• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2009년도 추계학술대회 논문집
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• pp.169-172
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• 2009

Multiple scale modeling has been applied to predict defect shape change during the wire rod rolling process. The size difference between bloom and defect prevent using usual FEM approaches due to the enormous number of elements required to depict the defect. The newly developed multiple scale model can visualize defect shape changes during the multi stands rolling process. The defect positioned at the top and side of bloom are smoothed out but the one at the middle evolved as folding or remained as crack. This approach can be used for defect control with roll shape design and initial bloom shape.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권12호
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• pp.943-947
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• 2009

소프트웨어의 결함을 예측하고 검증과 확인 활동을 통하여 효율적인 자원을 관리하기 위하여 많은 연구에서 결함 예측 모형을 제안하고 있다. 하지만 기존의 연구는 예측율이 최대 효과를 보이는 임계치에 결함 예측 모형의 예측율을 평가하고 있다. 이는 측정 시스템의 결함 정보를 알고 있는 가정하에서 평가가 이루어지는 것이기 때문에 실제 결함 정보를 알 수 없는 시스템에서는 최적의 임계치를 결정할 수 없다. 그러므로 임계치 선정의 중요성을 확인하기 위하여 본 연구에서는 결함 예측 모형으로 타 시스템의 결함을 예측하는 비교 실험을 하였다. 실험은 기존에 제안된 3개의 결함 예측 모형과 4개의 시스템을 대상으로 하였고 결함 예측 모형의 임계치별 예측의 정확성을 비교하였다. 실험결과에서 임계치는 모형의 예측율과 높은 관련이 있었지만 실제 결함 정보가 확인 안 되는 시스템에 대하여 결함을 예측하는 경우에는 임계치를 선정할 수 없음을 확인하였다. 따라서 결함 예측 모형을 타 시스템에 적용하기 위하석 임계치 선정에 관한 추후 연구가 필요함을 확인하였다.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제40권
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• pp.34.1-34.8
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• 2018

Background: Radiation therapy is widely employed in the treatment of head and neck cancer. Adverse effects of therapeutic irradiation include delayed bone healing after dental extraction or impaired bone regeneration at the irradiated bony defect. Development of a reliable experimental model may be beneficial to study tissue regeneration in the irradiated field. The current study aimed to develop a relevant animal model of post-radiation cranial bone defect. Methods: A lead shielding block was designed for selective external irradiation of the mouse calvaria. Critical-size calvarial defect was created 2 weeks after the irradiation. The defect was filled with a collagen scaffold, with or without incorporation of bone morphogenetic protein 2 (BMP-2) (1 ㎍/ml). The non-irradiated mice treated with or without BMP-2-included scaffold served as control. Four weeks after the surgery, the specimens were harvested and the degree of bone formation was evaluated by histological and radiographical examinations. Results: BMP-2-treated scaffold yielded significant bone regeneration in the mice calvarial defects. However, a single fraction of external irradiation was observed to eliminate the bone regeneration capacity of the BMP-2-incorporated scaffold without influencing the survival of the animals. Conclusion: The current study established an efficient model for post-radiation cranial bone regeneration and can be applied for evaluating the robust bone formation system using various chemokines or agents in unfavorable, demanding radiation-related bone defect models.

• 정보과학회 논문지
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• 제42권11호
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• pp.1349-1360
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• 2015

소프트웨어 품질을 향상하기 위해서는 소스에 내재된 결함을 효율적, 효과적으로 제거해야 한다. 개발현장에서는 결함 심각도와 결함 제거율로 결함을 제거하고 있다. 결함을 이용하여 품질을 향상하기 위한 연구로는 결함 발생 빈도과 ISO 품질속성을 이용하여 품질을 향상하려는 연구가 있고, 프로젝트 수행 시 결함을 심각도로 관리하여 품질을 향상시키는 연구가 있었다. 하지만, 결함 자체에 집중하여 결함을 유형화하여 결함 유형 간에는 어떤 영향력이 있는지, 그 영향력으로 인하여 어느 결함 유형이 더 중요한 지에 대한 연구는 미흡한 실정이었다. 이에 본 연구에서는 표준단체, 업체, 연구자들의 소프트웨어 결함 유형을 수집, 분류하여 ANP로 모형화하였다. 또한, 구성된 ANP 모형을 이용하여 일반 응용 소프트웨어에 대하여 결함 유형별 중요도 가중치를 산정하였다. 일반 응용 소프트웨어를 개발할 때, 산정된 가중치를 적용하여 결함을 제거한다면, 좀 더 효율적이고 효과적으로 소프트웨어 품질을 향상할 수 있으리라 기대한다.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• 소성∙가공
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• 제11권5호
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• pp.430-438
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• 2002

This paper suggests the new technology to control metal flow in orther to change of the cold forging from conventional deep drawing forming. This technology can be summarized the complex forming, which consists of bulk forming and sheet forming, and multi-action forging, which be performed double action press. The proposed technology is applied to hub clutch model which is part of auto-transmission for automobile. The purpose of this study is to investigate the material flow behavior of hub clutch through control the relative velocity ratio and the stroke of mandrel and punch using the flow forming technique. First of all, the finite element simulations are applied to analyse optimal process conditions to prevent flow defect(necking defect etc.) from non-uniform metal flow, then the results are compared with the plasticine model material experiments. The punch load for real material is predict from similarity law. Finally, the model material experiment results are in good agreement with the FE simulation ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4763-4775
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• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• 전기학회논문지
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• 제58권10호
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• pp.2000-2004
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• 2009

In general, for the condition monitoring of a power transformer using the UHF PD measuring technique, detection of any partial discharge, identifying the defect in the transformer and locating the insulation defect are necessary. In this paper one of the most frequent detects which can result in turn to turn fault in power transformer was examined for identifying the defect. In order to model the defect, as a discharge source, a partial discharge cell was used for experimental activity. Magnitude of electromagnetic wave signals and corresponding amount of apparent discharge were measured simultaneously against phase of applied voltage to the discharge cell. Frequency range and phase resolved partial discharge signals were measured and analyzed. The results will be contributed to build the defect database of power transformer and to decrease the occurrence of transformer faults.

• 스마트미디어저널
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• 제11권11호
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• pp.92-98
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• 2022

섬유산업에서 생산된 직물의 결함을 식별하는 것은 품질관리를 위한 핵심적인 절차이다. 본 연구는 직물의 이미지를 분석하여 결함을 검출하는 모델을 만들고자 하였다. 연구에 사용된 모델은 딥러닝 기반의 VGGNet 과 ResNet이었고, 두 모델의 결함 검출 성능을 비교하여 평가하였다. 정확도는 VGGNet 모델이 0.859, ResNet 모델이 0.893으로 ResNet 모델의 정확도가 더 높은 결과를 보여주었다. 추가적으로 딥러닝 모델이 직물의 이미지 내에서 결함으로 인식한 부분의 위치를 알아보기 위하여 XAI(eXplainable Artificial Intelligence)기법인 Grad-CAM 알고리즘을 사용하여 모델의 관심영역을 도출하였다. 그 결과 딥러닝 모델이 직물의 결함으로 인식한 부분이 육안으로도 실제 결함이 있는 것으로 확인되었다. 본 연구의 결과는 직물의 결함 검출에 있어서 딥러닝 기반의 인공지능을 활용함으로써 섬유의 생산과정에서 발생하는 시간과 비용을 줄일 수 있을 것으로 기대된다.