• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.372-372
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• 2019

본 논문에서는 격자 기반의 2차원 침수해석 모형인 G2D(Grid based 2-Dimensional land surface flood model)의 GUI 개발에 대해서 기술하였다. G2D 모형은 ASCII 래스터 포맷의 DEM을 이용하여 정형 사각격자로 구성되는 침수모의 도메인을 설정하고, 수위, 수심, 유량 등의 경계조건과 강우와 유량을 연속방정식의 생성항으로 사용하여 2차원 침수모의를 한다. 주요한 침수모의 결과는 ASCII 래스터 포맷을 가지는 수심과 수위 등이다. 이와 같이 G2D 모형은 ASCII 래스터 파일을 주로 이용하고 있다. 본 연구에서는 우선 래스터 파일의 전후처리와 침수모의 결과의 가시화에 대한 편의성을 높이기 위해서 GIS 소프트웨어를 이용하여 GUI를 개발하고자 하였다. 이와 더불어 사용자들이 소프트웨어 구매 비용에 대한 부담을 없애고, 편리하게 사용할 수 있는 오픈소스 소프트웨어를 이용하고자 하였으며, 이 두 가지 조건을 만족할 수 있는 QGIS를 이용해서 G2D 모형의 GUI인 QGIS-G2D를 개발하였다. QGIS-G2D는 QGIS의 plug-in으로 실행된다. QGIS-G2D는 G2D 모형의 실행에 필요한 프로젝트 파일(.g2p)을 GUI를 이용해서 만들 수 있으며, 모의결과를 애니매이션 등으로 가시화 할 수 있는 후처리 기능을 포함하고 있다. 또한 QGIS-G2D는 DEM 수정 기능과 같이 G2D 모형의 입력자료 전처리를 위해서 QGIS plug-in으로 제공되는 여러 가지 기능을 함께 이용할 수 있다. 또한 물리적 분포형 강우-유출 모형인 GRM(Grid based Rainfall-runoff Model)의 QGIS plug-in인 QGIS-GRM과 연계하여, 유역 유출모의와 침수모의를 QGIS 환경에서 함께 수행할 수도 있다. 개발된 소프트웨어는 오픈소스 플랫폼인 GitHub(https://github.com/floodmodel/)를 통해서 제공된다. 본 연구를 통해서 홍수해석에 필요한 강우-유출 모의와 침수모의를 위한 모형을 제공하고, 이를 편리하게 활용할 수 있는 오픈소스 소프트웨어를 제공할 수 있었다. 이러한 연구들은 홍수 분야의 전문가들에 의해서 다양한 분야의 홍수해석에 사용될 수 있을 것으로 기대한다.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.99-99
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• 2019

한국생명공학연구원 해외생물소재센터는 해외생물소재 확보 및 활용연구를 통하여 차세대 국가핵심전략 BT산업의 필수원자재인 생물소재의 범지구적 확보와 보존 관리의 임무를 수행하고 있다. 이를 위해 권역별 해외생물소재 공동연구센터(중국, 코스타리카, 인도네시아, 베트남) 및 37개국과의 국제협력 네트워크를 구축하고 있다. 세계적 수준의 생물소재 국가 인프라 구축을 목표로 국내 산 학 연 연구자들에게 다양한 해외생물소재 공급을 통한 지속 가능한 바이오 경제 기반 구축을 지원하고 있다. 해외생물소재센터에서는 2006년부터 특히 천연물 의약품 개발 분야에 많이 사용 되고 있는 해외 식물소재를 지속적으로 확보하고 있으며, 확보된 식물소재의 추출물 제조 및 추출물 은행을 구축, 이에 대한 생물활성 평가를 실시하며 연구자들에게 기초 자료로 제공하고 있다. 현재까지 총 36,500종의 해외식물소재를 확보하였으며, 추출물 약 320만점을 확보하였다. 확보된 소재의 분양활동을 통해 산 학 연 연구자들에게 180만여 점을 분양하였으며, 이를 활용한 다수의 논문과 특허를 획득하였고 중대형 기술이전을 실시하였다. 해외생물소재센터는 홈페이지 기반 분양 신청 시스템[https://www.ibmrc.re.kr]을 통하여 해외식물소재(Powder 와 Extact 형태) 분양서비스를 제공하고 있다. 또한 소재가치제고 연구를 통한 산업화 지원과 해외생물소재의 표본정보 서비스, 해외거점센터를 활용한 현지정보 제공 서비스를 지원하고 있다.

• Women's Health Nursing
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• v.27 no.1
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• pp.40-48
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• 2021

Purpose: This study compared maternal age at childbirth, the number of live births, and the weight of live births between Korean women and immigrant women using statistical data from the Republic of Korea for the period of 2008-2018. Methods: The analysis was conducted using data from the Microdata Integrated Service of Statistics Korea (https://mdis.kostat.go.kr/index.do). Results: Korean women and immigrant women showed a higher age at childbirth in 2018 than in 2008. The percentage of newborns of Korean women with a birth weight of less than 2.5 kg increased slightly for 3 consecutive years from 2016 to 2018, whereas for immigrant women, this percentage increased in 2017 compared to 2016 and then decreased again in 2018. Very low birth weight (less than 1.5 kg) became more common among immigrant women from 2016 to 2018. Birth at a gestational age of fewer than 37 weeks increased both among Korean and immigrant women from 2016 to 2018. In both groups, the percentage of women who had their first child within their first 2 years of marriage decreased from 2008 to 2018. Conclusion: Immigrant women had higher birth rates than Korean women, while both groups showed an increasing trend in preterm birth. Greater attention should be paid to the pregnancy and birth needs of immigrant women, and steps are needed to ensure health equity and access in order to prevent preterm births. It is also necessary to identify factors that affect preterm birth and birth of very low birth weight infants among immigrant women in the future.

• Journal of Radiation Protection and Research
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• v.46 no.1
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• pp.1-7
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• 2021

Background: The use of computed tomography (CT) device has increased in the past few decades in Japan. Dose optimization is strongly required in pediatric CT examinations, since there is concern that an unreasonably excessive medical radiation exposure might increase the risk of brain cancer and leukemia. To accelerate the process of dose optimization, continual assessment of the dose levels in actual hospitals and medical facilities is necessary. This study presents organ dose estimation using pediatric cerebral CT scans in the Kyushu region, Japan in 2012 and the web-based calculator, WAZA-ARI (https://waza-ari.nirs.qst.go.jp). Materials and Methods: We collected actual patient information and CT scan parameters from hospitals and medical facilities with more than 200 beds that perform pediatric CT in the Kyushu region, Japan through a questionnaire survey. To estimate the actual organ dose (brain dose, bone marrow dose, thyroid dose, lens dose), we divided the pediatric population into five age groups (0, 1, 5, 10, 15) based on body size, and inputted CT scan parameters into WAZA-ARI. Results and Discussion: Organ doses for each age group were obtained using WAZA-ARI. The brain dose, thyroid dose, and lens dose were the highest in the Age 0 group among the age groups, and the bone marrow and thyroid doses tended to decrease with increasing age groups. All organ doses showed differences among facilities, and this tendency was remarkable in the young group, especially in the Age 0 group. This study confirmed a difference of more than 10-fold in organ doses depending on the facility and CT scan parameters, even when the same CT device was used in the same age group. Conclusion: This study indicated that organ doses varied widely by age group, and also suggested that CT scan parameters are not optimized for children in some hospitals and medical facilities.

• Journal of International Academy of Physical Therapy Research
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• v.12 no.1
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• pp.2261-2266
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• 2021

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of 'Download All Images' and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.137-148
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• 2021

Purpose: This study aimed to assess the computer monitors used for analysis and interpretation of digital radiographs within the clinics of the Oral Health Centre of Western Australia. Materials and Methods: In total, 135 computer monitors(3 brands, 6 models) were assessed by analysing the same radiographic image of a combined 13-step aluminium step wedge and the Artinis CDDent 1.0^® (Artinis Medical Systems B.V.^®, Elst, the Netherlands) test object. The number of steps and cylindrical objects observed on each monitor was recorded along with the monitor's make, model, position relative to the researcher's eye level, and proximity to the nearest window. The number of window panels blocked by blinds, the outside weather conditions, and the number of ceiling lights over the surgical suite/cubicle were also recorded. MedCalc^® version 19.2.1 (MedCalc Software Ltd^®, Ostend, Belgium, https://www.medcalc.org; 2020) was used for statistical analyses(Kruskal-Wallis test and stepwise regression analysis). The level of significance was set at P<0.05. Results: Stepwise regression analysis showed that only the monitor brand and proximity of the monitor to a window had a significant impact on the monitor's performance (P<0.05). The Kruskal-Wallis test showed significant differences (P<0.05) in monitor performance for all variables investigated, except for the weather and the clinic in which the monitors were placed. Conclusion: The vast performance variation present between computer monitors implies the need for a review of monitor selection, calibration, and viewing conditions.

• Journal of Korean Dental Hygiene Science
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• v.4 no.1
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• pp.1-9
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• 2021

The purpose of the present study was to investigate the relationship between obesity and dental caries in children and adolescents. This study was analyzed by searching the following words in Google Scholar (https://scholar.google.co.kr), Kiss (kiss.kstudy.com), KCI (www.kci.go.kr), and RISS (riss.kr): "pediatric," "juvenile," "obesity," and "dental caries." A total of 19 Korean and 10 foreign studies out of 107 studies were selected after excluding the studies based on the exclusion criteria. When evaluating the relationship between childhood and adolescent obesity and dental caries, 16 out of 29 articles (55.2%) indicated a significant relationship, and 2 (6.9%) indicated different results based on sex, while 13 articles (44.8%) showed no significant relationship between obesity and dental caries. Among the significant studies, a total of 10 (34.5%) showed that the number of dental caries increased according to an increase in obesity, i.e., from normal to overweight to obese. A total of 5 studies (17.2%) reported that the number of dental caries increased in underweight individuals compared to those of normal weight, or decreased according to an increase in obesity, while 1 study (3.4%) indicated that the number of dental caries increased in both the underweight and obese groups compared to the normal weight group. Therefore, studies on the relationship between dental caries and the degree of obesity in children and adolescents have not shown a certain trend.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.709-725
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• 2021

The study focused on the design features of Chanel and Fendi, directed by Carl Lagerfeld, creative director of Chanel and Fendi until his recent death. The range of the study was from the 2017 S/S Collection to the 2019 F/W Collection, which collected a total of 767 fashion photographs, including 483 Chanel, 284 Fendi, with tops, bottoms and dresses at VOGUE (https://www.vogue.com). According to the data analysis criteria organized based on prior research and related literature, it was classified in the order of form, color, material, pattern, decoration, fashion image, item and coordination, and content analysis was conducted based on statistical analysis. Overall, the design characteristics of the Chanel collection, directed by Karl Lagerfeld, were rectangle form, tone in tone coloring, combination of identical materials, geometric patterns, and classical images as the main design characteristics of the Chanel collection. The design characteristics shown in the Fendi collection directed by Karl Lagerfeld were rectangle form, tone in tone coloration, hard material combination, abstract pattern, and total coordination. Comparing the design features of Chanel and Fendi, directed by Karl Lagerfeld, is as follows. Chanel and Fendi's designs show a lot of rectangle form, tone-in-tone colors, hard-materials and combination of the same material.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.