• Fire Science and Engineering
• /
• v.30 no.3
• /
• pp.94-103
• /
• 2016

This study analyzed the cause and liability of a car fire. The car was purchased 1 year ago and had traveled 8,500 km. When the car stopped at a red right and was about to start when the light turned green, the car next to it sounded itsa horn. As a result, the driver stopped the car and found that there was a fire in the engine compartment, which the fire brigade subsequently extinguished. This car fire may have been caused by an electrical spark from a screw hook inserted into the battery + terminal. Therefore, the courts decided that the car owner or driver was not responsible for the fire, because the electrical spark from the hook that caused it resulted from the automaker's manufacturing defect.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.10
• /
• pp.1030-1035
• /
• 2014

Machine vision inspection systems have replaced human inspectors in defect inspection fields for several decades. However, the inspection results of machine vision are often affected by small changes of illumination. When small changes of illumination appear in image histograms, the influence of illumination can be decreased by transformation of the histogram. In this paper, we propose an enhanced histogram matching algorithm which corrects distorted histograms by variations of illumination. We use the resolution resizing method for an optimal matching of input and reference histograms and reduction of quantization errors from the digitizing process. The proposed algorithm aims not only for improvement of the accuracy of defect detection, but also robustness against variations of illumination in machine vision inspection. The experimental results show that the proposed method maintains uniform inspection error rates under dramatic illumination changes whereas the conventional inspection method reveals inconsistent inspection results in the same illumination conditions.

• Industrial Engineering and Management Systems
• /
• v.9 no.3
• /
• pp.251-261
• /
• 2010

This article demonstrated the application of the Fuzzy Data Envelopment Analytical Hierarchy Process (FDEAHP) to evaluate the root causes of critical defect problems occurring in the production of liquid medicine. The methodology of the research began by collecting the defect data by using Check Sheets, and ranking the significant problems by using a Pareto Diagram. Two types of major problems were found to occur, including glass fragments in the medicine and damaged lid threads. The causes of each problem were then analyzed by using Cause and Effect Diagrams. The significant causes were ranked by FDEAHP under three criteria, Severity (S), Occurrence (O) and Detection (D), followed by the framework of the FMEA Technique. Two causes with the highest Final Weight (FW) of each problem were selected to be improved, such as installing auxiliary equipment, using the Poka-Yoke system, setting the scale of the shaft and lathing the bushes of each bottle size. The results demonstrated a reduction in defects from 3.209% to 1.669% and showed that improving a few significant root causes, identified by an experienced decision maker, was sufficient to reduce the defect rate.

• Journal of Information Processing Systems
• /
• v.8 no.2
• /
• pp.359-374
• /
• 2012

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

• Journal of IKEEE
• /
• v.26 no.2
• /
• pp.319-328
• /
• 2022

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

• Proceedings of the Korean Vacuum Society Conference
• /
• 1995.06a
• /
• pp.20-21
• /
• 1995

• 한국정보통신설비학회:학술대회논문집
• /
• 2004.08a
• /
• pp.386-389
• /
• 2004

This thesis complemented the weak points that the existing theses did not represented a phase characteristic as the equivalent circuit by applying 4-port simulation to DGS (Defected Ground Structure) characteristic and an equivalent circuit, which are the transmission line structure that has the defect made in the ground surface. We used a distribute device and a lumped device, obtained the equivalent circuit by applying the structure of balun to a discontinuous part. An indicated DGS (Defected Ground structure) is a dumbbells-shaped single defect, we indicated satisfying a magnitude and phase characteristics by applying this equivalent circuit.

• Journal of the Korean Ceramic Society
• /
• v.44 no.4 s.299
• /
• pp.202-207
• /
• 2007

High-Quality 1-Dimensional InN single crystalline have been grown by Halide Vapor-Phase Epitaxy on the Au catalyst coated Si substrate using the vapor-liquid-solid growth mechanism. We have been grown 1-dimension InN nanowires having controlled the growth conditions for substrate temperature and gases flow rate. The grown InN nanowire of characteristics for morphologies, crystal structure, and element analysis were carried out by SEM, HR-TEM, and EDS respectively. And the defects of InN crystalline were analyzed by indexing of selective area diffraction pattern with attached HR-TEM. We have successfully obtained the defect-free 1-dimensional InN single crystalline nanowire at the atmosphere pressure.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.403-406
• /
• 2019

논문에서는 스마트 공장 시스템의 표면 결함 검사 시에 영상의 특징인 SIFT, SURF, HOG 특징들을 이용하여 표면 결함 검출에 활용하는 연구를 다루었습니다. 먼저 SIFT, SURF, HOG 특징에 대하여 소개하고 실험에서 이 특징들이 사용될 수 있음을 결과를 통해 보였습니다.

• Journal of Ocean Engineering and Technology
• /
• v.27 no.3
• /
• pp.61-66
• /
• 2013

This paper presents the results of finite element simulations of elastic wave propagation in an underwater steel plate and the verification of a proposed method utilizing elastic wave-based damage detection. For the simulation and verification, we carried out the following procedures. First, three-dimensional finite element models were constructed using a general purpose finite element program. Second, two types of damages (mechanical defects and deteriorations) were applied to the underwater steel plate and three parameters (defect location, defect width, and depth) were considered to adjust the severity of the applied damages. Third, elastic waves were generated using the oblique incident method with a Gaussian tone burst, and the response signals were obtained at the receiving point for each defect or deterioration case. In addition, the received time domain signals were analyzed, particularly by measuring the magnitudes of the maximum amplitudes. Finally, the presence and severity of each type of damage were identified by the decreasing ratios of the maximum amplitudes. The results showed that the received signals for the models had the same global pattern with minor changes in the amplitudes and phases, and the decreasing ratio generally increased as the damage area increased. In addition, we found that the defect depth was more critical than the width in the decrease of the amplitude. This mainly occurred because the layout of the depth interfered with the elastic wave propagation in a more severe manner than the layout of the width. An inverse analysis showed that the proposed method is applicable for detecting mechanical defects and quantifying their severity.