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원태연,박용진 한국정보과학회 1982 한국정보과학회 학술발표논문집 Vol.9 No.2
3次元上의 多角形의 回轉은 Vector로서 分析可能하다. 그리고 回轉體의 潛線 (Hidden Line)의 제거는 그 回轉體를 多角形으로 分解 XZ平面上에 정사형을 이용해 판별가능하며 ROTATION TABLE을 이용해 계속 계산할 필요 없이 解決可能함을 보이고 있다. This paper explains the rotating objects in 3 dimensions with using Vector Theory, Polar Coordinate and Time, and describe. the Hidden Line Elimination Method of rotating objects in 3 dimensions as handling their projection and generating the Rotation Table-use to search the hidden line-on angle calculated in XZ Plane. Thus this solves the Hidden Line Elimination Problem of rotating objects in 3 dimensions as like 2 dimensions and shows the relationships between Velocity of object to Time and Distance changed to Time.
Particulate Matter Estimation from Public Weather Data and Closed-Circuit Television Images
원태연,어양담,성홍기,정규수,윤준희,이경욱 대한토목학회 2022 KSCE Journal of Civil Engineering Vol.26 No.2
This article proposes a method of estimating the concentrations of particulate matter (PM2.5 and PM10) using public data, including road-traffic closed-circuit television (CCTV) images, Smart Seoul City data sensor environment information, and Korea Meteorological Administrationdata. The region-of-interest images and full scenes derived from CCTV footage were used as the basis for the deep learning model, which combines a convolutional neural network and long short-term memory, to establish the particulate matter (PM) concentration prediction methodology. In the experiment, the prediction accuracies corresponding to various types of mean values were calculated by training the model with various mean measurement values for the surface PM2.5 and PM10 concentrations, as well as the corresponding CCTV images and weather data at different time points. In the experiments performed under relatively stable PM concentrations, R2 generally exceeded 0.9 and tended to increase with an increasing range of mean concentration values. In particular, in sections with rapid changes in the PM concentration within an hourly interval, higher R2 values were obtained by the model trained with the average PM concentrations of the time series before and after image capture, outperforming the method that used prior mean observation values and better reflecting the current PM concentration.
학습패치 크기와 ConvNeXt 적용이 CycleGAN 기반 위성영상 모의 정확도에 미치는 영향
원태연,조수민,어양담 한국측량학회 2022 한국측량학회지 Vol.40 No.3
A method of restoring the occluded area was proposed by referring to images taken with the same types of sensors on high-resolution optical satellite images through deep learning. For the natural continuity of the simulated image with the occlusion region and the surrounding image while maintaining the pixel distribution of the original image as much as possible in the patch segmentation image, CycleGAN (Cycle Generative Adversarial Network) method with ConvNeXt block applied was used to analyze three experimental regions. In addition, We compared the experimental results of a training patch size of 512*512 pixels and a 1024*1024 pixel size that was doubled. As a result of experimenting with three regions with different characteristics,the ConvNeXt CycleGAN methodology showed an improved R² value compared to the existing CycleGAN-applied image and histogram matching image. For the experiment by patch size used for training, an R² value of about 0.98 was generated for a patch of 1024*1024 pixels. Furthermore, As a result of comparing the pixel distribution for each image band, the simulation result trained with a large patch size showed a more similar histogram distribution to the original image. Therefore, by using ConvNeXt CycleGAN, which is more advanced than the image applied with the existing CycleGAN method and the histogram-matching image, it is possible to derive simulation results similar to the original image and perform a successful simulation. 본 연구에서는 딥러닝을 통해 고해상도 광학 위성영상에 동종센서로 촬영한 영상을 참조하여 폐색 영역을 복원하는 방법을 제안하였다. 패치 단위로 분할된 영상에서 원본 영상의 화소 분포를 최대한 유지하며 폐색 영역을 모의한 영상과 주변 영상의 자연스러운 연속성을 위해 ConvNeXt 블록을 적용한 CycleGAN (Cycle Generative Adversarial Network) 방법을 사용하여 실험을 진행하였고 이를 3개의 실험지역에 대해 분석하였다. 또한, 학습 패치 크기를 512*512화소로 하는 경우와 2배 확장한 1024*1024화소 크기의 적용 결과도 비교하였다. 서로 특징이 다른 3개의 지역에 대하여 실험한 결과, ConvNeXt CycleGAN 방법론이 기존의 CycleGAN을 적용한 영상, Histogram matching 영상과 비교하여 개선된 R² 값을 보여줌을 확인하였다. 학습에 사용되는 패치 크기별 실험의 경우 1024*1024화소의 패치를 사용한 결과, 약 0.98의 R²값이 산출되었으며 영상밴드별 화소 분포를 비교한 결과에서도 큰 패치 크기로 학습한 모의 결과가 원본 영상과 더 유사한 히스토그램 분포를 나타내었다. 이를 통해, 기존의 CycleGAN을 적용한 영상 및 Histogram matching 영상보다 발전된 ConvNeXt CycleGAN을 사용할 때 원본 영상과 유사한 모의 결과를 도출할 수 있었고, 성공적인 모의를 수행할 수 있음을 확인하였다.
원태연,윤용운 중앙대학교 통계연구소 1996 統計論文集 Vol.- No.3
공정의 계속적인 향상을 위해서는 여러 가지 통계적 방법이 필요하다. 본 논문에서는 우선 공정의 문제점 파악에 아주 중요한 역할을 하는 부분군의 형성에 대해 알아보고, 다음으로 공정의 향상에 많이 이용되는 관리도 및 실험계획법에 대해서 알아본다. The various statistical techniques are required for the process improvement. The process capability studies are active investigations whose purposes are to detect and remove special cause sources of variability to produce performance. Ration subgrouping is the basis for the identification and removal of possible problem in the process. Control charting techniques and desighed experiment generate process improvement by locating and removing causes of process variability.
원태연,오종양,조철범,조정기 대한척추신경외과학회 2010 Neurospine Vol.7 No.2
Various complications related to anterior lumbar interbody fusion(ALIF) have been reported in the literature. However, disseminated intravascular coagulation(DIC) after venous injury during ALIF has not been previously reported. We describe a rare case of DIC after ALIF.