• Applied Microscopy
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• v.47 no.3
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• pp.110-120
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• 2017

Non-isothermal thermogravimetry (TG) measurements on melt-quenched $Bi_xSe_{100-x}$ specimens (x=0, 2.5, 7.5 at%) were made at a heating rate ${\beta}=10^{\circ}C/min$ in the range $T=35^{\circ}C{\sim}950^{\circ}C$. The as-measured TG curves confirm that $Bi_xSe_{100-x}$ samples were thermally stable with minor loss at $T{\leq}400^{\circ}C$ and mass loss starts to decrease up to $600^{\circ}C$, beyond which trivial mass loss was observed. These TG curves were used to estimate molar (Se/Bi)-ratios of $Bi_xSe_{100-x}$ samples, which were not in accordance with initial composition. Shaping features of conversion curves ${\alpha}(T)-T$ of $Bi_xSe_{100-x}$ samples combined with a reliable flow chart were used to reduce kinetic mechanisms that would have caused their thermal mass loss to few nth-order reaction models of the form $f[{\alpha}(T)]{\propto}[1-{\alpha}(T)]^n$ (n=1/2, 2/3, and 1). The constructed ${\alpha}(T)-T$ and $(d{\alpha}(T)/dT)-T$ curves were analyzed using Coats-Redfern (CR) and Achar-Brindley-Sharp (ABS) kinetic formulas on basis of these model functions, but the linearity of attained plots were good in a limited ${\alpha}(T)-region$. The applicability of CR and ABS methods, with model function of kinetic reaction mechanism R0 (n=0), was notable as they gave best linear fits over much broader ${\alpha}(T)-range$.

• Journal of the Korean Ceramic Society
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• v.48 no.2
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• pp.127-133
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• 2011

Non-Alkali multicomponent $La_2O_3-Al_2O_3-SiO_2$ glasses has been designed and analyzed on the basis of a mixture design experiment with constraints. Fitted models for thermal expansion coefficient, glass transition temperature, Young's modulus, Shear modulus and density are as follows: ${\alpha}(/^{\circ}C)=8.41{\times}10^{-8}x_1+5.72{\times}10^{-7}x_2+2.13{\times}10^{-7}x_3+1.09{\times}10^{-7}x_4+1.10{\times}10^{-7}x_5+1.15{\times}10^{-7}x_6+2.72{\times}10^{-8}x_7+2.41{\times}10^{-7}x_8-1.08{\times}10^{-8}x_1x_2+4.28{\times}10^{-8}x_3x_7-2.02{\times}10^{-8}x_3x_8-1.60{\times}10^{-8}x_4x_5-2.71{\times}10^{-9}x_4x_8-2.19{\times}10^{-8}x_5x_6-3.89{\times}10^{-8}x_5x_7$ $T_g(^{\circ}C)=7.36x_1+15.35x_2+20.14x_3+8.97x_4+13.85x_5+4.22x_6+28.21x_7-1.44x_8-0.84x_2x_3-0.45x_2x_5-1.64x_2x_7+0.93x_3x_8-1.04x_5x_8-0.48x_6x_8$ $E(GPa)=2.04x_1+14.26x_2-1.22x_3-0.80x_4-2.26x_5-1.67x_6-1.27x_7+3.63x_8-0.24x_1x_2-0.07x_2x_8+0.14x_3x_6-0.68x_3x_8+0.29x_4x_5+1.28x_5x_8$ $G(GPa)=0.35x_1+1.78x_2+1.35x_3+1.87x_4+9.72x_5+29.16x_6-0.99x_7+3.60x_8-0.48x_1x_6-0.50x_2x_5+0.08x_3x_7-0.66x_3x_8+0.94x_5x_8$ ${\rho}(g/cm^3)=0.09x_1+0.51x_2-4.94{\times}10^{-3}x_3-0.03x_4+0.45x_5-0.07x_6-0.10x_7+0.07x_8-9.60{\times}10^{-3}x_1x_2-8.20{\times}10^{-3}x_1x_5+2.17{\times}10^{-3}x_3x_7-0.03x_3x_8+0.05x_5x_8$ The optimal glass composition similar to the thermal expansion coefficient of Si based on these fitted models is $65.53SiO_2{\cdot}25.00Al_2O_3{\cdot}5.00La_2O_3{\cdot}2.07ZrO_2{\cdot}0.70MgO{\cdot}1.70SrO$.

• Journal of Korean Elementary Science Education
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• v.35 no.2
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• pp.181-193
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• 2016

We examined the inquiry activities in the material domain of the elementary science textbooks and experimental workbooks based on 2009 revised curriculum. The analysis framework was SEP (Science and Engineering Practices) - 'Asking questions and defining problems', 'developing and using models', 'planning and carrying out investigations', 'analyzing and interpreting data', 'using mathematics and computational thinking', 'constructing explanations and designing solutions', 'engaging in argument from evidence', and 'obtaining, evaluating, and communicating information'. Sub-SEP of each grade band were also used. The results showed that the $3^{rd}{\sim}5^{th}$ grade science textbooks and workbooks mainly emphasized 'make observations and/or measurements', 'represent data in tables and/or various graphical displays', or 'use evidence to construct or support an explanation or design a solution to a problem' among around 40 sub-SEP. In the case of the inquiry activities for $6^{th}$ grade, majority of sub-SEP included were also only 'collect data to produce data to serve as the basis for evidence to answer scientific questions or test design solutions', 'analyze and interpret data to provide evidence for phenomena' or 'construct a scientific explanation based on valid and reliable evidence obtained from sources'. The type of 'asking questions and defining problems', 'using mathematics and computational thinking' or 'obtaining, evaluating, and communicating information' were little found out of 8 SEP. Educational implications were discussed.

• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• IE interfaces
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• v.10 no.1
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• pp.209-222
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• 1997

The paper provides a methodology to obtain the automaticity indicator of a factory and the sequence of enabling technologies of factory automation. The automaticity indicator is the measure of the current automation status of a factory and can be used as a crucial criteria for the future automation schedule and investment. Although most industries have their own computation methods which usually consider the number of workers in the shop floor, this research covers five evaluation items of automation, such as, production facility, material transfer system, inspection and test system, information system, and flexibility. The detailed evaluation models are developed for each item. Automation sequencing prioritizes the enabling technologies of factory automation on the basis of several criteria which consist of two phases. The first phase includes the automation indicator and the second phase includes six sub-criteria such as production rate, quality, number of workers, capital investment, development duration, development difficulty. For this evaluation, AHP(Analytical Hierarchy Process) is introduced to prevent the decision maker's subject intention. As results of the automaticity indicator and automation sequence, the manager can save time and cost in building constructive and transparent automation plans.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.166-172
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• 2010

Currently, vector control is used for speed control of trains because induction motor has high performance is installed in Electric railroad systems. Also, control of the induction motor is possible through various methods by developing inverters and control theory. Presently, rolling stocks which use the induction motor are possible to brake trains by using AC motor. Therefore model of motor block and induction motor is needed to adapt various methods. There is Variable Voltage Variable Frequency (VVVF) as the control method of the induction motor. The torque and speed is controlled in the VVVF. The propulsion system model in the electric railroad has many sub-systems. So, the analysis of performance of the speed control is very complex. In this paper, simulation models are suggested by using Matlab/Simulink in the speed control characteristic. On the basis of the simulation models, the response to disturbance input is analyzed about the load. Also, the current, speed and flux control model are proposed to analyze the speed control characteristic in the train propulsion system.

• Water for future
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• v.29 no.5
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• pp.223-233
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• 1996

Daily streamflow model, DAWAST, considering the meteorologic and geographic characteristics of the Korean watersheds has been developed to simulate the daily streamflow with the input data of daily rainfall and pan evaporation. The model is the conceptual one with three sub-models which are optimization, generalization, and regionalization models. The conceptual model consists of three linear reservoirs representing the surface, unsaturated, and saturated soil zones and water balance analysis was carried out in each soil zones on a daily basis. Optimization model calibrates the parameters by optimization technique and is applicable to the watersheds where the daily streamflow data are available Generalization model predicts the parameters by regression equations considering the geographic, soil type, land use, and hydrogeologic characteristics of watershed and is appicable to ungaged medium or small watersheds. Regionalization model cites the parameters from the analysed ones considering river system, latitude and longitude, and is applicable to ungaged large watersheds.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.37-43
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• 2001

Crop status monitoring and yield prediction at higher spatial resolution is a valuable tool in various decision making processes including agricultural policy making by the national and local governments. A prototype crop forecasting system was developed to project the size of rice crop across geographic areas nationwide, based on daily weather pattern. The system consists of crop models and the input data for 1,455 cultivation zone units (the smallest administrative unit of local government in South Korea called "Myun") making up the coterminous South Korea. CERES-rice, a rice crop growth simulation model, was tuned to have genetic characteristics pertinent to domestic cultivars. Daily maximum/minimum temperature, solar radiation, and precipitation surface on 1km by 1km grid spacing were prepared by a spatial interpolation of 63 point observations from the Korea Meteorological Administration network. Spatial mean weather data were derived for each Myun and transformed to the model input format. Soil characteristics and management information at each Myun were available from the Rural Development Administration. The system was applied to the forecasting of national rice production for the recent 3 years (1997 to 1999). The model was run with the past weather data as of September 15 each year, which is about a month earlier than the actual harvest date. Simulated yields of 1,455 Myuns were grouped into 162 counties by acreage-weighted summation to enable the validation, since the official production statistics from the Ministry of Agriculture and Forestry is on the county basis. Forecast yields were less sensitive to the changes in annual climate than the reported yields and there was a relatively weak correlation between the forecast and the reported yields. However, the projected size of rice crop at each county, which was obtained by multiplication of the mean yield with the acreage, was close to the reported production with the $r^2$ values higher than 0.97 in all three years.