• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4274-4281
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• 2023

Blanket is of vital importance for engineering application of the fusion reactor. Nuclear heat deposition in materials is the main heat source in blanket structure. In this paper, the three-dimensional method for thermal-hydraulics/neutronics coupling analysis is developed and applied for the full-scale module of the helium-cooled ceramic breeder tritium breeding blanket (HCCB TBB) designed for China Fusion Engineering Test Reactor (CFETR). The explicit coupling scheme is used to support data transfer for coupling analysis based on cell-to-cell mapping method. The coupling algorithm is realized by the user-defined function compiled in Fluent. The three-dimensional model is established, and then the coupling analysis is performed using the paralleled Coupling Analysis of Thermal-hydraulics and Neutronics Interface Code (CATNIC). The results reveal the relatively small influence of the coupling analysis compared to the traditional method using the radial fitting function of internal heat source. However, the coupling analysis method is quite important considering the nonuniform distribution of the neutron wall loading (NWL) along the poloidal direction. Finally, the structure optimization of the blanket is carried out using the coupling method to satisfy the thermal requirement of all materials. The nonlinear effect between thermal-hydraulics and neutronics is found during the blanket structure optimization, and the tritium production performance is slightly reduced after optimization. Such an adverse effect should be thoroughly evaluated in the future work.

• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.861-874
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• 2012

To fulfill applicability of Soil and Water Assessment Tool (SWAT) model, it is important that this model passes through a careful calibration and uncertainty analysis. In recent years, many researchers have come up with various uncertainty analysis techniques for SWAT model. To determine the differences and similarities of typical techniques, we applied three uncertainty analysis procedures to Chungju Dam watershed (6,581.1 $km^2$) of South Korea included in SWAT-Calibration Uncertainty Program (SWAT-CUP): Sequential Uncertainty FItting algorithm ver.2 (SUFI2), Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol). As a result, there was no significant difference in the objective function values between SUFI2 and GLUE algorithms. However, ParaSol algorithm shows the worst objective functions, and considerable divergence was also showed in 95PPU bands with each other. The p-factor and r-factor appeared from 0.02 to 0.79 and 0.03 to 0.52 differences in streamflow respectively. In general, the ParaSol algorithm showed the lowest p-factor and r-factor, SUFI2 algorithm was the highest in the p-factor and r-factor. Therefore, in the SWAT model calibration and uncertainty analysis of the automatic methods, we suggest the calibration methods considering p-factor and r-factor. The p-factor means the percentage of observations covered by 95PPU (95 Percent Prediction Uncertainty) band, and r-factor is the average thickness of the 95PPU band.

• Journal of Dental Rehabilitation and Applied Science
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• v.39 no.4
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• pp.187-194
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• 2023

Purpose: A mismatched size in the post and post space is a common problem during post-fixation. Since this discordance affects the bonding strength of the fiber-reinforced composite resin post (FRC Post), a corresponding luting agent is required. The aim of this study was to evaluate the bonding strength of the FRC post according to the fitness of the fiber post and the type of luting agent. Materials and Methods: Thirty mandibular premolar were endodontic-treated and assigned to two groups according to their prepared post space: Fitting (F) and Mismatching (M). These groups were further classified into three subgroups according to their luting agent: RelyX Unicem (ReX), Luxacore dual (Lux), and Duolink (Duo). A push-out test was performed to measure the push-out bond strengths. The fractured surfaces of each cross-section were then examined, and the fracture modes were classified. Results: In the ReX and Duo subgroups, the F group had a higher mean bond strength; however, the Lux subgroup had no significant difference between the F and M groups. In the analysis of the failure modes, the ReX subgroup had only adhesive failures between the cement and dentin. Conclusion: The result of this study showed that the bond strength of an FRC post was influenced by the type of luting agent and the mismatch between the diameter of the prepared post space and that of the post.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.12
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• pp.2115-2123
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• 2000

This research was designed to investigate the mathematical model and kinetics of phenol desorption from spent activated carbon. elucidating the desorption characteristics of phenol in the case of using acetone. The Freundlich isotherm constant ($k_e$) is expressed as a function of temperature: $k_e(T)=0.1exp(797.297/T)$. The Freundlich isotherm constant(n) is a weak temperature function and is rarely affected by temperature below $50^{\circ}C$. whereas it is necessary to correct the n value with respect to temperature above $100^{\circ}C$ owing to significant deviation (~5%). Based on the assumption that the surface desorption reaction of phenol is rate limiting, the desorption model was developed. Desorption reaction constant($k_d$) was determined by means of fitting the theoretical results best to experimental ones. The Arrhenius relationships for $k_d$ was expressed by: $k_d(sec^{-1})=0.0479{\cdot}exp(-3037/T)$. The model was verified by comparing the experimental ones under different reaction conditions with the theoretical results determined by the previously estimated $k_d$. Since the difference between them is with 5%, it is expected that the desorption model of this research seems to be appropriate to explain the desorption of phenol from activated carbon by acetone. According to studies of the model. regeneration time and ratio was estimated as a function of temperature under present conditions as follows: (1) regeneration time : ${\tau}_{reg}(hr)=-0.08130T_c+8.4775$. (2) regeneration ratio : ${\eta}(%)=0.2210T_c+83.745$. The regeneration time at 15, 55, and $100^{\circ}C$. respectively. was 7, 4.2, and 0.35 hours, whereas the regeneration ratio was 87. 96. and 99%. respectively. Also. studies of the model would make it possible to determine the regeneration time and ratio under other specific conditions (temperature, applied acetone volume, amount of activated carbon, and initially adsorbed phenol amount).

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.881-891
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• 2017

In this paper, we described the new TLE(Two Line Elements) generation method based on the compansation technique by using past TLEs(Two Line Elements) released by JSpOC(Joint Space Operation Center) in USA to reduce the orbit prediction error for long duration of SGP4(Simplified General Perturbations 4) which is a simplifed and analytical orbit propagator. The orbital residuals the orbital difference between two ephemeris for the first TLE only and for the all TLEs updated by JSpOC for the past some period was applied for this algorithm instead of general orbit determination software. Actually, in these orbital residuals, the trend of orbit prediction error from SGP4 is included. Thus, it is possible to make a simple residual function from these orbital residulas by using the fitting process. By using these residual functions with SGP4 prediction data for the currnet TLE data, the compansated orbit prediction can be reconstructed and the orbit prediction error for long duration of SGP4 is also reduced. And it is possible to generate new TLE data from it. In this paper, we demonstraed this algorithm in simple simulation, and the orbital error is decreased dramatically from 4km for the SGP4 propagation to 2km for it during 7 days as a result.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.56-62
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• 2009

In this paper we investigate an analytical approach for noise properties and geometric structure in Computed Radiography(CR) images of industrial steel-tubes. Over thirty diverse radiographic images are sampled from industrial radiography measurements according to radiation intensity. Each image consists of three regions; background, thickness and inner-tube. Among these the region of inner-tube is selected for the object of analysis. Geometric structure which includes the noise generation is analyzed by the statistical and functional methodology. The analysis is carried on spacially and line by line. It verifies the geometrical transfigure from the circle configuration of steel-tube and noise variation. The estimation of fitting function and its error are the geometric factors. The statistics such as standard deviation, mean and signal-to-noise ratio are noise parameters for discrimination. These factors are considered under the intensity variation which is the penetrative strength of radiation. The analysing results show that the original geometry of circle is preserved in the form of elliptic or short/long diameter circle, and the noise deviation has increased inverse proportional to the radiation intensity.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.97-107
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• 2011

For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.