicable to various processes, including excavation, writing reports, and research on numerical data from 3D models. With the breakthrough development of computer vision, the types of open-source software have been diversified and the performance has significantly improved. With the high accessibility to such digital technology, the acquisition of 3D model data in archaeology will be used as basic data for preservation and active research of cultural heritage.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.12
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• pp.2068-2075
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• 2013

The purpose of this study is to investigate the perception of university students on nutritional information according to food and nutrition labeling systems. A total of 310 customers, who visited family restaurant, were surveyed by a self-recorded questionnaire from March 2006 to April 2011. A total of 286 respondents were surveyed; of the respondents, 108 were males and 178 were females. Two surveys were conducted on the perception of the respondent's health: once in 2006 and once in 2011. According to these surveys, 63.6% and 54% of respondents perceived themselves as unhealthy, respectively. When ordering a meal, respondents were more concerned with price rather than taste, nutrition, new menu items, and food presentation. Compared with 2006, in 2011 more respondents felt that family restaurants provided enough nutritional information and practical use of that information to their customers. When surveyed, respondents felt that the total calories played a significantly higher role in ordering food than foods with higher nutritional values. There was a significant increase in satisfaction with the current nutrition labeling system; in 2006, $2.87{\pm}0.99$, and 2011, $3.35{\pm}0.84$. There was also a significant increase in individuals who felt that there was a need for an ingredient labeling system; $3.68{\pm}0.9$ in 2006 and $4.32{\pm}0.61$ in 2011. There was also a higher demand for nutritional information; $2.85{\pm}0.66$ in 2006, $3.06{\pm}0.65$ in 2011. From these results, it was concluded that the nutrition labeling system adopted by family restaurants did not affect the degree of customers' interest in nutritional information. Contrast to the results, the amount and frequency of nutritional information provided to customers have increased continuously since 2006. Therefore, the nutrition labeling system and recommended dietary allowance should be expanded in order to promote a healthy diet.

• Korean journal of food and cookery science
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• v.31 no.4
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• pp.497-504
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• 2015

The purpose of this study was to compare body image and dietary behavior in middle and high school girls in the Gyeongbuk area in September, 2014. Data were collected from a total of 194 middle school and 170 high school girls through a self-reported questionnaire. A total of 364 completed questionnaires were collected and used for the final analysis. The mean body mass index (BMI) of respondents was normal at 21.29. Generally, high school girls had greater height, weight and BMI than middle school girls. Height (p<0.001) and weight (p<0.001) were significantly different, while BMI was not. The ratio of students who perceived their body size as 'Fat' was significantly (p<0.05) higher in high school (43.9%) than in middle school (31.6%). The ratio of dissatisfaction with their current body image was significantly (p<0.001) higher in high school girls (64.1%) than in middle school girls (44.0%). Among respondents who perceived their body size as 'Fat', many high school girls actually (53.3%) had normal or low body weight and this was significantly (p<0.001) higher than in middle school girls (39.3%). Experience with weight control was higher in high school girls (67.3%) than in middle school girls (60.6%), but there was no significant difference. Regarding the weight control methods, respondents selected 'combination diet and exercise' (22.2%), 'diet control' (20.9%), 'exercise' (18.7%), and 'reduce snacks and midnight snack' (17.4%). 15 items under obesity-related dietary behavior were measured with 5-point scales and lower scores indicated obesity diet behavior. The mean score for all respondents was 3.19/5.00, and high school girls (3.06) scored significantly (p<0.001) higher than middle school girls (3.33). Our study suggests that the development of effective nutrition and health education for diet control is crucial for adolescent girls. This study will enable educators to plan more effective strategies to improve the dietary knowledge of adolescent girls.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.