An Inverse Parameter Estimation Method for Building Thermal Analysis

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
A. Moftakhari ◽  
C. Aghanajafi
Keyword(s):  
Thermal Analysis ◽  
Numerical Modeling ◽  
Parameter Estimation ◽  
Estimation Method ◽  
Real Data ◽  
Thermal Parameter ◽  
Thermal Design ◽  
Inverse Algorithm ◽  
Definition Of ◽  
Building Engineering

The aim of this study is to introduce a new solution methodology for thermal parameter estimation in building engineering science. By defining a good numerical modeling, inverse algorithm provides us a chance to enhance design conditions in building thermal analysis. The definition of mathematical governing equations and a good solution method to solve them direct the analysis procedure to find temperature distribution using dynamic coding in the computational field. In fact, inverse algorithm utilizes known data resulted from numerical modeling in order to determine the unknown value of important thermal design properties in building problems. The results obtained from implementation of such algorithms demonstrate the accuracy and precision of this new thermal analysis methodology with those of real data resulted from experiments in building problems.

Estimation of Failure Probability and its Applications in Reliability Data Analysis

2010 ◽  
Vol 118-120 ◽  
pp. 601-605
Author(s):  
Han Ming
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Bayesian Estimation ◽  
Failure Probability ◽  
Evaluation Method ◽  
Estimation Method ◽  
New Method ◽  
Reliability Parameter ◽  
Hierarchical Bayesian Estimation ◽  
Definition Of

Evaluation method of reliability parameter estimation needs to be improved effectively with the advance of science and technology. This paper develops a new method of parameter estimation, which is named E-Bayesian estimation method. In the case one hyper-parameter, the definition of E-Bayesian estimation of the failure probability is provided, moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation, and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.

scholarly journals Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Wu ◽  
Hanyang Xie ◽  
Jyun-You Chiang ◽  
Gang Peng ◽  
Yan Qin
Keyword(s):  
Parameter Estimation ◽  
Weibull Distribution ◽  
Glass Fiber ◽  
Fiber Strength ◽  
Estimation Method ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Strength Data ◽  
Parameter Estimation Method

Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.

scholarly journals Parameter Estimation and Covariance Matrix of the Linear Functional Relationship Model for Circular Variables with Unequal Error Concentrations

2020 ◽  
pp. 1-8
Author(s):  
Nurkhairany Amyra Mokhtar ◽  
Yong Zulina Zubairi ◽  
Abdul Ghapor Hussin ◽  
Nor Hafizah Moslim
Keyword(s):  
Parameter Estimation ◽  
Covariance Matrix ◽  
Linear Functional ◽  
Functional Relationship ◽  
Information Matrix ◽  
Estimation Method ◽  
Real Data ◽  
Circular Data ◽  
Data Set ◽  
Relationship Model

Functional relationship model is used to study the data that are subjected to errors. In this paper, we consider the linear functional relationship model with bivariate circular data where the pair of errors is with unequal concentration parameters. The parameter estimation of the model for circular data is different from linear data due to its wrapped around nature. We propose some improvements on the parameter estimation where some iterative procedures are considered. The concentration parameters are estimated based on the Bessel function. Also, we derive the corresponding covariance matrix of the model based on the Fisher Information matrix. Monte Carlo simulation studies were performed to study the suitability of the estimation method. It is found that the biasness of the estimates is small. Practical application of the method is illustrated by using real data set. Keywords: circular data; covariance matrix; Von Mises distribution; simulation study

The first flush in sewer systems

1996 ◽  
Vol 33 (9) ◽  
pp. 101-108 ◽  
Author(s):  
Agnès Saget ◽  
Ghassan Chebbo ◽  
Jean-Luc Bertrand-Krajewski
Keyword(s):  
Treatment Strategy ◽  
Real Data ◽  
First Flush ◽  
Clear Definition ◽  
Controversial Subject ◽  
Sewer Systems ◽  
Wet Weather ◽  
Definition Of ◽  
Appearance Frequency

The first flush phenomenon of urban wet weather discharges is presently a controversial subject. Scientists do not agree with its reality, nor with its influences on the size of treatment works. Those disagreements mainly result from the unclear definition of the phenomenon. The objective of this article is first to provide a simple and clear definition of the first flush and then to apply it to real data and to obtain results about its appearance frequency. The data originate from the French database based on the quality of urban wet weather discharges. We use 80 events from 7 separately sewered basins, and 117 events from 7 combined sewered basins. The main result is that the first flush phenomenon is very scarce, anyway too scarce to be used to elaborate a treatment strategy against pollution generated by urban wet weather discharges.

scholarly journals Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110269
Author(s):  
Lang Liang
Keyword(s):  
Parameter Estimation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Reference Value ◽  
Diffusion Problem ◽  
Bass Model ◽  
Weighted Least Squares Method ◽  
Estimated Parameters ◽  
Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

scholarly journals MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2092
Author(s):  
Songbai Song ◽  
Yan Kang ◽  
Xiaoyan Song ◽  
Vijay P. Singh
Keyword(s):  
Parameter Estimation ◽  
Confidence Intervals ◽  
Asymptotic Variance ◽  
Information Matrix ◽  
Estimation Method ◽  
Flood Frequency Analysis ◽  
Annual Precipitation ◽  
Precipitation Data ◽  
Marquardt Algorithm ◽  
Design Values

The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values

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