5results about How to "Avoid estimation" patented technology

The application discloses a deep learning DOA estimation method based on original IQ data. The application uses I and Q components of the original signal as the input of the model to improve the performance. The application aims to solve the DOA estimation problem of a single signal source, and models the single signal source DOA estimation problem as a single-label multi-classification problem. By discretizing the DOA range, the possible directions of arrival are taken as corresponding labels. A convolutional neural network is designed to adapt to different numbers of snapshots, and accurate DOA estimation can be adaptively obtained for input signals of different lengths. Experimental results show that, compared with existing deep learning DOA estimation methods based on covariance matrix as input, the scheme has more excellent performance, and can provide a more reliable solution for array signal processing.

This invention relates to the field of remote sensing image processing technology, specifically to a SAR control point extraction method, apparatus, electronic device, and storage medium. The SAR control point extraction method includes: acquiring multiple SAR images and matched candidate point observation data and imaging geometric parameters; establishing a multi-view positioning observation model; performing initial three-dimensional coordinate calculation to obtain initial calculation results; performing variance component estimation based on prior constraints of imaging geometric configuration to dynamically optimize the observation weights of each image; recalculating the three-dimensional coordinates; iteratively performing weight optimization and coordinate calculation until convergence conditions are met; and selecting and determining SAR control points based on the accuracy index of the final calculation results. This method constructs a robust and complete framework for multi-view stereo SAR control point extraction, embedding geometric prior constraints such as intersection angles and orbit combinations into the variance component estimation process, thus achieving end-to-end optimization processing from candidate corresponding points to high-reliability control points.

The application discloses to the technical field of artificial intelligence, computer vision and deep learning, and particularly relates to an image generation quality evaluation and optimization method based on sparse feedback for industrial design, which comprises the following steps: constructing an industrial design image dataset containing multi-dimensional expert scores; designing a physical flow branch to extract material texture and illumination statistical features, and a semantic flow branch to process picture-text splicing features through a self-attention mechanism; introducing a physical-visual outer product fusion module to capture second-order feature interaction; using the trained evaluation network as a reward model, combining a sparse sampling strategy and a LoRA fine-tuning mechanism, and guiding the diffusion model to align with the industrial design standard under extremely low computing resource consumption. The application significantly improves the material reality, illumination logic and picture-text consistency of the generated image, and provides an efficient and low-cost solution for automatic image generation in the field of industrial design.

The invention belongs to the technical field of risk assessment of malicious instruction attacks in the field of energy control, and particularly relates to an active power distribution network malicious instruction attack risk assessment method considering behavior characteristics. Comprising the steps of establishing a representation model of a non-uniform power distribution network information physical network; according to the representation model, constructing a malicious instruction attack behavior model so as to research the risk that the active power distribution network is attacked by malicious instructions, analyzing the invasion characteristics of the previous malicious instructions, and establishing a numerical simulation model based on historical event data analysis; and constructing a risk assessment model, considering influence factors that information nodes are attacked, and establishing a risk assessment method for the power distribution network under malicious instruction attack threats. According to the method, the evaluation is more comprehensive and accurate through a numerical simulation model of historical event data analysis, the risk value is directly quantified subsequently, the risk can be perceived and measured, the evaluation is more accurate compared with qualitative evaluation, and a targeted defense strategy can be formulated subsequently.

This application discloses a channel estimation method, apparatus, terminal, network-side device, and medium, belonging to the field of communication technology. The channel estimation method provided in the embodiments of this application includes: a UE receiving first target information from a communication device, the first target information being used to indicate: parameters of a backscatter signal sent by a BSC device, or whether to perform joint channel estimation on a first signal; the UE determining whether to perform joint channel estimation on the first signal based on the first target information; wherein the backscatter signal is obtained by the BSC device modulating the first signal.