• 한국전산유체공학회지
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• 제11권3호
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• pp.28-36
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• 2006

NSBT(No Slip Boundary Treatment) is a newly developed scheme for the treatment of a no slip condition on the solid wall of obstacle in a flow field. In our research, NSBT was used to perform LBM simulation of a flow over a circular cylinder to determine the flow feature and aerodynamics characteristic of the cylinder. To ascertain the applicability of NSBT on the complex shape of the obstacle, it was first simulated for the case of the flow over a circular and square cylinder in a channel and the results were compared against the solution of Navier-Stokes equation. The simulations were performed in a moderate range of Reynolds number at each cylinder position to identify the flow feature and aerodynamic characteristics of circular cylinder in a channel. The drag coefficients of the cylinder were calculated from the simulation results. We have numerically confirmed that the critical reynolds number for vortex shedding is in the range of 200$\sim$250. For the gap parameter $\gamma$ = 2 cases at Re > 240, the vortex shedding were symmetric and it resembled the Karmann vortex. As the cylinder approached to one wall, the vorticity significantly reduced in length while the vorticity on the other side elongated and the vorticity combined with the wall boundary-layer vorticity. The resultant $C_d$ by LBM concurred with the results of DNS simulation performed by previous researchers.

• 한국항행학회논문지
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• 제14권4호
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• pp.512-520
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• 2010

유비쿼터스와 모바일의 광범위한 응용과 더불어 통신 보안은 최근 중요한 관심사가 되고 있다. 따라서 각각의 보안 요소마다 다양한 기법 및 응용에 대한 연구와 시스템적 응용에 대한 연구가 활발히 이루어지고 있다. 본 논문에서는 음성의 특징을 이용한 모바일 OTP의 일회용 암호키를 생성하는 방법을 제안한다. 본 연구는 강력한 개인 인증에 사용되는 바이오매트릭스의 음성 정보를 이용하여 가변적이고 안전한 일회용 암호 키를 생성하였으며, 또한 제안 기법에 대한 덴드로그램(dendrogram)을 이용한 음성 특징점에 의한 준동형적(homomorphic) 가변성 그리고 음성 특징점의 분포를 시뮬레이션 하였다.

• 한국콘텐츠학회논문지
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• 제12권3호
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• pp.67-76
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• 2012

본 논문은 천 커버링 연산의 개념과 알고리즘을 제안한다. 천 커버링은 물리 법칙에 기반을 둔 연산으로 사물을 덮는 천의 모양을 계산학적으로 흉내낸다. 천 커버링의 목적은 사물을 천으로 덮어 표면의 상세함을 감추고 개략적인 외형이 드러나도록 하는 것이다. 이 연산은 천의 뻣뻣한 정도를 제어하는 하나의 크기 인자를 가지며, 이를 통해 외부로 드러나는 사물에 대한 정보의 상세함을 조절한다. 제안하는 연산의 가능성을 보이기 위해 문서 영상에 사용된 언어를 식별하는 문제에 천 커버링을 적용하였다. 실험 결과 가우시안을 이용한 특징 추출 방법보다 천 커버링을 이용한 특징 추출 방법이 더 우수한 식별 성능을 보였다. 토론에서 제안하는 연산이 우수한 이유를 제시한다.

• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.1865-1873
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• 2017

The feature-based visual SLAM requires 3D positions for the extracted feature points to perform 3D-2D motion estimation. LiDAR can provide reliable and accurate 3D position information with low computational burden, while stereo camera has the problem of the impossibility of stereo matching in simple texture image region, the inaccuracy in depth value due to error contained in intrinsic and extrinsic camera parameter, and the limited number of depth value restricted by permissible stereo disparity. However, the sparsity of LiDAR data may increase the inaccuracy of motion estimation and can even lead to the result of motion estimation failure. Therefore, in this paper, we propose three interpolation methods which can be applied to interpolate sparse LiDAR data. Simulation results obtained by applying these three methods to a visual odometry algorithm demonstrates that the selective bilinear interpolation shows better performance in the view point of computation speed and accuracy.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• 전자공학회논문지CI
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• 제37권1호
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• pp.11-20
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• 2000

본 논문은 도로영상에서 움직이는 물체 추적을 위한 윤곽선 및 형태 파라미터 추출방법을 제안코자 한다. 축소영상에서 차영상 방법을 이용하여 윤곽선을 추출하고 원영상에서 특징을 추출함으로써 추적의 정확성을 높이고자 한다. 사용된 특징은 물체화소의 원분포, 중심모멘트, 최대${\cdot}$최소비이다. 이를 이용하여 데이터 연상문제를 해결하였으며, 실시간 추적을 위하여 칼만필터를 사용하였다. 제안된 알고리즘에 의해 추출된 특징 벡터는 다중 차량 추적에 적합함을 실험을 통해 보였다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.167-175
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• 2006

Building accurate corporate bankruptcy prediction models has been one of the most important research issues in finance. Recently, support vector machines (SVMs) are popularly applied to bankruptcy prediction because of its many strong points. However, in order to use SVM, a modeler should determine several factors by heuristics, which hinders from obtaining accurate prediction results by using SVM. As a result, some researchers have tried to optimize these factors, especially the feature subset and kernel parameters of SVM But, there have been no studies that have attempted to determine appropriate instance subset of SVM, although it may improve the performance by eliminating distorted cases. Thus in the study, we propose the simultaneous optimization of the instance selection as well as the parameters of a kernel function of SVM by using genetic algorithms (GAs). Experimental results show that our model outperforms not only conventional SVM, but also prior approaches for optimizing SVM.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.874-877
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• 2012

This paper proposes the new method based on contour following method with directional angle to segment the cell into the nuclei. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. this paper, improved method of digital image analysis required in basic medical science for diagnosis of cells was proposed. The object image was the Thyroid Gland cell image with difference of chromatin patterns. To segment the cell nucleus from background, the region segmentation algorithm by edge tracing was proposed. And feature parameter was obtained from discrete Fourier transformation of image. After construct a feature sample group of each cells, experiment of discrimination was executed with any verification cells. As a result of experiment using features proposed in this paper, get a better segmentation rate(70-90%) than previously reported papers, and this method give shape to get objectivity and fixed quantity in diagnosis of cells. The methods described in this paper be used immediately for discrimination of neoplastic cells.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• 반도체디스플레이기술학회지
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• 제12권2호
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• pp.67-72
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• 2013

Temperature is a critical parameter in yield improvement for wafer manufacturing. In chemical vapor deposition (CVD) equipment, crack defect in ceramic heater leads to yield reduction, however, there is no suitable ceramic heater fault detection system for conventional CVD equipment. This paper proposes a short-time zero-crossing rate based fault detection method for the ceramic heater in CVD equipment. The proposed method measures the output signal ($V_{pp}$) of RF filter and extracts the zero-crossing rate (ZCR) as feature vector. The extracted feature vectors have a discriminant power and Gaussian mixture model (GMM) based fault detection method can detect fault in ceramic heater. Experimental results, carried out by measured signals provided by a CVD equipment manufacturer, indicate that the proposed method detects effectively faults in various process conditions.