• Journal of Industrial Technology
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• v.28 no.B
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• pp.91-99
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• 2008

Applicability of existing methods of predicting consolidation settlement was assessed by analyzing results of centrifuge tests modelling self-weight consolidation of soft marine clay. From extensive literature review about self-weight consolidation of soft marine clays located in southern coast in Korea, constitutive relationships of void ratio-effective stress-permeability and typical self-weight consolidation curves with time were obtained by centrifuge model experiments. For the condition of surcharge loading, exact solution of consolidation settlement curve was obtained by Terzaghi's consolidation theory and was compared with the results predicted by currently available methods such as Hyperbolic method, Asaoka's method, Hoshino's method and ${\sqrt{S}}$ method. All methods were found to have their own inherent error to predict final consolidation settlement. From results of analyzing the self-weight consolidation with time by using those methods, Asaoka's method predicted the best. Hyperbolic method predicted relatively well in error range of 2~24% for the case of showing the linearity in the relationship between T vs T/S in the stage of consolidation degree of 60~90 %. For the case of relation curve of T vs $T/S^2$ showing the lineality after the middle stage, error range from Hoshino method was close to those from Hyperbolic method. However, Hoshino method is not able to predict the final settlement in the case of relation curve of T vs $T/S^2$ being horizontal. For the given data about self-weight consolidation after the middle stage, relation curve of T vs T/S from ${\sqrt{S}}$ method shows the better linearity than that of T vs $T/{\sqrt{s}}$ from Hyperbolic method.

• Composites Research
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• v.16 no.3
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• pp.9-17
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• 2003

In order to simulate the crack connection between cords and the interply crack growth in the belt-layer of real tire, 2 ply rubber/cord laminate specimens with exposed edges were tested in 4~11mm displacement control. Measurement of the crack connection is evaluated when crack reaches the half of the length between 45$^{\circ}$ aligned cords, and the amount of the crack growth is measured by the steel probe method. 2 dimensional analytic modeling was performed to simulate the crack connection between cords at the exposed edges. Also, the theoretical life of the specimens was calculated from the crack connection life between cords(critical value) and from the critical value to the final failure by the use of Tearing energy(T); the strain energy release per unit area of one fracture surface of a crack. Then, theoretical life was compared with those of experiments. The life prediction up to the critical value has about 20% error compared to experimental life, and up to the final failure about 65% error. Therefore, total theoretical life has about 45% error compared to the experimental life, which is conceivable in the case of rubber.

• Journal of The Korean Astronomical Society
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• v.53 no.6
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.423-430
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• 2023

One recent notable method for real-time elimination of ionospheric errors in geodetic applications is the Predicted Global Ionosphere Map (PGIM). This study analyzes the level of accuracy achievable when applying the PGIM provided by the Center for Orbit Determination of Europe (CODE) to the Korean Peninsula region. First, an examination of the types and lead times of PGIMs provided by the International GNSS Service (IGS) Analysis Center revealed that CODE's two-day prediction model, C2PG, is available approximately eight hours before midnight. This suggests higher real-time usability compared to the one-day prediction model, C1PG. When evaluating the accuracy of PGIM by assuming the final output of the Global Ionosphere Map (GIM) as a reference, it was found that on days with low solar activity, the error is within ~2 TECU, and on days with high solar activity, the error reaches ~3 TECU. A comparison of the errors introduced when using PGIM and three solar activity indices-Kp index, F10.7, and sunspot number-revealed that F10.7 exhibits a relatively high correlation coefficient compared to Kp-index and sunspot number, confirming the effectiveness of the prediction model.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.377-387
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• 2012

Job competition among generations has become an important social issue that has yet to be studied from an academic viewpoint. This study performs statistical tests to investigate the interrelation of employment among generations using seasonally adjusted monthly time series data. Employment by generations is not found to be strongly interrelated, even if the employment of 30-year-olds appears to affect those of 40-yearolds in some tests.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.113-121
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• 2000

This paper presents a parallel-structure fuzzy system(PSFS) for prediction of time series data. The PSFS consists of a multiple number of fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts the same future data independently based on its past time series data with different embedding dimension and time delay. The component fuzzy systems are characterized by multiple-input singleoutput( MIS0) Sugeno-type fuzzy rules modeled by clustering input-output product space data. The optimal embedding dimension for each component fuzzy system is chosen to have superior prediction performance for a given value of time delay. The PSFS determines the final prediction result by averaging the outputs of all the component fuzzy systems excluding the predicted data with the minimum and the maximum values in order to reduce error accumulation effect.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Bulletin of the Korean Chemical Society
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• v.30 no.11
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• pp.2717-2722
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• 2009

The use of the classification and regression tree (CART) methodology was studied in a quantitative structure-activity relationship (QSAR) context on a data set consisting of the binding affinities of 39 imidazobenzodiazepines for the α1 benzodiazepine receptor. The 3-D structures of these compounds were optimized using HyperChem software with semiempirical AM1 optimization method. After optimization a set of 1481 zero-to three-dimentional descriptors was calculated for each molecule in the data set. The response (dependent variable) in the tree model consisted of the binding affinities of drugs. Three descriptors (two topological and one 3D-Morse descriptors) were applied in the final tree structure to describe the binding affinities. The mean relative error percent for the data set is 3.20%, compared with a previous model with mean relative error percent of 6.63%. To evaluate the predictive power of CART cross validation method was also performed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.66-71
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• 2016

For the manual transmission gearbox used in the automotive industry, gear meshing transmission error is the main source of noise known as gear whine, and radiated gear whine noise plays an important role in the whole gearbox. Therefore, in order to keep competitive in the automotive market, the NVH performance of transmission gearboxes is increasingly important for automotive manufacturers when a new product is developed. In this paper, in order to achieve an optimized tooth contact pattern, gear tooth modification is applied to make up for the deformation of the teeth owing to load. A five-speed MT gearbox is firstly modeled in RomaxDesign software and the prediction of transmission error under the loaded torques is studied and compared. From the simulation, the transmission error and maximum contact stress are also simulated and compared after tooth modification of the loaded torques. Finally, the simulation results are used to optimize the whole gearbox design and the final gearbox prototype is testified to obtain NVH performance in a semi-anechoic room.