• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.243-248
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• 1999

From the lessons after the Nakhodka oil-spill in Jan. 1997, oil slick detection by using remote sensing data and assimilating the data to the simulation program is important for monitoring the oil-drift pattern. For this object, we are going to construct the oil-spill warning system for estimating the oil-drift pattern using remotesensing/numerical simulation Model. Additionally we plan to use this system for restorating oil-spill damage domestically, such as estimating the ecological damage and making the priority fur restorating the oil-spilled shoreline. This report is intended to summarize the role of geo-informatics in the oil spill accident by not only paying attention to the effect of information provision/information management via the map, but also reporting the interim result in part based on the details discussed in the processes of recovery support and environmental impact assessment during the Nakhodka's accident.

• 한국세라믹학회지
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• 제35권12호
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• pp.1257-1265
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• 1998

Hertzian contact tests were used to investigate the evolution of fracturedamage in the coating layer as functions of contact load and coating thickness by studying crack patterns in porcelain on glass-infiltrated alumina bilayer system conceived to simulate the crown structure of a tooth. Cone cracks initiated at the coating top surface without delamination at interface and crack propagation to substrate. Preferentially the cracks made multi-cracks at the coating top surface rather than proceeding to interface. The cracks were highly stabilized with wide ranges between the loads to initiate first cracking and to cause final failure im-plying damage-tolerant capability. Finite element modelling was used to evaluate the stress distribution. Maximum tensile stress were responsible for the cracking at the coating layer and had a profound influence on the crack pattern and fracture damage in the layered structure materials.

• Structural Engineering and Mechanics
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• 제75권6호
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• pp.723-736
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• 2020

In this paper, acoustic emission (AE) and pattern recognition are utilized to identify the AE signal signatures caused by propagation of stress corrosion cracking (SCC) in a 304 stainless steel plate. The surface of the plate is under almost uniform tensile stress at a notch. A corrosive environment is provided by exposing the notch to a solution of 1% Potassium Tetrathionate by weight. The Global b-value indicated an occurrence of the first visible crack and damage stages during the SCC. Furthermore, a method based on linear regression has been developed for damage identification using AE data.

• 대한기계학회논문집A
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• 제27권4호
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• pp.537-543
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• 2003

Transient vibration generated by developing localized fault in gear can be used as indicators in gear fault detection. This vibration signal suffers from the background noise such as gear meshing frequency and its harmonics and broadband noise. Thus in order to extract the information about the only gear fault from the raw vibration signal measured on the gearbox this signal is processed to reduce the background noise with many kinds of signal-processing tools. However, these signal-processing tools are often very complex and time waste. Thus. in this paper. we propose a novel approach detecting the damage of gearbox and analyzing its pattern using the raw vibration signal. In order to do this, the residual signal. which consists of the sideband components of the gear meshing frequent) and its harmonics frequencies, is extracted from the raw signal by the power spectral density (PSD) to obtain the information about the fault and is used as the input data of the artificial neural network (ANN) for analysis of the pattern of gear fault. This novel approach has been very successfully applied to the damage analysis of a laboratory gearbox.

• Structural Engineering and Mechanics
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• 제75권3호
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• pp.295-309
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• 2020

The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design.

• 한국소음진동공학회논문집
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• 제24권11호
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• pp.840-845
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• 2014

Vibration profiles consist of two kinds of pattern, random and harmonic, at general engineering problems and the detailed vibration test mode of a target system is decided by the spectral condition that is exposed under operation. In moving mobility, random responses come generally from road source; whereas the harmonic responses are triggered from rotating machinery parts, such as combustion engine or drive shaft. Different spectral input may accumulate different damage in frequency domain since the accumulated fatigue damage dependent on the pattern of input spectrum in high cyclic loading condition. To evaluate the sensitivity of spectral damage according to different loading conditions, a linear elastic system is introduced to conduct a uniaxial vibration testing. Measured data, acceleration and strain, is analyzed using energy isocline function and then, the calculated fatigue damage is compared by different loading cases, random and harmonic.

• 비파괴검사학회지
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• 제31권4호
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• pp.351-359
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• 2011

최근 토목, 기계 및 항공 분야에서 구조물의 안전성 및 적정 성능 수준 확보를 위하여 구조물의 결함 및 노후화에 의한 성능저하 등을 상시적으로 모니터링하기 위한 관심이 높아지고 있다. 실제 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능한데, 복합 손상을 단일 모드 계측 시스템으로부터 진단하기는 매우 어렵다. 따라서 본 연구에서는 이러한 복합 손상을 효율적으로 진단하기 위하여 선행 연구에서 제안된 압전센서를 이용한 자가 계측 회로 기반의 다중 모드 계측 시스템을 적용하였다. 자가 계측 회로 기반 다중 모드 계측 시스템은 크게 두 가지 형태의 신호를 계측한다. 첫 번째 모드는 임피던스 계측으로부터 특정 주파수 대역의 구조 응답을 계측하며, 두 번째 모드는 유도 초음파 계측으로부터 단일 중심 주파수에 해당하는 구조 응답을 계측한다. 복합 손상을 손상 유형별로 분류하기 위하여 E/M 임피던스와 유도 초음파의 계측으로부터 추출한 특성을 이용하여 2차원 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법 중 확률론적 신경망 기법에 적용한다. 제안된 기법의 적용성 검토를 배관 구조물에 인위적으로 다중 손상을 생성시켜 실험을 수행하였다.

• Smart Structures and Systems
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• 제8권1호
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• 한국강구조학회 논문집
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• 제17권1호통권74호
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• pp.1-11
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• 2005

이 논문에서는 구조물의 동적응답을 입력으로 하고, 패턴인식을 위해 신경망기법(Neural Network, NN)을 사용하는 손상감지기법을 제시하였다. 입력된 동적응답, 즉 주파수응답함수(FRF) 또는 변형률 주파수응답함수(SFRF)의 변화를 정량적으로 표현하기 위해 신호변형지수(Signal Anomaly Index, SAI)를 고안하여 사용하였으며, 이 신호변형지수는 손상 전 및 손상 후의 구조물로부터 측정된 가속도 또는 동적 변형률 신호를 사용하여 계산된다. 제안된 알고리즘은 2단계로 구성되며, 1단계에서는 신호변형지수 값의 크기 변화를 사용하여 구조물의 손상발생 유무를 판별하고, 여기서 구조물에 손상이 발생한 것으로 분석되면 2단계에서 신경망기법을 사용한 패턴인식을 통해 손상의 위치를 찾아낸다. 이 방법의 타당성 및 적용성을 확인하기 위해 강교량 축소모형에 대한 실험을 수행하였다. 신경망의 학습에는 수치해석을 통해 생성한 가상 신호를 사용하였으며, 학습이 완료된 신경망과 실험을 통해 측정한 실제 신호를 사용하여 손상발견을 수행하였다. 모형 교량에 대한 적용 결과로부터 이 알고리즘의 타당성이 검증되었으며, 향후 실 교량에 대한 적용도 가능할 것으로 판단된다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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• pp.681-688
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• 2003

Rock damage induced by blasting can not be avoided during tunnel construction and may affect tunnel stability. But the mutual interaction between tunnel blasting design and tunnel stability design is generally not considered. Therefore this study propose a methodology to take into considration the results of the blasting damage in tunnel stability design. Rock damage is evaluated by dynamic numerical analysis for the most common blasting pattern adopted in road tunnel. Damage zone is determined by using the continuum damage model which is expressed as a function of volumetric strain. And the damage effect is taken into account by the damaged rock stiffness and the damaged failure criteria in tunnel stability assessment. The extend of plastic zone and deformation increase compared to the case of not considering blast-induced rock damage.