• 비파괴검사학회지
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• 제34권1호
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• pp.10-17
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• 2014

A 3D model based on the finite element method (FEM) was built to simulate the infrared thermography (IRT) inspection process. Thermal contrast is an important parameter in IRT and was proven to be a function of defect parameters. Parametric studies were conducted on internal defects with different depths, thicknesses, and orientations. Thermal contrast evolution profiles with respect to the time of the defect and host material were obtained through numerical simulation. The thermal contrast decreased with defect depth and slightly increased with defect thickness. Different orientations of thin defects were detected with IRT, but doing so for thick defects was difficult. These thermal contrast variations with the defect depth, thickness, and orientation can help in optimizing the experimental process and interpretation of data from IRT.

• Journal of Advanced Marine Engineering and Technology
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• 제20권1호
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• pp.13-23
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• 1996

Weld defects degrade the strength and safety of astructure and are resulted from the various cases. The complexity of causal relation of weld defects requires an expert for the analysis of weld defects and the measures counter to them. An expert system has the intelligent functions such as the representation of knowledge and the inference. On this research, weld defect are systematically analysed and their causal model is developed. This information is saved to the knowledge base. The suitable inference algorithm for the diagnosis of weld defects is developed and realized with C++ programming.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2003년도 International Meeting on Information Display
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• pp.1096-1100
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• 2003

A fast Q-tensor method, which can model the defect dynamics in a liquid crystal director field is presented. The method is used to model the defect dynamics occurring during the "warm-up" of a ${\pi}-cell$ device.

• Nuclear Engineering and Technology
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• 제55권3호
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• pp.958-967
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• 2023

This work studies the defect features in a dilute FeMnNi alloy by an Object Kinetic Monte Carlo (OKMC) model based on the "grey-alloy" method. The dose rate effect is studied at 573 K in a wide range of dose rates from 10^-8 to 10^-4 displacement per atom (dpa)/s and demonstrates that the density of defect clusters rises while the average size of defect clusters decreases with increasing dose rate. However, the dose-rate effect decreases with increasing irradiation dose. The model considered two realistic mechanisms for producing <100>-type self-interstitial atom (SIA) loops and gave reasonable production ratios compared with experimental results. Our simulation shows that the proportion of <100>-type SIA loops could change obviously with the dose rate, influencing hardening prediction for various dose rates irradiation. We also investigated ways to compensate for the dose rate effect. The simulation results verified that about a 100 K temperature shift at a high dose rate of 1×10^-4 dpa/s could produce similar irradiation microstructures to a lower dose rate of 1×10^-7 dpa/s irradiation, including matrix defects and deduced solute migration events. The work brings new insight into the OKMC modeling and the dose rate effect of the Fe-based alloys.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.625-627
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• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.310-315
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• 2003

This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2006년도 6th International Meeting on Information Display
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• pp.858-861
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• 2006

In this paper we model the liquid crystal director field in the Patterned Vertical Alignment (PVA) LC using the fast Q-tensor method, which can model multidimensional director configurations with defects in the liquid crystal director field. We observed the dynamic behaviors of the defect experimentally by applying the voltage and modeled the LC director field with defect in the active area of the PVA cell. As a result, we could also calculate the optical transmittance.

• EDISON SW 활용 경진대회 논문집
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• 제5회(2016년)
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• pp.75-79
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• 2016

A helical nanosturctrue can be obtained by self-assembly method. Utilizing DPD simulation coarse-grained model, we patterned 2D layer nanosheets with repeated diagonal defects and grafts, and programed to self-roll into hollow helix structure. The defected pattern side caused anisotropy, and formed helix or helix-like structure. This opens the possibility to control the helix pitch or cavity radius. In this work, we designed several patterns about diagonal defect with a variety of defect side densities and defect widths and then simulation was carried out. Thus, our results have that parameters are affecting self-assembly of nanosheets and their conformation.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권1호
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• pp.1-6
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• 2013

소프트웨어의 결함 경향 모듈 예측을 위해 SVM 분류기가 우수한 성능을 보인다는 연구들이 많지만, SVM에서 필요한 파라미터 선정 시 매 커널마다 다르게 선정해야 하고, 파라미터의 변경에 따른 결과예측을 위해 알고리즘을 반복적으로 수행해야 하는 불편함이 있다. 따라서 본 논문에서는 SVM의 파라미터 선정 시 유전알고리즘을 이용하여 스스로 찾게 하는 GA-SVM 모델을 구현하였다. 그리고 분류 성능 비교를 위해 신경망의 역전파알고리즘을 이용하여 분류했던 기존 논문과 비교 분석한 결과, GA-SVM 모델의 성능이 더 우수함을 확인하였다.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.