Behaviour of hardening and serration yield of a Fe-Ni-Cr alloy under isothermal cycling (ISC) and out-phase TMF was studied
on the basis of varied hysteresis loops. Cycling hardening and serrated yielding for ISC depend on the temperature and the
total strain range, stronger hardening with serrated yielding at higher strain range under ISC at 600 °C, but no hardening
and serrated yielding occurred under ISC at 800 °C. Stronger hardening with stress serration occurred at the thermal path
going to the lowest temperature, no stress serration occurred at the highest temperature under the out-phase. The hardening
also depends on the total strain range, higher total strain range with lower cycling temperature resulted in a stronger hardening
and remarkable serration yielding behavior. Weaker hardening without serrated yielding occurred at near 800 °C may due to
an obvious cycling stress drop under out-phase TMF. Change in the shape of the hysteresis loops also expresses the degree
of the damage of the tested alloy under out-phase and ISC. 相似文献
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
This paper proposes a sequential design scheme for switching ℌ∞ LPV (Linear Parameter-Varying) control, aiming to reduce the computational complexity of the associated optimization problem. Different from the traditional approach that simultaneously designs switching LPV controllers and solves a high-dimensional optimization problem, the proposed sequential design approach renders a bundle of low-dimensional optimization problems to be solved iteratively. Individual ℌ∞ LPV controller for each subregion is synthesized by independent PLMIs (Parametric Linear Matrix Inequalities) to guarantee ℌ∞ performance, and controller variables are interpolated on the overlapped subregions such that the ℌ∞ performance is also guaranteed on the overlapped subregion. Numerical examples are used to demonstrate the effectiveness of this method to reduce the computational load in each design iteration and improved ℌ∞ performance over the conventional simultaneous design method with well-tuned interpolation coefficient.
To simulate the firing pattern of biological grid cells, this paper presents an improved computational model of grid cells based on column structure. In this model, the displacement along different directions is processed by modulus operation, and the obtained remainder is associated with firing rate of grid cell. Compared with the original model, the improved parts include that: the base of modulus operation is changed, and the firing rate in firing field is encoded by Gaussian-like function. Simulation validates that the firing pattern generated by the improved computational model is more consistent with biological characteristic than original model. Besides, the firing pattern is badly influenced by the cumulative positioning error, but the computational model can also generate the regularly hexagonal firing pattern when the real-time positioning results are modified. 相似文献
The mechanical properties of three different commercially available closed cell Al alloys all made by foam casting are examined. The objective is to assess the roles of cell morphology and of imperfections in governing the basic properties: stiffness, yield strength and fracture resistance. This assessment provides goals for manufacturing strategies that enable attainment of good mechanical performance with affordable process technologies. A prevalent role of curves and wiggles in the cell walls on stiffness and strength (anticipated by models) is affirmed by the present measurements. Systematically larger stiffnesses and yield strengths found in tension than in compression are consistent with a prominent role exerted by such imperfections. Moreover, foam casting is apparently capable of cell morphologies that impart properties approaching the best achievable values for an isotropic closed cell solid, devoid of imperfections. There are associated implications for performance and affordability. Fracture measurements indicate crack growth occurring along the cell walls by a mechanism analogous to the plastic tearing of thin sheets. The crack growth resistances are in the range of 1 kJm−2. This mechanism infers a toughness that scales with the cell wall thickness and its yield strength. 相似文献