• Journal of the Korean Society of Industry Convergence
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• v.14 no.2
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• pp.59-65
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• 2011

$CO_2$ Welding which uses $CO_2$ instead of inert gas is most widely used in industrial sites. Welding rod for $CO_2$ Welding is roughly divided into solid wire and flux cored wire. $CO_2$ Welding has higher efficiency than any other welding methods, and also economic and speedy to handle, that's why is used frequently for welding general structures. As most of studies about $CO_2$ Welding are focused on metallurgical changes of successful joints, they developed theories about the change of configuration on weld parts. This study is especially focused on not only the change of configuration on weld parts, but also the change of the penetrating depth through changing the position of welding torch. For inspection, applied AWS A5.20 E70-1 among welding wires and fixed moving angles of torch, but controled the values of voltage and the position of welding. Also Automatic Feed Mechanism is used for exact movement of material, specimen is a piece of steel for general structures. By measuring and analyzing the configuration of sliced section and the values of welding leg length and welding throat after welding, the outcome about the changes turned out.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.171-179
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• 2020

Recently, studies have shown that convolution neural networks are achieving the best performance in image classification, object detection, and image generation. Vision based defect inspection which is more economical than other defect inspection, is a very important for a factory automation. Although supervised anomaly detection algorithm has far exceeded the performance of traditional machine learning based method, it is inefficient for real industrial field due to its tedious annotation work, In this paper, we propose ADGAN, a unsupervised anomaly detection architecture using the variational autoencoder and the generative adversarial network which give great results in image generation task, and demonstrate whether the proposed network architecture identifies anomalous images well on MNIST benchmark dataset as well as our own welding defect dataset.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.38-45
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• 2018

The purpose of this study is to deduct the structural damage / work process risks factors which can be occurred during the decommissioning in the NPP containment concrete structure. To achieve these purpose, risk profile specified in the construction industry is analyzed, and the work process of NPP decommissioning and the construction project were matched based on the similarity of each works. Accordingly, human and physical risk factors are classified. Finally, the risk associated with the building structure and work process was classified as per their process activities, and risk typology explaining the disaster which put the structure, equipments, machine and workers in serious danger was developed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.4
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• pp.119-125
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• 2010

The performance deformation of concrete can be caused by many factors such as load, thermal strain and creep at high temperature. Japan, Europe and America have been doing various experimental studies to solve these problems about thermal properties of concrete at high temperature, each study has generated different results due to a heating methods, heating hours, size of specimens and performance of a the loading, heating method, size of specimen and heating machine. There has been no unified experimental method so far. Therefore, this study reviewed experimental studies on the strength performance of concrete subject to heating and loading method. As a result, compressive strength of specimen prestressed increase in the temperature range of between $100^{\circ}C$ and about $400^{\circ}C$. Also, results can be analyzed as compare equation of compressive strength at elevated temperature with CEN and CEB code.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.852-859
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• 2009

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.4
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• pp.227-233
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• 2001

최근 비약적인 경제발전에 힘입어 장대교량, 항만, 댐, 도로, 원자력 발전소 등과 같은 대규모 기간구조물의 건설이 증가하고 있으며, 구조물은 대형화 혹은 고강도화되는 추세에 있다. 특히, 전술한 구조물을 매스콘크리트로 가설하게 되면 초기재령시에 수화열로 인한 균열이 발생할 가능성이 매우 높기 때문에 효율적인 매스콘크리트의 개발과 매스콘크리트 구조물의 설계기술 및 시공방법이 중요한 연구대상으로 등장하게 된다. 본 논문에서는 가로 52.6m, 세로 14.4m, 높이 8.5m의 기계기초 매스콘크리트의 시공에 적합한 온도관리기법을 다음과 같은 단계로 제안하고자 한다. 먼저 온도상승요인을 최소화하는 콘크리트의 배합비를 산정한다. 산정된 콘크리트의 열특성을 측정하기 위해 단열온도실험을 수행하여 각종 열특성상수와 단열온도 상승곡선식을 도출한다. 이와 같은 열특성치를 콘크리트 구조체에 적용하여 열응력해석을 수행한다. 이와 같은 열응력해석을 통하여 구조물의 분할타설높이에 따라 온도균열이 발생하지 않는 콘크리트 내외부의 온도차를 결정한다. 이때 열응력해석에 범용 유한요소 프로그램인 Diana을 사용한다. 콘크리트의 타설은 현장조건과 타설시점을 최대로 고려하고 양생방법으로 콘크리트 내외부의 온도차를 최소화하기 위해 이중단열효과가 있는 거푸집과 가열장비을 사용한다. 또한 콘크리트의 온도관리를 위하여 구조물 내외부에 온도게이지를 매립하고 30분마다 계측을 수행하면서 콘크리트 내외부 온도차가 허용 해석범위를 유지하도록 한다. 양생기간은 7-10일 정도를 유지한다. 전술한 온도관리기법을 통하여 완공후 수평정밀도가 기초의 허용침하량으로 환산하여 $1{\mu}m$ 인 고정밀도의 기계기초는 완벽하게 시공되었다. 따라서 매스콘크리트의 온도균열을 제어할 수 있는 시공방법으로 제안한다. 또한 매스콘크리트의 내외부 온도차를 단열온도실험과 온도해석으로부터 정한 값이내로 제어하고 충분한 양생관리를 병행하면 수화열에 의한 콘크리트의 온도균열을 최소화할 수 있을 것으로 기대한다.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.79-84
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• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.421-431
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• 2021

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.1
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• pp.30-36
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• 2023

In this study, an image segmentation model that can evaluate surface porosity based on concrete surface images was derived. Three types of concrete specimens with different water-cement ratios (w/c = 54, 35, and 30%) were prepared, and 2,729 surface images were obtained using an optical microscope. Benchmarking tests, parameter optimization, and final model derivation were performed using the surface images, and an image segmentation model with 97% verification accuracy was obtained. The model was verified by comparing the porosity obtained from the model and X-Ray Microscope (XRM). The model provided similar porosity to that of XRM for the specimens with a high water-cement ratio, but tended to give lower porosity for specimens with a low water-cement ratio.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.77-84
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• 2024

In this paper, we propose a method for accurately and rapidly detecting defects in wire harnesses by utilizing computer vision to calculate six crucial measurement values: the length of crimped terminals, the dimensions (width) of terminal ends, and the width of crimped sections (wire and core portions). We employ Harris corner detection to locate object positions from two types of data. Additionally, we generate reference points for extracting measurement values by utilizing features specific to each measurement area and exploiting the contrast in shading between the background and objects, thus reflecting the slope of each sample. Subsequently, we introduce a method using the Euclidean distance and correction coefficients to predict values, allowing for the prediction of measurements regardless of changes in the wire's position. We achieve high accuracy for each measurement type, 99.1%, 98.7%, 92.6%, 92.5%, 99.9%, and 99.7%, achieving outstanding overall average accuracy of 97% across all measurements. This inspection method not only addresses the limitations of conventional visual inspections but also yields excellent results with a small amount of data. Moreover, relying solely on image processing, it is expected to be more cost-effective and applicable with less data compared to deep learning methods.