• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• 정보처리학회논문지D
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• 제15D권3호
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• pp.313-320
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• 2008

전형적인 소프트웨어 생명주기 모델은 결점이 추가되거나 제거되는 단계들의 일련의 순서로 구성되어 있다. 우리가 원하는 수준의 품질을 달성하기 위해서는 소프트웨어 개발 전 과정에서 결점 제거를 수행하여야 한다. 잘 알려진 단계기반 결점 프로파일은 Gaffney 모델이 있다. 이 모델은 결점 제거 프로파일이 Rayleigh 분포를 따르며 단계 인덱스 번호를 모수로 하고 있다. 실제 개발되는 소프트웨어에 Gaffney 모델을 적용시 제거된 결점이 최대값이 되는 점을 위치 모수가 표현하지 못하는 문제가 있다. 그러므로 Gaffney 모델은 실제 결점 프로파일을 표현하지 못한다. 본 논문은 2개의 다른 모델을 제시한다. 하나는 수정된 Gaffney 모델로 위치 모수를 교체하기 위해 Putnam의 SLIM 모델의 모수를 도입하였다. 다른 하나는 누적 결점 프로파일이 S자 형태를 보여 성장곡선 모델을 제시하였다. 제안된 모델은 5개의 다른 소프트웨어 프로젝트로부터 얻어진 결점 프로파일 분석에 의해 검증하였다. 실험 결과 제안된 모델이 Gaffney 모델 보다 좋은 결과를 얻었다.

• 반도체디스플레이기술학회지
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• 제20권2호
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• pp.109-114
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• 2021

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

• 한국건설관리학회논문집
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• 제20권4호
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• pp.13-21
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• 2019

본 연구의 목적은 거주자가 하자정보를 정확하게 입력하고 건설회사와 정보를 공유 할 수 있는 하자정보 관리시스템에 대한 개선모델을 제시하는 것이다. 이를 위하여 기존의 하자정보 관리시스템을 고찰하고 거주자가 요구하는 기능을 적용하여 개선모델의 구조와 데이터 흐름 계통도를 제시하였다. 그리고 개선모델의 경제적 효과를 추정하였다. 상기와 같은 목적과 방법에 따라 도출된 결과는 다음과 같다. 빅 데이터와 연계하기 위한 하자정보 관리시스템의 정보입력화면에 대한 기본설계를 하였다. 또한 개선 모델을 활용함에 따라 유발되는 경제적 효과는 기존 방법 대비 약 151백만원이 절감되는 것으로 추정되었다.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• 한국안전학회지
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• 제18권4호
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• pp.28-34
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• 2003

In the present study, three-dimensional finite element analysis was performed to investigate the effects of internal wall thinning defect on the failure pressure of elbow in the piping system and to develop the failure pressure evaluation model. From the results of finite element analysis, the failure pressure was derived by employing local stress criteria, and the effects of thinning location, bend radius, and defect geometry on the failure pressure of internally wall thinned elbow were investigated. Also, based on these investigations and previous model developed to estimate the failure pressure of elbow with an external pitting defect, the failure pressure evaluation model to be applicable to the elbow containing an internal thinning defect was proposed and compared with the results of finite element analysis. The failure pressure calculated by the model agreed well with the results of finite element analysis.

• 반도체디스플레이기술학회지
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• 제20권1호
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 추계학술대회 논문집
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• pp.333-336
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• 2008

The groove rolling is a process that transforms the bloom or billet into a shape with circular section through a series of rolling. Inhibition of surface defect generation in groove rolling is a matter of great importance and therefore many research groups proposed a lot of models to find the location of surface defect initiation. In this study, we propose a model for maximum shear stress ratio over equivalent strain to catch the location of surface defect onset. This model is coupled with element removing method and applied to box groove rolling of POSCO No. 3 Rod Mill. Results show that proposed model in this study can find the location of surface defect initiation during groove rolling when finite element analysis results is compared with experiments. The proposed criterion has been applied successfully to design roll grooves which inhibits the generation of surface defect.

• 산업경영시스템학회지
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• 제27권2호
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.