Enhanced Assumed Strain Methods for Implicit Gradient‐Enhanced Damage‐Plasticity

islanding detection methods for microgrids

امروزه استفاده از منابع انرژی پراکنده کاربرد وسیعی یافته است . اگر چه این منابع بسیاری از مشکلات شبکه را حل می کنند اما زیاد شدن آنها مسائل فراوانی برای سیستم قدرت به همراه دارد . استفاده از میکروشبکه راه حلی است که علاوه بر استفاده از مزایای منابع انرژی پراکنده برخی از مشکلات ایجاد شده توسط آنها را نیز منتفی می کند . همچنین میکروشبکه ها کیفیت برق و قابلیت اطمینان تامین انرژی مشترکان را افزایش ...

Molecular Methods for Bacterial Strain Typing

ABSTRACT        Typing of bacteria is an important part of epidemiological studies on nosocomial infections. Bacterial identification methods have dramatically improved in recent years, which is mainly due to advancements in the field of molecular biotechnology. In many cases, molecular techniques have replaced phenotypic typing methods. Currently, a wide r...

Shear Locking in a Plane Elasticity Problem and the Enhanced Assumed Strain Method

The method of enhanced assumed strains (EAS) is a popular tool for avoiding locking phenomena, e.g., a remedy for shear locking in plane elasticity. We consider bending-dominated problems on thin bodies which can be treated as beams and prove that the degree of approximation of the EAS method is at least as good as that of a beam model. The hypercircle method is combined with arguments of nonco...

Assumed Density Filtering Methods for Learning Bayesian Neural Networks

Buoyed by the success of deep multilayer neural networks, there is renewed interest in scalable learning of Bayesian neural networks. Here, we study algorithms that utilize recent advances in Bayesian inference to efficiently learn distributions over network weights. In particular, we focus on recently proposed assumed density filtering based methods for learning Bayesian neural networks – Expe...

Implicit Numerical Methods for Magnetohydrodynamics