• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.6
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• pp.389-397
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• 2015

Recently, wave propagation imaging based on laser scanning-generated elastic waves has been intensively used for nondestructive inspection. However, the proficiency of the conventional software based system reduces when the scan area is large since the processing time increases significantly due to unavoidable processor multitasking, where computing resources are shared with multiple processes. Hence, the field programmable gate array (FPGA) was introduced for a wave propagation imaging method in order to obtain extreme processing time reduction. An FPGA board was used for the design, implementing post-processing ultrasonic wave propagation imaging (UWPI). The results were compared with the conventional system and considerable improvement was observed, with at least 78% (scanning of $100{\times}100mm^2$ with 0.5 mm interval) to 87.5% (scanning of $200{\times}200mm^2$ with 0.5 mm interval) less processing time, strengthening the claim for the research. This new concept to implement FPGA technology into the UPI system will act as a break-through technology for full-scale automatic inspection.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.40.1-40.1
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• 2014

We have classified barred galaxies for 418 RC3 sample galaxies within z < 0.01 from SDSS DR7 using the visual inspection, ellipse fitting method and Fourier analysis. We found the bar fraction to be ~60%, 43% and 70% for each method and that the ellipse fitting method tends to miss the bar when a large bulge hides the transition from bar to disk in early spirals. We also confirmed that the Fourier analysis cannot distinguish between a bar and spiral arm structure. These systematic difficulties may have produced the long-time controversy about bar fraction dependence on Hubble sequence, mass and color. We designed a new method to fine bars by analyzing the ratio map of bar strength in polar coordinates, which yields the bar fraction of ~27% and ~32% for SAB and SB, respectively. The consistency with visual inspection reaches around 70%, and roughly 90% of visual strong bar are classified as SAB and SB in our classification. Although our method also has a weakness that a large bulge lowers the value of bar strength, the missing bar fraction in early spirals is reduced to the level of ~1/4 compared to the ellipse fitting method. Our method can make up for the demerits of the previous automatic classifications and provide a quantitative bar classification that agrees with visual classification.

• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.58-67
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• 2020

In this paper, the maintenance training manual of EV vehicle was produced by utilizing digital twin technology and various sensors such as IR-based light house tracking and head tracker. In addition, through digital twin technology and VR to provide high immersiveness to users, sensory content creation technology was secured through animation and effect realization suitable for EV vehicle maintenance situation. EV vehicle maintenance training manual is 3D engine programming and real-time creation of 3D objects and minimization of screen obstacles and selection of specific menus in virtual space in the form of training simulation. In addition, automatic output from the Head Mount Display (HUD), EV vehicle maintenance and inspection, etc., user can easily operate content was produced. This technology development can enhance immersion to users through implementation of detailed scenarios for maintenance / inspection of EV vehicles" and 3D parts display by procedure, realization of animations and effects for maintenance situations. Through this study, familiarity with improving the quality of education and safety accidents and correct maintenance process and the experienced person was very helpful in learning how to use equipment naturally and how to maintain EV vehicles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.308-314
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• 2006

A line scan camera is applied to enhance COG(Chip On Glass) inspection accuracy to be measured a few micro unit. The foreign substance detection among various faulty factors has been the most difficult technology in the faulty automatic inspection step since COG pattern is very miniature and complexity. In this paper, we proposed two step area segmentation template matching method to increase matching speed. Futhermore to detect foreign substance(such as dust, scratch) with a few micro unit, the new method using gradient mask and AND operation was proposed. The proposed 2 step template matching method increased 0.3 - 0.4 second matching speed compared with conventional correlation coefficient. Also, the proposed foreign substance applied masks enhanced $5-8\%$ faulty detection rate compared with conventional no mask application method.

• Journal of Sensor Science and Technology
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• v.17 no.5
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• pp.338-345
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• 2008

Steel bearing balls are important component in machines having moving parts. In this paper we describe a vision-based automatic inspection system designed for sensing defects on the surface of steel bearing balls. The system has a camera looking down over a rail on which balls roll. Two mirrors are installed at both sides of the rail so that the side parts of a ball can be well inspected. The entire ball surface can be sufficiently seen by taking three images at $120^{\circ}$ rotation interval. Defects are detected by thresholding the difference image between an image captured and the reference image of a good ball.

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.121-127
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• 2018

A system for diagnosing surface defects of long and large pipe inner overlay welds, 1m in diameter and 6m in length, was developed using a Liquid Penetrant Test (PT). First, CATIA was used to model all major units and PT machines in 3-dimensions. They were used for structural strength analysis and strain analysis, and to check the motion interference phenomenon of each unit to produce two-dimensional production drawings. Structural strength analysis and deformation analysis using the ANSYS results in a maximum equivalent stress of 44.901 MPa, which is less than the yield tensile strength of SS400 (200 MPa), a material of the PT Machine. An examination of the performance of the developed equipment revealed a maximum travel speed of 7.2 m/min., maximum rotational speed of 9 rpm, repeatable position accuracy of 1.2 mm, and inspection speed of $1.65m^2/min$. The results of the automatic PT-inspection system developed to check for surface defects, such as cracks, porosity, and undercut, were in accordance with the method of ASME SEC. V&VIII. In addition, the results of corrosion testing of the overlay weld layer in accordance with the ferric chloride fitting test by the method of ASME G48-11 indicated that the weight loss was $0.3g/m^2$, and met the specifications. Furthermore, the chemical composition of the overlay welds was analyzed according to the method described in ASTM A375-14, and all components met the specifications.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.377-382
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• 2006

In this paper, two methods based on the use of morphology and pattern matching prior to detect classified defects automatically on the back light unit which is a part of display equipments are proposed. One is the template check method which detects small size defects by using closing and opening method, and the other is the block matching method which detects big size defects by comparing with four regions of uniform blocks. The TC algorithm also can detect defects on the non-uniform pattern of BLU by using revised Otsu method. The proposed method has been implemented on the automatic defect detection system we developed and has been tested image data of BLU captured by the system.

• International Journal of Highway Engineering
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• v.15 no.3
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• pp.127-135
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• 2013

PURPOSES : The purpose of this study is development of automatic equipment to measure the road water-reservoir which can be one of factors for road traffic safety inspection and its application to safety analysis. METHODS : The scopes of this study are the examination of the riskiness and location of road water-reservoir through literature review, development of appropriate sensor and automatic equipment to survey the road water-reservoir and evaluation of field application. RESULTS: The laser lighting and IR camera were selected to develop the equipment. It was found from the field calibration that there is a high correlation between rutting and road water-reservoir and road water-reservoir caused by rutting can be correctly calculated. About 20.2km of national highway were inspected for case study and field application. It was found from correlation of traffic incident that 2.08km of the latent length for water-reservoir which is related to 12 traffic incidents were analyzed. CONCLUSIONS : This technique can be utilized evaluation method for road condition such as road water-reservoir for conventional evaluation system such as road traffic safety assessment and safety analysis and it can be use to new evaluation system to apply various road condition and traffic condition.