WAVELET-BASED ANALYSIS OF CEREBROVASCULAR DYNAMICS IN NEWBORN RATS WITH INTRACRANIAL HEMORRHAGES

Intracranial hemorrhage(I)is a major problem of neonatal intensive care.The incidence of Iistypically asymptomatic and'canmot be effectively detected by standard diagnostic methods.The mechanisms underlying IH are unknown but there is evidence that stress-induced disorders inadrenergic regulation of cerebral venous blood flow (CVBF) are among the main reasons.Quantitative and qualitative:could significantly advance understanding ofthe nature of I in newbornslth1sions of CVBF in newborn rats withan experimental model of stinjection.Our analysis is bas ed on theDoppler optical coheavelet-based approachthat provides sensitiv external factors.Theobtained resultsccompanied by asupprectivity to adrenaline.Weintroducd show that the values0<1.23 estimated ithelodinto the sympathicusindicate abnormal reactions associated with the developent of I.We conclude that t he revealed areactivity of the cerebral veins to adrenaline represents a possible mechanism responsible forpat hological changes in CVBF.

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study.In order to reduce the number of design variables for aerodynamic optimizations,the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters,the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output,and it is trained using the k-step contrastive divergence algorithm.The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils,and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models.Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization(PSO)framework,and applied to the robust aerodynamic design optimizations of Natural Laminar Flow(NLF)airfoil and transonic wing.The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing.By employing the PCA-DBN-based surrogate model,the developed efficient method improves the optimization efficiency obviously.