• 한국과학교육학회지
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• 제17권1호
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• pp.1-10
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• 1997

The purpose of this study was to analyze the structural model of causal effects of students' variables on science process skills. Student characteristics investigated in the study included attitude related to the science, logical thinking ability, scientific experiences, cognitive style. Covariance structural modeling procedures were used to test causal inferences about hypothesized relationships. The sample consisted of 319 6th grade students and 321 8th grade students in Seoul City, Korea. Five instruments were used in the study, TSPS(test of science process skills), GALT(group assessment of logical thinking), CEFT(children embedded figures test), questionnaire of attitude related to the science, questionnaire of scientific experience. For statistical analysis, the study adopted the structural equation modeling with LlSREL, a computer statistical program developed by J reskog and S rbom. Major findings of the study are as follows:1) Logical thinking ability has a most strong direct effect on science process skills. 2) The structural coefficient of scientific experience influence on attitude related to the science has the greatest direct one than the others in the covariance structural model. According to the results of this study, it is very importance that various scientific experiences, particularly hands-on activity, should be offer to students to improve science process skills. Also, understanding the relationships of student variable to science process skills will be helpful to decision making on the part of curriculum developers, science teachers and researchers.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.43-48
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• 2007

In this study, an ANN-based damage detection algorithm using acceleration signals is developed for alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed for damage detection in real time. The cross-covariance of two acceleration signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained for potential loading patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• Smart Structures and Systems
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• 제2권2호
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• 응용통계연구
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• 제22권1호
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• pp.181-188
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• 2009

본 논문은 실험단위들의 구조적 특성으로 지분관계를 갖는 실험을 행해야 하는 경우를 가정한다. 지분계획하에서 처리를 구성하는 요인으로 반복측정 요인을 고려한다. 반복측정 요인의 수준들이 비확률화에 의해 지분구조의 실험단위들에 배정될 때, 비확률화에 따른 실험의 특성을 감안한 모형으로 복합대칭의 공분산 구조하에서 혼합효과 모형을 논의하고 있다. 처리의 일부 요인들이 시간 또는 공간상의 제약으로 인해 지분구조의 실험단위들에 임의적으로 배정될 수 없을 때, 지분구조의 실험단위들에 대한 반응 값들은 어떤 구조적 상관관계를 나타내는 값들로 관측될 수 있음을 예상할 수 있다. 자료의 구조적 상관성을 고려한 공분산 구조하의 선형모형으로 확률요인과 고정요인을 포함하는 혼합효과의 모형을 제시하고 모형내 미지모수들에 대한 추론방법을 다루고 있다.

• Structural Engineering and Mechanics
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• 제56권5호
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• 한국전산구조공학회논문집
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• 제34권1호
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• pp.35-42
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• 2021

이 논문에서는 공용중인 구조물의 상시 계측 자료를 사용한 온라인 유한요소 모델 업데이트 방법을 제안한다. 일반적인 최적화 방법에 기반한 기존의 방법은 최적해를 찾기까지 반복적으로 고유치 해석을 수행해야 하므로 상시 업데이트에 사용하기에는 효과적이지 못하다. 제안하는 방법은 별도의 오프라인 작업이나 사용자의 개입이 없이 자동화된 과정으로 계측과 동시에 온라인 유한요소모델 업데이트를 수행할 수 있는 새로운 방법이다. 자동화된 Cov-SSI 알고리즘을 통해 구조물의 진동 계측 신호로부터 고유진동수 및 모드 형상을 식별하고, 이를 다시 역 고유치 신경망에 입력하여 최종적으로 업데이트된 유한요소 모델의 파라미터를 추정한다. 풍하중을 받는 20층 전단 빌딩 구조 모형에 대한 수치예제를 통해 제시한 방법이 자동으로 연속적인 유한요소모델 업데이트를 할 수 있었음을 확인하였다. 또한, 계측 도중 구조물의 특성이 변화하는 시나리오에 대한 예제에서 구조물의 변화가 일어나는 시점과 변화 후 변동된 구조 모델 파라미터 값을 성공적으로 추정할 수 있음을 확인하였다.

• Structural Engineering and Mechanics
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• 제44권3호
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• pp.379-403
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• 2012

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

• 대한간호학회지
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• 제34권2호
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• pp.333-343
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• 2004

Purpose: This study was designed to construct a structural model for symptom management of life of the patients with chronic fatigue. The hypothetical model was developed based on the literature review and Self-regulating Model. Method: Data were collected by questionnaires from 252 patients with chronic fatigue in the 8 community from December 2002 to April 2003 in Seoul. Data analysis was done with SAS for descriptive statistics and PC-LISREL Program for Covariance structural analysis. Result: The fit of the hypothetical model to the data was moderate, thus it was modified by excluding 4 path and including free parameters and 3 path to it The modified model with path showed a good fitness to the empirical data($x^2$=318.11, p=0.0, GFI=.98, AGFI=.98, NNFI=.95, RMSR=.03, RMSEA=.05). The symptoms of stress, self-efficacy, and present fatigue level were found to have significant direct effect on symptom management of the patients with chronic fatigue. The ways of coping, perceived stress, and fatigue symptom were found to have indirect effects on symptom management of the patients with chronic fatigue. Conclusion: The derived model is considered appropriate in explaining and predicting symptom management of the patients with chronic fatigue. Therefore, it can effectively be used as a reference model for further studies and suggested direction in nursing practice.

• Structural Engineering and Mechanics
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• 제28권2호
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• pp.129-152
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• 2008

This paper considers the solution of the stochastic differential equations (SDEs) with random operator and/or random excitation using the spectral SFEM. The random system parameters (involved in the operator) and the random excitations are modeled as second order stochastic processes defined only by their means and covariance functions. All random fields dealt with in this paper are continuous and do not have known explicit forms dependent on the spatial dimension. This fact makes the usage of the finite element (FE) analysis be difficult. Relying on the spectral properties of the covariance function, the Karhunen-Loeve expansion is used to represent these processes to overcome this difficulty. Then, a spectral approximation for the stochastic response (solution) of the SDE is obtained based on the implementation of the concept of generalized inverse defined by the Neumann expansion. This leads to an explicit expression for the solution process as a multivariate polynomial functional of a set of uncorrelated random variables that enables us to compute the statistical moments of the solution vector. To check the validity of this method, two applications are introduced which are, randomly loaded simply supported reinforced concrete beam and reinforced concrete cantilever beam with random bending rigidity. Finally, a more general application, randomly loaded simply supported reinforced concrete beam with random bending rigidity, is presented to illustrate the method.

• Smart Structures and Systems
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• 제32권1호
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• pp.9-21
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• 2023

The parameters of civil engineering structures have time-variant characteristics during their service. When extremely large external excitations, such as earthquake excitation to buildings or overweight vehicles to bridges, apply to structures, sudden or gradual damage may be caused. It is crucially necessary to detect the occurrence time and severity of the damage. The unscented Kalman filter (UKF), as one efficient estimator, is usually used to conduct the recursive identification of parameters. However, the conventional UKF algorithm has a weak tracking ability for time-variant structural parameters. To improve the identification ability of time-variant parameters, an adaptive UKF with forgetting factor (AUKF-FF) algorithm, in which the state covariance, innovation covariance and cross covariance are updated simultaneously with the help of the forgetting factor, is proposed. To verify the effectiveness of the method, this paper conducted two case studies as follows: the identification of time-variant parameters of a simply supported bridge when the vehicle passing, and the model updating of a six-story concrete frame structure with field test during the Yangbi earthquake excitation in Yunnan Province, China. The comparison results of the numerical studies show that the proposed method is superior to the conventional UKF algorithm for the time-variant parameter identification in convergence speed, accuracy and adaptability to the sampling frequency. The field test studies demonstrate that the proposed method can provide suggestions for solving practical problems.