http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
반사 경계를 이용한 광선 추적법 기반 간접 조명 시뮬레이션
김성대(Seongdae Kim),김형석(Hyungseok Kim),김지인(Jeein Kim) 한국HCI학회 2013 한국HCI학회 학술대회 Vol.2013 No.1
본 논문은 전역 조명에서 가장 많은 계산 량을 요구하는 간접 조명 계산을 단순화 하는 방법을 제안하였다. 조명의 강도는 거리에 반비례 하는데, 이 특징을 이용하여 간접 조명의 유효 반사 경계 범위를 지정한다. 유효 반사 경계 범위에 벗어나는 요소들은 간접 조명 계산에서 제외되는 방법이다. 이 방법은 광선 추적 법 기반으로 하여 시점 종속적인 반사 경계를 구성하였고 계산 단순화를 위하여 반사광만 고려한다. 그리고 반사 경계 구성과 구성하지 않는 결과를 비교하였으며, 반사 경계 구성의 규모에 따른 결과도 비교하였다. This paper provides the simplified computation on indirect illumination that requies high computing power. As the intensity of illumination is inverse proportional to a distance between a surface and a light source, this utilizes that the inverse proportion for valid reflection boundary construction. in a case of out of reflection boundary, these elements(e.g. surfaces) will be not on indirect illumination. this paper uses view-independent reflection boundary construction based on ray tracing and considers reflection on indirect illumination for the simplification of computing. and it compares results with and without reflection boundary, and the extent to reflection boundary scales.
3차원 물체의 프로젝티브 재구성을 위한 카메라행렬의 추정
김수정(Sujung Kim),박재희(Jaehee Park),김성대(SeongDae Kim) 대한전자공학회 2006 대한전자공학회 학술대회 Vol.2006 No.11
본 논문에서는 다중시점 영상들로부터 3 차원 물체를 프로젝티브 재구성하기 위한 카메라행렬(Camera matrix, 혹은 투영행렬(Projection matrix))추정 기법을 제안한다. 제안기법은 영상선택 방법에 따른 추정결과의 오차를 통계적으로 분석하고, 시차간격, 대응점수 등을 이용하여 재사영오차를 근사하는 목적함수 도출과 이를 이용한 영상의 배열방법으로 구성된다. 이를 통하여 기존의 순차적 영상선택을 통한 카메라행렬 추정의 성능저하를 개선한다. 실험에서는 가상데이터를 이용한 기존 기법과 제안기법의 성능 비교 분석을 통하여 제안 기법의 우수함을 보인다.
Do Hee Kim,Woojae Choi,Younghye Ro,Leegon Hong,Seongdae Kim,Ilsu Yoon,Eunhui Choe,김단일 한국임상수의학회 2022 한국임상수의학회지 Vol.39 No.5
Postpartum diseases should be predicted to prevent productivity loss before calving especially in organic dairy farms. This study was aimed to inves- tigate the incidence of postpartum metabolic diseases in an organic dairy farm in Korea, to confirm the association between diseases and prepartum blood biochemical parameters, and to evaluate the accuracy of these parameters with a receiver operating characteristic (ROC) analysis for identifying vulnerable cows. Data were collected from 58 Holstein cows (16 primiparous and 42 multiparous) having calved for 2 years on an organic farm. During a transition period from 4 weeks prepartum to 4 weeks postpartum, blood biochemistry was performed through blood collection every 2 weeks with a physical examination. Thirty-one (53.4%) cows (9 primiparous and 22 multiparous) were diagnosed with at least one postpartum disease. Each incidence was 27.6% for subclinical ketosis, 22.4% for subclinical hypocalcemia, 12.1% for retained placenta, 10.3% for displaced abomasum and 5.2% for clinical ketosis. Between at least one disease and no disease, there were significant differences in the prepartum levels of parameters like body condition score (BCS), non-esterified fatty acid (NEFA), total bilirubin (T-bil), direct bilirubin (D-bil) and NEFA to total cholesterol (T-chol) ratio (p < 0.05). The ROC analysis of each of these prepartum parameters had the area under the curve (AUC) <0.7. However, the ROC analysis with logistic regression including all these parameters revealed a higher AUC (0.769), sensitivity (71.0%), and spec- ificity (77.8%). The ROC analysis with logistic regression including the prepartum BCS, NEFA, T-bil, D-bil, and NEFA to T-chol ratio can be used to identify cows that are vulnerable to postpartum diseases with moderate accuracy.
( Yoonjoo Kim ),( Jeongsuk Koh ),( Seongdae Woo ),( Songi Lee ),( Dahyun Kang ),( Dongil Park ),( Chaeuk Chung ),( Jeongeun Lee ) 대한결핵 및 호흡기학회 2021 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.129 No.-
Introduction Endocrine hormones such as thyroxine and estrogen are known to influence the tumor progression and response to treatment. Despite the importance of ICIs (immune checkpoint inhibitors) in the treatment of advanced NSCLC, few studies have explored the effects of endocrine hormones on NSCLC receiving ICI therapy. Biomarkers like PD-L1 expression in the tumor have been developed to predict the treatment response to ICI. However, only several markers have been clinically verified using standard measurement techniques. We investigated the effects of baseline endocrine hormones in advanced NSCLC on ICI treatment and discovered an easily measurable and useful predictor. Method 156 patients with advanced NSCLC who received PD-1/PD-L1 inhibitors, excluding those with positive driver mutations, were retrospectively analyzed. We collected their clinical information and baseline laboratory findings including endocrine hormones, cytokines, CBC parameters, blood chemistry panels from peripheral blood. We identified the relationship between endocrine hormones and clinical outcomes (OS, PFS, best response), liver metastasis, and other blood markers. Result Shorter PFS was independently associated with liver metastasis, higher cortisol, lower Hb, while shorter OS was associated with liver metastasis, lower T3, higher LDH, lower albumin. According to T3 level, a newly found predictor, patients were divided into two groups, and patients with low T3 levels exhibited shorter PFS, OS, and worse best response. And we confirmed a significant association between the low T3 level and liver metastasis, a negative predictive marker for the treatment response of ICI in NSCLC. Conclusion This study shows that the baseline T3 level, relatively underrated in its clinical importance, is associated with the prognosis and response to ICIs in advanced NSCLC. The specific mechanism is probably related to the decreased function of the liver and the systemic inflammation induced by the interaction with other biomarkers such as IL-6, ACTH, cortisol, C-peptide, Hb, LDH, albumin.
Reduction of Vibration for an Elastic Structure by means of a Relocation of Part
Giman Kim(김기만),Seongdae Choi(최성대) 한국기계가공학회 2020 한국기계가공학회지 Vol.19 No.7
This study deals with the passive control of the dynamic characteristics of a theoretical model which is a string with fixed ends and loaded by two point masses - a main mass (Mo) and a secondary mass (Ms). It has been controlled passively by means of a relocation of a secondary mass. A main mass placed on the string is considered as a vibrating receiver which be forced to vibrate by a vibrating source being positioned on the string. By analyzing the motion of a string, the equation of motion for a string was derived by using a method of variation of parameters. To define the optimal conditions for the vibration reduction, the governing equation, which denotes the dynamic response of a string was derived in the closed form and then evaluated numerically. The possibility of reduction of an amplitude and a power being transmitted to a main mass were found to depend on the location and the magnitude of a secondary mass as well as the range of a forcing frequency.
Deep Learning-based Classification of Respiratory Sounds and Its Clinical Value
( Yoonjoo Kim ),( Jeongsuk Koh ),( Seongdae Woo ),( Songi Lee ),( Dahyun Kang ),( Dongil Park ),( Jeongeun Lee ),( Sungsoo Jung ),( Chaeuk Chung ) 대한결핵 및 호흡기학회 2021 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.129 No.-
Introduction Auscultation of respiratory sound is a non-invasive and relatively simple diagnostic method that can be performed anytime, but the information provided is quite useful. Breath sound information markedly improves the accuracy of diagnosis and monitoring of disease status. Despite these advantages, auscultation has a major limitation, subjectivity. Since the interpretation of respiratory sounds requires significant expertise and clinical experience, doctors in training sometimes misidentify respiratory sounds. To overcome such a drawback, we developed an automated classification system of respiratory sounds. Method 2840 respiratory sounds were recorded by a digital stethoscope in a real clinical setting. Three pulmonologists classified them and 1918 sounds including normal sounds, crackles, wheezes, rhonchi were selected. We applied deep learning convolutional neural network (CNN) to identify the classified database. We developed the predictive model for respiratory sound classifcation combining pretrained image feature extractor of series, respiratory sound, and CNN classifer. To evaluate the accuracy of human auscultation ability and compare it with our predictive model, 70 participants were asked to listen to classified sounds and identify them. Result Deep learning-based classification model detected abnormal sounds with an accuracy of 86.5% and the AUC of 0.93. It further classifed abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. Meanwhile, the accuracy of human auscultation was different depending on the group; 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Conclusion This respiratory sound classification model using deep learning is expected to complement the limitation of inaccurate auscultation of clinicians and help the rapid diagnosis and appropriate treatment of respiratory diseases. In addition, this model will be useful to meet the current medical demands such as non-face-to-face care due to COVID-19 and telemedicine in hard-to-reach area.