• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.35-43
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• 2023

Along with the increase in Multi-unit housing defect disputes, the importance of defect management is also increased. However, previous studies have mostly focused on the Multi-unit housing's 'common part'. In addition, there is a lack of research on the system for the 'management office', which is a part of the subject of defect management. These resulted in the lack of defect management capability of the management office and the deterioration of management quality. Therefore, this paper proposes a machine learning-based defect data management system for management offices. The goal is to solve the inconvenience of management by using Optical Character Recognition (OCR) and Natural Language Processing (NLP) modules. This system converts handwritten defect information into online text via OCR. By using the language model, the defect information is regenerated along with the form specified by the user. Eventually, the generated text is stored in a database and statistical analysis is performed. Through this chain of system, management office is expected to improve its defect management capabilities and support decision-making.

• 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.

• 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 Biosystems Engineering
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• v.26 no.2
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• pp.169-176
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• 2001

Defect of apple was depreciated the product value and causes storage disease seriously. To detect the defect of ‘Fuji’apple with machine vision system, the optical characteristics of defect should be investigated. In this research, absorbance spectra of defect were acquired by spectrophotometer in the range of visible and NIR region(400∼1,100nm) and L*a*b* color values were also acquired by colorimeter. NIR machine vision system was constructed with B&W camera, frame grabber, 16 tungsten-halogen lamps, variable focal length lens and NIR bandpass filter which was mounted to lens outward. Average gray values of defect at 15 NIR wavelength were acquired and the significant NIR wavelength was selected by comparing Mahalanobis distance between sound and defective apple. As the result of Mahalanobis distance analysis, the significant wavelength to discriminate the defectives in ‘Fuji’apple were found to be 720nm for scab and 970nm for bruise and cuts and 920nm was also effective regardless of defective types.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.585-589
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• 2004

This paper deals with the algorithm development that inspects defects such as Lens Focus, Black Defect, Dim Defect, Color Defect, White Balance, and Line Defect caused by the process of Compact Camera Module (CCM). These days the demand of CCM goes on increasing in various types like PDA, a cellular phone and PC camera every year. However, owing to the defect inspection of CCM by the semiskilled work the average inspection time of CCM takes about 40 to 50 seconds. As time goes by the efficiency takes a sudden turn for the worse because workers must inspect with seeing a monitor directly. In this paper, to solve these problems, we developed the imaging processing algorithm to inspect the defects in captured image of assembled CCM. The performances of the developed inspection system and its algorithm are tested on many samples. Experimental results reveal that the proposed system can focus the lens of CCM within 5s and we can recognize various types of defect of CCM modules with good accuracy and high speed.

• Carbon letters
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• v.16 no.3
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• pp.198-202
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• 2015

Carbon-supported Pt catalyst systems containing defect adsorption sites on the anode of direct methanol fuel cells were investigated, to elucidate the mechanisms of H₂ dissociation and carbon monoxide (CO) poisoning. Density functional theory calculations were carried out to determine the effect of defect sites located neighboring to or distant from the Pt catalyst on H₂ and CO adsorption properties, based on electronic properties such as adsorption energy and electronic band gap. Interestingly, the presence of neighboring defect sites led to a reduction of H₂ dissociation and CO poisoning due to atomic Pt filling the defect sites. At distant sites, H₂ dissociation was active on Pt, but CO filled the defect sites to form carbon π-π bonds, thus enhancing the oxidation of the carbon surface. It should be noted that defect sites can cause CO poisoning, thereby deactivating the anode gradually.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.195-198
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• 2001

Backside defect of plate structure may grow due to fatigue or overload to cause critical failure during operation, so it is important to detect this kind of defect in line. For this purpose, nondestructive, non-contact and highly sensitive method is required. ESPI and Shearography are considered as useful method to satisfy these requirements. In this paper, the possibility of application of ESPI and Shearography to detect the backside defect of steel plate and to quantify the defect size was tested. For the experiment, some steel plates with defect on the backside were prepared. Experimental results for these plates showed that location and size of defect could be detected correctly by both of ESPI and Shearography.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.3 no.3
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• pp.23-25
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• 2004

The defect formation energy of boron nitride (BN) nanotubes is investigated using molecular-dynamics simulation. Although the defect with tetragon-octagon pairs (4-88-4) is favored in the flat cap of BN nanotubes, BN clusters, and the growth of BN nanotubes, the formation energy of the 4-88-4 defect is significantly higher than that of the pentagon-heptagon pairs (5-77-5) defect in BN nanotubes. The 5-77-5 defect reduces the effect of the structural distortion caused by the 4-88-4 defect, in spite of homoelemental bonds.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1288-1296
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• 2016

The reliable detection of the limited defect in TFT-LCD images is difficult due to the small intensity difference with the background. However, the proposed detection method reliably detects the limited defect by enhancing the TFT-LCD image based on the cumulative histogram and then detecting the defect through the mean and standard deviation of the enhanced image. Notably, an image enhancement using a cumulative histogram increases the intensity contrast between the background and the limited defect, which then allows defects to be detected by using the mean and standard deviation of the enhanced image. Furthermore, through the comparison with the histogram equalization, we confirm that the proposed algorithm suppresses the emphasis of the noise. Experimental comparative results using real TFT-LCD images and pseudo images show that the proposed method detects the limited defect more reliably than conventional methods.