• Smart Structures and Systems
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• 제14권5호
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• pp.831-845
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• 2014

This paper proposed a discrete wavelet transform based method for time-varying physical parameter identification of shear type structures. The time-varying physical parameters are dispersed and expanded at multi-scale as profile and detail signal using discrete wavelet basis. To reduce the number of unknown quantity, the wavelet coefficients that reflect the detail signal are ignored by setting as zero value. Consequently, the time-varying parameter can be approximately estimated only using the scale coefficients that reflect the profile signal, and the identification task is transformed to an equivalent time-invariant scale coefficient estimation. The time-invariant scale coefficients can be simply estimated using regular least-squares methods, and then the original time-varying physical parameters can be reconstructed by using the identified time-invariant scale coefficients. To reduce the influence of the ill-posed problem of equation resolving caused by noise, the Tikhonov regularization method instead of regular least-squares method is used in the paper to estimate the scale coefficients. A two-story shear type frame structure with time-varying stiffness and damping are simulated to validate the effectiveness and accuracy of the proposed method. It is demonstrated that the identified time-varying stiffness is with a good accuracy, while the identified damping is sensitive to noise.

• 대한기계학회논문집A
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• 제34권7호
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• pp.873-879
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• 2010

일반적으로 설계파라미터 변경에 의한 구조물의 동특성변화를 예측하는 연구에 비해 동특성 변화로부터 변경된 설계파라미터를 예측하는 연구는 잘 알려져 있지 않다. 여기에서는 감도계수와 반복법을 사용하여 비례감쇠계의 변경된 설계파라미터를 예측하였다. 감도계수는 변경에 의한 고유벡터의 변화로부터 구하였다. 이 방법을 3 층 전단 구조물에 적용하여 변경된 설계 파라미터를 예측하였으며 재 해석한 결과와 잘 일치함을 알았다.

• Bulletin of the Korean Chemical Society
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• 제28권9호
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.59-64
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• 2003

The parameter modification of the initial FEM model to match it with the experimental results needs the modal information and the modal sensitivity matrix to the parameter change. There are two cases this methodology is ill-equip to deal with; the deficiency of the necessary modal information and the ill-conditioning of the sensitivity matrix. In this research, a novel concept of the feedback exciter that uses the summation of the white noise and the signals from the measurement sensors multiplied with feedback gains as the reference signal is proposed. There are 2 advantages using this external feedback excitation. First, we can use the change of the system response such as modal data by the active energy Path from the sensor to the exciter. This change of the system response can be additional clues to the system dynamics that we want to know. Secondly, the external energy Path alternates the offset of the Parameter change to the system response. That means the modal sensitivity of the parameters becomes different from the original sensitivities by the feedback excitation. Through the feedback loop, we can change the similar modal sensitivities of some updating parameters and consequently discriminate the parameters using the closed-loop modal data. To demonstrate the discrimination performance, the parameter estimation of an indeterminate structure by use of the feedback method is introduced.

• 대한수학회보
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• 제57권3호
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• pp.751-762
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• 2020

We define the average of a set of continuous functions of two variables (surfaces) using the structure of the two-parameter Wiener space that constitutes a probability space. The average of a sample set in the two-parameter Wiener space is defined employing the two-parameter Wiener process, which provides the concept of distribution over the two-parameter Wiener space. The average defined in our work, called an average function, also turns out to be a continuous function which is very desirable. It is proved that the average function also lies within the range of the sample set. The average function can be applied to model 3D shapes, which are regarded as their boundaries (surfaces), and serve as the average shape of them.

• Steel and Composite Structures
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• 제43권2호
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• pp.221-228
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• 2022

Two-dimensional stagnation point slip flow of a Casson fluid impinging normally on a flat linearly shrinking surface is considered. The modeled PDEs are changed into nonlinear ODEs through appropriate nonlinear transformations.The flow is assumed to be steady and incompressible, with external magnetic field acting on it. Similarity transformation is utilized to investigate the behavior of many parameters for heat and velocity distributions using truncation approach.The influence of buoyancy parameter, slip parameter, shrinking parameter, Casson fluid parameter on the heat profile. The effect of the magnetic parameter on the streamwise velocity profile is also investigated.

• Structural Engineering and Mechanics
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• 제45권4호
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• pp.423-437
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• 2013

Complex structures are usually assembled from several substructures with joints connecting them together. These joints have significant effects on the dynamic behavior of the assembled structure and must be accurately modeled. In structural analysis, these joints are often simplified by assuming ideal boundary conditions. However, the dynamic behavior predicted on the basis of the simplified model may have significant errors. This has prompted the researchers to include the effect of joint stiffness in the structural model and to estimate the stiffness parameters using inverse dynamics. In the present work, structural joints have been modeled as a pair of translational and rotational springs and frequency equation of the overall system has been developed using sub-structure synthesis. It is shown that using first few natural frequencies of the system, one can obtain a set of over-determined system of equations involving the unknown stiffness parameters. Method of multi-linear regression is then applied to obtain the best estimate of the unknown stiffness parameters. The estimation procedure has been developed for a two parameter joint stiffness matrix.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권2호
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• pp.143-149
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• 2017

Purpose: In recent years, weight reduction for improving fuel efficiency of the vehicle and cost reduction have been developed. The structure of suspension link is widely used as a single plate press structure which can reduce process and weight compared to existing pipe welding method. However, it was found that the lifetime of a single plate press structure is determined by initial defects that occurred during initial manufacturing rather than fatigue damage caused by driving. Methods: I research the mechanism of failure phenomenon of the single plate press assist arm of rear wheel. In addition, I investigate durability effect parameters to determine the link lifetime in inserting bush into single plate press process through durability test. Conclusion: I discover significant durability effect parameter in inserting bush into single plate press process. It is expected that the durability can be improved by suggesting a bush inserting process inspection guide for similar suspension link like single plate press structure.

• 한국구조물진단유지관리공학회 논문집
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• 제12권4호
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• pp.149-160
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• 2008

본 논문에서는 구조물의 응답 신호만을 스마트 센서 시스템으로 획득하여 모달 인자들을 추출하는 연구를 수행하였다. 본 연구의 목적은 최근에 차세대 계측시스템으로 활발히 연구 되고 있는 스마트 센서 시스템의 성능과 현장 적용 가능성을 검증하는데 있다. 본 연구에 사용된 스마트 센서 시스템은 MEMS형 가속도 센서와 8bit CPU, 무선모뎀을 이용하여 실시간 동적계측이 가능하도록 개발되었다. 모달 인자 추출 실험은 모형 캔틸레버 보에 임의 가진을 가한 후, 구조물의 응답을 스마트 센서와 범용계측장비로 각각 획득하였다. 데이터 분석은 NExT & ERA 알고리즘을 이용하여 모달 인자를 추출하였다. 또한, 양질의 데이터를 획득하기 위하여 EOT알고리즘으로 최적의 계측위치를 선정하였다. 실험 결과, 스마트 센서의 현장 적용 가능성을 확인할 수 있었다.