• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.51-57
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• 2021

If a company's actions to save or avoid taxes are judged to be tax evasion rather than legal tax action by the tax authorities, the company will not only pay tax but also non-tax costs such as damage to corporate image and stock price decline due to a series of tax evasion-related news articles. Therefore, this study measures the frequency of occurrence of tax evasion controversial keywords in internet news portal as a factor to measure the severity of the case, and analyzes the effect of the frequency of occurrence on corporate value. In the Korean stock market, we crawl related articles from internet news portal by using keywords that are controversial for tax evasion targeting top companies based on market capitalization, and generate a time series of the frequency of occurrence of keywords about tax evasion by company and analyze the effect of frequency of appearance on book value versus market capitalization. Through panel regression and impulse response analysis, it is analyzed that the frequency of appearance has a negative effect on the market capitalization and the effect gradually decreases until 12 months. This study examines whether the tax evasion issue affects the corporate value of Korean companies and suggests that it is necessary to take these influences into account when entrepreneurs set up tax-planning schemes.

• Explosives and Blasting
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• v.40 no.4
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• pp.15-26
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• 2022

With the recent increase in old and dangerous buildings, the demand for technology in the field of structure demolition is rapidly increasing. In particular, in the case of structures with severe deformation of damage, there is a risk of deterioration in stability and disaster due to changes in the load distribution characteristics in the structure, so rapid structure demolition technology that can be efficiently dismantled in a short period of time is drawing attention. However, structural deformation such as unauthorized extension or illegal remodeling occurs frequently in many old structures, which is not reflected in structural information such as building drawings, and acts as an obstacle in the demolition design process. In this study, as an effective way to overcome the discrepancy between the structural information of old structures and the actual structure, access to actual structures through 3D modeling was considered. 3D point cloud data inside and outside the building were obtained through LiDAR and drone photography for buildings scheduled to be blasting demolition, and precision matching between the two spatial data groups was performed using an open-source based spatial information construction system. The 3D structure model was completed by importing point cloud data matched with 3D modeling software to create structural drawings for each layer and forming each member along the structure slab, pillar, beam, and ceiling boundary. In addition, the modeling technique proposed in this study was verified by comparing it with the actual measurement value for selected structure member.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.105-122
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• 2022

According to the statistics about the fatal crashes that have occurred on the expressways for the last 5 years, those who died on the shoulders of the road has been as 3 times high as the others who died on the expressways. It suggests that the crashes on the shoulders of the road should be fatal, and that it would be important to prevent the traffic crashes by cracking down on the vehicles intruding the shoulders of the road. Therefore, this study proposed a method to detect a vehicle that violates the shoulder lane by using the Faster R-CNN. The vehicle was detected based on the Faster R-CNN, and an additional reading module was configured to determine whether there was a shoulder violation. For experiments and evaluations, GTAV, a simulation game that can reproduce situations similar to the real world, was used. 1,800 images of training data and 800 evaluation data were processed and generated, and the performance according to the change of the threshold value was measured in ZFNet and VGG16. As a result, the detection rate of ZFNet was 99.2% based on Threshold 0.8 and VGG16 93.9% based on Threshold 0.7, and the average detection speed for each model was 0.0468 seconds for ZFNet and 0.16 seconds for VGG16, so the detection rate of ZFNet was about 7% higher. The speed was also confirmed to be about 3.4 times faster. These results show that even in a relatively uncomplicated network, it is possible to detect a vehicle that violates the shoulder lane at a high speed without pre-processing the input image. It suggests that this algorithm can be used to detect violations of designated lanes if sufficient training datasets based on actual video data are obtained.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.574-593
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• 2023

In this study, the effectiveness and applicability of a newly designed Compact CNS shear box for conducting direct shear tests on jointed rock specimens were investigated. CNS joint shear tests were conducted on jointed rocks with Artificially generated roughness while varying the fracture surface roughness coefficient and initial normal stress conditions. In addition, displacement data were validated by Digital image correlation analysis, fracture patterns were observed, and comparative analysis was conducted with previously studied shear behavior prediction models. Furthermore, the accuracy of the displacement data was confirmed through DIC analysis, the fracture patterns were observed, and the shear properties obtained from the tests were compared with existing models that predict shear behavior. The findings exhibited a strong correlation with specific established empirical models for predicting shear behavior. Furthermore, the potential linkage between the characteristics of shear behavior and fracture patterns was deliberated. In conclusion, the CNS shear box was shown to be applicable and effective in providing data on the shear characteristics of the joint.

• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.1
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• pp.63-71
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• 2024

Through the development of construction technology, various construction projects such as redevelopment projects, undergrounding of roads, expansion of subways, and metro railways are being carried out. However, this has led to an increase in the number of construction projects in existing urban centers and neighborhoods, resulting in an increase in the number of damages and disputes between neighboring buildings and residents, as well as an increase in safety accidents due to the aging of existing buildings. In this study, digital data was applied to a graphics program to objectify the progress of cracks by comparing the creation of cracks and the increase in length and width through photographic images and presenting the degree of cracks numerically. Through the application of the program, the error caused by the subjective judgment of crack change, which was mentioned as a shortcoming of the existing field survey, was solved. It is expected that the program can be used universally in the building diagnosis process by improving its reliability if supplemented and improved in the process of use. As a follow-up study, it is necessary to apply the extraction algorithm of the digital graphic data program to calculate the length and width of the crack by itself without human intervention in the preprocessing work and to check the overall change of the building.

• Applied Chemistry for Engineering
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• v.29 no.3
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• pp.342-349
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• 2018

In this study, the physicochemical properties, emulsifying capacity, moisture content and cytotoxicity of the composite material produced by transesterification reactions of the olive oil (olive oil esters) were investigated for cosmetic applications. Olive oil esters with short (S) and long (L) reaction times were studied. From the TLC-image analysis, composition ratios of the olive oil esters S were found to be 5.2, 24.1, 46.4, and 21.9% for mono-, di-, tri-glyceride, and fatty acid ethyl ester, respectively. Those of the olive oil esters L were 4.1, 24.7, 40.6, and 28.8% for mono-, di-, tri-glyceride, and fatty acid ethyl ester, respectively. The iodine value, acid value, saponification value, unsaponified matter, refractive index, and specific gravity were determined and purity tests were also carried out and normalized to establish standards and testing methods for using olive oil esters in cosmetics. To evaluate their emulsifying capacities, the O/W emulsion was prepared without surfactants and the formation of the emulsified particles were confirmed. After 5 days of applying the olive oil esters to human skin, the skin moisture retention was improved by 13.1% from the initial state. For the evaluation of toxicity on human skin cells, the olive oil esters showed 90% or more of the cell viability at $0.2-200{\mu}g/mL$. These results suggested that olive oil esters can be applied as natural/non-toxic ingredients to cosmetics industries.

• Food Science and Preservation
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• v.20 no.4
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• pp.554-563
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• 2013

The changes in quality properties and nutritional components for two mugworts, namely, Artemisia capillaris Thumberg Artemisiae asiaticae Nakai fermented by Bacillus strains were characterized followed by rapid pattern analysis of volatile flavor compounds through the SAW-based electronic nose sensor in the GC system. After fermentation, the pH has remarkably decreased from 6.0~6.4 to 4.6~5.1 and there has been a slight change in the total soluble solids. The L (lightness) and b (yellowness) values in the Hunter's color system significantly decreased, whilst the a (redness) value increased via fermentation. The HPLC analysis demonstrated that the total amino acids increased in quantity and the essential amino acids were higher in the A. asiaticae Nakai than in the A. capillaris Thumberg, specially with high contents of glutamic and aspartic acid. After fermentation, the monounsaturated fatty acid increased in the A. asiaticae Nakai and the polyunsaturated fatty acids increased in the A. capillaris Thumberg. While the total polyphenol contents have not been affected by fermentation, the total sugar contents have dramatically decreased. Scopoletin, which is one of the most important index components in mugworts, was highly abundant in the A. capillaris Thumberg; however, it was not detected in the A. asiaticae Nakai. Small pieces of plant tissue in the surface microstructure were found in the fermented mugworts through the use of the scanning electron microscope (SEM). Volatile flavor compounds via electronic nose showed that the intensity of several peaks has increased and additional seven flavor peaks have been produced after fermentation. The VaporPrintTM images demonstrated a notable difference in flavors between the A. asiaticae Nakai and A. capillaris Thumberg, and the fermentation enabled the mugworts to produce subtle differences in flavor.

• Clean Technology
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• v.27 no.4
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• pp.341-349
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• 2021

In combustion facilities, the nitrogen and sulfur in fossil fuels react with oxygen to generate air pollutants such as nitrogen oxides (NO_X) and sulfur oxides (SO_X), which are harmful to the human body and cause environmental pollution. There are regulations worldwide to reduce NO_X and SO_X, and various technologies are being applied to meet these regulations. There are commercialized methods to reduce NO_X and SO_X emissions such as selective catalytic reduction (SCR), selective non-catalytic reduction (SNCR) and wet flue gas desulfurization (WFGD), but due to the disadvantages of these methods, many studies have been conducted to simultaneously remove NO_X and SO_X. However, even in the NO_X and SO_X simultaneous removal methods, there are problems with wastewater generation due to oxidants and absorbents, costs incurred due to the use of catalysts and electrolysis to activate specific oxidants, and the harmfulness of gas oxidants themselves. Therefore, in this research, microbubbles generated in a high-pressure disperser and reducing agents were used to reduce costs and facilitate wastewater treatment in order to compensate for the shortcomings of the NO_X, SO_X simultaneous treatment method. It was confirmed through image processing and ESR (electron spin resonance) analysis that the disperser generates real microbubbles. NO_X and SO_X removal tests according to temperature were also conducted using only microbubbles. In addition, the removal efficiencies of NO_X and SO_X are about 75% and 99% using a reducing agent and microbubbles to reduce wastewater. When a small amount of oxidizing agent was added to this microbubble system, both NO_X and SO_X removal rates achieved 99% or more. Based on these findings, it is expected that this suggested method will contribute to solving the cost and environmental problems associated with the wet oxidation removal method.