• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 춘계합동학술대회 논문집
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• pp.117-120
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• 2002

Electrical power system has very complexity problem that it is plan measurement system to achieve Harmonic State Estimation (HSE). This complexity problem depends on discord of necessary accuracy, certainty of noise that exist in data communication damage and converter, adaptability of network modification and minimum of expense size of system, estimated monitering. Also, quantity of available measurement equipment for harmonic measurement has been limited. Therefore, systematic method that choose measurement location for harmonic state estimation. This paper is that see proposed HSE that use Observability Analysis(OA) for harmonic state estimation of electrical power system. OA depends on measurement number, measurement location and measurement form here, it is analysis method that depend on network form and admittance of the system. OA used achieve harmonic state estimation that it is Applied to New Zealand electrical power system to prove validity of HSE algorithm that propose. This study result about harmonic state estimation of electrical power system displayed very economical and effective method by OA.

• Smart Structures and Systems
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• 제6권9호
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Nuclear Engineering and Technology
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• 제43권4호
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• pp.343-354
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• 2011

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

• Structural Engineering and Mechanics
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• 제86권6호
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.

• 한국강구조학회 논문집
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• 제14권5호통권60호
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• pp.641-646
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• 2002

본 논문에서는 정적 처짐데이터를 이용한 교량의 강성추정에 관한 기법을 개발하였다. 제안된 기법은 주기적으로 교량의 자중에 의한 처짐을 계측하여 이들 처짐값의 변화량을 이용하여 손상의 진행정도를 규명하는 기법으로써 재하시험이 필요하지 않으며, 최근 활발히 진행되고 있는 계측모니터링 시스템으로부터 획득된 계측데이터를 정량적으로 분석하는 기법으로 활용이 가능하리라 판단된다. 손상평가를 위한 정식화과정에서 부재의 손상은 강성의 저감으로 표현하였으며, 부재의 질량과 초기강성은 실측 또는 도면을 통하여 획득이 가능하다고 가정하였다. 제안된 기법의 타당성 검증을 위하여 수치모형을 통한 손상도 추정결과를 제시하였으며, 기존의 손상도 추정기법 중 비교적 손상에 대한 민감도가 높은 모드형상을 이용한 기법과 손상도 추정결과를 비교하였다. 또한, 손상추정결과를 정확도를 감소시키는 노이즈의 영향을 분석하기 위하여 정적응답자료에 백색잡음을 추가하여 손상도를 추정하였으며, 손상부재의 수가 손상추정 결과에 미치는 영향을 검토하였다.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• 한국지진공학회논문집
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• 제2권1호
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• pp.35-46
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• 1998

대부분의 손상도 추정법들을 부재의 손상을 해당부재의 평균적인 강성감소로 표현하였다. 본 연구에서는 보다 실제적인 손상도를 추정하기 위하여, 접합부의 손상을 도입하였다. 접합부의 모형화를 위하여 보의 양단에 회전스프링을 추가하였으며, 접합부 손상을 접합부 강성의 감소로 정의하였다. 접합부의 손상도를 계측된 모드벡터를 바탕으로하여, 신경망기법을 추정하였다. 효율적인 훈련패턴을 만들기 위하여 Latin Hypercube Sampling 기법을 도입하였으며, 국부영역에서의 손상도추정을 위하여 부구조법을 도입하였다. 제안된 기법의 효율성을 검증하기 위하여 10층 프레임구조물에 대한 수치해석결과를 이용하였다. 예제해석을 통하여 추정결과가 상당히 정확함을 확인하여, 실제 적용 가능한 방법임을 알수 있었다.

• Smart Structures and Systems
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• 제30권5호
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.553-558
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• 2002

The blast vibration can generate occupants dissatisfy as well as damage of physics nearby building. Then blast vibration estimation issue important problems. But, now blast vibration prediction inside-outside country not established objective method to express magnitude of vibration according to blast number. In this study, Our propose show our country problem of blast vibration about blast vibration measurement and this problems be able to find improve method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.315-318
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• 2007

Non-Destructive Health Monitoring using PZT sensors is a major concern and has great significance for research about NDT (Non-Destructive Test). In this paper, we study about the guided wave measurement method using PZT sensors to find cracks and estimate locations. Two aluminum beams bonded with PZT sensors were tested for estimating about the guided wave propagation characteristics and shape of each beam are decided in terms of analytical purpose. NI Signal Acquisition Device and specially designed LabVIEW VI program were used for data acquisition and analysis. The measured data were progressed by using a high-pass filtering.