• Title/Summary/Keyword: 변위벡터

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Application of Displacement-Vector Objective Function for Frequency-domain Elastic Full Waveform Inversion (주파수 영역 탄성파 완전파형역산을 위한 변위벡터 목적함수의 적용)

  • Kwak, Sang-Min;Pyun, Suk-Joon;Min, Dong-Joo
    • Geophysics and Geophysical Exploration
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    • v.14 no.3
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    • pp.220-226
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    • 2011
  • In the elastic wave equations, both horizontal and vertical displacements are defined. Since we can measure both the horizontal and vertical displacements in field acquisition, these displacements compose a displacement vector. In this study, we propose a frequency-domain elastic waveform inversion technique taking advantage of the magnitudes of displacement vectors to define objective function. When we apply this displacement-vector objective function to the frequency-domain waveform inversion, the inversion process naturally incorporates the back-propagation algorithm. Through the inversion examples with the Marmousi model and the SEG/EAGE salt model, we could note that the RMS error of the solution obtained by our algorithm decreased more stably than that of the conventional method. Particularly, the density of the Marmousi model and the low-velocity sub-salt zone of the SEG/EAGE salt model were successfully recovered. Since the gradient direction obtained from the proposed objective function is numerically unstable, we need additional study to stabilize the gradient direction. In order to perform the waveform inversion using the displacementvector objective function, it is necessary to acquire multi-component data. Hence, more rigorous study should be continued for the multi-component land acquisition or OBC (Ocean Bottom Cable) multi-component survey.

Non-statistical Stochastic Finite Element Method Employing Higher Order Stochastic Field Function (고차의 추계장 함수와 이를 이용한 비통계학적 추계론적 유한요소해석)

  • Noh, Hyuk-Chun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.2A
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    • pp.383-390
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    • 2006
  • In this paper, a stochastic field that is compatible with Monte Carlo simulation is suggested for an expansion-based stochastic analysis scheme of weighted integral method. Through investigation on the way of affection of stochastic field function on the displacement vector in the series expansion scheme, it is noticed that the stochastic field adopted in the weighted integral method is not compatible with that appears in the Monte Carlo simulation. As generally recognized in the field of stochastic mechanics, the response variability is not a linear function of the coefficient of variation of stochastic field but a nonlinear function with increasing variability as the intensity of uncertainty is increased. Employing the stochastic field suggested in this study, the response variability evaluated by means of the weighted integral scheme is reproduced with high precision even for uncertain fields with moderately large coefficient of variation. Besides, despite the fact that only the first-order expansion is employed, an outstanding agreement between the results of expansion-based weighted integral method and Monte Carlo simulation is achieved.

Disparity Vector Derivation Method for Texture-Video-First-Coding Modes of 3D Video Coding Standards (3차원 동영상 압축 표준의 텍스쳐 비디오 우선 부호화 방식을 위한 변위 벡터 추정 기법)

  • Kang, Je-Won
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.10
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    • pp.2080-2089
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    • 2015
  • In 3D video compression, a disparity vector (DV) pointing a corresponding block position in an adjacent view is a key coding tool to exploit statistical correlation in multi-view videos. In this paper, neighboring block-based disparity vector (NBDV) is shown with detail algorithm descriptions and coding performance analysis. The proposed method derives a DV from disparity motion vector information, obtained from spatially and temporally neighboring blocks, and provides a significant coding gain about 20% BD-rate saving in a texture-video-first-coding scheme. The proposed DV derivation method is adopted into the recent 3D video coding standards such as 3D-AVC and 3D-HEVC as the state-of-the-art DV derivation method.

An Embedding Similarity-based Deep Learning Model for Detecting Displacement in Cultural Asset Images (목조 문화재 영상에서의 크랙을 감지하기 위한 임베딩 유사도 기반 딥러닝 모델)

  • Kang, Jaeyong;Kim, Inki;Lim, Hyunseok;Gwak, Jeonghwan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.133-135
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    • 2021
  • 본 논문에서는 목조 문화재 영상에서의 변위 현상 중 하나인 크랙이 발생하는 영역을 감지하기 위한 임베딩 유사도 기반 모델을 제안한다. 우선 변위가 존재하지 않는 정상으로만 구성된 학습 이미지는 사전 학습된 합성 곱 신경망을 통과하여 임베딩 벡터들을 추출한다. 그 이후 임베딩 벡터들을 가지고 정상 클래스에 대한 분포의 파라미터 값을 구한다. 실제 추론 과정에 사용되는 테스트 이미지에 대해서도 마찬가지로 임베딩 벡터를 구한다. 그런 다음 테스트 이미지의 임베딩 벡터와 이전에 구한 정상 클래스를 대표하는 가우시안 분포 정보와의 거리를 계산하여 이상치 맵을 생성하여 최종적으로 변위가 존재하는 영역을 감지한다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 우리가 제안한 임베딩 유사도 기반 모델이 목조 문화재에서 크랙이 발생하는 변위 영역을 잘 감지함을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 크랙 현상에 대한 변위 영역 검출에 있어서 매우 적합함을 보여준다.

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Fractal Deformation using Code and Displacement Vectors (코드와 변위 벡터를 이용한 프랙탈 변형)

  • Han, Yeong-Deok;Kim, Gi-Ok
    • The Journal of the Korea Contents Association
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    • v.7 no.12
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    • pp.322-332
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    • 2007
  • We consider a deformation method suitable for fractal. In IFS fractal, the position of a point is characterized by its code as well as by its coordinates. Code has a meaning of address for fractal. If we move a point by changing its code, the resulting movement shows fractal behavior. We propose three deformation methods based on code information. For the deformation vector of a point in fractal, 1) we use the vector of a given vector field at the point obtained by code transformation, 2) we use the vector constructed by adding predefined displacement vectors according to the code information of the point. Both methods show a fractal-like character as well as an ordinary continuous deformation character. Also, 3) we can deform fern-fractal more naturally by restricting its deforming region using code form.

Electromagnetic Force Calculation Using Magnetic Vector Potentials in 3-D Problems (자기벡터포텐셜을 이용한 3차원 전자력 계산)

  • 양재진;이복용;이기식
    • Journal of the Korean Magnetics Society
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    • v.6 no.2
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    • pp.106-111
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    • 1996
  • Electric machines such as motors which have rmving parts are designed for producing mechanical force or torque. The accurate calculations of electromagnetic force and torque are important in the design these machines. Electromagnetic force calculation method using the results of Finite Element Method(FEM) has been presented variously in 2-D problems. Typically the Maxwell's Stress Tensor method and the method of virtual work are used. The former calculates forces by integrating the surface force densities which can be expressed in terms of Maxwell Stress Tensor(MST), and the latter by differentiating the electromagnetic energy with respect to the virtual dis¬placement of rigid bodies of interest. In the problems including current source, magnetic vector potentials(MVP) have rmstly been used as unknown variables for field analysis by a numerical method; e. g. FEM. This paper, thus, introduces the two both methods using MVP in 3-D case. To verify the usefulness of presented methods, a solenoid model is chosen and analyzed by 3-D and axisymmetric FEM. It is found that the force calculation results are in good agreement for several mesh schemes.

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Fast Disparity Motion Vector Searching Method for the MV-HEVC (MV-HEVC에서 빠른 변위 움직임 벡터 탐색 방법)

  • Lee, Jae-Yung;Han, Jong-Ki
    • Journal of Broadcast Engineering
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    • v.22 no.2
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    • pp.240-252
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    • 2017
  • Multi-view video codec based on the High Efficiency Video Coding (MV-HEVC) has high encoding complexity because it exploits an additional reference picture for disparity compensation prediction (DCP) when the picture of dependent view is encoded. In this paper, we propose an efficient method to reduce the complexity of disparity motion vector search for the MV-HEVC. The proposed method includes the initial search point decision method using affine transform and the adaptive search range decision method. The simulation results show that the proposed method reduces the complexity of disparity motion vector search up to 90.78% with negligible coding efficiency degradation. Also the results show that the proposed method outperforms other conventional techniques reducing complexity.

An Displacement Detection Model in Cultural Asset Images using Object-centric Augmentation (객체 중심 증강 기법을 사용한 목조 문화재 영상에서의 변위 감지 모델)

  • Kang, Jaeyong;Kim, Inki;Lim, Hyunseok;Gwak, Jeonghwan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.137-139
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    • 2021
  • 본 논문에서는 목조 문화재 영상에서의 변위를 효율적으로 감지하기 위한 객체 중심 증강 기법을 사용한 모델을 제안한다. 우선 객체 중심 증강 기법을 적용하여 변위 객체들이 이미지 공간상의 어느 곳이든 위치할 수 있게끔 데이터를 구성한 이후 사전 학습된 합성 곱 신경망을 사용하여 입력 이미지에 대한 심층 특징 벡터를 추출한다. 그 이후 심층 특징 벡터는 완전 연결 계층의 입력 값으로 들어와서 최종적으로 변위가 존재하는지 아닌지에 대한 예측을 수행하게 된다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 우리가 제안한 객체 중심 증강 기법을 사용한 모델이 객체 중심 증강 기법을 사용하지 않은 모델보다 목조 문화재에서 변위 영역을 더 잘 감지함을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 변위 검출에 있어서 매우 적합함을 보여준다.

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Determination of Optimal Locations for Measuring Displacements to Adjust Cable Tension Forces of Cable-Stayed Bridges (사장교 시공 중 케이블 장력 보정을 위한 최적 변위계측 위치 결정)

  • Shin, Soobong;Lee, Jung-Yong;Kim, Jae-Cheon;Jung, Kil-Je
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.13 no.2 s.54
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    • pp.129-136
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    • 2009
  • The paper presents an algorithm of selecting optimal locations for measuring displacements(OLD) to adjust cable tension forces during the construction of cable-stayed bridges. The rank for optimal locations can be determined from the effective independence distribution vectors(EIDV) that are computed from the Fisher Information Matrices(FIM) formulated with the displacement sensitivities. To examine the efficiency and reliability of the proposed algorithm for determining OLD, a simulation study on a cable-stayed bridge has been carried out. The results using FIM formulated with displacements are compared with those using FIM with displacement sensitivities through the simulation study. The effects of measurement noise and error in cable length on the adjustment of cable tension forces are evaluated statistically by applying the Monte Carlo scheme.

An Ensemble Deep Learning Model for Measuring Displacement in Cultural Asset images (목조 문화재 영상에서의 변위량 측정을 위한 앙상블 딥러닝 모델)

  • Kang, Jaeyong;Kim, Inki;Lim, Hyunseok;Gwak, Jeonghwan
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.141-143
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    • 2021
  • 본 논문에서는 목조 문화재의 변위량을 감지할 수 있는 앙상블 딥러닝 모델 모델을 제안한다. 우선 총 2개의 서로 다른 사전 학습된 합성 곱 신경망을 사용하여 입력 영상에 대한 심층 특징들을 추출한다. 그 이후 2개의 서로 다른 심층 특징들을 결합하여 하나의 특징 벡터를 생성한다. 그 이후 합쳐진 특징 벡터는 완전 연결 계층의 입력 값으로 들어와서 최종적으로 변위의 심각 단계에 대한 예측을 수행하게 된다. 데이터 셋으로는 충주시 근처의 문화재에 방문해서 수집한 목조 문화재 이미지를 가지고 정상 및 비정상으로 구분한 데이터 셋을 사용하였다. 실험 결과 앙상블 딥러닝 기법을 사용한 모델이 앙상블 기법을 사용하지 않는 모델보다 더 좋은 성능을 나타냄을 확인하였다. 이러한 결과로부터 우리가 제안한 방법이 목재 문화재의 변위량 예측에 있어서 매우 적합함을 보여준다.

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