• Smart Media Journal
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• v.11 no.11
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.1
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• pp.34-39
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• 2013

In the advanced research deducted infrared thermography (IRT) test using 4 inch pipe with artificial wall-thinning defect to measure on the wall-thinned nuclear pipe components. This study conducted for defect detection condition of nuclear small-bore pipe research using deducted condition in the advanced research. Defect process is processed by change for defect length, circumferential direction angle, wall-thinning depth. In the used equipment IR camera and two halogen lamps, whose full power capacitany is 1 kW, halogen lamps and Target pipe experiment performed to the distance of the changed 1 m, 1.5 m, 2 m. To analysis of the experimental results ensure for the temperature distribution data, by this data measure for defect length. artificial defect of 4 inch pipe is high reliability in the 2 m, but small-bore pipe is in the 1.5 m from the defect clearly was detected.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1673-1675
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• 2001

In this paper, Partial Discharge(PD) patterns are compared by means of Low Frequency PD(LFPD, based on the IEC 270) and recently proposed High Frequency PD(HFPD). For this purpose, three different types of artificial defects are provided such as corona in air, void in epoxy insulator and needle defect in XLPE cable insulation. PD were generated from each defect and then detected respectively by two different methods such as LFPD and HFPD. As a result, remarkable resemblance in PD pattern for differ detecting method have been observed from each defect. Accordingly, it could be deduced that the pattern recognition by LFPD could be regarded as the reference for the investigations by HFPD.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.289-292
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• 2006

In this paper, the hybrid algorithm of Support Vector Machine md Artificial Neural Network is used for the defect diagnostics algorithm for the aircraft turbo-shaft engine. The results of learning of ANN, especially, accuracy or speed of convergence are sensitive to the number of data, so a comparison between design point and off-design area, especially, Mach number and fuel flow variable area, is essential research. From application results for diagnostics of gas turbine engine, it was confirmed that the hybrid algorithm could detect well in the of-design area as well as design point.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.102-109
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• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.

• Transactions of Materials Processing
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• v.32 no.5
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• pp.276-286
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• 2023

The field of sheet metal forming using press technology has become essential in modern mass production systems. Draw bead is often used to enhance formability. However, optimal draw bead design often requires excessive time and cost due to iterative experimentation and sometimes results in some defects. Given these challenges, there is a need to enhance formability by introducing segmented blank holders without draw beads. In this paper, the feasibility of a localized holding strategy using segmented blank holders is evaluated without the use of draw beads. The possibility for improving the formability was evaluated by utilizing a combination of the forming limit diagram and the wrinkle pattern-based defect indicators. Artificial neural networks were used for predicting defect indicators corresponding to arbitrary input holding forces and the NSGA-II optimization algorithm is used to find optimum blank holder forces yielding better defect indicators than the original process with drawbeads. Using optimum holding forces obtained from the proposed procedure, the stamping process with the segmented blank holders can yield better formability than the conventional process with drawbeads.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Journal of Power System Engineering
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• v.15 no.4
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• pp.37-41
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• 2011

This paper introduces the special eddy current sensor and its characteristic for bolt hole defect evaluation in gas turbine rotor. In the past, Fluorescent penetration inspection method was used for qualitative defect evaluation in gas turbine rotor bolt hole. This method can defect the bolt hole defect but can not evaluate the defect size. Nowadays, eddy current method is used quantitative defect evaluation due to advanced sensor design technology. And eddy current method is more time and cost saving than the old method. We developed bolt shape eddy current sensor for the rotor bolt hole defect detection and evaluation. The eddy current sensor moves to the bolt hole guided by screw nut and detects the defect on the bolt hole. The bolt hole mock-up and artificial defects were made and used for the signal detection & resolution analysis of eddy current sensor. The results show that signal detection capability is enough to detect 0.2 mm depth defect. And the resolution capability is enough to differentiate 02, 0.5, 1.0 and 2.0 mm depth defect.

• Journal of Ocean Engineering and Technology
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• v.10 no.1
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• pp.53-64
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• 1996

In this paper, rotating bending fatigue tests have been carried out to investigate the growth behabiors of surface fatigue crack initiated from a small artificial surface defect, that might exist in real structures, on 2024-T3 and 6:4 brass. The test results are analysed in the viewpoints of both strength of materials and fracture mechanics, it can be concluded as follows. The effect of a small artificial surface defect upon the fatigue strength is very large. The sensitivity of 2024-T3 on the defect is higher than that of 6:4 brass. The growth behavior of the surface fatigue crack of 2024-T3 is different from that of 6:4 brass. The growth rate of the surface fatigue crack of 2024-T3 is considerably rapid in the early stage of the fatigue life and apt to decrease in the later stage. It was impossible to establish a unifying approach in the analysis of crack growth begabior of 2024-T3 and 6:4 brass using the maximum stress intensity factor because of their dependence on stress level. But if the elastic strain and cyclic total strain intensity factor range were applied to obtain the growth rate of surface fatigue cracks of the materials, the data were found to be nearly coincided.

• Journal of Chest Surgery
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• v.10 no.1
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• pp.143-147
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• 1977

Atrial septal defect is the most common of the congenital heart disease in the adult. Since the first description of atrial septal defect by Rokitansky in 1857, this anomaly has been studied by many workers in past one century. In 1953, Lewis had first corrected the atrial septal defect under direct vision with deep hypothermia, and Gibbon [1954] had first done the atrial septal defect under direct vision with extracorporeal circulation. In our college [May 2’ 1977], we have first repaired the A.S.D. under direct vision with artificial heart-lung machine and, the defect was 4x5cm in size which was closed by Dacron patch. This patient was 12 year old girl who is well now. [postoperative 13 days]