• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1094-1098
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• 2013

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. Defect detection is not an easy process because of noises from various sources and optical distortion. In this paper the acquired images from a TFT panel are enhanced with the adoption of an HVS (Human Visual System). A human visual system is more sensitive on the defect area than the illumination components because it has greater sensitivity to variations of intensity. In this paper we modified an MTF (Modulation Transfer Function) in the Wavelet domain and utilized the characteristics of an HVS. The proposed algorithm flattens the inner illumination components while preserving the defect information intact.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.261-270
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• 2010

This paper presents an analytical investigation for a baseline-free imaging of a defect in plate-like structures using the time-reversal of Lamb waves. We first consider the flexural wave (A0 mode) propagation in a plate containing a defect, and reception and time reversal process of the output signal at the receiver. The received output signal is then composed of two parts: a directly propagated wave and a scattered wave from the defect. The time reversal of these waves recovers the original input signal, and produces two additional side bands that contain the time-of-flight information on the defect location. One of the side band signals is then extracted as a pure defect signal. A defect localization image is then constructed from a beamforming technique based on the time-frequency analysis of the side band signal for each transducer pair in a network of sensors. The simulation results show that the proposed scheme enables the accurate, baseline-free detection of a defect, so that experimental studies are needed to verify the proposed method and to be applied to real structure.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.11
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• pp.989-995
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• 2015

In the literature, various stochastic anomaly detection methods, such as limit checking and PCA-based approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by $Na{\ddot{i}}ve$ Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC's LWM$^{TM}$ and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.317-321
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• 2008

Piping in the Nuclear Power plants (NPP) are mostly consisted of carbon steel pipe. The wall thinning defect is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid which flows in carbon steel pipes. This type of defect becomes the cause of damage or destruction of piping. Therefore, it is very important to measure defect which is existed not only on the welding partbut also on the whole field of pipe. Over the years, Infrared thermography (IRT) has been used as a non destructive testing methods of the various kinds of materials. This technique has many merits and applied to the industrial field but has limitation to the materials. Therefore, this method was combined with lock-in technique. So IRT detection resolution has been progressively improved using lock-in technique. In this paper, the quantitative analysis results of the location and the size of wall thinning defect that is artificially processed inside the carbon steel pipe by using IRT are obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.43-51
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• 2014

TFT-LCD image includes a defect of various properties. TFT-LCD image have a recognizable defects in the human inspector. On the other hand, it is difficult to detect defects that difference between the background and defect is very low. In this paper, we proposed sequentially detect algorithm from pixels included in the defect region to limited defects. And blob analysis methods using the blob size and gray difference are applied to the defect candidate image. Finally, we detect an accurate defect blob to distinguish the noise. The experimental results show that the proposed method finds the various defects reliably.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.270-279
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• 2003

Parametric studies have been conducted into the variability of the factors affecting the ultrasonic testing applied to weldments. The influence of ultrasonic equipment, transducer parameters, test technique, job parameters, defect type and characteristics on reliability far defect detection and sizing was investigated by experimentation. The investigation was able to build up substantial bank of information on the reliability of manual ultrasonic method for testing weldments. The major findings of the study separate into two parts, one dealing with correlation between ultrasonic techniques, equipment and defect parameters and inspection performance effectiveness and other with human factors. Defect detection abilities are dependent on the training, experience and proficiency of the UT operators, the equipment used, the effectiveness of procedures and techniques.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.55-61
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• 2021

In this paper, we proposed and implemented defect detection methods of polarized film with half-wave phase retardation plates. We investigated the principles of phase retardation compensation and optical principle of half-wave phase retardation plates. We analyzed of samples of polarized film with half-wave phase retardation plates. The optical defect detection methods are proposed and the performance is validated with experiments.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1509-1511
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• 2015

Defect detection is one of the most challenging problems in industrial quality control. In this study we developed a vision-based defect detection system for wafer production. To achieve high-accuracy detection, Otsu method was improved so that it can handle both unimodal and bimodal distributions. After thresholding, detected defect regions in the wafer are classified and grouped into user-defined defect categories. The experimental result has proved the efficiency of our system.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1973-1979
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• 2000

Driving the need for machine vision system is growing consumer demand for quality and defect-free products. Especially it is the most important in tarpaulin's manufacturing process achieves automatically by machine vision instead of by man vision. In this paper pinholes detection is performed by using morphology algorithms. Top hat transform is one of morphology applications. This transform take high performance of defect detection in the case that unexpected changes occur in some non-uniform background. For pinholes defect, automatic visual inspection system has been developed, which was composed by a line-scan camera, illumination, a frame grabber, a motor driver and control units. This system has excellent capacity to defect pinholes to the 0.1 mm by 0.5 mm in size and to work in moving objects by maximum 20 m/min in speed.