• Smart Structures and Systems
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• 제18권3호
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Nuclear Engineering and Technology
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• 제55권3호
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• pp.968-980
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• 2023

The dynamic structural responses are sensitive to the time-frequency content of seismic waves, and seismic input motions in time-history analysis are usually required to be compatible with design response spectra according to nuclear codes. In order to generate spectra-compatible input motions while maintaining the intrinsic non-stationarity of seismic waves, an improved time-domain approach is proposed in this paper. To maintain the nonstationary characteristics of the given seismic waves, a new time-frequency envelope function is constructed using the Hilbert amplitude spectrum. Based on the intrinsic mode functions (IMFs) obtained from given seismic waves through variational mode decomposition, a new corrective time history is constructed to locally modify the given seismic waves. The proposed corrective time history and time-frequency envelope function are unique for each earthquake records as they are extracted from the given seismic waves. In addition, a dimension reduction iterative technique is presented herein to simultaneously superimpose corrective time histories of all the damping ratios at a specific frequency in the time domain according to optimal weights, which are found by the genetic algorithm (GA). Examples are presented to show the capability of the proposed approach in generating spectra-compatible time histories, especially in maintaining the nonstationary characteristics of seismic records. And numerical results reveal that the modified time histories generated by the proposed method can obtain similar dynamic behaviors of AP1000 nuclear power plant with the natural seismic records. Thus, the proposed method can be efficiently used in the design practices.

• 한국정보통신학회논문지
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• 제26권11호
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• pp.1586-1591
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• 2022

Kirchhoff-Love 판 (KLP) 방정식은 특정 외력이 얇은 막에 끼치는 변형을 기술하는 잘 알려진 이론이다. 한편, frequency 도메인에서 진동하는 판을 해석하는 것은 주요 진동 주파수와 고유함수들을 구하는 것과 판의 진동을 예측하는데 중요하다. 다양한 모드 분석 방법들 중 dynamic mode decomposition (DMD)는 효율적인 data 기반 방법이다. 이 논문에서 우리는 DMD를 기반으로 sine 유형 외력의 영향력 안에 있는 KLP의 모드 분석을 수행한다. 우리는 먼저 유한차분법을 사용하여 이산적으로 표현된 시계열 형식의 KLP 해를 구한다. 720,00개의 FDM으로 생성된 해중에서, 오직 500개의 해만을 DMD의 구현을 위해 선택한다. 우리는 결과적으로 얻어진 DMD-mode를 보고한다. 또한, DMD를 통하여 KLP의 해를 예측하는 효율적인 방법을 소개한다.

• Smart Structures and Systems
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• 제10권2호
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• pp.131-154
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• 2012

In this paper, it is aimed to determine the seismic behaviour of highway bridges by nondestructive testing using ambient vibration measurements. Eynel Highway Bridge which has arch type structural system with a total length of 216 m and located in the Ayvaclk county of Samsun, Turkey is selected as an application. The bridge connects the villages which are separated with Suat U$\breve{g}$urlu Dam Lake. A three dimensional finite element model is first established for a highway bridge using project drawings and an analytical modal analysis is then performed to generate natural frequencies and mode shapes in the three orthogonal directions. The ambient vibration measurements are carried out on the bridge deck under natural excitation such as traffic, human walking and wind loads using Operational Modal Analysis. Sensitive seismic accelerometers are used to collect signals obtained from the experimental tests. To obtain experimental dynamic characteristics, two output-only system identification techniques are employed namely, Enhanced Frequency Domain Decomposition technique in the frequency domain and Stochastic Subspace Identification technique in time domain. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of boundary conditions to reduce the differences between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. After finite element model updating, maximum differences between the natural frequencies are reduced averagely from 23% to 3%. The updated finite element model reflects the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring. Analytical model of the bridge before and after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behaviour. It can be seen from the analysis results that displacements increase by the height of bridge columns and along to middle point of the deck and main arches. Bending moments have an increasing trend along to first and last 50 m and have a decreasing trend long to the middle of the main arches.

• 한국지진공학회논문집
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• 제10권6호
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• pp.115-123
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• 2006

본 논문에서는 구조건전성모니터링(SHM)시스템이 설치된 케이블교량을 대상으로 장기적인 동적거동 특성을 분석하였다. 3차원 유한요소모델을 사용한 모드해석을 통해 모드변수를 추출하였다. 이를 교량에서 측정된 상시진동신호에 대해 주파수영역에서 분석한 고유진동수와 비교하여 정확한 기저모델이 구축되었음을 알 수 있었다. 지난 5년간의 고유진동수와 온도를 통계분석하여 고유진동수가 온도에 선형 반비례하고 있음을 확인하였고, 이러한 온도효과에 대한 평가를 수행하였다. 또한 상시진동신호를 시간영역에서 TDD기법을 적용하여 모드형상을 추출하였으며, 모드해석 결과와의 비교를 통해 케이블교량에 적용이 가능함을 검증하였다.

• 한국구조물진단유지관리공학회 논문집
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• 제15권1호
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• pp.120-128
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• 2011

변전소 구조물은 가스절연개폐기의 개폐로 유발되는 주기적인 충격하중을 받게 된다. 이러한 주기적인 충격하중은 구조물의 노후화 및 열화를 가속화하여 구조물의 일부 부재 혹은 전반적인 강성 감소를 초래할 수 있다. 본 연구에서는 이러한 주기적인 충격하중이 구조물에 미치는 영향을 정량적으로 평가하고, 손상 정도를 평가하는 방법을 선정하고자 한다. 따라서, 개략적인 손상 정도를 파악할 수 있는 효율적인 방법으로 인지되고 있는 시스템식별기법을 선정하였다. 선정된 기법에 대한 신뢰성을 검증하기 위해 3층 1경간 축소 모형을 대상으로 모의 손상실험 및 해석을 통해 모드형상 및 고유진동수를 산정하고 결과를 비교하였다.

• Smart Structures and Systems
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• 제19권1호
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• pp.39-46
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• 2017

Finite element model updating is very effective procedure to determine the uncertainty parameters in structural model and minimize the differences between experimentally and numerically identified dynamic characteristics. This procedure can be practiced with manual and automatic model updating procedures. The manual model updating involves manual changes of geometry and analyses parameters by trial and error, guided by engineering judgement. Besides, the automated updating is performed by constructing a series of loops based on optimization procedures. This paper addresses the ambient vibration based finite element model updating of long span reinforced concrete highway bridges using manual model updating procedure. Birecik Highway Bridge located on the $81^{st}km$ of Şanliurfa-Gaziantep state highway over Firat River in Turkey is selected as a case study. The structural carrier system of the bridge consists of two main parts: Arch and Beam Compartments. In this part of the paper, the arch compartment is investigated. Three dimensional finite element model of the arch compartment of the bridge is constructed using SAP2000 software to determine the dynamic characteristics, numerically. Operational Modal Analysis method is used to extract dynamic characteristics using Enhanced Frequency Domain Decomposition method. Numerically and experimentally identified dynamic characteristics are compared with each other and finite element model of the arch compartment of the bridge is updated manually by changing some uncertain parameters such as section properties, damages, boundary conditions and material properties to reduce the difference between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of long span highway bridges. Maximum differences between the natural frequencies are reduced averagely from %49.1 to %0.6 by model updating. Also, a good harmony is found between mode shapes after finite element model updating.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• 한국통신학회논문지
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• 제29권11C호
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• pp.1518-1526
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• 2004

본 논문은 확장 QR-RLS 알고리즘을 이용한 시스토릭 어레이 구조를 갖는 적응 결정 궤환 등화기에 대해서 소개한다. 무선 이동 통신 시스템의 경우 빠른 시변환 채널로 인해 고속의 수렴 특성을 갖는 등화기가 필수적으로 요구된다. 최근에 이러한 성질을 만족하는 QR-RLS 알고리즘 기반의 등화기가 소개되었으며, RLS 알고리즘이 갖는 높은 수렴 속도와 시스토릭 어레이의 병렬 파이프라인 형태로 구현 가능함으로 인해 계산상의 높은 효율성을 가진다. 그러나 일반적인 QR-RLS 알고리즘은 별도의 등화기 가중치 추출과정을 필요로 하며, 이로 인해 적응 처리 과정을 완전한 파이프라인 형태로 수행하기는 어렵다. 본 논문에서는 확장 QR-RLS 알고리즘을 기반으로 제곱근 연산을 배제한 계산과정을 통해 채널 출력의 입력으로부터 가중치 갱신까지 완전환 파이프라인 방식으로 처리가 가능한 시스토릭 어레이 구조의 결정 궤환 등화기를 소개한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.