• Journal of the Korean Wood Science and Technology
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• v.39 no.4
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• pp.318-325
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• 2011

Pitch pine (Pinus rigida Miller) retaining walls using Steel bar, of which the constructability and strength performance are good at the construction site, were manufactured and their strength properties were evaluated. The wooden retaining wall using Steel bar was piled into four stories stretcher and three stories header, which is 770 mm high, 2,890 mm length and 782 mm width. Retaining wall was made by inserting stretchers into Steel bar after making 18 mm diameter of holes at top and bottom stretcher, and then stacking other stretchers and headers which have a slit of 66 mm depth and 18 mm width. The strength properties of retaining walls were investigated by horizontal loading test, and the deformation of structure by image processing (AlCON 3D OPA-PRO system). Joint (Type-A) made with a single long stretcher and two headers, and joint (Type-B) made with two short stretchers connected with half lap joint and two headers were in the retaining wall using Steel bar. The compressive shear strength of joint was tested. Three replicates were used in each test. In horizontal loading test the strength was 1.6 times stronger in wooden retaining wall using Steel bar than in wooden retaining wall using square timber. The timber and joints were not fractured in the test. When testing compressive shear strength, the maximum load of type-A and Type-B was 130.13 kN and 130.6 kN, respectively. Constructability and strength were better in the wooden retaining wall using Steel bar than in wooden retaining wall using square timber.

• Journal of the Korean Applied Science and Technology
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• v.37 no.4
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• pp.893-905
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• 2020

This study is a study on solubilization and emulsifying power of tocopheryl acetate using vegetable surfactants. High purity polyglyceryl-10 isostearate and polyglyceryl-10 oleate were mixed to synthesize a vegetable surfactant with excellent solubilizing power and emulsifying power. The mixed raw material was named Solubil EWG-1100. The appearance of this raw material was a pale yellowish paste with a specific smell, specific gravity of 1.12, and acid value of 0.085. The HLB value of this surfactant was calculated by the Griffin's equation with an average value of 15.17. The behavior of this surfactant to solubilize tocopheryl acetate was mechanically verified. The performance of solubilization was evaluated by a method of visual evaluation and was measured by a transmittance rate at 650 nm using a UV spectrophotometer. As a result, in the formulation using 3% ethanol as a co-solvent, the concentration of surfactant was required to solubilize tocopheryl acetate was required about 5 times of natural surfactant. In the formulation without ethanol as a co-solvent, the concentration of surfactant was required to solubilize tocopheryl acetate required about 7 times of natural surfactant. In addition, the concentration of surfactant required to make an emulsifivation 10 % of tocopheryl acetate was 1 wt% of Solubil EWG-1100, and the emulsified particle size was 3.5 mm in cream formula. In order to obtain stable and fine emulsified particles, it was found that as the concentration of tocopheryl acetate increased, the concentration of Solubil EWG-1100 also was to increase. As a result of testing the solubilizing power of the surfactant according to the pH various change, it showed stable solubilizing power in the acidic region of pH=3.2, the neutral region of pH=7.0, and the alkaline region of pH=11.8. As application, based on these results, it is expected that it can be widely applied to the cosmetics field that develops skin care prescriptions, sensitive skin products, and heavy dry skin products.

• Journal of the Korea Concrete Institute
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• v.25 no.5
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• pp.485-496
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• 2013

In recent years, frequent terror or military attacks by explosion or impact accidents have occurred. Examplary case of these attacks were World Trade Center collapse and US Department of Defense Pentagon attack on Sept. 11 of 2001. These attacks of the civil infrastructure have induced numerous casualties and property damage, which raised public concerns and anxiety of potential terrorist attacks. However, a existing design procedure for civil infrastructures do not consider a protective design for extreme loading scenario. Also, the extreme loading researches of prestressed concrete (PSC) member, which widely used for nuclear containment vessel, gas tank, bridges, and tunnel, are insufficient due to experimental limitations of loading characteristics. To protect concrete structures against extreme loading such as explosion and impact with high strain rate, understanding of the effect, characteristic, and propagation mechanism of extreme loadings on structures is needed. Therefore, in this paper, to evaluate the impact resistance capacity and its protective performance of bi-directional unbonded prestressed concrete member, impact tests were carried out on $1400mm{\times}1000mm{\times}300mm$ for reinforced concrete (RC), prestressed concrete without rebar (PS), prestressed concrete with rebar (PSR, general PSC) specimens. According to test site conditions, impact tests were performed with 14 kN impactor with drop height of 10 m, 5 m, 4 m for preliminary tests and 3.5 m for main tests. Also, in this study, the procedure, layout, and measurement system of impact tests were established. The impact resistance capacity was measured using crack patterns, damage rates, measuring value such as displacement, acceleration, and residual structural strength. The results can be used as basic research references for related research areas, which include protective design and impact numerical simulation under impact loading.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.4
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• pp.404-411
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• 2007

Adsorption performance of odorous compounds such as geosmin and 2-MIB on granular activated carbon were evaluated in this study. The coal-based activated carbon was found more effective than other carbons in adsorption of geosmin and 2-MIB. The wood-based virgin activated carbon was less effective than coconut- and coal-based carbon in adsorption nevertheless having larger pore volume and specific surface area than others carbons. The maximum adsorption capacity(X/M) of coal-based activated carbon for geosmin and 2-MIB was $1.2\sim1.9$ and $2.1\sim2.6$ times larger than coconut- and wood-based virgin activated carbon, respectively. Carbon usage rate (CUR) of coal-, coconut- and wood-based virgin activated carbons for geosmin and 2-MIB were 1.72 and 1.44 g/day, 1.72 and 2.05 g/day and 2.12 and 1.90 g/day, respectively. In the evaluation of adsorption isotherm of geosmin and 2-MIB for coal-, coconut- and wood-based virgin activated carbons, k value of 2-MIB was lower than geosmin, It menas 2-MIB is more difficult to remove by activated carbon adsorption than geosmin. The relationship of max. adsorption versus total pore volume of coconut- and wood-based virgin and used activated carbon for geosmin and 2-MIB were $y=264,459\times-79,047(R^2=0.95)$, $y=319,650\times-101,762(R^2=0.93)$.

• Horticultural Science & Technology
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• v.30 no.4
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• pp.442-448
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• 2012

Many outbreaks of food-borne illnesses have been associated with the consumption of fresh vegetables and fruits contaminated with food-borne pathogens. Contaminated medium, manure and irrigation water are probable vehicles for the pathogen in many outbreaks. The aim of this study was to determine the potential transfer of Escherichia coli and Bacillus cereus from medium and soil fertilized with contaminated compost or irrigation with contaminated water to the edible parts of lettuce. Moreover, survivals of the two pathogens on lettuce contaminated medium, soil and irrigation water were estimated. Lettuce seeds were planted in medium contaminated with 7.5 log colony forming unit (CFU)/g of E. coli and B. cereus. Seedlings grown in the contaminated medium were transplanted in soil fertilized with contaminated pig manure compost or uncontaminated soil. Contaminated irrigation water with E. coli and B. cereus at 8.0 log CFU/mL was applied only once on the plant by sprinkle irrigation and surface irrigation. Although E. coli and B. cereus in medium and sprouted lettuce after planting seeds were reduced as time passed, these pathogens survived in seedling raising stage for extended periods. The numbers of E. coli and B. cereus in lettuce grown on contaminated soil were detected over 4.0 log CFU/g for 21 days. The numbers of E. coli and B. cereus in lettuce applied by sprinkle irrigation were higher than those of surface irrigation by 5.0 log CFU/g. Our results indicated that contaminated medium, soil and irrigation water can play an important role in the presence of food-borne pathogens on vegetables.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.7
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• pp.906-913
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• 2019

To evaluate the removal performance of PO₄-P and NH₃-N, laboratory experiments were conducted by filling a container with oyster shells, pyrolyzed at 100℃ (POS100), 600℃ (POS600) and 800℃ (POS800), and passing artificial wastewaters through the container. The pH in the ef luent was found to increase due to CaO eluted from oyster shell. Removal amounts of PO₄-P of ~23.1 mg/kg, 16.1 mg/kg, and 15.9 mg/kg were obtained when POS100, POS600, and POS800, respectively, were used; therefore, the highest PO₄-P removal amount was obtained when POS100 was used. It is considered that Ca and dolomite in the oyster shells adsorbed and precipitated PO₄-P. Removal amounts of NH₃-N were of ~3.56 mg/kg, 5.72 mg/kg, and 3.97 mg/kg were obtained when POS100, POS600, and POS800, respectively, were used The low removal rate for NH₃-N is probably due to unstable nitrification, use of sealed containers, and the effect of NH₃-N being converted to NH₄⁺ upon increasing pH. Based on these results, pyrolyzed oyster shell is expected to promote changes in PO₄-P and NH₃-N concentrations through chemical reactions. These results can also be used for basic research in the development of wastewater treatment.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.19-26
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• 2023

Humans have adapted and evolved to natural light. However, as humans stay in indoor longer in modern times, the problem of biorhythm disturbance has been induced. To solve this problem, research is being conducted on lighting that reproduces the correlated color temperature(CCT) of natural light that varies from sunrise to sunset. In order to reproduce the CCT of natural light, multiple LED light sources with different CCTs are used to produce lighting, and then a control index DB is constructed by measuring and collecting the light characteristics of the combination of input currents for each light source in hundreds to thousands of steps, and then using it to control the lighting through the light characteristic matching method. The problem with this control method is that the more detailed the steps of the combination of input currents, the more time and economic costs are incurred. In this paper, an LED lighting control method that applies interpolation and combination calculation based on the minimum spectral power distribution information for each light source is proposed to reproduce the CCT of natural light. First, five minimum SPD information for each channel was measured and collected for the LED lighting, which consisted of light source channels with different CCTs and implemented input current control function of a 256-steps for each channel. Interpolation calculation was performed to generate SPD of 256 steps for each channel for the minimum SPD information, and SPD for all control combinations of LED lighting was generated through combination calculation of SPD for each channel. Illuminance and CCT were calculated through the generated SPD, a control index DB was constructed, and the CCT of natural light was reproduced through a matching technique. In the performance evaluation, the CCT for natural light was provided within the range of an average error rate of 0.18% while meeting the recommended indoor illumination standard.

• 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.