• Journal of the Korean Wood Science and Technology
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• v.35 no.5
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• pp.16-23
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• 2007

This study was performed as part of a research project aimed at developing an ultrasonic computed tomography (CT) system of wood for field application. In this reports, we investigate the variation of wave velocities on the cross section of real size wooden structural member to confirm the reason of image distortion on CT image of wood, and then proposed a new image reconstruction method by considering the velocity variation on wood cross section. First of all, the effect of wood anisotropy on ultrasonic velocities of wooden members was investigated. Based on the relationship between ultrasonic velocity and annual ring angle, which was obtained from test results of small clear specimens, ultrasonic velocities of each measuring angle were predicted. Next, they were compared with the ultrasonic velocities measured on five wood disks. There were very large differences between predicted and measured results, thought to be caused by the skewing effect of ultrasound and the presence of juvenile-wood. Based on these findings, a new method was proposed to reconstruct cross-sectional image of wood. By using this method, some distortions on reconstructed images could be removed, and defects were more easily and clearly detected. The minimum size of detectable defect was decreased remarkably, from 33 mm to 13 mm. However, the size of the detected defect was enlarged and the position somewhat shifted to the specimen surface on the CT images, which was also thought to be caused by the skewing effect of ultrasound. Additional research has been planned to solve these problems.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.451-458
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• 2004

A leather quality inspection by naked eyes has known as unreliable because of its biological characteristics like accumulated fatigue caused from an optical illusion and biological phenomenon. Therefore it is necessary to automate the leather quality inspection by computer vision technique. In this paper, we present automatic leather qua1ity classification system get information from leather surface. Leather is usually graded by its information such as texture density, types and distribution of defects. The presented algorithm explain how we analyze leather information like texture density and defects from the gray-level images obtained by digital camera. The density data is computed by its ratio of distribution area, width, and height of Fourier spectrum magnitude. And the defect information of leather surface can be obtained by histogram distribution of pixels which is Windowed from preprocessed images. The information for entire leather could be a standard for grading leather quality. The proposed leather inspection system using machine vision can also be applied to another field to substitute human eye inspection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.53-60
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• 2005

The wire rope of container crane is a important component to container transfer system and is used in a myriad of various applications such as elevator, mine hoist, construction machinery, and so on. If it happen wire rope failures in operating, it may lead to the safety accident and economic loss, which is productivity decline, competitive decline of container terminal, etc. To solve this problem, we developed the active and portable wire rope fault detecting system. The developed system consists of three parts that are the fault detecting, signal processing, and remote monitoring part. All detected signal has external noise or disturbance according to circumstances. Therefore we applied to discrete wavelet transform to extract a signal from noisy data that was used filter. As experimental result, we can reduce the expense for container terminal because of extension of exchange period of wire rope for container crane and this system is possible to apply in several fields to use wire rope.

• Journal of Sensor Science and Technology
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• v.28 no.6
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• pp.371-376
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• 2019

We have developed an acoustic resonance inspection system to inspect the welding defects in the mechanical parts fabricated using friction stir welding method. The inspection system was consisted of a DAQ board, a microphone sensor, an impact hammer, and controlled by a PC software. The system was developed to collect and analyze the sound signal generated by hitting the sample with an impact hammer to determine whether it is defective. In this study, 100% welded good samples were compared with 95%, 90%, and 85% welded samples, respectively. The variation of the completeness in welding did not affect the visual appearance in the samples. As a result of analyzing the natural frequencies of the good samples, the five natural frequency peaks were identified. In the case of the defective samples, the frequency change was observed. The welding failure detection time was fast enough to be only 0.7 seconds. Employing our welding defect inspection system to the actual industrial field will maximize the efficiency of quality inspection and thus improve the productivity.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.5
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• pp.465-472
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• 2016

This study materialized an FPGA-based system to extract PCB patterns. The Printed Circuit Boards that are produced these days are becoming more detailed and complex. Therefore, the importance of a vision system to extract defects of detailed patterns is increasing. This study produced an FPGA-based system that has high speed handling for vision automation of the PCB production process. A vision library that is used to extract defect patterns was also materialized in IPs to optimize the system. The IPs materialized are Camera Link IP, pattern matching IP, VGA IP, edge extraction IP, and memory IP.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1061-1066
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• 2013

In the steel industry, the detection of tiny defects including its 3D characteristics on steel surfaces is very important from the point of view of quality control. A multi-spectral photometric stereo method is an attractive scheme because the shape of the defect can be obtained based on the images which are acquired at the same time by using a multi-channel camera. Moreover, the calculation time required for this scheme can be greatly reduced for real-time application with the aid of a GPU (Graphic Processing Unit). Although a more reliable shape reconstruction of defects can be possible when the numbers of available images are increased, it is not an easy task to construct a camera system which has more than 3 channels in the visible light range. In this paper, a new 6-channel camera system, which can distinguish the vertical/horizontal linearly polarized lights of RGB light sources, was developed by adopting two 3-CCD cameras and two polarized lenses based on the fact that the polarized light is preserved on the steel surface. The photometric stereo scheme with 6 images was accelerated by using a GPU, and the performance of the proposed system was validated through experiments.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.83-88
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• 2021

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1279-1287
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• 2009

An increasingly competitive business environment has been concentrated on industries to reduce the operating costs. Industries such as gas, oil, petrochemical, chemical, and electric power have employed for the operation and used for large equipment or structures that require a high capital investment. In order to meet these requirements, the industries are increasingly moving toward saving the experimental verification and computer simulation. Therefore industries to reduce the maintenance costs without compromising the operational safety have been forced on finding for better and more efficient methods to inspect their equipment and structures. In this study, it focused on the development the real-time non-contract monitoring system as an efficient tool for the experimental study of weld defects based on the relationship between the measured voltage and input parameters.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.