• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1209-1215
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• 2022

The selective catalytic reduction (SCR) is known as a very efficient method to reduce nitrogen oxides (NOx) and the catalyst performs reduction from nitrogen oxides (NOx) to nitrogen (N₂) and water vapor (H2O). The catalyst, which is one of the factors determining the performance of the nitrogen oxide (NOx) ruduction method, is known to increase catalyst efficiency as cell density increases. In this study, the reduction characteristics of nitrogen oxides (NOx) under various engine loads investigated. A 100CPSI(60Cell) catalysts was studied through a laboratory-sized simulating device that can simulate the exhaust gas conditions from the power generation engine installed in the training ship SEGERO. The effect of 100CPSI(60Cell) cell density was compared with that of 25.8CPSI(30Cell) cell density that already had NOx reduction data from the SCR manufacturing. The experimental catalysts were honeycomb type and its compositions and materials of V₂O₅-WO₃-TiO₂ were retained, with only change on cell density. As a result, the NOx concentration reduction rate from 100CPSI(60Cell) catalyst was 88.5%, and IMO specific NOx emission was 0.99g/kwh satisfying the IMO Tier III NOx emission requirement. The NOx concentration reduction rate from 25.8CPSI(30Cell) was 78%, and IMO specific NOx emission was 2.00g/kwh. Comparing the NOx concentration reduction rate and emission of 100CPSI(60Cell) and 25.8CPSI(30Cell) catalysts, notably, the NOx concentration reduction rate of 100CPSI(60Cell) catalyst was 10.5% higher and its IMO specific NOx emission was about twice less than that of the 25.8CPSI(30Cell) catalysts. Therefore, an efficient NOx reduction effect can be expected by increasing the cell density of catalysts. In other words, effects to production cost reduction, efficient arrangement of engine room and cargo space can be estimated from the reduced catalyst volume.

• Progress in Medical Physics
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• v.22 no.3
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• pp.140-147
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• 2011

The purpose of this study was to estimate internal motion using molecular sieve for quantitative improvement of lung tumor and to localize lung tumor in the small animal PET image by evaluated data. Internal motion has been demonstrated in small animal lung region by molecular sieve contained radioactive substance. Molecular sieve for internal lung motion target was contained approximately 37 kBq Cu-64. The small animal PET images were obtained from Siemens Inveon scanner using external trigger system (BioVet). SD-Rat PET images were obtained at 60 min post injection of FDG 37 MBq/0.2 mL via tail vein for 20 min. Each line of response in the list-mode data was converted to sinogram gated frames (2~16 bin) by trigger signal obtained from BioVet. The sinogram data was reconstructed using OSEM 2D with 4 iterations. PET images were evaluated with count, SNR, FWHM from ROI drawn in the target region for quantitative tumor analysis. The size of molecular sieve motion target was $1.59{\times}2.50mm$. The reference motion target FWHM of vertical and horizontal was 2.91 mm and 1.43 mm, respectively. The vertical FWHM of static, 4 bin and 8 bin was 3.90 mm, 3.74 mm, and 3.16 mm, respectively. The horizontal FWHM of static, 4 bin and 8 bin was 2.21 mm, 2.06 mm, and 1.60 mm, respectively. Count of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.10, 4.83, 5.59, 5.38, and 5.31, respectively. The SNR of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.18, 4.05, 4.22, 3.89, and 3.58, respectively. The FWHM were improved in accordance with gate number increase. The count and SNR were not proportionately improve with gate number, but shown the highest value in specific bin number. We measured the optimal gate number what minimize the SNR loss and gain improved count when imaging lung tumor in small animal. The internal motion estimation provide localized tumor image and will be a useful method for organ motion prediction modeling without external motion monitoring system.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of the Daesoon Academy of Sciences
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• v.27
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• pp.135-169
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• 2016

While examining the religious ideas implied by Jeungsan's Great Works of the Reordering of Universe, we find special ideas which cannot be found in any other religions, and these ideas are presented in diverse ways. Most of all, the representative idea is that of human nobility; a distinctive idea which makes Daesoonjinrihoe different from other religions. Thus, this research focuses on the following questions: when was Human Nobility concretely realized? What kind of organic relationship does human nobility have between the divine world and the world of humanity? In light of the forthcoming Era of Human Nobility, what are some concrete images which can be drawn from the interaction between the realms of heaven and humanity wherein preordinations are plotted in heaven and then carried out by humankind? Prior to formulating my own sense of the subject matter, I consulted 43 previous discussions and dissertations and arranged them chronologically so as to examine their correlation. From these sources and my own insights, I was able to gain a sense of the starting point of the era of human nobility and its tenor. I have found the following problems in previous research on the uniqueness and distinctness of human nobility: ①The conceptual undertones of human nobility have not been adequately gleaned. ②There do not seem to be any dissertations which examine the way in which human nobility is connected with the doctrines of the creative conjunction between yin and yang, the harmonious union of divine beings and human beings, and the resolution of grievances for mutual beneficence. ③In most dissertations, not only is the starting point of the Era of Human Nobility regarded as concurrent with the start of the 50,000 years of earthly paradise in the Later World, but also the point of division between the former world and the later world is widely disputed. ④In-depth and fully realized studies dealing with the subject of human nobility are not easily found. ⑤There is little sense of progression in the research on human nobility because scholars are not sufficiently engage with one another to achieve common consensus. Therefore, in this dissertation, I have provided answers to the problems I discovered in previous research. I have developed my own tenor as follows: ①By giving priority to the Jeongyeong, I have closely investigated the period which divides the Former World and the Later World. Then, I produced a chronological timeline to demonstrate the progression: the Former World → the Era of the Resolution of Grievances → the Later World. This aids in the comprehension of human nobility. ②The Era of Human Nobility was preceeded by the opening of the Era of the Resolution of Grievances of human world which began in 1901. Human nobility is stipulated as a regulatory system for the universe set in motion by the opening the Era of Resolution of Grievances. ③While synthetically examining the aspects of transition which enable the Ear of Human Nobility to be realized, the period to be studied is stipulated as beginning from 1901 and ending at the start of the Later World. The subjects are defined as the flowing from Jeungsan, the first leader of human nobility, to the noble individuals empowered by Dao and the noble populace. In the Era of Human Nobility, studying the transition process by which human nobility is realized requires delving into the resolution of grievances. Although this method is essential to understanding Daesoon ideas, in actuality it does not hinge upon speculative exegetical theorizing but instead it was gained through eisegetical rigor.