• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.4
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• pp.383-390
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• 2004

IGS provides so accute a final precise ephmerides which is offered in the 13rd, and it also offers a rapid precise ephmerides for more prompt application and an ultra-rapid precise ephmerides for real-time application. The purpose of this study is to analyze the accuracy of a rapid precise ephemerides and an ultra-rapid precise ephemerides based on a final precise ephmerides and determine the degree of the Lagrange Interpolation which needs to decide the location of a satellite. As the result of this study, the root mean square error of x,y,z coordinates of a rapid precise ephemerides was $\pm$0.0l6m or so, and the root mean square error of an observed ultra-rapid precise ephemerides was approximately $\pm$0.024m. The root mean square error of an ultra-rapid precise ephemerides predicted for 24 hours was $\pm$0.07m or so and the one of an ultra-rapid precise ephemerides predicted for 6 hours was $\pm$0.04m or so. Therefore, I could figure out that it had higher accuracy than a broadcast ephemerides. Also, in case that the location of a satellite was calculated with the method of the Lagrange Interpolation, it was confirmed that using the 9th order polynomial was efficient.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.1-9
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• 2023

For optimal image quality of computer tomography angiography (CTA), different iodine concentrations and scan parameters were applied to quantitatively evaluate the image quality characteristics of filtered back projection (FBP), hybrid-iterative reconstruction (hybrid-IR), and deep learning reconstruction (DLR). A 320-row-detector CT scanner scanned a phantom with various iodine concentrations (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4, and 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm. Data obtained using each reconstruction technique was analyzed through noise, coefficient of variation (COV), and root mean square error (RMSE). As the iodine concentration increased, the CT number value increased, but the noise change did not show any special characteristics. COV decreased with increasing iodine concentration for FBP, adaptive iterative dose reduction (AIDR) 3D, and advanced intelligent clear-IQ engine (AiCE) at various tube voltages and tube currents. In addition, when the iodine concentration was low, there was a slight difference in COV between the reconstitution techniques, but there was little difference as the iodine concentration increased. AiCE showed the characteristic that RMSE decreased as the iodine concentration increased but rather increased after a specific concentration (4.9 mg/mL). Therefore, the user will have to consider the characteristics of scan parameters such as tube current and tube voltage as well as iodine concentration according to the reconstruction technique for optimal CTA image acquisition.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.559-570
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• 2017

A partial least squares regression (PLSR) model was developed to map the internal soluble solids content (SSC) of apples using a ground-based hyperspectral scanner that could simultaneously acquire outdoor data and capture images of large quantities of apples. We evaluated the applicability of various preprocessing techniques to construct an optimal prediction model and calculated the optimal band through a variable importance in projection (VIP)score. From the 515 bands of hyperspectral images extracted at wavelengths of 360-1019 nm, 70 reflectance spectra of apples were extracted, and the SSC ($^{\circ}Brix$) was measured using a digital photometer. The optimal prediction model wasselected considering the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP) and coefficient of determination of prediction $r_p^2$. As a result, multiplicative scatter correction (MSC)-based preprocessing methods were better than others. For example, when a combination of MSC and standard normal variate (SNV) was used, RMSECV and RMSEP were the lowest at 0.8551 and 0.8561 and $r_c^2$ and $r_p^2$ were the highest at 0.8533 and 0.6546; wavelength ranges of 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, and 992-1019 nm were most influential for SSC determination. The PLSR model with the spectral value of the corresponding region confirmed that the RMSEP decreased to 0.6841 and $r_p^2$ increased to 0.7795 as compared to the values of the entire wavelength band. In this study, we confirmed the feasibility of using a hyperspectral scanner image obtained from outdoors for the SSC measurement of apples. These results indicate that the application of field data and sensors could possibly expand in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.493-504
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• 2008

Space-Based Augmentation System (SBAS), which is one of the GPS augmentation systems, is a Wide-Area Differential GPS that provides differential GPS corrections and integrity data. In this study, we did performance analysis of kinematic SBAS surveying by conducting Real-Time Kinematic (RTK), DGPS, standalone, and SBAS surveys. Considering static survey results as truth, 2-D Root Mean Square (RMS) error and 3-D RMS error were computed to evaluate the positioning accuracy of each survey method. As a result, the 3-D positioning error of RTK was 13.1cm, DGPS 126.0cm, standalone (L1/L2) 135.7cm, standalone (C/A) 428.9cm, and SBAS 109.2cm. The results showed that the positioning accuracy of SBAS was comparable to that of DGPS.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Earthquakes and Structures
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• v.15 no.6
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• pp.595-603
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• 2018

Shake table testing has been regarded as one of the most effective experimental approaches to evaluate seismic response of structural systems subjected to earthquakes. However, reproducing a prescribed acceleration time history precisely over the frequency of interest is challenging because shake table test systems are eventually nonlinear by nature. In addition, interaction between the table and specimen could affect the control accuracy of shake table testing significantly. Various novel control algorithms have been proposed to improve the control accuracy of shake table testing; however, reference values for control performance assessment remain rare. In this study, reference values for control performance assessment of shake table testing are specified based on the statistical analyses of 1,209 experimental data provided by the Seismic Simulator Laboratory of National Center for Research on Earthquake Engineering in Taiwan. Three individual reference values are considered for the assessment including the root-mean-square error of the achieved acceleration time history; the percentage of the spectral acceleration that exceeds the determined tolerance range over the frequency of interest; and the error-ratio of the achieved peak ground acceleration. Quartiles of the real experimental data in terms of the three objective variables are obtained, providing users with solid and simple references to evaluate the control performance of shake table testing. Finally, a set of experimental data of a newly developed control framework implementation for uni-axial shake tables are used as an application example to demonstrate the significant improvement of control accuracy according to the reference values provided in this study.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.383-390
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• 2024

When measuring the radiated noise of an underwater vehicle, the range information between the vehicle and the receiver is an important factor, but since Global Positioning System (GPS) is not available in underwater, an alternative method is needed. As an alternative, the range is measured by estimating the arrival time, arrival time difference, and arrival frequency difference using a separate acoustic signal. However, errors occur due to the channel environment, and these outliers become obstacles in continuously measuring range. In this paper, we propose a method to reduce errors by curve fitting with a function in the form of a V-curve as a post-processing to remove outliers that occurred in the process of measuring range information. Simulation, lake and sea trials were conducted to verify the performance of the proposed method. In the results of the lake trial, the range estimation error was reduced by about 85 % from the Root Mean Square Error (RMSE) point of view.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• Wind and Structures
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• v.31 no.5
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• pp.419-430
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• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1405-1410
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• 2016

Background: The European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Bone Metastases Module (EORTC QLQ-BM22) is a recently designed supplement to EORTC Quality of Life Questionnaire-C30 (EORTC QLQ-C30). Additional psychometric properties, especially using confirmatory factor analysis (CFA) and the Rasch model, are warranted. Materials and Methods: A total of 573 patients with bone metastases were enrolled from eight countries with a mean${\pm}$SD age of $55.8{\pm}13.7years$. Slightly more than two thirds of them were female (n=383; 66.8%). CFA was used to examine the BM22 framework; Rasch models were applied to understand misfit items and differential item functioning (DIF). Results: The fit indices were satisfactory in CFA (comparative fit index=0.972, Tucker-Lewis index=0.964, root mean square error of approximation=0.076, and standardized root mean square residual=0.045). All items fit well in the Rasch models (mean square values were between 0.5 and 1.5), and only one item (number 17) displayed DIF across gender. However, there were six DIF items across Canada and Taiwan, ten across Canada and Iran, and six across Taiwan and Iran. Conclusions: The BM22 has satisfactory psychometric properties, and could assess the QoL of patients with bone metastases specifically focusing on their symptoms. Clinicians may want to use it to capture the underlying QoL for patients with bone metastases. However, the score of item 17 should be interpreted with caution when comparing male and female patients. In addition, researchers should note that variation in DIF items may occur when conducting an international study.