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RISS 인기검색어
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- 저자 : Dang Lien Minh (검색결과 5 건)
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Le Thi Thuy Lien,Nguyen Khoi Viet,Hoang Van Hoa,Phung Bao Ngoc,Nguyen Ngoc Trang,Vu Thi Kim Thoa,Nguyen Cong Tien,Phan Anh Phuong,Pham Minh Thong,Vu Dang Luu 아시아심장혈관영상의학회 2022 Cardiovascular Imaging Asia Vol.6 No.2
Objective: To compare left ventricular (LV) function, ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), and regional wall motion analyzed in 256-slice dualsource coronary CT angiography (DSCT) with 2D transthoracic echocardiography (TTE). Materials and Methods: One hundred twelve patients suspected of coronary artery disease underwent DSCT and 2D-TTE within one week for LVEF, EDV, and ESV. The correlation between DSCT and 2D-TTE measurements was analyzed through linear regression and Bland- Altman analysis. Regional wall motion was visually scored with a 3-point scale (1, normal; 2, hypokinesia; 3, dysphagia, akinesia). Results: Average LVEF at 66.45%±1.27% (range 23%–85%) as determined on DSCT compared with 66.09%±1.01% (range 25%–84%) on 2D-TTE. LVEF exhibited a good correlation between DSCT and 2D-TTE (r=0.715; p<0.001). Good correlations between DSCT and 2D-TTE were demonstrated for LVEDV (r=0.732; p<0.001) and LVESV (r=0.841; p<0.001). Mean differences (±SD) of 1.78±24.10 mL (p<0.05) and 0.77±13.70 mL (p<0.05) were observed between DSCT and 2D-TTE for LVEDV and LVESV, respectively. LVEF was slightly overestimated with DSCT (0.52%±9.59%; p<0.05). Although the LVEF values calculated by DSCT and 2D-TTE were similar, EDV and ESV from DSCT were statistically higher than those from 2D-TTE (p<0.05). Agreement between DSCT and 2D-TTE in regional wall motion was 96.4%, κ=0.840. Conclusion: DSCT can provide comparable results to those using 2D-TTE for LV function (EF, EDV, and ESV) and regional wall motion assessment in a heterogeneous population.
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CNN을 이용한 딥러닝 기반 하수관 손상 탐지 분류 시스템
Syed Ibrahim Hassan,Dang Lien Minh,임수현(Su-hyeon Im),민경복(Kyung-bok Min),남준영(Jun-young Nam),문현준(Hyeon-joon Moon) 한국정보통신학회 2018 한국정보통신학회논문지 Vol.22 No.3
연구는 인공지능 분야의 딥러닝 기술을 기반으로 한 하수관 손상의 자동 탐지 분류 시스템을 제안한다. 성능의 최적화를 위하여 DB 획득 시 발생된 조도 및 그림자 변화와 같은 다양한 환경변화에 강인한 시스템을 구현하였다. 제안된 시스템에서는 Convolutional Neural Network(CNN) 기반의 균열 탐지 및 손상 분류 기법을 구현하였다. 최적의 결과를 위하여 256 x 256 픽셀 해상도의 CCTV 영상 9,941개를 이용하여 CNN모델을 적용하여 손상부위에 대한 딥러닝을 수행하였고 그 결과 98.76 %의 인식률을 획득하였다. 기계학습을 통한 딥러닝 모델을 기반으로 다양한 환경의 하수도 DB에서 720 x 480 픽셀 해상도의 646개의 이미지를 추출하여 성능 평가를 수행 하였다. 본 시스템은 다양한 환경에서 구축된 하수관 데이터베이스 에서 손상 유형의 자동 탐지 및 분류에 최적화된 인식률을 제시한다. We propose an automatic detection and classification system of sewer damage database based on artificial intelligence and deep learning. In order to optimize the performance, we implemented a robust system against various environmental variations such as illumination and shadow changes. In our proposed system, a crack detection and damage classification method using a deep learning based Convolutional Neural Network (CNN) is implemented. For optimal results, 9,941 CCTV images with 256 x 256 pixel resolution were used for machine learning on the damaged area based on the CNN model. As a result, the recognition rate of 98.76% was obtained. Total of 646 images of 720 x 480 pixel resolution were extracted from various sewage DB for performance evaluation. Proposed system presents the optimal recognition rate for the automatic detection and classification of damage in the sewer DB constructed in various environments.
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무인기로 촬영한 무 재배지 영상의 정규식생지수(NDVI)를 활용한 병충해 분석 연구
임수현(Su-Hyeon Im),Syed Ibrahim Hassan,Lien Minh Dang,민경복(Kyung-Bok Min),문현준(Hyeonjoon Moon) 대한전기학회 2018 전기학회논문지 Vol.67 No.10
This paper compares and analyzes Fusarium wilt of radish by using an unmanned aerial vehicle(UAV) with the NDVI-7 camera. The UAV have taken near-infrared images of the Radish field in Gangwon area, which is affected by Fusarium wilt. Based on those images, we analyzed NDVI(Normalized difference vegetation index) and compared conditions of radish by using the Blue value among Regular Vegetation Index in NDVI. First, the radish field is divided into three fields for radish, soil and vinyl. Each field has separate Blue values that are radish 0.4890, soil 0.2959, vinyl -0.0605 respectively. Second, radish condition levels are divided into four stages which are normal, early, middle, and late stage of Fusarium wilt. The average values of each stage are normal 0.5165(100%), early 0.4565(88%), middle 0.3444(66%), and late 0.1772(34%) respectively. This result shows that this NDVI value is validated by measuring conditions of Radish and soil.
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Phung Bao Ngoc,Nguyen Khoi Viet,Hoang Van Hoa,Nguyen Ngoc Trang,Le Thi Thuy Lien,Pham Minh Thong,Vu Dang Luu 아시아심장혈관영상의학회 2022 Cardiovascular Imaging Asia Vol.6 No.2
Left pulmonary artery sling (LPAS) is a rare congenital anomaly in which the left pulmonary artery (LPA) originates from the posterior aspect of the right pulmonary artery and courses between the trachea and esophagus to reach the left lung. This anomaly causes distal tracheal and/or right main stem bronchus compression. Most LPAS cases are associated with early symptom onset, around 2 month-old, and have severe respiratory distress within the first year of life. There are two major types of LPAS based on the location of LPA and abnormal bronchial branching. The diagnosis can be made by using various imaging modalities. Herein, we present the imaging characteristics on multidetector computed tomography of 5 LPAS cases with respiratory distress (2 months to 12 months).
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Han Thi Vo,Tien Duc Dao,Tuyen Van Duong,Tan Thanh Nguyen,Binh Nhu Do,Tinh Xuan Do,Khue Minh Pham,Vinh Hai Vu,Linh Van Pham,Lien Thi Hong Nguyen,Lan Thi Huong Le,Hoang Cong Nguyen,Nga Hoang Dang,Trung 질병관리청 2024 Osong Public Health and Research Persptectives Vol.15 No.1
Objectives: The incidence of posttraumatic stress disorder (PTSD) has increased, particularly among individuals who have recovered from coronavirus disease 2019 (COVID-19) infection. Health literacy is considered a “social vaccine” that helps people respond effectively to the pandemic. We aimed to investigate the association between long COVID-19 and PTSD, and to examine the modifying role of health literacy in this association.Methods: A cross-sectional study was conducted at 18 hospitals and health centers in Vietnam from December 2021 to October 2022. We recruited 4,463 individuals who had recovered from COVID-19 infection for at least 4 weeks. Participants provided information about their sociodemographics, clinical parameters, health-related behaviors, health literacy (using the 12-item short-form health literacy scale), long COVID-19 symptoms and PTSD (Impact Event Scale-Revised score of 33 or higher). Logistic regression models were used to examine associations and interactions.Results: Out of the study sample, 55.9% had long COVID-19 symptoms, and 49.6% had PTSD. Individuals with long COVID-19 symptoms had a higher likelihood of PTSD (odds ratio [OR], 1.68; 95% confidence interval [CI], 1.63–2.12; p<0.001). Higher health literacy was associated with a lower likelihood of PTSD (OR, 0.98; 95% CI, 0.97–0.99; p=0.001). Compared to those with long COVID-19 symptoms and the lowest health literacy score, those with long COVID-19 symptoms and a 1-point health literacy increment had a 3% lower likelihood of PTSD (OR, 0.97; 95% CI, 0.96–0.99; p=0.001).Conclusion: Health literacy was found to be a protective factor against PTSD and modified the negative impact of long COVID-19 symptoms on PTSD.
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