Abstract: A few-shot urban remote sensing image information extraction method based on meta learning and attention includes building a few-shot urban remote sensing information pre-trained model. During a pre-training stage, pre-training network learning is performed for a few-shot set to fully learn feature information of existing samples and obtain initial feature parameters and a deep convolutional network backbone of the few-shot set; the few-shot urban remote sensing information pre-trained model is a network structure including a convolutional layer, a pooling layer and a fully-connected layer, and includes five sections of convolutional network where each section includes two or three convolutional layers, and an end of each section is connected to one maximum pooling layer to reduce a size of a picture; the number of convolutional kernels inside each section is same, and when closer to the fully-connected layer, the number of convolutional kernels is larger.

Abstract: A system for in-situ testing of mechanical properties of materials in static and dynamic load spectra, that includes: an Arcan biaxial clamping subsystem, a press-in test subsystem, a biaxial fatigue test subsystem, a biaxial pre-tension loading subsystem, a signal detection subsystem, and a support and adjustment subsystem. A combined guide mechanism in the Arcan biaxial clamping subsystem is rigidly connected to a guide mechanism support block, an x-direction three sensor base and a y-direction force sensor base in the support and adjustment subsystem by threaded connections, respectively. A laser transmitter, a voice coil motor and a laser receiver in the press-in test subsystem are rigidly connected to a two-degree-of-freedom electric moving platform for the laser transmitter, a two-degree-of-freedom electric moving platform for the voice coil motor and a two-degree-of-freedom electric moving platform for the laser receiver in the support and adjustment subsystem by threaded connections, respectively.

Abstract: A system for in-situ testing of mechanical properties of materials in static and dynamic load spectra, that includes: an Arcan biaxial clamping subsystem, a press-in test subsystem, a biaxial fatigue test subsystem, a biaxial pre-tension loading subsystem, a signal detection subsystem, and a support and adjustment subsystem. A combined guide mechanism in the Arcan biaxial clamping subsystem is rigidly connected to a guide mechanism support block, an x-direction force sensor base and a y-direction force sensor base in the support and adjustment subsystem by threaded connections, respectively. A laser transmitter, a voice coil motor and a laser receiver in the press-in test subsystem are rigidly connected to a two-degree-of-freedom electric moving platform for the laser transmitter, a two-degree-of-freedom electric moving platform for the voice coil motor and a two-degree-of-freedom electric moving platform for the laser receiver in the support and adjustment subsystem by threaded connections, respectively.

Abstract: An in-situ testing equipment for testing micromechanical properties of a material in a multi-load and multi-physical field coupled condition is disclosed. The equipment comprises a frame supporting module, a tension/compression-low cycle fatigue module, a torsioning module (21), a three-point bending module (6), an impressing module (33), a thermal field and magnetic field application module (34), an in-situ observation module (32) and a clamp body module (22).

Abstract: An in-situ testing equipment for testing micromechanical properties of a material in a multi-load and multi-physical field coupled condition is disclosed. The equipment comprises a frame supporting module, a tension/compression-low cycle fatigue module, a torsioning module (21), a three-point bending module (6), an impressing module (33), a thermal field and magnetic field application module (34), an in-situ observation module (32) and a clamp body module (22).