• 한국지진공학회:학술대회논문집
• /
• 한국지진공학회 2003년도 춘계 학술발표회논문집
• /
• pp.85-92
• /
• 2003

Hysteretic behaviors of a seismic isolator are identified by using the regularized output error estimator (OEE) based on the secant stiffness model. A proper regularity condition of tangent stiffness for the current OEE is proposed considering the regularity condition of Duhem hysteretic operator. The proposed regularity condition is defined by 12-norm of the tangent stiffness with respect to time. The secant stiffness model for the OEE is obtained by approximating the tangent stiffness under the proposed regularity condition by the secant stiffness at each time step. A least square method is employed to minimize the difference between the calculated response and measured response for the OEE. The regularity condition of the secant stiffness is utilized to alleviate ill-posedness of the OEE and to yield numerically stable solutions through the regularization technique. An optimal regularization factor determined by geometric mean scheme (GMS) is used to yield appropriate regularization effects on the OEE.

• Journal of Positioning, Navigation, and Timing
• /
• 제2권1호
• /
• pp.81-89
• /
• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제24권1호
• /
• pp.1-22
• /
• 2020

An accurate and efficient gradient estimation method on unstructured grid is presented by proposing a switching process between two Least-Square methods. Diverse test cases show that the gradient estimation by Least-Square methods exhibit better characteristics compared to Green-Gauss approach. Based on the investigation, switching between the two Least-Square methods, whose merit complements each other, is pursued. The condition number of the Least-Square matrix is adopted as the switching criterion, because it shows clear correlation with the gradient error, and it can be easily calculated from the geometric information of the grid. To illustrate switching process on general grid, condition number is analyzed using stencil vectors and trigonometric relations. Then, the threshold of switching criterion is established. Finally, the capability of Switching Weighted Least-Square method is demonstrated through various two- and three-dimensional applications.

• Nuclear Engineering and Technology
• /
• 제53권4호
• /
• pp.1199-1209
• /
• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• 대한전자공학회논문지SP
• /
• 제41권5호
• /
• pp.251-258
• /
• 2004

본 논문은 디지털 카메라로 획득된 실영상에서 카메라의 응답분포의 특성을 이용하여 광원의 색도값을 추정하는 방법을 제안한다. 광휘도 영역을 이용하는 방법은 물체의 표면에 의한 색과 광원에 의한 색이 일정하게 변하는 특징을 이용하여 광원의 색도값을 추정한다. 일반적인 디지털 카메라 영상의 경우, 광휘도 영역의 화소들은 실영상에서 야기되는 광원의 기하학적 불균일성, 카메라에 의한 양자화 오차 및 CCD 센서의 불균일한 특성들을 포함하는 값이다. 그러므로 전처리 과정이 없는 카메라의 응답을 이용하여 광원을 추정한 결과, 정확한 광원의 색도값 추정이 어려웠다. 따라서 이 문제를 해결하기 위해서 본 논문에서는 카메라의 응답 특성을 조사하고, 광휘도 영역에서 Mahalanobis distance를 이용하여 화소들을 선택함으로써, 광원 추정의 정확성을 높이고자 하였다. 카메라 응답에서 Mahalanobis distance의 사용함으로써 광휘도 영역에서 분포된 화소들 중에서 유효한 화소들을 선택하는 것이 가능하다. 선택된 화소들을 주성분 분석 과정을 이용하여 r-g 좌표계에서 직선을 만들었으며, 그 직선들의 교차점으로부터 광원의 색도값을 추정하였다. 제안한 방법을 이용하여 다양한 실영상에서 실험한 결과 기존의 방법에 비해 광원 추정에 대한 오차가 감소함을 확인하였다.

• Journal of Biosystems Engineering
• /
• 제17권1호
• /
• pp.65-78
• /
• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• 한국정보통신학회논문지
• /
• 제17권1호
• /
• pp.248-254
• /
• 2013

신호원으로부터 발생하는 전파를 이격된 다중 센서에서 수신하여 신호원의 위치를 추정하는 시스템에서는 신호원의 위치와 센서들의 위치에 따라 위치탐지 정확도가 저하되는 현상이 나타난다. 이러한 현상을 기하학적 정밀도 저하(GDOP) 효과라 하며, 이러한 효과를 최소화하여 위치탐지 정확도 성능을 향상시키기 위한 방법에 대해 연구가 필요하다. 본 논문에서는 이격 배치된 센서들의 방위 정보를 이용하여 GDOP 효과의 발생 가능성을 추정하고, 위치 추정에서 오차 요인이 되는 센서를 제거하여 GDOP 효과로 인한 성능 저하를 최소화하는 방법에 관한 연구 결과를 서술하겠다.

• 대한교통학회지
• /
• 제32권2호
• /
• pp.170-178
• /
• 2014

지금까지 교통사고발생과 기하구조와의 관계파악을 위한 모형정립에 관한 연구가 많이 이루어져 왔다. 이러한 연구들은 도로선형, 기하구조의 개선 혹은 위험구간 선정 등에 사용되어 교통사고 건수 및 사고심각도를 줄이는데 기여를 하여왔다. 하지만, 모형정립에 사용되었던 변수들은 자료수집 부족 등의 이유로 변수 혹은 대상구간이 가지고 있는 기하구조의 비동질성을 고려하지 못한 측면이 있었으며, 이는 모형 정립시 계수의 표준오차값이 과소 추정되어 모형전체의 신뢰성에 영향을 미쳐왔다. 따라서, 이번 연구에서는 사용되는 변수의 비동질성 고려가 모형의 결과에 미치는 영향을 알아봄으로써, 비동질성의 중요성을 파악하고자 하는데 목적이 있다. 그 결과, 모든 기하구조에 대한 비동질성을 고려하지는 못하였으나, 몇몇 사용된 기하구조 변수들의 경우, 의미 있는 결과가 도출되었다.

• 한국군사과학기술학회지
• /
• 제21권4호
• /
• pp.447-455
• /
• 2018

If a hidden enemy is shooting, there is a threat against soldiers in recent conflicts. This paper aims to improve the localization of a muzzle using microphone array. Gunshot noise can provide information about the location of muzzle with two signals, the muzzle blast from the gun barrel and the projectile sound from the bullet. Two signals arrive to the microphone array with different arrival time and angle. If the arrival angles of the two signals are estimated, distance between sniper location and the microphone array can be calculated by using geometric principles. This method was established in 2003 by Pare. But this method has a limitation that it cannot calculate the distance when the arrival angles of the two signals are same. Also it has an error when the angle difference of arrival is small. In order to overcome this limitation, a new method is proposed that uses the change of characteristic of the projectile sound with respect to vertical distance from the trajectory. The proposed method estimates the distance correctly when the arrival angle of two signals are same, and when the angle difference between two signals is increased, the estimation error increases with respect to the angle. Therefore these two methods can be selected according to the angle difference between two signals to estimate the distance of the muzzle. Below the threshold of the angle difference, the proposed method can be used to estimate distance with smaller error than the existing method. This was demonstrated by shooting tests using actual sniper rifles.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제7권4호
• /
• pp.834-848
• /
• 2013

To effectively use these functions many kinds of human-phone interface are used such as touch, voice, and gesture. However, the most important touch interface cannot be used in case of hand disabled person or busy both hands. Although eye tracking is a superb human-computer interface method, it has not been applied to smartphones because of the small screen size, the frequently changing geometric position between the user's face and phone screen, and the low resolution of the frontal cameras. In this paper, a new eye tracking method is proposed to act as a smartphone user interface. To maximize eye image resolution, a zoom lens and three infrared LEDs are adopted. Our proposed method has following novelties. Firstly, appropriate camera specification and image resolution are analyzed in order to smartphone based gaze tracking method. Secondly, facial movement is allowable in case of one eye region is included in image. Thirdly, the proposed method can be operated in case of both landscape and portrait screen modes. Fourthly, only two LED reflective positions are used in order to calculate gaze position on the basis of 2D geometric relation between reflective rectangle and screen. Fifthly, a prototype mock-up design module is made in order to confirm feasibility for applying to actual smart-phone. Experimental results showed that the gaze estimation error was about 31 pixels at a screen resolution of $480{\times}800$ and the average hit ratio of a $5{\times}4$ icon grid was 94.6%.