• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.157-163
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• 2007

Crack detection technique for concrete structures has been developed in this study. Experimental tests were carried out to detect a surface and internal crack, employing common fiber optic cables and OTDR(optical time domain reflectometry), an optical signal analyzer which is widely used to detect damages at fiber optic cables in the field of optical engineering. While initial concrete crack is ready to occur under cracking force, the occurrence and location of the crack are simultaneously detected to give the same damage to fiber optic cables which have been attached to and/or embedded into concrete in advance. It is obtained through successive tests that the principal factors for crack detection is the covering state of fiber optic cable, and total 4 tests including a preliminary test were conducted and the crack detection technique was verified. The practical usefulness would be expected at crack management and maintenance of concrete structures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.405-412
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• 2012

After the spatial discrepancy between SLI(Street-Level Imagery) and vector map is removed by their conflation, the corresponding building regions can be found based on SLI parameters. The building region correspondence, however, is not perfect even after the conflation. This paper aims to improve the correspondence of building regions by region splitting of an SLI. Regions are initialized by the seed lines, projection of building objects onto SLI scene. First, sky images are generated by filtering, segmentation, and sky region detection. Candidates for split lines are detected by edge detector, and then images are splitted into building regions by optimal split lines based on color difference and sky existence. The experiments demonstrated that the proposed region splitting method had improved the accuracy of building region correspondence from 83.3% to 89.7%. The result can be utilized effectively for enhancement of SLI services.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.305-312
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• 2012

This paper aims at improving the accuracy and computational efficiency in reconstructing building boundaries from airborne Lidar points. We proposed an adaptive convex hull algorithm, which is a modified version of local convex hull algorithm in three ways. The candidate points for boundary are first selected to improve efficiency depending on their local density. Second, a searching-space is adjusted adaptively, based on raw data structure, to extract boundary points more robustly. Third, distance between two points and their IDs are utilized in detecting the seed points of inner boundary to distinguish between inner yards and inner holes due to errors or occlusions. The practicability of the approach were evaluated on two urban areas where various buildings exist. The proposed method showed less shape-dissimilarity(8.5%) and proved to be two times more efficient than the other method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.57-65
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• 2010

In this paper, we propose a new real-time algorithm detecting the flame and smoke in digital CCTV images. Because the forest fire causes the enormous human life and damage of property, the early management according to the early sensing is very important. The proposed algorithm for monitoring forest fire is classified into the flame sensing and detection of smoke. The flame sensing algorithm detects a flame through the conditional test at YCbCr color model from the single frame. For the detection of smoke, firstly the background range is set by using differences between current picture and the average picture among the adjacent frames in the weighted value, and the pixels which get out of this range and have a gray-scale are detected in the smoke area. Because the proposed flame sensing algorithm is stronger than the existing algorithms in the change of the illuminance according to the quantity of sunshine, and the smoke detection algorithm senses the pixel of a gray-scale with the smoke considering the amount of change for unit time, the effective early forest fire detection is possible. The experimental results indicate that the proposed algorithm provides better performance than existing algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.77-89
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• 2024

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.2
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• pp.236-246
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• 2004

Artificial carious lesions in various depths were observed with visual examination using light transillumination, bite-wing radiography, laser fluorescence, and dye-enhanced laser fluorescence to determine the reproducibility, correlation of each diagnostic method, diagnostic sensitivity and diagnostic specificity. And optical densities according to demineralized times were measured whether laser fluorescence could be used as a quantitative diagnostic method. The following results were obtained whether laser fluorescence could be used for diagnosis of initial proximal caries. 1. Tau-c values of visual examination was 0.08 which showed lowest reproducibility and those of bite-wing radiography, laser fluorescence, dye-enhanced laser fluorescence were 0.60, 0.48, and 0.64, respectively which showed relatively high reproducibility. 2. The correlation between demineralization time and each examination was the highest in dye-enhanced laser fluorescence$({\gamma}=0.51)$ followed by laser fluorescence$({\gamma}=0.43)$, bite-wing radiograph$({\gamma}=0.35)$, and visual examination$({\gamma}=0.33)$. Dye-enhanced laser fluorescence and laser fluorescence showed significant correlation with demineralization time. 3. The sensitivity of laser fluorescence and dye-enhanced laser fluorescence for diagnosing approximal caries based on bite-wing radiography were 67%, 100% and those of specificity were 57%, 11% which showed diagnostic specificity was relatively lower than sensitivity. 4. The difference in optical density(DFR) between sound teeth and carious lesions according to lesion depth was high with dye-enhanced laser fluorescence compared with laser fluorescence. DFR measured with laser fluorescence according to changes in lesion depth was statistically significant but was not statistically significant with dye-enhanced laser fluorescence. Based on these results, laser fluorescence and dye-enhanced laser fluorescence have comparable diagnostic power as bite-wing radiography in early diagnosis of proximal caries.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.361-373
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• 2013

The monitoring of urban area, target detection and building reconstruction have been actively studied and investigated since high resolution X-band SAR images could be acquired by airborne and/or satellite SAR systems. This paper describes an efficient approach to reconstruct artificial structures (e.g. apartment, building and house) in urban area using high resolution X-band SAR images. Building footprint was first extracted from 1:25,000 digital topographic map and then a corner line of building was detected by an automatic detecting algorithm. With SAR amplitude images, an initial building height was calculated by the length of layover estimated using KS-test (Kolmogorov-Smirnov test) from the corner line. The interferometric SAR phases were simulated depending on SAR geometry and changable building heights ranging from -10 m to +10 m of the initial building height. With an interferogram from real SAR data set, the simulation results were compared using the method of the phase consistency. One of results can be finally defined as the reconstructed building height. The developed algorithm was applied to repeat-pass TerraSAR-X spotlight mode data set over an apartment complex in Daejeon city, Korea. The final building heights were validated against reference heights extracted from LiDAR DSM, with an RMSE (Root Mean Square Error) of about 1~2m.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.1-22
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• 2022

Lavie's perceptual load theory (Lavie, 1995) proposes that the influence of distractors would be blocked as the load gets higher. Studies of perceptual load have usually adopted the flanker task, developed by Eriksen and Eriksen (1974), which measures reaction time on the target flanked by distractors. In the post-cueing forced task, participants should report the identity of the target cued later, and negative repetition effect (NRE) has often been observed. NRE means the effect that the accuracy of identification is worse when the target is flanked by the same nontargets than when flanked by different nontargets. This study has tried to check whether perceptual load has an effect on identification rate and NRE. Experiment 1 manipulated the similarity between targets and a distractor, and observed a tendency of NRE, but not the effect of perceptual load. Experiment 2 used 4, 2 (in two kinds of diagonal arrangement), or none distractors of the same identity to burden more perceptual load. NRE was significant and perceptual load showed significance but not a linear trend. Experiment 3 checked again whether NRE would be varied according to two levels of perceptual load strengthened by positional variability of load stimuli, but did not find the effect of perceptual load. It is concluded that perceptual load might have a limited effect on the early stage of perceptual processing due to divided attentional processing of the targets briefly exposed. Implications of this study were discussed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.56-64
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• 2021

Leakage due to deterioration and damage is one of the major causes of volume change by freezing and thawing, and it leads micro-cracking and surface scaling in concrete structures. The deterioration of damaged concrete accelerates with the chloride attack. Thus, in the detailed guidelines for facility performance evaluation (2020), the quality of cover concrete and the freeze-thaw (FT) repetition cycle were newly suggested for concrete durability assessment. The quality of cover concrete should be evaluated by the rebound hammer test and the FT repetition cycle should be also considered in the deterioration environmental assessment. This study suggested the application of fast dynamic based nonlinear ultrasound method to monitor initial micro-scale damage under freezing and thawing environment. Concrete specimens were fabricated with different water-cement ratios (40%, 60%) and air contents (1.5% and 3.0%). The compressive strength, rebound number, relative dynamic modulus, and nonlinear ultrasound were measured with different FT cycles. The scanning electron microscopy was also performed to investigate the micro-scale FT damage. As a result, both the rebound number and the relative dynamic modulus had difficulty to detect early damage but the proposed method showed a potential to detect initial micro-scale damage and predict the FT resistance performance of concrete.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.