• 한국멀티미디어학회논문지
• /
• 제20권11호
• /
• pp.1720-1728
• /
• 2017

Active shape model is widely used in the field of image processing especially on arbitrary meaningful shape extraction from single gray level image. Cootes et. al. showed efficient detection of variable shape from image by using covariance and mean shape from learning. There are two stages of learning and testing. Hahn applied enhanced shape alignment method rather than using Cootes's rotation and scale scheme. Hahn did not modified the profile itself. In this paper, the method using directional one dimensional profile is proposed to enhance Cootes's one dimensional profile and the shape alignment algorithm of Hahn is combined. The performance of the proposed method was superior to Cootes's and Hahn's. Average landmark estimation error for each image was 27.72 pixels and 39.46 for Cootes's and 33.73 for Hahn's each.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2004년도 추계 학술발표회 제16권2호
• /
• pp.421-424
• /
• 2004

Model updating is a very active research field, in which significant efforts has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are-unavoidably-corrupted with uncorrelated noise content. In this paper, Reanalysis using frequency response functions for correlating and updating dynamic systems is presented. A transformation matrix is obtained from the relationship between the complex and the normal frequency response functions of a structure. The transformation matrix is employed to calculate the modified damping matrix of the system. The modified mass and stiffness matrices are identified from the normal frequency response functions by using the least squares method. Full scale pseudo dynamic pier test is employed to illustrate the applicability of the proposed method. The result indicate that the damping matrix of correlated finite element model can be identified accurately by the proposed method. In addition, the robustness of the new approach uniformly distributed measurement noise is also addressed.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2000년도 하계학술대회 논문집
• /
• pp.320-323
• /
• 2000

In the resent years, the Thin Film Transistor Liquid Crystal Display(TFT-LCD) have trend toward larger panel sizes and higher spatial and/or gray-scale resolution. In this trend, Because of its low field effect mobility, a-Si TFT is change to poly-Si TFT. In this paper, both effective-medium model of poly-Si TFTs and empirical capacitance model are applied to Pixel Design Array Simulation Tool (PDAST) and the pixel characteristics of TFT-LCD array were simulated, which were compared with the results calculated by Aim-Spice.

• Smart Structures and Systems
• /
• 제23권4호
• /
• pp.347-357
• /
• 2019

A new methodology for mitigation of the wheel squealing is proposed and investigated based on the dithering control. The idea can be applied in railway lines particularly in urban areas. The idea is clearly presented, and applied to a validated model. A full-scale model including the vehicle, curved track and wheel/rail contact is developed in the time domain to analyze the possibility and level of wheel squeal noise. Comparing the numerical results with a field test, the model is validated in different levels namely i) occurrence, ii) squealing frequency and iii) noise level. Two different approaches are proposed a) dithering of the wheel with piezoelectric patches and b) dithering of the rail with piezoelectric stacks. The noise level as well as the wheel responses is compared after applying the control strategy. A parametric study is carried out and effect of the dithering voltage and frequency on the squealing noise is investigated. It is found that both the strategies perform quite effectively within the saturating threshold of piezoelectric actuators.

• 한국지하수토양환경학회지:지하수토양환경
• /
• 제25권3호
• /
• pp.52-64
• /
• 2020

The universal soil loss equation (USLE), a model for estimating the potential soil loss, has been used not only in research areas but also in establishing national policies in South Korea. Despite its wide applicability, USLE cannot adequately address the effect of seasonal variances. To overcome this limit, the ArcGIS-based Sediment Assessment Tool for Effective Erosion (ArcSATEEC) has been developed as an alternative model. Although the field-scale (< 100 ㎡) application of this model produced reliable estimation results, it is still challenging to validate accuracy of the model estimation because it only estimates potential soil losses, not the actual sediment yield. Therefore, in this study, a method for estimating actual soil loss based on the ArcSATEEC model was suggested. The model was applied to eight watersheds in South Korea to estimate sediment yields. Correction factor was introduced for each watershed, and the estimated sediment yield was compared with that of the estimated yield by LOAD ESTimator (LOADEST). Sediment yield estimation for all watersheds exhibited reliable results, and the validity of the proposed correction factor was confirmed, suggesting the correction factor needs to be considered in estimating actual soil loss.

• 폴리머
• /
• 제30권6호
• /
• pp.453-463
• /
• 2006

분자 모델에 의한 전산 모사는 단백질 접힘, 미셀화, 블록공중합체의 규칙구조화 등 다양한 고분자 계의 자기조립 현상을 예측하거나 그 조립 메커니즘을 밝히는 데 특별히 유용한 연구방법이다. 자기조립 현상은 분자 수, 분자 크기 등, 계의 속성에 따라 나노미터 이하의 현상으로부터 마이크론이나 그 이상의 길이 스케일의 현상까지 조립 구조의 길이 스케일이 매우 광범위하기 때문에 다양한 계의 모든 조립 현상을 양자역학적 방법과 같은 궁극의 근본원칙에 의해 모사하는 것은 현실적인 시간 내에서 불가능하다. 이러한 문제들을 해결하기 위해 계를 기술하는 과정에서 필요 이상으로 세밀한 표현을 생략하여 모델을 다른 관점에서 재구성하는 방법이 있는데 재구성된 모델은 그 관점에 따라 크게 '원자 수준'의 모델과 '메조 스케일 수준'의 모델로 분류할 수 있다. 본 총론에서는 고분자 자기조립 현상과 관련하여 이 두 가지 관점에 따른 모델과 모사 방법들에 대해 살펴보고자 한다.

• Wind and Structures
• /
• 제2권1호
• /
• pp.25-40
• /
• 1999

In this paper, artificial neural networks, a new kind of intelligent method, are employed to model and predict amplitude dependent damping in buildings based on our full-scale measurements of buildings. The modelling method and procedure using neural networks to model the damping are studied. Comparative analysis of different neural network models of damping, which includes multi-layer perception network (MLP), recurrent neural network, and general regression neural network (GRNN), is performed and discussed in detail. The performances of the models are evaluated and discussed by tests and predictions including self-test, "one-lag" prediction and "multi-lag" prediction of the damping values at high amplitude levels. The established models of damping are used to predict the damping in the following three ways : (1) the model is established by part of the data measured from one building and is used to predict the another part of damping values which are always difficult to obtain from field measurements : the values at the high amplitude level. (2) The model is established by the damping data measured from one building and is used to predict the variation curve of damping for another building. And (3) the model is established by the data measured from more than one buildings and is used to predict the variation curve of damping for another building. The prediction results are discussed.

• Composites Research
• /
• 제36권5호
• /
• pp.329-334
• /
• 2023

기존의 멀티스케일 유한요소법(Multiscale finite element, FE² )은 거시 스케일의 모든 적분점에서 대표 체적요소(representative volume element, RVE)의 미시 경계치 문제를 반복적으로 계산하기 때문에 긴 해석 시간과 많은 데이터 저장 공간을 필요로 한다. 이를 해결하기 위해 본 연구에서 평균장 균질화 데이터 기반 멀티스케일 해석 기법을 개발하였다. 데이터 기반 전산역학(data-driven computational mechanics, DDCM) 해석은 변형률-응력 데이터 셋을 직접적으로 사용하는 모델-프리(model-free)접근 방식이다. 멀티스케일 해석을 수행하기 위해, 평균장 균질화(mean-field homogenization)를 활용하여 복합재의 미세구조에 대한 변형률-응력 데이터베이스(database)를 효율적으로 구축하고, 이를 기반으로 데이터 기반 전산역학 시뮬레이션을 수행하였다. 본 논문에서는 개발한 멀티 스케일 해석 프레임워크(framework)를 예제에 적용하여, 초탄성(hyperelasticity) 복합재의 미세 구조를 고려한 데이터 기반 전산역학 시뮬레이션 결과를 확인하였다. 따라서, 데이터 기반 전산역학 접근 방식을 활용한 멀티스케일 해석기법은 다양한 재료 및 구조에 적용될 수 있으며, 멀티스케일 해석 연구 및 응용 가능성을 열어줄 것으로 기대된다.

• Journal of Astronomy and Space Sciences
• /
• 제38권3호
• /
• pp.157-164
• /
• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• Smart Structures and Systems
• /
• 제11권1호
• /
• pp.53-67
• /
• 2013

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.