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Owing to the comprehensive effects of dimensional disaster, interpolation error, and Markov characteristics of the controlled objects, the traditional dynamic programming algorithm has difficulty ensuring the efficiency of calculation, the accuracy of the results, and the rationality of the optimal control law when solving the optimal fuel economy problem for a multi-mode and multi-gear hybrid electric vehicles (HEVs). To solve this problem, an improved dynamic programming algorithm, CQU-DP, is proposed herein. The algorithm can rapidly generate optimal solutions with high accuracy and rationality through grid size configuration, matrix expansion, filtering, and the introduction of state and control variable penalties. The optimal fuel economy of a parallel HEV with five gears and six modes was solved using this algorithm. The results indicated that compared with the traditional basic dynamic programming algorithm (B-DP) and an improved dynamic programming algorithm (SJTU-DP), the proposed optimization algorithm reduced the calculation time by 96.36% and 93.79%, and the fuel economy was increased by 26.63% and 1.92%, respectively. Additionally, the optimal control law was more reasonable. • Global optimal economy solution of hybrid electric vehicles (HEVs). • Account efficiency, accuracy, and rationality. • A fast dynamic-programming algorithm is developed. • The algorithm is verified by comparing the results with two other dynamic programming algorithms. [ABSTRACT FROM AUTHOR]

Finding a reliable correspondence between two feature point sets efficiently and accurately is the first step in many computer vision applications. It is a crucial prerequisite for many remote sensings, target recognition, and photogrammetry applications. However, in different application scenarios, images may undergo different degrees of deformation, which makes the predefined geometric model no longer appropriate. This article attempts to remove mismatches between the set of assumed matching points. In order to achieve this goal, we propose an effective matching method, called "visualized local structure generation-Siamese attention" network (VLSG-SANet), which transform the task of eliminating mismatching of feature points into a dynamic visual similarity assessment. The VLSG-SANet can generate visual descriptors by using the local structure information about each feature point. We embed the attention module based on the Siamese network for the generated feature descriptors, allowing the network to adjust the descriptors dynamically. In order to prove the robustness and versatility of the VLSG-SANet algorithm, we have performed good experiments on open remote sensing data images and got good experimental results. [ABSTRACT FROM AUTHOR]