Long Short-Term Memory Spatial Transformer Network

Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially. In this paper, we propose a combined spatial transformer network (STN) and a Long Short-Term Memory network (LSTM) to classify digits in sequences formed by MINST elements. This LSTM-STN model has a top-down attention mechanism profit from LSTM layer, so that the STN layer can perform short-term independent elements for the statement in the process of spatial transformation, thus avoiding the distortion that may be caused when the entire sequence is spatially transformed. It also avoids the influence of this distortion on the subsequent classification process using convolutional neural networks and achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional Neural Network with STN layer

Creep fatigue life assessment of a pipe intersection with dissimilar material joint by linear matching method

As the energy demand increases the power industry has to enhance both efficiency and environmental sustainability of power plants by increasing the operating temperature. The accurate creep fatigue life assessment is important for the safe operation and design of current and future power plant stations. This paper proposes a practical creep fatigue life assessment case of study by the Linear Matching Method (LMM) framework. The LMM for extended Direct Steady Cycle Analysis (eDSCA) has been adopted to calculate the creep fatigue responses due to the cyclic loading under high temperature conditions. A pipe intersection with dissimilar material joint, subjected to high cycling temperature and constant pressure steam, is used as an example. The closed end condition is considered at both ends of main and branch pipes. The impact of the material mismatch, transitional thermal load, and creep dwell on the failure mechanism and location within the intersection is investigated. All the results demonstrate the capability of the method, and how a direct method is able to support engineers in the assessment and design of high temperature component in a complex loading scenario

Analytical approach of late-time evolution in a torsion cosmology

In this letter, we study the late-time evolution of a torsion cosmology only with the spin-$0^+$ mode. We find three kinds of analytical solutions with a constant affine scalar curvature. In the first case, it is not physical because the matter density will be negative. In the second case, it shows that the dark energy can be mimicked in the torsion cosmological model. In the third case, the characteristic of late-time evolution is similar to that of the universe of matter dominant. And we also find a kind of expression with the non-constant curvature that the periodic character of numerical calculation is only the reflection of solution in a specific period of evolution. Using these expressions, we shall be able to predict the evolution over the late-time. From this prediction, we know the fate of universe that the universe would expand forever, slowly asymtotically to a halt.Comment: 12pages,6 figure