• Smart Structures and Systems
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• v.32 no.4
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• pp.253-266
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• 2023

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.19-26
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• 2020

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

• The Journal of Sustainable Design and Educational Environment Research
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• v.16 no.2
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• pp.1-9
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• 2017

Defect dispute in apartment building has become a debating social issue. The system of defect lawsuit and the conciliation process are applicable to solve defect problems in South Korea. Among various defects, painting work defect is a critical issue because it requires large area works and entails a lot of cost. Accordingly, disputes on work procedure and cost calculation are argued oftenly between residents and housing providers. This study reviewed detailed main issues of painting work and propose relevant systems and standards. In this analysis, the main issues are categorized into pre-works, main work, and others. The most recent cases are compared and analyzed for each issue. After the analysis, following conclusions are obtained, (1) In defect lawsuit system, even though surface treatment work in pre-work step is part of main work, it has been separated and regarded as a separate work. (2) Although the main painting work are not significantly different from two systems, it is still necessary to achieve a consensus to close the gap in the methodology of painting area calculation and determining whole painting or partial painting. (3) In addition, unlike the profit rate of general construction works, that of painting work remained the maximum rate and additional charge rate for works carried out in higher place are different among cases. Therefore, it is determined that establishing consistent standards is urgent.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.273-275
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• 2005

Active thermography is being used since several years for remote non-destructive testing. It provides thermal images for remote detection and imaging of damages. Also, it is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. For energy deposition, it use external heat sources (e.g., halogen lamp or convective heating) or internal heat generation (e.g., microwaves, eddy current, or elastic wave). Among the external heat sources, the ultrasound is generally used for energy deposition because of defect selective heating up. The heat source generating a thermal wave is provided by the defect itself due to the attenuation of amplitude modulated ultrasound. A defect causes locally enhanced losses and consequently selective heating up. Therefore amplitude modulation of the injected ultrasonic wave turns a defect into a thermal wave transmitter whose signal is detected at the surface by thermal infrared camera. This way ultrasound thermography(UT) allows for selective defect detection which enhances the probability of defect detection in the presence of complicated intact structures. In this paper the applicability of UT for fast defect detection is described. Examples are presented showing the detection of defects in PCB material. Measurements were performed on various kinds of typical defects in PCB materials (both Cu metal and non-metal epoxy). The obtained thermal image reveals area of defect in row of thick epoxy material and PCB.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1922-1928
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• 1999

Rotary bending fatigue tests were carried out to investigate the influence of artificial defects on fatigue limit in annealed and austempered ductile iron. Obtained main results are as follows : (1) Artificial defect(micro hole type, dia.<0.4 mm) on specimen surface did not bring about a obvious reduction of fatigue limit in austempered ductile iron(ADI) as compared with annealed ductile iron. (2) According to the investigation of $\sqrt{area}_c$ which is the critical defect size to crack initiation at artificial defect, $\sqrt{area}_c$ of ADI is larger than that of annealed ductile iron. This shows that the situation of crack initiation at artificial defect in ADI is more difficult in comparison with annealed ductile iron. (3) One of the reasons for the low rate of crack initiation from artificial defect in ADI is that the resistance of matrix to crack initiation is higher than that of annealed ductile iron. (4) In case that the $\sqrt{area}$ of artificial defect and graphite nodule is the same, the rate of crack initiation from graphite nodule is higher than that from artificial defect. This reason is that the serious ruggedness around graphite nodule is formed by austempering treatment.

• 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 %.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2357-2363
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• 2021

Silicon carbide is widely used in radiation environments due to its excellent properties. However, when exposed to the strong radiation environment constantly, plenty of defects are generated, thus causing the material performance downgrades or failures. In this paper, the two-temperature model (2T-MD) is used to explore the defect recovery process by applying the electronic energy loss (Se) on the pre-damaged system. The effects of defect concentration and the applied electronic energy loss on the defect recovery process are investigated, respectively. The results demonstrate that almost no defect recovery takes place until the defect density in the damage region or the local defect density is large enough, and the probability of defect recovery increases with the defect concentration. Additionally, the results indicate that the defect recovery induced by swift heavy ions is mainly connected with the homogeneous recombination of the carbon defects, while the probability of heterogeneous recombination is mainly dependent on the silicon defects.

• Journal of Chest Surgery
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• v.17 no.3
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• pp.398-401
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• 1984

Aortopulmonary septal defect is rare congenital heart disease. An 8-year-old girl was diagnosed as a ventricular septal defect with patent ductus arteriosus at Department of Thoracic and Cardiovascular Surgery of Chungnam National University Hospital. On operation, the defect was confirmed as an aortopulmonary septal defect [Type I], anomalous origin of right pulmonary artery from aorta [Type Ill] and patent ductus arteriosus. The defect was repaired anatomically with cardiopulmonary bypass. But she was not survived because of uncontrollable bleeding from aorta.

• Journal of the Korean Ceramic Society
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• v.44 no.12
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• pp.660-682
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• 2007

An SOFC consists of all ceramic complex oxides each with different electrochemical-property requirements. These requirements, in principle, can be made met to a great extent by controlling or tailoring the defect structure of the oxide. This paper reviews the defect structure, nonstoichiometry as a measure of the total defect concentration, and the defect relaxation kinetics of complex oxides that are currently involved in a variety of growing applications today.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.637-640
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• 1999

In this Paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. The proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.6 respectively.