Efficient Building Extraction for High Spatial Resolution Images Based on Dual Attention Network

Building extraction with high spatial resolution images becomes an important research in the field of computer vision for urban-related applications. Due to the rich detailed information and complex texture features presented in high spatial resolution images, the distribution of buildings is non-proportional and their difference of scales is obvious. General methods often provide confusion results with other ground objects. In this paper, a building extraction framework based on deep residual neural network with a self-attention mechanism is proposed. This mechanism contains two parts: one is the spatial attention module, which is used to aggregate and relate the local and global features at each position (short and long distance context information) of buildings; the other is channel attention module, in which the representation of comprehensive features (includes color, texture, geometric and high-level semantic feature) are improved. The combination of the dual attention modules makes buildings can be extracted from the complex backgrounds. The effectiveness of our method is validated by the experiments counted on a wide range high spatial resolution image, i.e., Jilin-1 Gaofen 02A imagery. Compared with some state-of-the-art segmentation methods, i.e., DeepLab-v3+, PSPNet, and PSANet algorithms, the proposed dual attention network-based method achieved high accuracy and intersection-over-union for extraction performance and show finest recognition integrity of buildings

Foreign Object Damage to Fan Rotor Blades of Aeroengine Part I: Experimental Study of Bird Impact

AbstractThe conditions of experiment for bird impact to blades have been improved. The experiment of bird impact to the fan rotor blades of an aeroengine is carried out. Through analyzing the transient state response of blades impacted by bird and the change of blade profile before and after the impact, the anti-bird impact performance of blades in the first fan rotor is verified. The basis of anti-foreign object damage design for the fan rotor blades of an aeroengine is provided

Kernel Feature Extraction for Hyperspectral Image Classification Using Chunklet Constraints

A novel semi-supervised kernel feature extraction algorithm to combine an efficient metric learning method, i.e. relevant component analysis (RCA), and kernel trick is presented for hyperspectral imagery land-cover classification. This method obtains projection of the input data by learning an optimal nonlinear transformation via a chunklet constraints-based FDA criterion, and called chunklet-based kernel relevant component analysis (CKRCA). The proposed method is appealing as it constructs the kernel very intuitively for the RCA method and does not require any labeled information. The effectiveness of the proposed CKRCA is successfully illustrated in hyperspectral remote sensing image classification. Experimental results demonstrate that the proposed method can greatly improve the classification accuracy compared with traditional linear and conventional kernel-based methods

Foreign Object Damage to Fan Rotor Blades of Aeroengine Part II: Numerical Simulation of Bird Impact

AbstractBird impact is one of the most dangerous threats to flight safety. The consequences of bird impact can be severe and, therefore, the aircraft components have to be certified for a proven level of bird impact resistance before being put into service. The fan rotor blades of aeroengine are the components being easily impacted by birds. It is necessary to ensure that the fan rotor blades should have adequate resistance against the bird impact, to reduce the flying accidents caused by bird impacts. Using the contacting-impacting algorithm, the numerical simulation is carried out to simulate bird impact. A three-blade computational model is set up for the fan rotor blade having shrouds. The transient response curves of the points corresponding to measured points in experiments, displacements and equivalent stresses on the blades are obtained during the simulation. From the comparison of the transient response curves obtained from numerical simulation with that obtained from experiments, it can be found that the variations in measured points and the corresponding points of simulation are basically the same. The deforming process, the maximum displacements and the maximum equivalent stresses on blades are analyzed. The numerical simulation verifies and complements the experiment results

The ϕ(2170)\phi(2170) production in the process γp→ηϕp\gamma p\to \eta \phi p

We have studied the $\gamma p\to \eta \phi p$ reaction within the effective Lagrangian approach, and our results show that there may be a peak, at least a bump structure around 2180 MeV associated to the resonance $\phi(2170)$ in the $\eta\phi$ mass distribution. We suggest to search for the resonance $\phi(2170)$ in this reaction, which would be helpful to shed light on its nature.Comment: 4 pages, 2 figures, contribution to 18th International Conference on Hadron Spectroscopy and Structure (HADRON 2019

An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF