• International conference on construction engineering and project management
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• 2013.01a
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• pp.7-13
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• 2013

Recently, defect management have been considered as one of the major issues for more large-sized and complicated in domestic construction industry. However, the defect management have not been performed systematically because of special manpower, excessive amount of documents, 2D based inspection work, unclear traditional checklists, complicated work process and difficulty in communicating construction information. Therefore, the construction field manager could not performed the quality inspection and defect management work on time as well as the reliability of recorded quality and defect factors was decreased. The primary objective of this study is develop a Construction Defect Management Application CDMA) using a mobile (smartphone). The application can be sharing a huge information and communication technology based on RFID (Radio-Frequency Identification), BIM (Building Information Modeling) which enables field mangers to efficiently gather the information of defection in construction on-site.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.55-61
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• 2009

The wall thinning defect of nuclear power pipe is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid. This type of defect becomes the cause of damage or destruction of in carbon steel pipes. Therefore, it is very important to measure defect which is existed not only on the welding part but also on the whole field of pipe. This study use dual-beam Shearography, which can measure the out-of-plane deformation and the in-plane deformation by using another illuminated laser beam and simple image processing technique. And this study proposes Infrared thermography, which is a two-dimensional non-contact nondestructive evaluation that can detect internal defects from the thermal distribution by the inspection of infrared light radiated from the object surface. In this paper, defect of nuclear power pipe were, measured using dual-beam shearography and infrared thermography, quantitatively evaluated by the analysis of phase map and thermal image pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1079-1082
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• 2005

This paper deals with the algorithm development that inspects defects such as Lens Focus, Focus check, Black Defect, Dark Defect, Dim Defect, Color Defect, and Line Defect, Angle Defect, IrisAgc Defect caused by the process of 2M Class Compact Camera Module (CCM). Domestic market was majorly comprised of VGA(0.3 million pixel) market. But in the middle of year 2004, camera phone with Mega Pixel has appeared, and it is estimated that the camera phone with Mega Pixel will take up to 28% of total phone sales if it is released in the end of year 2004. Since the inspection of finished products is done manually, it is major obstacle in production increment 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 we can recognize various types of defect of CCM modules with good accuracy and high speed

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.852-862
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• 1996

This study was carried out to develop tools to detect defects of apple using machine vision. For the purpose, 6 kinds of frame for color images, R, G, B, h, S, and I frame, and a frame for near infra-red images (NIR frame) were tested first to select one which is useful to segment defect areas from apple images. After then, several methods to classify kind of defect for the segmented defect areas were developed and tested. Five kinds of apple defect -bruise , decay ,fleck worm hole and scar were investigated . The results are as follows: NIR frame was selected as the best one among the 7 kinds of image frame, and R, G and I frames showed favourable result to segment areas of apple defect. Various features of the segmented defect areas were measured to classify the defect areas. Eight kids of feature of the areas-size, roundness, axes length ratio, mean and variance of pixel values, variance of real part of spectrum, mean and variance of power spectrum resulted from spacial ourier transform were observed for the segmented defect areas in the selected 4 frames. then procedures to classify defects using the features were developed for the 4 frames and tested with 75-113 defects on apples. The test resulted that NIR and I frames showed high accuracies to classify the kind of defect as 77% and 76% , respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Computers and Concrete
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• v.33 no.5
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• pp.621-630
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• 2024

This study presents novel electromagnetic wave-based methods for evaluating the integrity of cement-based structures using time domain reflectometry (TDR). Two cement-based plates with embedded rebars are prepared under sound and defective conditions. TDR tests are carried out using transmission lines with various numbers of artificial joints, and electromagnetic waves are measured to assess the integrity of the plates. The experimental results show that the travel time of electromagnetic waves is consistently longer in sound plates than in defective ones, and an increase in the reflection coefficients is observed in the defect zone of the defective plates. Electromagnetic wave velocities are higher in the defective plates, especially when connectors are present in the transmission line. A novel approach based on the area of the reflection coefficient provides larger areas in the defective plates, and the attenuation effect of the electromagnetic waves induces a difference in the areas of the reflection coefficient between the two defect conditions. An alternative method using the centroid of the defect zone slightly overestimates the location of the defect zone. The length of the defect zone is estimated using the defect ratio and wave velocities of cement, air, and plate. The length of the defect zone can also be calculated using the travel times within the plate, total measured length of the plate, and wave velocities in the cement and air. Therefore, the electromagnetic wave-based methods proposed in this study may be useful for estimating the location and length of defect zones by considering attenuation effects.

• Journal of the Korean Ceramic Society
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• v.44 no.12
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• pp.660-682
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• 2007

An SOFC consists of all ceramic complex oxides each with different electrochemical-property requirements. These requirements, in principle, can be made met to a great extent by controlling or tailoring the defect structure of the oxide. This paper reviews the defect structure, nonstoichiometry as a measure of the total defect concentration, and the defect relaxation kinetics of complex oxides that are currently involved in a variety of growing applications today.

• Journal of KSNVE
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• v.9 no.2
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• pp.393-400
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• 1999

Defect frequencies of rolling element bearings are experimentally investigated utilizing the two-sided directional spectra of the complex-valued vibration signals measured from the outer ring of defective bearings. The directional spectra make it possible to discern backward and forward defect frequencies. The experimental results show that the directional zoom spectrum is superior to the conventional spectrum in identification of bearing defect frequencies, in particular the inner race defect frequencies.