• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• Tribology and Lubricants
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• v.26 no.6
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• pp.329-335
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• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

• Korean Journal of Veterinary Research
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• v.53 no.2
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• pp.73-76
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• 2013

The aim of this study was to produce a simple and inexpensive technique for estimating the obturator foramen area (OFA) from young calves based on the hypothesis that OFA can be extrapolated from simple linear measurements. Three linear measurements - dorsoventral height, craneocaudal width and total perimeter of obturator foramen - were obtained from 55 bovine hemicoxae. Different algorithms for determining OFA were then produced with a regression analysis (curve fitting) and statistical analysis software. The most simple equation was OFA ($mm^2$) = [3,150.538 + ($36.111^*CW$)] - [147,856.033/DH] (where CW = craneocaudal width and DH = dorsoventral height, both in mm), representing a good nonlinear model with a standard deviation of error for the estimate of 232.44 and a coefficient of multiple determination of 0.846. This formula may be helpful as a repeatable and easily performed estimation of the obturator foramen area in young bovines. The area of the obturator foramen magnum can thus be estimated using this regression formula.

• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.65-76
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• 2013

Traffic accidents at signalized intersections have been increased annually so that it is required to examine the causation to reduce the accidents. However, the current existing accident models were developed mainly by using non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal the complicated causation for traffic accidents, though they are the right choice to study randomness and non-linearity of accidents. Therefore, it is required to utilize another statistical method to make up for the lack of the non-linear regression methods. This study developed accident prediction models for 4 legged signalized intersections with Poisson methods and compared them with structural equation models. This study used structural equation methods to reveal the complicated causation of traffic accidents, because the structural equation method has merits to explain more causational factors for accidents than others.

• Journal of applied mathematics & informatics
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• v.42 no.5
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• pp.1063-1076
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• 2024

This research introduces an innovative approach to revolutionize inventory management strategies amid unpredictable demand and uncertainties. Introducing a Fuzzy Economic Order Quantity (EOQ) model, enriched with the centroid defuzzification method and supervised machine learning, the study offers a comprehensive solution for optimized decision-making. The model transcends traditional inventory paradigms by seamlessly integrating fuzzy logic and advanced machine learning, emphasizing adaptability in fast-paced business landscapes. The research unfolds against the backdrop of agile inventory management advocacy, with key contributions including the centroid defuzzification method for crisp interpretation and the integration of linear regression for cost prediction. The study employs a real-life bakery scenario to demonstrate the efficacy of both crisp and fuzzy models, underscoring the latter's superiority in handling uncertainties. Comparative analysis reveals nuanced impacts of uncertainty on inventory decisions, while linear regression establishes statistical relationships for cost predictions. The findings underscore the pivotal role of fuzzy logic in optimizing inventory management, paving the way for future enhancements, advanced machine learning integration, and real-world validation. This research not only contributes to adaptive inventory management evolution but also sets the stage for further exploration and refinement in dynamic business landscapes.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• Smart Structures and Systems
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• v.6 no.4
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• pp.363-372
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• 2010

Accurate peak response estimation of a seismically excited structure with frictional damping system (FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated the peak response of the structure with FDS by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In case that earthquake excitation is defined probabilistically, corresponding response of the structure with FDS becomes to have probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake excitation generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Coefficients of the proposed PDF are obtained by regression of the statistical distribution of the time history responses. Finally, the correlation between the resulting PDFs and statistical response distribution is investigated.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.639-644
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• 2007

The accurate peak response estimation of a seismically excited structure with frictional damping system(FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated that by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In the case that on earthquake load is defined with probabilistic characteristics, the corresponding response of the structure with FDS has probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake loads generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Finally, coefficients of the proposed PDF is obtained by regression analysis of the statistical distribution of the time history responses. Finally, the correlation between PDFs and statistical response distribution is presented.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.148-153
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• 2016

Hydraulic and hydrological flood routing methods are commonly used to analyze temporal and spatial flood influences of flood wave through a river reach. Hydrological flood routing method has relatively more simple and reasonable performance accuracy compared to the hydraulic method. Storage constant used in Muskingum method widely applied in hydrological flood routing is very similar to the travel time. Focusing on this point, in this study, we estimate the travel time from HEC-RAS results to estimate storage constant, and develop a non-linear regression equation for the travel time using reach length, channel slope, and discharge. The estimated flow by Muskingum model with storage constant of nonlinear equation is compared with the flow calculated by applying the HEC-RAS 1-D unsteady flow simulation. In addition, this study examines the effect on the weighting factor changes and interval reach divisions; peak discharge increases with the bigger weighting factor, and RMSE decreases with the fragmented division.