• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• MALSORI
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• no.67
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• pp.79-102
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• 2008

In this paper, we investigate the relation between Bayes factor and likelihood ratio test (LRT) approaches and apply the neighborhood information of Bayes factor to building an alternate hypothesis model of the LRT system. To consider the neighborhood approaches, we contemplate a distance measure between models and algorithms to be applied. We also evaluate several methods to improve performance of utterance verification using neighborhood information. Among these methods, the system which adopts anti-models built by collecting mixtures of neighborhood models obtains maximum error rate reduction of 17% compared to the baseline, linear and weighted combination of neighborhood models.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.43-48
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• 2005

We consider the problem of testing for parameter changes in time series models based on a cusum test. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case has not been discussed in the literature. Therefore, here we develop a cusum test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model and that of the autocovariances of a linear process. We also consider the variance change test for unstable models with unit roots and GARCH models.

• The korean journal of orthodontics
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• v.44 no.2
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• pp.69-76
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• 2014

Objective: This study aimed to evaluate the accuracy and precision of polyurethane (PUT) dental arch models fabricated using a three-dimensional (3D) subtractive rapid prototyping (RP) method with an intraoral scanning technique by comparing linear measurements obtained from PUT models and conventional plaster models. Methods: Ten plaster models were duplicated using a selected standard master model and conventional impression, and 10 PUT models were duplicated using the 3D subtractive RP technique with an oral scanner. Six linear measurements were evaluated in terms of x, y, and z-axes using a non-contact white light scanner. Accuracy was assessed using mean differences between two measurements, and precision was examined using four quantitative methods and the Bland-Altman graphical method. Repeatability was evaluated in terms of intra-examiner variability, and reproducibility was assessed in terms of interexaminer and inter-method variability. Results: The mean difference between plaster models and PUT models ranged from 0.07 mm to 0.33 mm. Relative measurement errors ranged from 2.2% to 7.6% and intraclass correlation coefficients ranged from 0.93 to 0.96, when comparing plaster models and PUT models. The Bland-Altman plot showed good agreement. Conclusions: The accuracy and precision of PUT dental models for evaluating the performance of oral scanner and subtractive RP technology was acceptable. Because of the recent improvements in block material and computerized numeric control milling machines, the subtractive RP method may be a good choice for dental arch models.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1051-1061
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• 2010

This study hydrologically re-analysed the Muskingum channel routing method to represent it as a linear combination of the linear channel model considering only the translation and the linear reservoir model considering only the storage effect. The resulting model becomes a kind of instantaneous unit hydrograph, whose parameters are identical to those of the Muskingum model. That is, the outflow occurs after the routing interval ${\Delta}t$ or concentration time $T_c$, and among the total amount of inflow, the x portion is translated by the linear channel model and the remaining (1-x) portion is routed by the linear reservoir model with the storage coefficient ��$K_c$. The application result of both the Muskingum channel routing method and its corresponding instantaneous unit hydrograph to an imaginary channel showed that these two models are basically identical. This result was also assured by the application to the channel flood routing to the Kumnam and Gongju Station for the discharge from the Daechung Dam.

• Korean journal of applied entomology
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• v.56 no.1
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• pp.1-18
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• 2017

Temperature-dependent development model is an essential component for forecasting models of insect pests as well as for insect population models. This study reviewed the nonlinear models which explain the relationship between temperature and development rate of insects. In the present study, the types of models were classified largely into empirical and biophysical model, and the groups were subdivided into subgroups according to the similarity of mathematical equations or the connection with original idea. Empirical models that apply analytical functions describing the suitable shape of development curve were subdivided into multiple subgroups as Stinner-based types, Logan-based types, performance models and Beta distribution types. Biophysical models based on enzyme kinetic reaction were grouped as monophyletic group leading to Eyring-model, SM-model, SS-mode, and SSI-model. Finally, we described the historical development and characteristics of non-linear development models and discussed the availability of models.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1585-1592
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• 2016

Estimation of winning percentage in baseball has always been particularly interesting to many baseball fans. We have fitted models including linear regression and Pythagorean formula to the Korean baseball data of seasons from 1982 to 2015. Using RMSE criterion for both the linear formula and the Pythagorean formula, we compared two models in predicting the actual winning percentage. Pythagorean expectation is superior to linear formula when there is either high or low winning percentage. Two methods yield very similar efficiencies when the actual winning percentage is about 50%. To understand and use for estimating winning percentage, it is easier linear formula as estimated equations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.61-67
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• 2007

The load forecasting has been an important part of power system Accordingly, it has been proposed various methods for the load forecasting. The load patterns of the special days is quite different than those of ordinary weekdays. It is difficult to accurately forecast the load of special days due to the insufficiency of the load patterns compared with ordinary weekdays, so we have proposed fuzzy least squares linear regression algorithm for the load forecasting. In this paper we proposed four models for fuzzy least squares linear regression. It is separated by coefficients of fuzzy least squares linear regression equation. we compared model of H1 with H4 and prove it H4 has accurately forecast better than H1.