3results about How to "High separation accuracy" patented technology

A fabric defect classification method and system fusing multi-scale dynamic convolution and graph embedding constraints, aiming at the problems that existing methods are difficult to model irregular defects, similar defects are easy to confuse, and small sample generalization is weak, hierarchical multi-scale features are extracted through Swin Transformer; then a dynamic multi-scale refining module is designed, different receptive field features are fused through context-aware dynamic convolution, scale-dependent is modeled through cross-scale collaborative attention, and high discriminative features are output; the refined features are input into an integrated deep random vector functional connection network, and a boundary-aware graph embedding constraint based on local Fisher discriminant analysis is innovatively introduced in the optimization target, the feature manifold structure is explicitly modeled by constructing same-class intrinsic graph and different-class penalty graph, and the intra-class compactness and inter-class separation degree are strengthened. The present invention significantly reduces the misjudgment rate of similar defects, and meets the needs of industrial real-time detection.

This invention belongs to the technical field of aviation transponder pulse overlap signal separation methods, specifically relating to a separation method applicable to Mode A / C aviation transponder pulse collision conditions. By analyzing the differential characteristics of the Mode A / C aviation transponder signal waveform in the time and energy domains, the pulse collision signal is effectively split. Based on the waveform definition characteristics of the Mode A / C aviation transponder signal, the pulse frame is accurately recovered from the separated signal, ultimately completing the separation process of the Mode A / C collision pulse signal. This invention requires only a single-channel receiver to achieve signal separation, reducing system implementation costs and engineering difficulty, effectively improving the system monitoring capability in densely overlapping electromagnetic environments, and providing an efficient and engineered solution for transponder pulse signal separation tasks in scenarios such as air traffic surveillance and low-altitude airspace management.

The present application belongs to the field of microseismic monitoring, and relates to a microseismic P wave and S wave separation method based on CNN-LSTM and a multi-head attention mechanism. The technology comprises: constructing a data set, using microseismic data collected in a hot dry rock hydraulic fracturing field, and manually labeling the data; the data set includes microseismic data of different time periods and different background noise intensities; inputting the data set into a neural network composed of a convolution layer, an excitation layer, a pooling layer, a random hidden neuron (Dropout) layer, a long short-term memory (LSTM) layer, a multi-head attention mechanism layer and a full connection layer for training; after the training is completed, the model is saved; inputting the microseismic data to be separated into the trained model for separation, and finally obtaining the separation result of P wave and S wave.