• Journal of the Society of Naval Architects of Korea
• /
• v.53 no.5
• /
• pp.388-399
• /
• 2016

A motion analysis of an underwater towed cable is a complex task due to its nonlinear nature of the problem. The major source of the nonlinearity of the underwater cable analysis is that the motion of the cable involves large rigid-body motion. This large rigid-body motion makes difficult to use standard displacement-based finite element method. In this paper, the authors apply recently developed nodal position-based finite element method which can deal with the geometric nonlinearity due to the large rigid-body motion. In order to enhance the stability of the large-scale nonlinear cable motion simulation, an efficient time-integration scheme is proposed, namely predictor/multi-corrector Newmark scheme. Three different predictors are introduced, and the best predictor in terms of stability and robustness for impulsive cable motion analysis is proposed. As a result, the nonlinear motion of underwater cable is predicted in a very efficient manner compared to the classical finite element of finite difference methods. The efficacy of the method is demonstrated with several test cases, involving static and dynamic motion of a single cable element, and also under water towed cable composed of multiple cable elements.

• Survey Research
• /
• v.11 no.1
• /
• pp.19-41
• /
• 2010

Although it is possible to provide statistically reliable estimators of the entire population parameters based on each independent rolling sample, estimators of the small areas may not have the required statistical efficiency. Thus, in general, small area estimators are calculated based on the combined rolling sample after entire rolling sample survey is finished. In this study, we considered the construction of weights that is necessary in the analysis of the combined rolling sample. Unlike the past studies that provided the empirical results for the corresponding specific rolling sample survey, we considered linear models that depends only on design variables and rolling period and provided the corresponding Best Linear Unbiased Predictor(BLUP). Through a simulation study, we proposed the estimators for the population parameters that are robust to model failure and the BLUP under the assumed model. The results are applied to the 4th Korea National Health and Nutrition Examination Survey.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.6
• /
• pp.499-505
• /
• 2012

Vibratory loads imposed by the rotating blade upon the fuselage has been one of major obstacles in rotorcrafts. A new concept of rotor blade is currently developed to adopt an Active Trailing-edge Flap (ATF) to alleviate such obstacles. The flap is mounted at 65~85% spanwise location from the rotor hub. The nominal rotational speed of the blade is as high as 1,528 RPM, to match the required tip Mach number. Structural integrity is one of the important design aspects to be maintained and monitored in this special type of rotor. This is due to that many detailed components, which drive the flap, are inserted inside the rotating blade. To conduct its structural design and analysis, CAMRAD-II and the one-dimensional beam analysis are used. At the same time, three-dimensional finite element analysis are also used, such as MSC. PATRAN/NASTRAN, in order to analyze the details of the present active blade. As a result, comparable characteristics for the present rotor are predicted by both approaches.

• Proceedings of the KWS Conference
• /
• 2009.11a
• /
• pp.28-28
• /
• 2009

최근 전세계적으로 유가 상승 및 배기가스 배출 저감과 관련된 각종 환경규제에 대응하기 위하여 선진 자동차회사들은 $CO_2$ 배출 저감 기술과 기존 내연기관 차량의 연비향상을 위해 엔진성능 개선, 구동시스템의 최적화, 차량 경량화, 공기저항 감소 등에 초점을 맞춰 차량의 연비향상과 배기가스 규제에 대응하고 있다. 특히, 자동차 중량의 30%를 차지하는 차체의 경량화는 엔진효율을 높여 자동차의 성능향상을 극대화시키고, 그로 인해 연비향상을 도모할 수 있으므로 환경오염 방지와 연료절감에 가장 적합하고 효과적인 방법이다. 이에 기존의 강재에 비해 비중이 낮으면서 유사한 강도와 내식성이 뛰어난 알루미늄 합금의 차체 적용에 대한 연구가 진행되고 있다. 본 연구에서는 자동차 Door에 알루미늄 합금(Al 5052)의 적용 가능성을 판단하기 위해 반응표면분석법(Response surface methodology)을 이용하여 저입열 Pulse MIG 용접 공정변수를 최적화하였다. 첫째, 저입열 Pulse MIG 용접에서 용접 공정 변수(용접전압, 용접속도, Gap)의 변화가 비드 형상에 미치는 영향에 대해 평가하였다. 요인분석법을 이용하여 용접 공정 변수와 비드 형상 변수와의 주효과와 교호작용효과를 분석하였고, 이를 통해 비드 형상 변수에 영향을 크게 미치는 용접 공정변수를 선별하여 다중회귀분석을 통해 용접 공정 변수 변화에 따른 비드형상 예측 회귀모델을 제안하였다. 둘째, 자동차 Door 생산 현장에서 박판 알루미늄 합금 겹치기 용접 이음부의 0~1 mm 갭 발생에 대해 강건한 용접조건을 제시하기 위해 반응표면법(Response surface methodology)을 이용하여 저입열 Pulse MIG 용접 공정 변수를 최적화하였고, 그 적용 가능성을 확인하였다.

• Journal of International Area Studies (JIAS)
• /
• v.22 no.1
• /
• pp.91-112
• /
• 2018

This study examined the dynamic relationship between urbanization and energy consumption in China. As an alternative to the conventional method of having the same integration of time series and large samples, ARDL method and Toda-Yamamoto causality analysis were applied. As a result, urbanization income, income, and energy consumption have a long-term stable equilibrium. Urbanization and income have a positive effect on energy consumption in the long run, but short-term changes of urbanization and income have no significant effect on energy consumption changes. The adjusted coefficient was -0.2395, which was statistically significant. In the causality test, income and energy consumption are useful to predict each other, but urbanization is exogenous because there are no causality with other variables. Since the process of urbanization in China has been proceeding slowly and deliberately by the government, it can be seen that the long-term effects of urbanization are clear and exogenous.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.3
• /
• pp.147-157
• /
• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of Broadcast Engineering
• /
• v.21 no.1
• /
• pp.36-42
• /
• 2016

Recently, many 3D contents have been produced through the multiview camera system. In this system, since a difference of the viewpoint between color and depth cameras is inevitable, the camera parameter plays the important role to adjust the viewpoint as a preprocessing step. The conventional camera calibration method is inconvenient to users since we need to choose pattern features manually after capturing a planar chessboard with various poses. Therefore, we propose a semi-auto camera calibration method using a circular sampling and an homography estimation. Firstly, The proposed method extracts the candidates of the pattern features from the images by FAST corner detector. Next, we reduce the amount of the candidates by the circular sampling and obtain the complete point cloud by the homography estimation. Lastly, we compute the accurate position having the sub-pixel accuracy of the pattern features by the approximation of the hyper parabola surface. We investigated which factor affects the result of the pattern feature detection at each step. Compared to the conventional method, we found the proposed method released the inconvenience of the manual operation but maintained the accuracy of the camera parameters.

• The Korean Journal of Nuclear Medicine
• /
• v.33 no.6
• /
• pp.502-511
• /
• 1999

Purpose: F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) has been proven to be useful in the differential diagnosis of breast mass and staging of breast cancer. This study assessed the diagnostic accuracy of F-18-FDG PET in the differentiation of breast mass and staging of breast cancer. Materials and Methods: Total 42 patients who had breast mass underwent F-18-FDG PET (all female, 40: 10 year old). We compared F-18 FDG PET results with pathologic findings in 24 patients Results: In the differentiation of breast mass, sensitivity and specificity were 95% (19/20) and 77% (7/9) respectively. Three false positive cases were due to inflammation and one false negative patient had small tumor less than 1 cm. In the assessment of axillary lymph node status, sensitivity and specificity were 73% (8/11) and 100% (7/7) respectively. We found distant metastasis that were not suspected before F-18-FDG PET in 2 patients. Conclusion: F-18-FDG PET shows good diagnostic performance in differentiation of breast mass and staging of breast cancer.

• Journal of Internet Computing and Services
• /
• v.19 no.6
• /
• pp.41-51
• /
• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.373-380
• /
• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.