• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.540-544
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• 2012

Like the phase array based ultrasonic system, complicated electronics has been used for real time signal processing in the magnetostrictive long-range ultrasonic testing(LRUT) system. This study shows that the software approach including the phase compensation, noise filtering and waveform transformation takes advantage rather than the previous hardware approach. Furthermore, it is possible for the software approach to be able more flexible and efficient realtime signal processing. These results will contribute to a cost-effective LRUT system and analysis of the inspection data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.3
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• pp.51-55
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• 2019

The loss of safety for reservoirs brought about by climate change and facility aging leads to reservoir failures, which results in the loss of lives and property damage in downstream areas. Therefore, it is necessary to provide a Reservoir Failure Forecasting System for downstream residents to detect the early signs of failure (with sensors) in real-time and perform safety management to prevent and minimize possible damage. For the verification of established water level management criteria, 10 water level data up to reservoir capacity was selected. Weight factor and trend line were applied to dramatic increase section of water level in the 1 year period data. The results shows that water level criteria based on three even parts shows less than 7% of standard deviation and it is appropriate to verify management criteria.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.1 s.53
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• pp.145-151
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• 2009

The span deflection among the monitoring items of bridge measurement system in real time is representative behaviour and important index of superstructure condition. the limit of span deflection in Standard Specifications for Highway Bridge in Korea has been applied to the method that is making the management-criteria for span deflection in bridge measurement system. But the limit concern mainly serviceability of divers. So it is difficult to find the safety factor of the limit from the viewpoint of bridge safety. This study estimated the safety factor of the limit of span-deflection in Standard Specifications for Highway Bridge in Korea from the viewpoint of bridge safety by the indoor structural test.

• Journal of the Korean Wood Science and Technology
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• v.37 no.1
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• pp.37-47
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• 2009

For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.605-610
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• 2008

Through welding fabrication, user can feel unsatisfaction of surface quality because of welded defects, Generally speaking, these are called weld defects. For checking these defects effectively without time loss effectively, weldability estimation system setup is an urgent thing for detecting whole specimen quality. In this study, by laser vision camera, catching a rawdata on welded specimen profiles, treating vision processing with these data, qualitative defects are estimated from getting these information at first. At the same time, for detecting quantitative defects, whole specimen weldability estimation is pursued by multifeature pattern recognition, which is a kind of fuzzy pattern recognition. For user friendly, by weldability estimation results are shown each profiles, final reports and visual graphics method, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line weldability estimation.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.141-146
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• 1998

Recently, the computer vision such as part measurement, and product inspection is very popular to achieve the factory automation since the labor cost is dramatically increasing. In this paper, the diameter and the length of rope are measured by CCD camera which is orthogonally mounted on the ceiling. Two parameters which are the diameter and the length of rope are used to measure the weight of rope. If the weight of rope is reached to predetermined weight, the information is transmitted to PLC(programmable logic control) to cut the rope on the wheel. The cutting machine cuts the rope according to the information obtained from the CCD camera. To measure the diameter and length of rope on real time, the searching space for image segmentation is restricted the predetermined area according to the camera calibration position. Finally, to estimate the weight of rope, the knowledge base system which depends on the diameter, the length of rope, and weight relation between these information are constructed according to diameters of rope. This method contributes to achieve the factory automation, and reduce the production cost since the operators are unnecessary to measure the weight of rope by try-and-error method.