• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.63-68
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• 2021

In this study, data was augmentation through the SinGAN algorithm using small image data, and defects in rubber O-rings were detected using the random forest algorithm. Unlike the commonly used data augmentation image rotation method to solve the data imbalance problem, the data imbalance problem was solved by using the SinGAN algorithm. A study was conducted to distinguish between normal products and defective products of rubber o-ring by using the random forest algorithm. A total of 20,000 image date were divided into transit and testing datasets, and an accuracy result was obtained to distinguish 97.43% defects as a result of the test.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1060-1066
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material efficiently. In this paper a detection of the welding defect of thin SUS 304 plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (20kHz) ultrasonic transducer was used to infuse the welded thin SUS 304 plates with a short pulse of sound for 280ms. The ultrasonic source has a maximum power of 2kW. The surface temperature of the area under inspection is imaged by a thermal infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the defect tip and heated up highly, are observed. From the sequence of the thermosonic images, the location of defective or inhomogeneous regions in the welded thin SUS 304 plates can be detected easily.

• The Journal of the Acoustical Society of Korea
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• v.14 no.4
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• pp.12-20
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• 1995

This paper describes principles and experimental results in conjunction with examining the possibility of the detection of effects from a cylindrical objects. Cylindrical objects have its own resonance frequency. The frequency varies according to the diameter, length etc. And acoustic sound, whose frequency and the harmonic components corresponding to resonance frequency, is radiated from the object. When an object have defect in itself, the radiated acoustic sound is different from normal one. So we can detect its defect by analysing frequency components of acoustic sound. We proved that detection of defective objects by acoustic signal analysis is possible automatically. Also the result can be applied to other kinds of objects.

• Steel and Composite Structures
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• v.19 no.1
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• pp.131-152
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• 2015

Easy detection and evaluation of defect in the tube structure is a continuous problem and remains a significant demand in tube inspection technologies. This study is aimed to automate defect detection using the pattern recognition approach based on the classification of high frequency stress wave signals. The stress wave signals from vibrational impact excitation on several tube conditions were captured to identify the defect in ASTM A179 seamless steel tubes. The variation in stress wave propagation was captured by a high frequency sensor. Stress wave signals from four tubes with artificial defects of different depths and one reference tube were classified using the autoregressive (AR) algorithm. The results were demonstrated using a dendrogram. The preliminary research revealed the natural arrangement of stress wave signals were grouped into two clusters. The stress wave signals from the healthy tube were grouped together in one cluster and the signals from the defective tubes were classified in another cluster. This approach was effective in separating different stress wave signals and allowed quicker and easier defect identification and interpretation in steel tubes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.576-579
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• 2004

Eventhough the productivity has been improved remarkably by introducing automatic facilities, the 100％ inspection is necessary because the possibility to produce large amount of defective goods is also increased. Since it is extremely unreasonable that workers inspect very large amount of products as 100％ inspection, there has been many researches for the automatic inspection system. In this thesis, we develop an automatic detection system of suspension insulator's minutes cracks System The automatic detection system of suspension insulator's minute cracks： To detect the minute cracks of suspension insulators, images of the insulator are acquired with a progressive scan camera, rotating a suspension insulator on a turning table. And after the shadow and noises are eliminated by preprocessing techniques, we detect minute cracks using the features of them.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.15-21
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• 2019

X-rays with shorter wavelengths than ultraviolet light have very good penetration power. It is convergence in industrial and medical fields has been used a lot. n particular, in the industrial field, various researches have been conducted on the detection of foregin body inside metal that can occur in the production process of products such as metal using x-ray, a non-destructive inspection device. Detectors are becoming increasingly popular for the popularization of DR (Digital Radiography) photography methods that digitally acquire X-ray video images. However, there are cases where foreign body detection is impossible depending on the sensor noise and sensitivity inside the detector. When producing a metal product, since the defective rate of the produced product may increase due to contamination of the foreign body, accurate detection is necessary. In this paper, we provide a correction model for X-ray images acquired in order to improve the efficiency of defect detection such as foreign body inside metal. When applied to defect detection in the production process of metal products through the proposed model, it is expected that the detection of product defects can be processed accurately and quickly.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.426-431
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material in real time. In this paper a realtime detection of the brazing defect of thin Inconel plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (23 kHz) ultrasonic transducer was used to infuse the welded Inconel plates with a short pulse of sound for 280 ms. The ultrasonic source has a maximum power of 2 kW. The surface temperature of the area under inspection is imaged by an infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the bound between the two faces of the Inconel plates near the defective brazing point and heated up highly, are observed. And the weak thermal signal is observed at the defect position of brazed plate also. Using the image processing technology such as background subtraction average and image enhancement using histogram equalization, the position of defective brazing regions in the thin Inconel plates can be located certainly.

• Genomics & Informatics
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• v.18 no.1
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• pp.4.1-4.6
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• 2020

Transposable elements (TEs) constitute approximately half of Bovine genome. They can be a powerful species-specific marker without regression mutations by the structure variation (SV) at the time of genomic evolution. In a previous study, we identified the Hanwoo-specific SV that was generated by a TE-association deletion event using traditional PCR method and Sanger sequencing validation. It could be used as a molecular marker to distinguish different cattle breeds (i.e., Hanwoo vs. Holstein). However, PCR is defective with various final copy quantifications from every sample. Thus, we applied to the droplet digital PCR (ddPCR) platform for accurate quantitative detection of the Hanwoo-specific SV. Although samples have low allele frequency variation within Hanwoo population, ddPCR could perform high sensitive detection with absolute quantification. We aimed to use ddPCR for more accurate quantification than PCR. We suggest that the ddPCR platform is applicable for the quantitative evaluation of molecular markers.