• International Area Studies Review
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• v.21 no.1
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• pp.25-57
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• 2017

In this study, we analyzed the information asymmetry and the agency problem in major affiliated companies on the basis of the R&D investment. As a result of comparing how the R&D investment effects on major affiliated companies and the independent companies, even the achievement of R&D investment effects in a positive way to the firm value, the positive effect appears much lower on major affiliated companies comparing independent companies. In order to analyze the case, we investigated in a separate way according to the shareholding ratio and the affiliated market using the sample of the independent company and the group affiliated company. As a result of such analysis, the cause of this comes from the agency problem in major affiliated company, not the asymmetry information of affiliated company. After we analyzed the sample of the research depending on the affiliation market, we could observe there is a little impact of the asymmetry information in the outcome of the R&D investment of the major affiliated companies. In contrast, the companies which rated lower in the ratio of the shareholding appears much less in the positive effect of R&D investment compared to the companies which rated at a higher level. This phenomenon was also consistently observed when changing the research method or further subdividing the sample of companies belonging to the group based on the ownership share of major shareholders.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.215-226
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• 2015

In this study, with variables the cross section area ($97m^2$, $58m^2$, $38m^2$) and the wind velocity(0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5 m/s), the time of getting off train dependent on the way of itself and the width of the evacuation route was analyzed, and also fire and evacuation characteristics is reviewed by cross section area of each wind velocity. As the result, if cross section become smaller, the density of harmful gases in the tunnel increased more than the ratio of decreasing cross section area. In the case of small cross sectional area, the surrounding environment from initial fire is indicated to exceed the limit criteria suggested in performance based design. In the analysis of effective evacuation time for evacuation characteristics, the effective evacuation time was the shortest in the case of evaluating effective evacuation time by the visibility. Also, there was significant difference between the effective evacuation time on the basis of performance based evaluation and the effective evacuation time obtained by analyzing FED (Fractional effective dose), one of the analysis method obtaining the point that deaths occur, against harmful gases.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.6
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• pp.1159-1170
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• 2000

It is necessary to improve the ambient air quality through the proper treatment and control of pollutants by designating air pollutants to regulatory ones. Especially, human took a concern for particulate matters which raised visibility reduction, public health effects and injury of property for urban areas. In order to reduce the effect of particulate matters, we need to establish proper control strategies based on the concentration characteristics of particulate matters. In this study. to evaluate the characteristics of $PM_{2.5}$ and $PM_{10}$. thirty-eight samples of $PM_{2.5}$ and $PM_{10}$ were collected at Nam-Gu sampling site where continuous air monitoring system has been operated, from May, 1999 to November, 1999, and their concentrations for the mass and anion components($Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$) were analyzed. The important conclusions obtained in this study were as followings. Total average mass concentrations of $PM_{2.5}$ and $PM_{10}$ were 35.016 and $50.293{\mu}g/m^3$ respectively. and $PM_{2.5}/PM_{10}$ ratio was calculated 0.692. Total average concentrations of anion components in $PM_{2.5}$ were $1.581(Cl^-)$, $3.690(NO_3{^-})$ and $12.825(SO_4{^{2-}}){\mu}g/m^3$ and those in $PM_{10}$ were $2.471(Cl^-)$, $5.819(NO_3{^-})$ and $14.414(SO_4{^{2-}}){\mu}g/m^3$ respectively. From the correlations analysis. the correlation coefficient between mass concentration of $PM_{2.5}$ and $PM_{10}$ was calculated as 0.945. The correlation coefficients between $PM_{2.5}$ and anion components were analyzed as $Cl^-$(0.025), $NO_3{^-}$(0.788) and $SO_4{^{2-}}$(0.500) respectively, and the correlation coefficients between $PM_{10}$ and anion components were analyzed as $Cl^-$(-0.019), $NO_3{^-}$(0.806) and $SO_4{^{2-}}$)(0.535) respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.