http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
CRITICAL EXPONENTS FOR A DOUBLY DEGENERATE PARABOLIC SYSTEM COUPLED VIA NONLINEAR BOUNDARY FLUX
Yongsheng Mi,Chunlai Mu,Botao Chen 대한수학회 2011 대한수학회지 Vol.48 No.3
The paper deals with the degenerate parabolic system with nonlinear boundary flux. By constructing the self-similar supersolution and subsolution, we obtain the critical global existence curve. The critical Fujita curve is conjectured with the aid of some new results.
CRITICAL EXPONENTS FOR A DOUBLY DEGENERATE PARABOLIC SYSTEM COUPLED VIA NONLINEAR BOUNDARY FLUX
Mi, Yongsheng,Mu, Chunlai,Chen, Botao Korean Mathematical Society 2011 대한수학회지 Vol.48 No.3
The paper deals with the degenerate parabolic system with nonlinear boundary flux. By constructing the self-similar supersolution and subsolution, we obtain the critical global existence curve. The critical Fujita curve is conjectured with the aid of some new results.
An effective method for quantifying and incorporating uncertainty in metamodel selection
Mi Xiao,Yongsheng Yi,Jinhao Zhang,Wei Li 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.3
In complex engineering systems, metamodels are extensively used to replace the computationally expensive simulation and analysis process. Different metamodels can be constructed by using different metamodeling techniques. Uncertainty exists in selecting the best metamodel among a set of alternatives. This study introduces an effective method based on Bayes’ theorem and additive adjustment factor for quantifying and incorporating this uncertainty in metamodel selection. In this method, the posterior probability of each metamodel evaluated by Bayes’ theorem is employed to quantify the uncertainty. The metamodel with the largest posterior probability is considered as the best. Then, the uncertainty in metamodel selection is accounted for by an additive adjustment factor and incorporated into the prediction of the best metamodel. Mathematical examples and the resistance and volume prediction example of a submersible are presented to showcase the introduced method. In these examples, metamodels are constructed by different metamodeling techniques, such as polynomial response surface (PRS), locally weighted regression (LWP), k-nearest neighbors (KNN), radial basis functions (RBF), multivariate adaptive regression splines (MARS) and Kriging. Results indicate that the introduced method can effectively quantify the uncertainty in metamodel selection and incorporate it into the prediction of a system output, hence achieving a prediction with good accuracy and small uncertainty.