飞机大迎角机动非线性气动力模型的辨识

给出一种基于最大似然辨识的方法,用以分析飞机模型的大迎角大幅强迫简谐振动获得的气动力和力矩数据,在泛函分析和非定常气动力线性理论的基础上,通过合理的简化建立了能够包括气动力动态失速和涡流破裂及显著气动迟滞效应的大线性气动力模型表达式,并应用飞机模型的试验数据验证了该方法的计算结果,实际计算结果表明该非线性气动力模型能够较为精确地计算飞机模型大幅度迎角简谐振动产生的气动力响应。     

Pose matters: Pose guided graph attention network for person re-identification

Person re-Identification (reID), aiming at retrieving a person across different cameras, has been playing a more and more important role in the construction of smart city and social security. For deep-learning-based reID methods, it has been proved that using local feature together with global feature could help to give robust representation for person retrieval. Human pose information can provide the locations of human skeleton to effectively guide the network to pay more attention to these key areas, and can also help to reduce the noise distractions from background or occlusions. Based on human pose, a Pose Guided Graph Attention (PGGA) network is proposed in this paper, which is a multi-branch architecture consisting of one branch for global feature and two branches for local key-point features. A graph attention convolution layer is carefully designed to re-assign the contribution weight of each extracted local feature by modeling the similarity relations. The experimental results demonstrate the effectiveness of our approach on discriminative feature learning. Our model achieves the state-of-the-art performance on several mainstream evaluation datasets. A plenty of ablation studies and different kinds of comparison experiments are conducted to prove the effectiveness of this work, including the tests on occluded datasets and cross-domain datasets. Moreover, we further design supplementary tests in practical scenario to indicate the advantage of our work in real-word applications.