• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• Industry Promotion Research
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• v.9 no.1
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• pp.21-29
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• 2024

In the rapidly advancing information society, electronic devices, including smartphones and tablets, are increasingly digitized and equipped with high-performance features such as flexible displays. This study focused on optimizing the manufacturing process for Transparent Conductive Films (TCF) by using the cost-effective conductive polymer PEDOT and transparent substrate PET as alternatives to expensive materials in flexible display technology. The variables considered are production speed (m/min), coating maximum temperature (℃), and PEDOT supply speed (rpm), with surface resistivity (Ω/□) as the response parameter, using Response Surface Methodology (RSM). Optimization results indicate the ideal conditions for production: a speed of 22.16 m/min, coating temperature of 125.28℃, and PEDOT supply at 522.79 rpm. Statistical analysis validates the reliability of the results (F value: 18.37, P-value: < 0.0001, R²: 0.9430). Under optimal conditions, the predicted surface resistivity is 145.75 Ω/□, closely aligned with the experimental value of 142.97 Ω/□. Applying these findings to mass production processes is expected to enhance production yields and decrease defect rates compared to current practices. This research provides valuable insights for the advancement of flexible display manufacturing.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• 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 Life Science
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• v.23 no.6
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• pp.757-769
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• 2013

A microorganism producing carboxymethylcellulase (CMCase) was isolated from seawater, identified as Bacillus velezensis by analyses of 16S rDNA and partial sequences of the gyrA, and designated as B. velezensis A-68. The optimal conditions for production of CMCase by B. velezensis A-68 were established using response surface methodology (RSM). The optimal concentrations of rice hulls and yeast extract, and initial pH of the medium for cell growth were 60.2 g/l, 7.38 g/l, and 7.18, respectively, whereas those for production of CMCase were 50.0 g/l, 5.00 g/l, and 7.30. The analysis of variance (ANOVA) implied that the most significant factor for cell growth as well as production of CMCase was yeast extract. The optimal concentrations of $K_2HPO_4$, NaCl, $MgSO_4{\cdot}7H_2O$, and $(NH_4)_2SO_4$ in the medium for cell growth were 7.50, 1.00, 0.10, and 0.80 g/l, respectively, which were the same as those for production of CMCase. The optimal temperatures for cell growth and production of CMCase were 30 and $35^{\circ}C$, respectively. The maximal production of CMCase under optimized conditions was 83.8 U/ml, which was 3.3 times higher than that before optimization. In this study, rice hulls, agro-byproduct, were developed as a substrate for production of CMCase and time for production of CMCase was reduced to 3 days using a newly isolated marine bacterium.

• KSBB Journal
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• v.23 no.3
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• pp.257-262
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• 2008

In this study, the reaction conditions for lipase-catalyzed resolution of racemic naproxen 2,2,2-trilfluoroethyl thioester were optimized, and the effect of surfactants was investigated. Among the organic solvents tested, the isooctane showed the highest conversion (92.19%) in a hydrolytic reaction of (S)-naproxen 2,2,2-trifluoroethyl thioester. In addition, the isooctane induced the highest initial reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=2.34{\times}10^{-2}mM/h$), the highest enantioselectivity (E = 36.12) and the highest specific activity ($V_s/(E_t)=7.80{\times}10^{-4}mmol/h{\cdot}g$) of lipase. Furthermore, reaction conditions such as temperature, concentration of the substrate and enzyme, and agitation speed were optimized using response surface methodology (RSM), and the statistical analysis indicated that the optimal conditions were $48.2^{\circ}C$, 3.51 mM, 30.11 mg/mL and 180 rpm, respectively. When the optimal reaction conditions were used, the conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester was 96.5%, which is similar to the conversion (94.6%) that was predicted by the model. After optimization of reaction conditions, the initial reaction rate, lipase specific activity and conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester increased by approximately 19.54%, 19.12% and 4.05%, respectively. The effect of surfactants such as Triton X-100 and NP-10 was also studied and NP-10 showed the highest conversion (89.43%), final reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=1.175{\times}10^{-2}mM/h$) and enantioselectivity (E = 59.24) of lipase.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.6
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• pp.115-128
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• 2019

Huam-dong is a representative area from the Japanese colonial period and is the space where most Japanese-style buildings remain in Seoul. Interest in modern cultural heritage continues to increase, including the registration of cultural properties in 2001, building assets in 2015, and the registration of cultural property units in 2018. As the debate continues over the necessity of preserving cultural heritage that reminds us of the Japanese colonial, there is a need for research to grasp the perceptions of stakeholders along with the perceived value of such spaces. This study identified the subjective perception types of the stakeholders concerned with the Japanese-style building group in Huam-dong, analyzed characteristics by types, and debated the issues. For this purpose, Q methodology, which is a statistical technique for measuring human self-subjectivity and extracting common human perspectives, was used. A literature study on the values of Huam-dong and modern cultural heritage was conducted, and a Q questionnaire based on five aspects of modern cultural heritage values (historical, architectural, sociocultural, landscape, and economic) was applied. The results of the study depicted three types of cognition and showed different attitudes toward the Japanese building group. This study found a conflict comparing the perceptional differences between the types of cognition. This study is meaningful in that it provides an in-depth approach to the perspectives of the stakeholders concerned with the Japanese-style buildings clustered in central Seoul. It is also meant to present a theoretical framework that can be applied to the use area as sustainable cultural heritage through the establishment of preservation and utilization of Japanese-style areas and conflict resolution.

• Applied Biological Chemistry
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• v.47 no.4
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• pp.414-421
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• 2004

3D QSAR studies for protox inhibition activities against root and shoot of the rice plant (Orysa sativa L.) and barnyardgrass (Echinochloa crus-galli) by a series of new 1-(5-methyl-3-phenylisoxazolin-5-yl)methoxy-2-chloro-4-fluorobenzene derivatives were conducted based on the results (Sung, N. D. et al.'s, (2004) J. Korean Soc. Appl. Biol. Chem. 47(3), 351-356) using comparative molecular similarity indices analysis (CoMSIA) methodology. Four CoMSIA models, without hydrogen bond donor field for the protox inhibition activities against root and shoot of the two plants, were derived from the combination of several fields using steric field, hydrophobic field, hydrogen bond acceptor field, LUMO molecular orbital field, dipole moment (DM) and molar refractivity (MR) as additional descriptors. The predictabilities and fitness of CoMSIA models for protox inhibition activities against barnyard-grass were higher than that of rice plant. The statistical results of these models showed the best predictability of the protox inhibition activities against barnyard-grass based on the cross-validated value $r^2\;_{cv}\;(q^2=0.635{\sim}0.924)$, non cross-validated, conventional coefficient $r^2\;_{ncv.}$ value $(r^2=0.928{\sim}0.977)$ and PRESS value $(0.255{\sim}0.273)$. The protox inhibition activities exhibited a strong correlation with the steric $(5.4{\sim}15.7%)$ and hydrophobic $(68.0{\sim}84.3%)$ factors of the molecules. Particularly, the CoMSIA models indicated that the groups of increasing steric bulk at ortho-position on the C-phenyl ring will enhance the protox inhibition activities against barnyard-grass and subsequently increase the selectivity.

• The Korean Journal of Pesticide Science
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• v.8 no.3
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• pp.151-161
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• 2004

Three dimensional quantitative structure-activity relationships (3D-QSAR) studies for the protox inhibition activities against root and shoot of rice plant (Orysa sativa L.) and barnyardgrass (Echinochloa crus-galli) by a series of new A=3,4,5,6-tetrahydrophthalimino, B=3-chloro-4,5,6,7-tetrahydro-2H-indazolyl and C=3,4-dimethylmaleimino group, and R-group substituted on the phenyl ring in 1-(5-methyl-3-phenylisoxazolin-5-yl)methoxy-2chloro-4-fluorobenzene derivatives were performed using comparative molecular field analyses (CoMFA) methodology with Gasteiger-Huckel charge. Four CoMFA models for the protox inhibition activities against root and shoot of the two plants were generated using 46 molecules as training set and the predictive ability of the each models was evaluated against a test set of 8 molecules. And the statistical results of these models with combination (SIH) of standard field, indicator field and H-bond field showed the best predictability of the protox inhibition activities based on the cross-validated value $r^2_{cv.}$ $(q^2=0.635\sim0.924)$, conventional coefficient $(r^2_{ncv.}=0.928\sim0.977)$ and PRESS value $(0.091\sim0.156)$, respectively. The activities exhibited a strong correlation with steric $(74.3\sim87.4%)$, electrostatic $(10.10\sim18.5%)$ and hydrophobic $(1.10\sim8.30%)$ factors of the molecules. The steric feature of molecule may be an important factor for the activities. We founded that an novel selective and higher protox inhibitors between the two plants may be designed by modification of X-subsitutents for barnyardgrass based upon the results obtained from CoMFA analyses.