• Journal of KIISE
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• v.42 no.11
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• pp.1349-1360
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• 2015

In order to improve software quality, it is necessary to efficiently and effectively remove software defects in source codes. In the development field, defects are removed according to removal ratio or severity of defects. There are several studies on the removal of defects based on software quality attributes, and several other studies have been done to improve the software quality using classification of the severity of defects, when working on projects. These studies have thus far been insufficient in terms of identifying if there exists relationships between defects or whether any type of defect is more important than others. Therefore, in this study, we collected various types of software defects, standards organization, companies, and researchers. We modeled the defects types using an ANP model, and developed the weighted severities of the defects types, with respect to the general application software, using the ANP model. When general application software is developed, we will be able to use the weight for each severity of defect type, and we expect to be able to remove defects efficiently and effectively.

• Computational Structural Engineering
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• v.3 no.4
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• pp.14-23
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• 1990

구조물의 초기결함 민감도해석과 관련하여 카타스트로피 이론을 적용할 수 있는 가능성을 다음과 같이 요약할 수 있다. 첫째, 카타스트로피 이론은 현재까지 수행된 구조불안정현상의 분류에 대한 일반화된 수학적 근거를 제공해 준다. 둘째, 카타스트로피 이론에 의하면 구조물에서의 초기결함 민감도 특성을 위상수학적인 방법론에 의해 적은 계산량으로 구할 수 있다. 셋째, 복잡한 좌굴현상 예를 들면 Modal Interaction, Compound Buckling의 현상이 발생하는 경우 좌굴점근처에서의 분기특성, 초기결함 민감도 특성을 효과적으로 규명하는 모델로서 고차카타스트로피를 이용할 수 있다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1310-1310
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• 2015

대단위 태양광 발전소 또는 고층건물에 설치된 태양광 모듈의 결함을 분석하는데 있어 열화상 카메라를 통한 온도로써 태양광 모듈의 결함을 검출하는 방식이 대두되고 있다. 본 논문에서는 열화상 카메라로 얻은 영상을 온도로 표현하는데 필요한 영상처리를 각각의 태양광 모듈들을 셀 단위로 분류하고 해당 셀을 기준으로 행 이미지를 ROI로 잡은 후 이미지 저장을 하는 방법을 제안한다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.4 no.1
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• pp.3-26
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• 1993

Leo Kanner (1943)의 자폐증에 관한 획기적 논문발표 50주년을 맞아, 자폐증의 개념변천, 분류, 의학, 원인설 및 자폐증의 본질을 문헌고찰을 통해 살펴 보았다. 초기에 자폐증을 정신병의 아형으로 보다가 1980년 (DMS-III)을 기점으로 전반적 발달장애로의 개념 변천이 일어났다 원인설도, 초기의 심리${\cdot}$환경설은 지지 받지 못하고 1960년대에는 신경${\cdot}$생물학적 이상이 자폐병리의 기저를 이룸이 분명해 졌고 1970년대에는 지각과 운동, 감각과 인지 통합의 결함, 심각한 언어, 인지의 장해가 일차적인 결함으로 생각 되었다 최근 1980년 후반기 부터 상징적${\cdot}$표상적 인지의 결함, 타인의 감정과 생각의 이해 결함, 사회적${\cdot}$정감적 표현의 결함등 사회${\cdot}$정서발달의 이상이 자폐의 근본적 결함이라는 비교 관찰 연구가 많이 보고되어, 자폐증의 근본적이고 일차적인 결함이 정감적 접촉의 선천적 장애라는 Kanner의 놀라운 임상적 통찰을 증명해 주고 있다. 저자는 이상의 광범위한 문헌 고찰을 통해 자폐장애를 일차성 애착장애로 개념화하고 앞으로 치료, 교육의 방향도 일차적으로 사회${\cdot}$정서발달에 촛점을 두어야 하며, 특히 어머니와의 애착증진 치료가 필요함을 제안하고 있다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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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.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.62-67
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• 1995

In this paper, the features extraction of reflected ultrasonic signals from various type of defects existing in Al metal has been studied by digital signal processing. Since the reflected signals from various type of the defects are ambiguous in features distinction from effects of noise, Wiener filtering using AR (auto-regressive) technique and least-absolute-values norm method has been used in features extraction and comparison of signals. In this experiment, three types of the defect in aluminum specimen have been considered: a flat cut, an angular cut, a circular hole. And the reflected signal have been measured by pulse-echo methods. In the result of digital signal processing of the reflected signal, it has been found that the features extraction method have been effective for classification of the reflected signals from various defects.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.2
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• pp.102-109
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• 2000

This paper describes two different theories used to model the scattering of ultrasound by a volumetric flaw and a crack-like flaw. The elastodynamic Kirchhoff approximation (EKA) and the geometrical theory of diffraction (GTD) are applied respectively to a cylindrical cavity and a semi-infinite crack. These methods are known as high frequency approximations. The 2-D elastodynamic scattering problems of a plane wave incident on these model defects are considered and the scattered fields are expressed in terms of the reflection and diffraction coefficients. The ratio of the scattered far field amplitude to the incident wave amplitude is computed as a function of the angular location and compared with the boundary element solutions.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2000.04a
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• pp.22-22
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• 2000

본 연구에서는 초음파 탐촉자의 접촉방식을 다양화하여 Immersion법/Bubbler법/Squirtor법으로 복합재 연소관을 탐상하는 방법에 대하여 연구하였으며, 이를 위하여 FRP의 delamination, FRP-EPDM간의 미접착을 모사한 복합재 연소관 모의결함시편을 제작하여 시험하였다. Immersion법에서는 일반적으로 사용되는 방법을 적용하였으며 Bubbler 및 Squirter법에서는 자체적으로 설계 및 제작한 치구를 이용하여 시험하였다. 이들 각 방법에 대하여 복합재 연소관 모의결함 시편의 결함부위에 대한 초음파 신호를 A-scan으로 비교하고 또한 C-scan하여 결함의 검출 정도를 비교하였다. 그리고 시험 자료를 바탕으로 데이터 값을 분석함으로써 복합재 연소관에서의 초음파 속도, 음향 임피던스, 반사율 및 투과율 등 복합재료의 초음파 특성을 산출하였으며 또한 건전신호와 결함신호에 대한 분류기준을 제시하였다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.209-212
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• 2006

In this study, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. Effect of altitude variation on the Defect Diagnostics algorithm has been included and evaluated. Separate learning Algorithm(SLA) suggested with ANN to loam the performance data selectively after classifying the position of defects by SVM improves the classification speed and accuracy.