• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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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 %.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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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.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.313-320
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• 2008

A typical software development life cycle consists of a series of phases, each of which has some ability to insert and detect defects. To achieve desired quality, we should progress the defect removal with the all phases of the software development. The well-known model of phase-based defect profile is Gaffney model. This model assumes that the defect removal profile follows Rayleigh curve and uses the parameters as the phase index number. However, these is a problem that the location parameter cannot present the peak point of removed defects when you apply Gaffney model to the actual situation. Therefore, Gaffney model failed to represent the actual defect profile. This paper suggests two different models: One is modified Gaffney model that introduce the parameter of Putnam's SLIM model to replace of the location parameter, the other is the growth function model because the cumulative defect profile shows S-shaped. Suggested model is analyzed and verified by the defect profile sets that are obtained from 5 different software projects. We could see from the experiment, the suggested model performed better result than Gaffney model.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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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.

• Korean Journal of Construction Engineering and Management
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• v.20 no.4
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• pp.13-21
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• 2019

The purpose of this study is to suggest an Improvement Model of defect information management system. The improvement model adapts methods for the residents to input defect information correctly and share to defect information with construction company. The adapted research method is review for existing defect information management system and suggested for data flow diagram of improvement model. The results of this study are as follows: The basic design of the information input window of the defect information management system for connecting with big data was made. And 5 point scale was applied to evaluate the convenience, simplicity, accuracy, necessity, and usability of the improvement model. It is evaluated that the economic effect caused by using the improvement model is saved by about 151 million KRW compared to the existing method. The Improvement model is used utilize big data in correct defect management and decision making.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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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.

• Journal of the Korean Society of Safety
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• v.18 no.4
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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.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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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%.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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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.