599 research outputs found
Metamagnetic transitions and anomalous magnetoresistance in EuAgAs single crystal
In this paper, the magnetic and transport properties were systematically
studied for EuAgAs single crystals, crystallizing in a centrosymmetric
trigonal CaCuP type structure. It was confirmed that two magnetic
transitions occur at = 10 K and = 15 K,
respectively. With the increasing field, the two transitions are noticeably
driven to lower temperature. At low temperatures, applying a magnetic field in
the plane induces two successive metamagnetic transitions. For
both and
, EuAgAs shows a positive, unexpected large
magnetoresistance (up to 202\%) at low fields below 10 K, and a large negative
magnetoresistance (up to -78\%) at high fields/intermediate temperatures. Such
anomalous field dependence of magnetoresistance may have potential application
in the future magnetic sensors. Finally, the magnetic phase diagrams of
EuAgAs were constructed for both
and
High-Dimensional Reliability Method Accounting for Important and Unimportant Input Variables
Reliability analysis is a core element in engineering design and can be performed with physical models (limit-state functions). Reliability analysis becomes computationally expensive when the dimensionality of input random variables is high. This work develops a high-dimensional reliability analysis method through a new dimension reduction strategy so that the contributions of unimportant input variables are also accommodated after dimension reduction. Dimension reduction is performed with the first iteration of the first-order reliability method (FORM), which identifies important and unimportant input variables. Then a higher order reliability analysis is performed in the reduced space of only important input variables. The reliability obtained in the reduced space is then integrated with the contributions of unimportant input variables, resulting in the final reliability prediction that accounts for both types of input variables. Consequently, the new reliability method is more accurate than the traditional method which fixes unimportant input variables at their means. The accuracy is demonstrated by three examples
Active learning with generalized sliced inverse regression for high-dimensional reliability analysis
It is computationally expensive to predict reliability using physical models at the design stage if many random input variables exist. This work introduces a dimension reduction technique based on generalized sliced inverse regression (GSIR) to mitigate the curse of dimensionality. The proposed high dimensional reliability method enables active learning to integrate GSIR, Gaussian Process (GP) modeling, and Importance Sampling (IS), resulting in an accurate reliability prediction at a reduced computational cost. The new method consists of three core steps, 1) identification of the importance sampling region, 2) dimension reduction by GSIR to produce a sufficient predictor, and 3) construction of a GP model for the true response with respect to the sufficient predictor in the reduced-dimension space. High accuracy and efficiency are achieved with active learning that is iteratively executed with the above three steps by adding new training points one by one in the region with a high chance of failure
Dunhuang murals contour generation network based on convolution and self-attention fusion
Dunhuang murals are a collection of Chinese style and national style, forming
a self-contained Chinese-style Buddhist art. It has very high historical and
cultural value and research significance. Among them, the lines of Dunhuang
murals are highly general and expressive. It reflects the character's
distinctive character and complex inner emotions. Therefore, the outline
drawing of murals is of great significance to the research of Dunhuang Culture.
The contour generation of Dunhuang murals belongs to image edge detection,
which is an important branch of computer vision, aims to extract salient
contour information in images. Although convolution-based deep learning
networks have achieved good results in image edge extraction by exploring the
contextual and semantic features of images. However, with the enlargement of
the receptive field, some local detail information is lost. This makes it
impossible for them to generate reasonable outline drawings of murals. In this
paper, we propose a novel edge detector based on self-attention combined with
convolution to generate line drawings of Dunhuang murals. Compared with
existing edge detection methods, firstly, a new residual self-attention and
convolution mixed module (Ramix) is proposed to fuse local and global features
in feature maps. Secondly, a novel densely connected backbone extraction
network is designed to efficiently propagate rich edge feature information from
shallow layers into deep layers. Compared with existing methods, it is shown on
different public datasets that our method is able to generate sharper and
richer edge maps. In addition, testing on the Dunhuang mural dataset shows that
our method can achieve very competitive performance
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