• 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 Korean Academy of Fundamentals of Nursing
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• v.6 no.3
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• pp.532-543
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• 1999

This study was conducted to provide the basic data necessary for the improvement of the teaching method for basic nursing practice as well as the effectiveness of the practice by examining the students' competency in cleansing enema after doing the self study instead of the traditional education. To examine the competency in cleansing enema after the self study, this study is an one group pretest-posttest design that subjects did the enema practice through the self study. The subjects were 89 sophomore students at Y University. College of Nursing. In basic nursing lab practice class, cleansing enema self study module was given to the students which was developed by the researcher based on the literature review and asked them to finish doing the pre study and checking the self study evaluation criteria after reading the goal, learning activities and theoretical guideline. After watching the video tape, students practiced the process in the module by themselves. For the competency in cleansing enema. repeated autonomous practices were done during the open lab other than the regular class. Whenever the practice was done, the frequency and time were measure and documented. When the student felt confident through repeated practices, the competency was evaluated by the researcher and two assistants based on the evaluation criteria. And the process was repeated till the student could perform all the items on evaluation criteria completely. The data were collected for 42 days from Oct. 15 to Nov. 26 in 1996. Collected data were analyzed by frequency, percentage, Pearson correlation coefficient and variance analysis. The results are summarized as follows : 1. 43.2% of the students were favorable to nursing and 63.6% like lecture, but 71.6% like practice. So they were more interested in practice than in lecture. 2. 62.3% of the students scored high in written test, 97.8% scored high in practice. So the practice score was better. 3. The frequency of repeated practice to pass the test ranged from 1 to 4 and the average is 2.2. 4. The average time needed in preparation and the performance was nearly the same regardless of the frequency. It took 5 to 38 minutes for those who passed the test after practicing once and the average was 16 minutes. 5 to 60 minutes were taken for those who practiced twice to pass the test and the average was 21 minutes. Those who passed the test after three practices needed 8 to 30 minutes and the average was 15 minutes, which was similar to the time that the students who passed the test for the first trial. Only one student passed the test after 4 practices and it took 10 minutes. 5. 64% of the students agreed that the context and the content of the module were appropriate for the self study and 68.2% were satisfied. And 71.9% said that the module helped them to practice the enema self study 6. Though only 42% of the students were satisfied with the video. 50.6% said that it was helpful for the self study. 7. 52.3% of the students were satisfied with the self study method, and 86.6% obtained self-confidence when performing the enema. 8. The lower the student's practice score was, the more practices were needed for them to pass the test(r=-.213, P<.05). As a result, for performing the enema practice competently, two or more practice opportunities were needed to be given. And it is possible to obtain the less complex nursing skills through the self study, when enough learning resources and assistance such as learning guidance or video tapes are provided. Based on this study. I want to suggest that. 1. There must be college policy that can support the new method instead of the traditional learning method for the students to attain the proficiency in basic nursing skills. 2. The assistant materials should be developed as soon as possible to promote the self study of basic nursing skills.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.974-984
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• 2007

The purpose of this study was to find differences in evaluation criteria and to find differences in preferred images based on benefits segmented groups of jeans products consumers. Male and female Korean university students participated in the study. Quota sampling method was used to collect the data based on gender and a residential area of the respondents. Data from 492 questionnaires were used in the analysis. Factor analysis, Cronbach's alpha coefficient, cluster analysis, one-way ANOVA, and post-hoc test were conducted. As a result, respondents who seek multi-benefits considered aesthetic criteria(e.g., color, style, design, fit) and quality performance criteria(e.g., durability, ease of care, contractibility, flexibility) more importantly when evaluating and purchasing jeans products. Respondents who seek brand name considered extrinsic criteria(e.g., brand reputation, status symbol, country of origin, fashionability) more importantly than respondents who seek economic efciency. Respondents who seek multi-benefits such as attractiveness, fashion, individuality, and utility tend to prefer all the images: individual image, active image, sexual image, sophisticated image, and simple image when wearing jeans products. Respondents who seek fashion are likely to prefer individual image, and respondents who seek brand name more prefer both individual image and polished image. Mean while, respondents who seek economical efficiency less prefer sexual image and polished image.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Journal of the Korea Concrete Institute
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• v.26 no.2
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• pp.151-157
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• 2014

Since recently developed Ultra-High-Performance-Concrete (UHPC) provides very high strength, stiffness, and durability, many studies have been made on the application of the UHPC to bridge decks. Due to high strength and stiffness of UHPC bridge deck, the structural contribution of top flange of steel girder composite to UHPC deck would be much lower than that of conventional concrete deck. At this point of view, this study proposes a inverted-T shaped steel girder composite to UHPC deck. This girder requires a new type of shear connector because conventional shear connectors are welded on top flange. This study also proposes three different types of shear connectors, and evaluate their ultimate strength via push-out static test. The first one is a stud shear connector welded directly to the web of the girder in the transverse direction. The second one is a puzzle-strip type shear connector developed by the European Commission, and the last one is the combination of the stud and the puzzle-strip shear connectors. Experimental results showed that the ultimate strength of the transverse stud was 26% larger than that given in the AASHTO LRFD Bridge Design Specifications, but a splitting crack observed in the UHPC deck was so severe that another measure needs to be developed to prevent the splitting crack. The ultimate strength of the puzzle-strip specimen was 40% larger than that evaluated by the equation of European Commission. The specimens combined with stud and puzzle-strip shear connectors provided less strength than arithmetical sum of those. Based on the experimental observations, there appears to be no advantage of combining transverse stud and puzzle-strip shear connectors.

• Journal of Animal Science and Technology
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• v.48 no.4
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• pp.503-512
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• 2006

This study was conducted to investigate the effects of dietary Lactobacillus brevis (3.4×108 CFU/g) supplementation on growth performance, DM and N digestibilities, blood cell counts and fecal odor emission compounds in growing pigs. Ninety six crossbred [(Landrace×Yorkshire)×Duroc] pigs with an initial BW of 24.60±1.28kg were used for 42-d feeding trial according to a completely randomized design. Three corn- soybean meal based dietary treatments included: 1) CON (basal diet); 2) LB1 (basal diet + Lactobacillus brevis 0.2%) and 3) LB2 (basal diet+Lactobacillus brevis 0.4%). There were three dietary treatments with eight replicate pens per treatment and four pigs per pen. Through the entire experimental period, ADG, ADFI and gain/feed had no significant differences among treatments(P>0.05). Nitrogen digestibility was increased in LB1 and LB2 treatments compared to CON treatment (linear effect, P<0.05), however, DM digestibility had no significant difference among all the treatments (P>0.05). The WBC, RBC and lymphocyte concentrations in whole blood were not affected by treatments (P>0.05). Fecal NH3N and H2S concentrations were significant decreased in LB2 treatment compared to CON treatment (linear effect, P<0.05). Fecal VFA (acetic acid and propionic acid) concentration was also reduced in LB2 treatment compared to CON treatment (linear effect, P<0.05). In conclusion, Lactobacillus brevis (3.4×108 CFU/g) supplementation at the level of 0.4% can improve nitrogen digestibility and decrease the concentrations of fecal odor emission compounds in growing pigs.

• Korean Journal of Poultry Science
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• v.44 no.3
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• pp.189-198
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• 2017

This study was conducted to develop a new synthetic breed of Korean native chicken. The combining ability and reciprocal effects for production traits were estimated on 1,157 hens from a $5{\times}5$ diallel cross-mating design using grand parent stock (GPS) lines of Korean native chicken. Body weight, viability, age at first egg laying, egg weight, and hen-day egg production were measured and analyzed. The results showed that the general combining ability (GCA) of the survival rate during laying periods was -9.6 to 11.1, with the highest value obtained in the W strain. Additionally, the GCA of the body weight at 12 weeks was -209.7 to 162.2, with the highest value obtained in the F strain. The GCA for age at fist egg laying was estimated to be -2.8 to 3.7, while the GCA of egg weight was -0.91 to 0.96, and the GCA of hen-day egg production was -4.9 to 6.0. In the estimation of specific combining ability, the YW combination showed the highest survival rate, FW showed the highest body weight at 12 weeks, and GW showed the highest hen-day egg production. The reciprocal effects were significantly different among crosses for almost all productivity traits. In identical breeding combinations, differences in ability were observed when the maternal or paternal breeds were switched. The mean value based on combining ability was higher in WY, WF, and GW combinations for survival rate; GF, HG, and HF combinations for body weight at 12 weeks; and GW, YW, and FW combinations for hen-day egg production. It is concluded that the GF and HF combinations, which have excellent growth performance and moderate survival rate, are the most desirable paternal parent stock (PS) strains, and the GW and FW combinations, which have great laying performance and moderate body weight, are the most desirable maternal PS strains.

• Clean Technology
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• v.24 no.2
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• pp.127-134
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• 2018

To reduce the environmental pollution by $NO_x$ from ship engine, International maritime organization (IMO) announced Tier III regulation, which is the emmision regulation of ship's exhaust gas in Emission control area (ECA). Selective catalytic reduction (SCR) process is the most commercial $De-NO_x$ system in order to meet the requirement of Tier III regulation. In generally, commercial ceramic honeycomb SCR catalyst has been installed in SCR reactor inside marine vessel engine. However, the ceramic honeycomb SCR catalyst has some serious issues such as low strength and easy destroution at high velocity of exhaust gas from the marine engine. For these reasons, we design to metallic structured catalyst in order to compensate the defects of the ceramic honeycomb catalyst for applying marine SCR system. Especially, metallic structured catalyst has many advantages such as robustness, compactness, lightness, and high thermal conductivity etc. In this study, in order to support catalyst on metal substrate, coating slurry is prepared by changing binder. we successfully fabricate the metallic structured catalyst with strong adhesion by coating, drying, and calcination process. And we carry out the SCR performance and durability such as sonication and dropping test for the prepared samples. The MFC01 shows above 95% of $NO_x$ conversion and much more robust and more stable compared to the commercial honeycomb catalyst. Based on the evaluation of characterization and performance test, we confirm that the proposed metallic structured catalyst in this study has high efficient and durability. Therefore, we suggest that the metallic structured catalyst may be a good alternative as a new type of SCR catalyst for marine SCR system.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• Clean Technology
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• v.27 no.4
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• pp.332-340
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• 2021

With the recent reinforcement of emission standards, it is necessary to make efforts to reduce NOx from air pollutant-emitting workplaces. The NOx reduction method mainly used in industrial facilities is selective catalytic reduction (SCR), and the most commercial SCR catalyst is the ceramic honeycomb catalyst. This study was carried out to reduce the NOx emitted from steel plants by applying De-NOx catalyst coated on metallic monolith. The De-NOx catalyst was synthesized through the optimized coating technique, and the coated catalyst was uniformly and strongly adhered onto the surface of the metallic monolith according to the air jet erosion and bending test. Due to the good thermal conductivity of metallic monolith, the De-NOx catalyst coated on metallic monolith showed good De-NOx efficiency at low temperatures (200 ~ 250 ℃). In addition, the optimal amount of catalyst coating on the metallic monolith surface was confirmed for the design of an economical catalyst. Based on these results, the De-NOx catalyst of commercial grade size was tested in a semi-pilot De-NOx performance facility under a simulated gas similar to the exhaust gas emitted from a steel plant. Even at a low temperature (200 ℃), it showed excellent performance satisfying the emission standard (less than 60 ppm). Therefore, the De-NOx catalyst coated metallic monolith has good physical and chemical properties and showed a good De-NOx efficiency even with the minimum amount of catalyst. Additionally, it was possible to compact and downsize the SCR reactor through the application of a high-density cell. Therefore, we suggest that the proposed De-NOx catalyst coated metallic monolith may be a good alternative De-NOx catalyst for industrial uses such as steel plants, thermal power plants, incineration plants ships, and construction machinery.