Investigation of the Influence of Nanodispersed Compositions Obtained by Plasmochemical Synthesis on the Crystallization Processes of Structural Alloys

The state of the problem of stabilizing the structure, improving the quality and properties of structural alloys is studied. To solve the problem, it is proposed to modify melts of low–alloyed alloys with nanodispersed compositions obtained by plasma–chemical synthesis. Process technological parameters are developed. Nanopowders of carbide and carbonitride class SiC and Ti (C, N) with a size of 50 ... 100 nm are obtained. The crystallographic parameters of the nanocompositions and the specific surface are determined, and the dependency curves are plotted. The macro– and microstructure of structural steels and alloys was studied before and after the modification. A significant (in 2 ... 3.5 times) grain refinement and stabilization of the alloy structure as a result of nanopowder modification of titanium carbonitride have been achieved. Thermodynamic calculations of the dimensions of crystalline seeds during the crystallization of steels and alloys are carried out. A complex criterial estimation of the efficiency of nanodispersed compositions in a steel melt is proposed. The features of crystallization and structure formation of modified structural steels are studied. The obtained results are of theoretical and practical importance for production of critical parts from structural steels and high–quality alloys

FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations

Neural network-based methods for image processing are becoming widely used in practical applications. Modern neural networks are computationally expensive and require specialized hardware, such as graphics processing units. Since such hardware is not always available in real life applications, there is a compelling need for the design of neural networks for mobile devices. Mobile neural networks typically have reduced number of parameters and require a relatively small number of arithmetic operations. However, they usually still are executed at the software level and use floating-point calculations. The use of mobile networks without further optimization may not provide sufficient performance when high processing speed is required, for example, in real-time video processing (30 frames per second). In this study, we suggest optimizations to speed up computations in order to efficiently use already trained neural networks on a mobile device. Specifically, we propose an approach for speeding up neural networks by moving computation from software to hardware and by using fixed-point calculations instead of floating-point. We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations. We also show an example of how existing datasets can be modified and adapted for the recognition task in hand. Finally, we present the design and the implementation of a floating-point gate array-based device to solve the practical problem of real-time handwritten digit classification from mobile camera video feed

Nanometer-scale mapping of irreversible electrochemical nucleation processes on solid Li-ion electrolytes

Electrochemical processes associated with changes in structure, connectivity or composition typically proceed via new phase nucleation with subsequent growth of nuclei. Understanding and controlling reactions requires the elucidation and control of nucleation mechanisms. However, factors controlling nucleation kinetics, including the interplay between local mechanical conditions, microstructure and local ionic profile remain inaccessible. Furthermore, the tendency of current probing techniques to interfere with the original microstructure prevents a systematic evaluation of the correlation between the microstructure and local electrochemical reactivity. In this work, the spatial variability of irreversible nucleation processes of Li on a Li-ion conductive glass-ceramics surface is studied with ~30 nm resolution. An increased nucleation rate at the boundaries between the crystalline AlPO4 phase and amorphous matrix is observed and attributed to Li segregation. This study opens a pathway for probing mechanisms at the level of single structural defects and elucidation of electrochemical activities in nanoscale volumes