Atomic Structure Evolution of Pt–Co Binary Catalysts: Single Metal Sites versus Intermetallic Nanocrystals

Due to their exceptional catalytic properties for the oxygen reduction reaction (ORR) and other crucial electrochemical reactions, PtCo intermetallic nanoparticle (NP) and single atomic (SA) Pt metal site catalysts have received considerable attention. However, their formation mechanisms at the atomic level during high-temperature annealing processes remain elusive. Here, the thermally driven structure evolution of Pt–Co binary catalyst systems is investigated using advanced in situ electron microscopy, including PtCo intermetallic alloys and single Pt/Co metal sites. The pre-doping of CoN4 sites in carbon supports and the initial Pt NP sizes play essential roles in forming either Pt3Co intermetallics or single Pt/Co metal sites. Importantly, the initial Pt NP loadings against the carbon support are critical to whether alloying to L12-ordered Pt3Co NPs or atomizing to SA Pt sites at high temperatures. High Pt NP loadings (e.g., 20%) tend to lead to the formation of highly ordered Pt3Co intermetallic NPs with excellent activity and enhanced stability toward the ORR. In contrast, at a relatively low Pt loading (<6 wt%), the formation of single Pt sites in the form of PtC3N is thermodynamically favorable, in which a synergy between the PtC3N and the CoN4 sites could enhance the catalytic activity for the ORR, but showing insufficient stability

A StereoSAR Matching Method Based on Disparity Maps Fusion

A matching algorithm based on disparity maps fusion is proposed. Firstly, on the basis of normalized cross correlation(NCC), various disparity maps are computed using several different matching window sizes. Then, for each disparity of each disparity maps, the confidence level is evaluated by a new confidence measure, which combined left right consistency(LRC) with signal to noise ratio(SNR). Finally, a new proposed disparity maps fusion strategy is used for formation of weighted disparity map in terms of confidence level. This disparity maps fusion strategy considers not only the confidence level of the disparity itself but also its neighbors. The algorithm has been applied to a pair of TanDEM-X spotlight stereo images. The results demonstrate that the accuracy of DEM generated with the proposed algorithm is improved from 11.28 m to 8.41 m and the gross errors are effectively reduced

Development and application of ANN model for property prediction of supercritical kerosene

Three artificial neural network (ANN) models were developed to predict the fluid properties of China RP-3 kerosene under supercritical pressure in replacement of the time-consuming property calculations by the principle of Extended Corresponding State (ECS). The analysis shows that the properties predicted by the trained ANN models agree well with the calculations by the ECS method. The correlation coefficients (R) between the ANN predictions and the ECS calculations are higher than 0.99, and most of the relative errors are lower than 0.1%. The prediction by the ANN models is of several orders (10(4)) faster than that by the ECS method, especially near the critical points. The trained ANN model was further coupled with the CFD modeling of a realistic kerosene jet, where high efficiency and satisfactory accuracy were shown compared with the direct ECS calculations. (C) 2020 Elsevier Ltd. All rights reserved

Knowledge-Aided Ground Moving Target Relocation for Airborne Dual-Channel Wide-Area Radar by Exploiting the Antenna Pattern Information

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios

Knowledge-Aided Ground Moving Target Relocation for Airborne Dual-Channel Wide-Area Radar by Exploiting the Antenna Pattern Information

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios

Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario

Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data