• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1853-1858
• /
• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.99-104
• /
• 2011

In this paper the approach of neural network was proposed which detects a variety of defects in the molded parts. In an attempt to improve the response of the system, It is designed to minimize the use of memory via LookUp table in software. The goal of these methods was to extract the features of samples in learning of neural networks, overcoming the algorithms of defects detection and classification. Through the learning of 500 sample patterns of molded parts, defects of 3% molded parts was detected and classified as the incorrect diameter parts. We expect that proposed approach is an effective alternative to save test time and cost for defect detection of a fine pattern within the molded parts.

• Composites Research
• /
• v.35 no.1
• /
• pp.8-12
• /
• 2022

Analysis of defect signals inside glass fiber reinforced polymer (GFRP) was conducted through deconvolution of terahertz (THz) wave. The GFRP specimen with internal defects was manufactured and the THz signal was measured through the reflection mode of the Terahertz Time-Domain Spectroscopy (THz-TDS) system. For deconvolution of the measured THz signal, the peak position of the THz signal was amplified through Normalized Cross Correlation (NCC) of the incident and detected THz signals. The position and intensity of the amplified peak were extracted as impulse, and the extracted signal of the impulse position was removed from the THz original signal. By repeating the process, the critical impulses, which represent boundary of the specimen, were derived. The deconvolution process was verified by confirming that the original THz signal without noise can be restored through the convolution of the critical impulses and the incident signal. From the derived critical impulses, the thickness of the internal defect in the GFRP was calculated through the detection time of impulses within 15 ㎛ accuracy.

• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.4
• /
• pp.185-192
• /
• 2021

Various methods have been proposed to determine the defect detection of welding bead, and recently sensor data and image data inspection have been steadily announced. There are advantages that sensor data inspection is highly accurate, and two-dimensional-based image data inspection is able to determine the position of the welding bead. However, when analyzing only with sensor data, it is difficult to determine whether the welding has been performed at the correct position. On the other hand, the image data inspection does not have high accuracy due to noise and measurement errors. In this paper, we propose a method that can complement the shortcomings of each inspection method and increase its advantages to improve accuracy and speed up inspection by fusing sensor data inspection which are average current, average volt, and mixed gas data, and image data inspection methods and is implemented as software. In addition, it is intended to allow users to conveniently and intuitively analyze and grasp the results by performing analysis using a graphical user interface(GUI) and checking the data and inspection results used for the inspection. Sensor inspection is performed using the characteristics of each sensor data, and image data is inspected by applying a morphology geodesic active contour algorithm. The experimental results showed 98% accuracy, and when performing the inspection on the four image data, and sensor data the inspection time was about 1.9 seconds, indicating the performance of software that can be used as a real-time inspector in the welding process.

• Journal of Information Processing Systems
• /
• v.14 no.3
• /
• pp.751-770
• /
• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.2
• /
• pp.153-166
• /
• 2020

DDI(Display Driver IC) are used to drive numerous pixels that make up display. For stable driving of DDI, it is necessary to attach a protective film to shield electromagnetic waves. When the protective film is attached, defects often occur if the film is inclined or the center point is not aligned. In order to minimize such defects, an algorithm for correcting the center point and the inclined angle using camera image information is required. This technology detects the corner coordinates of the protective film by image processing in order to correct the positional defects where the protective film is attached. Corner point coordinates are detected using an algorithm, and center point position finds and correction values are calculated using the detected coordinates. LUT (Lookup Table) is used to quickly find out whether the angle is inclined or not. These algorithms were described by Verilog HDL. The method using the existing software requires a memory to store the entire image after processing one image. Since the method proposed in this paper is a method of scanning by adding a line buffer in one scan, it is possible to scan even if only a part of the image is saved after processing one image. Compared to those written in software language, the execution time is shortened, the speed is very fast, and the error is relatively small.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.233-236
• /
• 1997

In modem industry, the accuracy and the surface-finish requirements for machined parts have been becoming ever more stringent. Optical method in measurements is playing an important role in vibration measurement, crack and defect detection and surface topography with the advent of opto-mechatronics. In this study, the principle of the general confocal microscope is introduced for surface measurement, and the advanced confocal microscope that has better measuring speed than the traditional confocal microscope is developed. A study on improving the resolution of the advanced confocal microscope is followed. Finally, Software for data acquisition and analysis of various parameters in surface geometrical features has been developed.

• Journal of the Semiconductor & Display Technology
• /
• v.11 no.3
• /
• pp.1-6
• /
• 2012

In this paper, inspection system based on optical scanning mechanism is designed and developed for solar cell wafer. It consists of optical scanning mechanism, NIR camera optics, machinery and control system, algorithm of defect detection and software. Optical scanning mechanism is composed of geometrical camera optics and structured hybrid illumination system. It is used to inspection of surface defects. NIR camera optics is used for inspection of defects inside solar cell wafer. It is shown that surface and internal micro defects can be detected in developed inspection system for solar cell wafer.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.123-128
• /
• 2016

In manufacturing processing of a secondary battery, the visual inspection system is studied and developed to check the surface defects of the electrode plates. It consists of two parts, one is the hardware control and the other software implementation. The former is made up to the system configuration and the design of the optical system, the illuminations and the controllers. The latter is the detection algorithms of the surface defects. This system achieves the quality improvement of the electrode process and the price competitiveness. By using the proposed defects detection algorithms this system demonstrates the high reliability of spot, line, manhole, extraneous substance, scratch, and crater defect of a electrode plate surface.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.2493-2502
• /
• 2011

For Static analysis of software code, an experienced tester prefer detecting defects with using selective static technique. Many cases of static method have been reported such as coding rules, software metrics, defect data, etc. However, many of analysis case only present effectiveness of static analysis, not enough description for how the tester judged to classify code defects used in code analysis and removed them properly for ensure high quality. Occasionally, there are materials to show the effect of through some examples through some examples. But difficult to gain trust, because of not enough detail for application process. In this paper, introduced the static technique commonly used in railway and applied to the real development challenges. And the each of results were compared and analyzed. It is hard to generalize the results of this parer. But can be used and referenced as a case of study.