• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1177-1186
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• 2009

High temperature steam pipes of thermal power plant are subject to a severe thermal range and usually operates well into the creep range. Cyclic operation of the plant subjects the piping system to mechanical and thermal fatigue damages. Also, poor or malfunctional supports can impose massive loads or stress onto the piping system. In order to prevent the serious damage and failure of the critical piping system, various inspection methods such as visual inspection, computational analysis and on-line piping displacement monitoring were developed. 3-dimensional piping displacement monitoring system was developed with using the aluminum alloy rod and rotary encoder sensors, this system was installed and operated on the high temperature steam piping of "Y" thermal power plant successfully. It is expected that this study will contribute to the safety of piping system, which could minimize stress and extend the actual life of critical piping.

• The Korean Journal of Community Living Science
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• v.24 no.2
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• pp.251-258
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• 2013

Korean oil and honey Yukwa has been paid attention as formal cake for traditional national seasons' holiday and religious service. Quality of Yukwa, however, has been maintained arbitrarily by each Yukwa manufacturer. Since even same Yukwa had severe differences in size, weight, and pattern, it has given the negative effect to the consumer. Yukwa industries need to setup the quantitative quality specifications instead of qualitative ones to maintain the uniformity of Yukwa quality. Efficient and economical inspection and process control system should be developed. In developing quality standards of Yukwa, features which can measure quality quantitatively in real time should be properly chosen. Existing quality features such as acidity, oxidization, hardness, viscosity, and texture were measured by the chemical or physical base destructive methods. Many research and developments have been performed in investigating and analyzing chemical transition states of those quality features as environment or storage condition changes. Most methods, however, require either off-line or complex treatment or time consuming process of analysis in evaluating quality features. Consumer, however, selects products mostly based on the external features such as shape, size, and color. Therefore, critical visual quality features should be chosen and the efficient real time measurement system must be developed. In this paper, computer image acquisition and processing system were developed and software modules were developed to extract the quantitative data of those features in real-time. Computer image processing system will promote in maintaining uniform quality of Yukwa and establishing quality standards of Yukwa.

• 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.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.293-298
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• 2021

In general, the guided weapon seeker and the guided control device process the target, search, recognition, and capture information to indicate the state of the guided missile, and play a role in controlling the operation and control of the guided weapon. The signals required for guided weapons are gaze change rate, visual signal, and end-stage fuselage orientation signal. In order to process the complex and difficult-to-process missile signals of recent missiles in real time, it is necessary to increase the data processing speed of the missiles. This study showed the processing speed after applying the stop and go and inverse enumeration algorithm among the parallel algorithm methods of PINQ and comparing the processing speed of the signal data required for the guided missile in real time using the guided missile inspection program. Based on the derived data processing results, we propose an effective method for processing missile data when applying a parallel processing algorithm by comparing the processing speed of the multi-core processing method and the single-core processing method, and the CPU core utilization rate.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.10-16
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• 2012

For high speed visual inspection in semiconductor industries, it is essential to acquire two-dimensional images on regions of interests with a large field of view (FOV) and a high resolution simultaneously. In this paper, an imaging system is newly proposed to achieve high quality image in terms of precision and FOV, which is composed of single lens, a beam splitter, two camera sensors, and stereo image grabbing board. For simultaneously acquired object images from two camera sensors, Zhang's camera calibration method is applied to calibrate each camera first of all. Secondly, to find a mathematical mapping function between two images acquired from different view cameras, the matching matrix from multiview camera geometry is calculated based on their image homography. Through the image homography, two images are finally registered to secure a large inspection FOV. Here the inspection system of using multiple images from multiple cameras need very fast processing unit for real-time image matching. For this purpose, parallel processing hardware and software are utilized, such as Compute Unified Device Architecture (CUDA). As a result, we can obtain a matched image from two separated images in real-time. Finally, the acquired homography is evaluated in term of accuracy through a series of experiments, and the obtained results shows the effectiveness of the proposed system and method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.8
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• pp.1339-1347
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• 1997

The automation of final inspection and tuning process in the manufacturing of electric products is hot issue now, because it is the only part that has not been wholey automized yet, mainly due to the difficulties to handle so small size of VR which is the final tuning point in the most of electric products. For the automation of this process, at first the accurate measurement of position and orientation of SMD VR on PCB in real time is strongly needed. In this paper, a new image processing algorithm to detect the center position and orientation of target VR by using machine vision is proposed for automatic final tuning of the 8mm camcoder's performance. In the method, the outline feature of object is used actively. The usefulness of the proposed methods were tested by several experiments, and the results showed enough accuracy for both of position and orientation. Additatively, we discussed about the total visual system construction and preprocessing of image.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.163-170
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• 2019

Most of the current crack investigation work consists of visual inspection using simple measuring equipment such as crack scale. These methods involve the subjection of the inspector, which may lead to differences in the inspection results prepared by the inspector, and may lead to a large number of measurement errors. So, this study proposes an image-based crack detection method to enhance objectivity and efficiency of concrete crack investigation. In this study, YOLOv2 was used to determine the presence of cracks in the image information to ensure the speed and accuracy of detection for real-time analysis. In addition, we extracted shapes of cracks and calculated quantitatively, such as width and length using various image processing techniques. The results of this study will be used as a basis for the development of image-based facility defect diagnosis automation system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.58-65
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• 2020

3D Models of offshore plant piping materials designed by 3D CAD systems are provided to the production processes in the form of 2D piping drawings and 2D piping installation drawings. In addition to the standard engineering information, the purchasing, procurement, manufacturing, installation, and inspection of raw materials are managed systematically in an integrated process control system. The existing integrated process management system can help reduce the processing time by managing the flow and progress of resources systematically, but it does not include 3D design model information. Hence, it is difficult to understand complicated pipe structures before installing the pipe. In addition, when design changes or immediate design modifications are required, it is difficult to find related data or exchange information quickly with each other. To solve this problem, an offshore plant-piping process-monitoring system was developed based on a 3D model. The 3D model-based piping monitoring system is based on Visual Studio 2017 C# and UNITY3D so that the piping-process work information can be linked to the 3D CAD model in real time. In addition, the 3D model could check the progress of the pipe installation process, such as block, size, and material, and the progress of functional inspection items, such as cleaning, hydraulic inspection, and pneumatic inspection.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.