• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.593-599
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• 2022

In this study, a crack monitoring system capable of detecting cracks based on image processing techniques was developed to effectively check cracks, which are the main damage of concrete structures, and a program capable of imaging and analyzing cracks was developed using machine vision. This system provides objective and quantitative data by replacing the appearance inspection that checks cracks with the naked eye. The verification of the development system was applied to the construction site of a self-healing concrete water tank to monitor the crack and the amount of change in the crack width according to age. In the case of crack width detected by image analysis, the difference from the measured value using a digital microscope was up to 0.036 mm, and the crack healing effect of self-healing concrete could be confirmed through the reduction of crack width.

• Korean Journal of Veterinary Service
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• v.45 no.1
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• pp.19-30
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• 2022

In this study, a simple loop-mediated isothermal amplification (LAMP) combined with visual detection method (vLAMP) assay was developed for the rapid and specific detection of African swine fever virus (ASFV), overcoming the shortcomings of previously described LAMP assays that require additional detection steps or pose a cross-contamination risk. The assay results can be directly detected by the naked eye using hydroxynaphthol blue after incubation for 40 min at 62℃. The assay specifically amplified ASFV DNA and no other viral nucleic acids. The limit of detection of the assay was <50 DNA copies/reaction, which was ten times more sensitive than conventional polymerase chain reaction (cPCR) and comparable to real-time PCR (qPCR). For clinical evaluation, the ASFV detection rate of vLAMP was higher than cPCR and comparable to OIE-recommended qPCR, showing 100% concordance, with a κ value (95% confidence interval) of 1 (1.00~1.00). Considering the advantages of high sensitivity and specificity, no possibility for cross-contamination, and being able to be used as low-cost equipment, the developed vLAMP assay will be a valuable tool for detecting ASFV from clinical samples, even in resource-limited laboratories.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.637-654
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• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

• Journal of Animal Science and Technology
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• v.44 no.1
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• pp.31-38
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• 2002

Interval mapping using microsatellite markers was employed to detect quantitative trait loci (QTL) in the experimental cross between Berkshire and Yorkshire pigs. In order to derive critical values (CV) for test statistics for declaring significance of QTL, permutation test (PT) of Churchill and Doerge method(1994) and the analytical method(LK) of Lander and Kruglyak(1995) were used by each trait and chromosome. 525 $F_2$ progeny phenotypes of five traits(carcass weight, loin eye area, marbling score, cholesterol content, last back fat thickness) and genotypes of 125 markers covering the genome were used. Data were analyzed by line cross regression interval mapping with an F-test every by 1cM. PT CV were based on 10,000 permutations. CV at genome-wise test were 10.5 for LK and ranged from 8.1 to 8.3 for PT, depending on the trait. CV, differed substantially between methods, led to different numbers of quantitative trait loci (QTL) to be detected. PT results in the least stringent CV compared at the same % level.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• Journal of the Korean Geosynthetics Society
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• v.19 no.4
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• pp.41-50
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• 2020

Since cracks in concrete structures expedite corrosion of reinforced concrete over a long period of time, regular on-site inspections are essential to ensure structural usability and prevent degradation. Most of the safety inspections of facilities rely on visual inspection with naked eye, so cost and time consuming are severe, and the reliability of results differs depending on the inspector. In this study, a portable measuring device that can be used for safety diagnosis and maintenance was developed as a device that measures the width and length of concrete cracks through image analysis of cracks photographed with a camera. This device captures the cracks found within a close distance (3 m), and accurately calculates the unit pixel size by laser distance measurement, and automatically calculates the crack length and width with the image processing algorithm developed in this study. In measurement results using the crack image applied to the experiment, the measurement of the length of a 0.3 mm crack within a distance of 3 m was possible with a range of about 10% error. The crack width showed a tendency to be overestimated by detecting surrounding pixels due to vibration and blurring effect during the binarization process, but it could be effectively corrected by applying the crack width reduction function.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• Korean Journal of Cognitive Science
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• v.34 no.4
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• pp.259-275
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• 2023

The aim of this study was to investigate font change blindness based on text difficulty in the "Moving Window Task", as originally introduced by McConkie and Rayner(1975). During the reading process where the moving window was applied, different target words in terms of font style compared to the text were presented. As participants' gaze reached the position of the target word, the font of the target word was changed to match the text font. The font of the target word before the change was either sans-serif when the text font was serif, or serif when the text font was sans-serif. After completing the reading task, more than half of the participants(62.5%) reported not detecting the font change. Observation of eye movements at the target word positions revealed that when understanding the content within the text was difficult, there was an increase in the number of regressions, an extended gaze duration, and a reduction in saccade length. Specifically, the increase in the number of regressions was evident only when the text font was serif, in other words, when the font of the target word shifted from sans-serif to serif. These results suggest that sensory interference unrelated to content understanding is not easily detected during reading. However, the possibility of detection increases when comprehension of the content becomes challenging. Furthermore, this exceptional detection possibility implies that it may be higher when the text font is serif compared to when it is sans-serif.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.19-29
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• 2006

As the security of personal information is becoming more important in mobile phones, we are starting to apply iris recognition technology to these devices. In conventional iris recognition, magnified iris images are required. For that, it has been necessary to use large magnified zoom & focus lens camera to capture images, but due to the requirement about low size and cost of mobile phones, the zoom & focus lens are difficult to be used. However, with rapid developments and multimedia convergence trends in mobile phones, more and more companies have built mega-pixel cameras into their mobile phones. These devices make it possible to capture a magnified iris image without zoom & focus lens. Although facial images are captured far away from the user using a mega-pixel camera, the captured iris region possesses sufficient pixel information for iris recognition. However, in this case, the eye region should be detected for accurate iris recognition in facial images. So, we propose a new fast iris detection method, which is appropriate for mobile phones based on corneal specular reflection. To detect specular reflection robustly, we propose the theoretical background of estimating the size and brightness of specular reflection based on eye, camera and illuminator models. In addition, we use the successive On/Off scheme of the illuminator to detect the optical/motion blurring and sunlight effect on input image. Experimental results show that total processing time(detecting iris region) is on average 65ms on a Samsung SCH-S2300 (with 150MHz ARM 9 CPU) mobile phone. The rate of correct iris detection is 99% (about indoor images) and 98.5% (about outdoor images).

• The Korean Journal of Nuclear Medicine
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• v.33 no.5
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• pp.439-451
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• 1999

Purpose : It is important to differentiate malignant from benign lesions of intraocular masses in choosing therapeutic plan. Biopsy of intraocular tumor is not recommended due to the risk of visual damage. We evaluated the usefulness of F-18-FDG PET imaging in diagnosing intraocular neoplasms. Materials and Methods: F-18-FDG PET scan was performed in 13 patients (15 lesions) suspected to have malignant intraocular tumors. There were 3 benign lesions (retinal detachment, choroidal effusion and hemorrhage) and 10 patients with 12 malignant lesions (3 melanomas, 7 retinoblastomas and 2 metastatic cancers). Regional eye images ($256{\times}256$ and $128{\times}128$ matrices) were obtained with or without attenuation correction. Whole body scan was also performed in eight patients (3 benign and 6 malignant lesions). Results: All malignant lesions were visualized while all benign lesions were not visualized. The mean peak standardized uptake value (SUV) of malignant lesions was $2.64{\pm}0.57g/ml$. There was no correlations between peak SUV and tumor volume. Two large malignant lesions ($> 1000 mm^3$) showed hot uptake on whole body scan. But two medium-sized lesions ($100-1000mm^3$) looked faint and two small ($<100mm^3$) lesions were not visualized. The images reconstructed with $256{\times}256$ matrix showed lesions more clearly than those with $128{\times}128$ matrix Conclusion: F-18-FDG PET scan is highly sensitivity in detecting malignant intraocular tumor For the evaluation of small-sized intraocular lesions, whole body scan is not appropriate because of low sensitivity. A regional scan with sufficient acquisition time is recommended for that purpose. Image reconstruction in matrix size of $256{\times}256$ produced clearer images than the ones in $128{\times}128$, but it does not affect the diagnostic sensitivity.