• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.537-543
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• 2007

Since the GPS absolute positioning with pseudorange measurements can significantly be affected by the observation error, the time series analysis of the GPS receiver clock errors was performed in this study. From the estimated receiver clock errors, the time series model is generated, and constrained back in the absolute positioning process. One of the CORS (Continuously Operating Reference Stations) network is used to analyze the behavior of the receiver clock. The dominant part of the model is the linear trend during 24 hours, and the seasonal component is also estimated. After constraining the modeled receiver clock errors, the estimated position error compared to the published coordinates is improved from ${\pm}11.4\;m\;to\;{\pm}9.5\;m$ in 3D RMS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• The Journal of Advanced Prosthodontics
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• v.12 no.2
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• pp.100-106
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• 2020

PURPOSE. The purpose of this in vitro study was to evaluate the fitting accuracy of single crowns made from a novel presintered Co-Cr alloy prepared with a computer-aided design and computer-aided manufacturing (CAD/CAM) technique, as compared with crowns manufactured by other digital and the conventional casting technique. Additionally, the influence of oxide layer on the fitting accuracy of specimens was tested. MATERIALS AND METHODS. A total of 40 test specimens made from Co-Cr alloy were investigated according to the fitting accuracy using a replica technique. Four different methods processing different materials were used for the manufacture of the crown copings (milling of presintered (Ceramill Sintron-group_cer_sin) or rigid alloy (Tizian NEM-group_ti_nem), selective laser melting (Ceramill NPL-group_cer_npl), and casting (Girobond NB-group_gir_nb)). The specimens were adapted to a resin model and the outer surfaces were airborne-particle abraded with aluminum oxide. After the veneering process, the fitting accuracy (absolute marginal discrepancy and internal gap) was evaluated by the replica technique in 2 steps, before removing the oxide layer from the intaglio surface of the crowns, and after removing the layer with aluminum oxide airborne-particle abrasion. Statistical analysis was performed by multifactorial analysis of variance (ANOVA) (α=.05). RESULTS. Mean absolute marginal discrepancy ranged between 20 ㎛ (group_cer_npl for specimens of Ceramill NPL) and 43 ㎛ (group_cer_sin for crowns of Ceramill Sintron) with the oxide layer and between 19 ㎛ and 28 ㎛ without the oxide layer. The internal gap varied between 33 ㎛ (group_ti_nem for test samples of Tizian NEM) and 75 ㎛ (group_gir_nb for the base material Girobond NB) with the oxide layer and between 30 ㎛ and 76 ㎛ without the oxide layer. The absolute marginal discrepancy and the internal gap were significantly influenced by the fabrication method used (P<.05). CONCLUSION. Different manufacturing techniques had a significant influence on the fitting accuracy of single crowns made from Co-Cr alloys. However, all tested crowns showed a clinically acceptable absolute marginal discrepancy and internal gap with and without oxide layer and could be recommended under clinical considerations. Especially, the new system Ceramill Sintron showed acceptable values of fitting accuracy so it can be suggested in routine clinical work.

• The Journal of the Korean dental association
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• v.44 no.2 s.441
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• pp.115-122
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• 2006

Objective : The purpose of this study was to evaluate the dimensional errors between real tooth, 3D CT image and CARP model. Materials and Methods : Two maxilla and two mandible block bones with intact teeth were taken from two cadavers. Computed tomography was taken either in dry state and in wet state. After then, all teeth were extracted and the dimensions of the real teeth were measured using a digital caliper at mesio-distal and bucco-lingual width both in crown and cervical portion. 3D CT image was generated using the V-works $4.0^{TM}$ (Cybemed Inc., Seoul, Korea) software. Twelve teeth were randomly selected for CARP model fabrication. All the measurements of 3D Ct images and CARP models were made in the same manner of the real tooth group. Dimensional errors between real tooth, 3D CT image model and CARP model was calculated. Results : 1) Average of absolute error was 0.199 mm between real teeth and 3D CT image model, 0.169 mm between 3D CT image model and CARP model and 0.291 mm between real teeth and CARP model, respectively. 2) Average size of 3D CT image was smaller than real teeth by 0.149 mm and that of CARP model was smalier than 3D CT image model by 0.067mm. Conclusion : Within the scope of this study, CARP model with the 0.291 mm average of absolute eror can aid to enhance the success rate cf autogenous tooth transplantation due to the increased accuracy of recipient bone and donor tooth.

• Structural Engineering and Mechanics
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• v.82 no.2
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• pp.245-258
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• 2022

Many Structural Health Monitoring (SHM) methods have been proposed for structural damage diagnosis and prognosis. However, SHM for pinched hysteretic structures can be problematic due to the high level of nonlinearity. The model-free hysteresis loop analysis (HLA) has displayed notable robustness and accuracy in identifying damage for full-scaled and scaled test buildings. In this paper, the performance of HLA is compared with seven other SHM methods in identifying lateral elastic stiffness for a six-story numerical building with highly nonlinear pinching behavior. Two successive earthquakes are employed to compare the accuracy and consistency of methods within and between events. Robustness is assessed across sampling rates 50-1000 Hz in noise-free condition and then assessed with 10% root mean square (RMS) noise added to responses at 250 Hz sampling rate. Results confirm HLA is the most robust method to sampling rate and noise. HLA preserves high accuracy even when the sampling rate drops to 50 Hz, where the performance of other methods deteriorates considerably. In noisy conditions, the maximum absolute estimation error is less than 4% for HLA. The overall results show HLA has high robustness and accuracy for an extremely nonlinear, but realistic case compared to a range of leading and recent model-based and model-free methods.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.445-458
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• 2020

The least absolute shrinkage and selection operator (LASSO) is a popular method for a high-dimensional regression model. LASSO has high prediction accuracy; however, it also selects many irrelevant variables. In this paper, we consider the moderately clipped LASSO (MCL) for the high-dimensional generalized linear model which is a hybrid method of the LASSO and minimax concave penalty (MCP). The MCL preserves advantages of the LASSO and MCP since it shows high prediction accuracy and successfully selects relevant variables. We prove that the MCL achieves the oracle property under some regularity conditions, even when the number of parameters is larger than the sample size. An efficient algorithm is also provided. Various numerical studies confirm that the MCL can be a better alternative to other competitors.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.675-683
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• 2015

In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.