4 results about "Local statistics" patented technology

The application discloses a strip steel hot rolling process multi-performance index abnormality monitoring method and system, and belongs to the technical field of industrial process control and fault diagnosis, and the method comprises the following steps: collecting process variable data and performance index data in a historical strip steel hot rolling process and performing standardization pretreatment; dividing the strip steel hot rolling process into multiple different process subblocks; for each process subblock, performing space-time feature extraction on the corresponding process variable data to obtain a space-time feature representation; constructing a local statistic quantity of each performance index; fusing the local statistic quantities to obtain a global statistic quantity and a global control limit; and based on this, realizing multi-performance index abnormality monitoring and alarm. The application effectively solves the problems that cross-process time lag dependence is difficult to capture, space-time feature extraction is not comprehensive, and single performance index monitoring leads to abnormality missed reports in the strip steel hot rolling process, significantly improves the fault detection rate and reduces the false alarm rate.

The application discloses a kind of based on deep learning electric power load multi-period prediction method and system, it utilizes instance-level reversible distribution adaptation mechanism, and the original tensor is stationary mapping in input end dynamic extraction local statistics, convert time-varying distribution into unified feature space that model is easy to converge.On this basis, design time-frequency double-flow interactive network, and extract local mutation feature in time domain and global periodicity feature in frequency domain in parallel, and compensate the information missing of single perspective through cross-domain gate fusion mechanism.Finally, cooperate with future-oriented generative decoder and distribution recovery module, and the stationary prediction result is reversely mapped back to the original dimension space with physical meaning.Through this closed-loop logic of stationary reasoning first and distribution restoration later, effectively eliminate the negative influence of statistical drift on model generalization, realize high-precision multi-period prediction under the premise of retaining environment-driven physical characteristics.

The application discloses a kind of narrow river detection methods suitable for spaceborne wide swath interferometric radar altimeter image, comprising: reading into spaceborne wide swath interferometric radar altimeter image;It is enhanced river linear feature by Gaussian difference preprocessing to suppress background;Curvature structure perception detector is constructed, curvature response map is calculated based on Hessian matrix eigenvalue, and feature fusion enhancement is carried out in conjunction with multi-direction structure consistency score;The binary image obtained by adaptive threshold segmentation based on local statistics is used as region label by carrying out adaptive threshold segmentation to enhanced feature map, maximum value is extracted in the region corresponding to enhanced feature map, seed point set is formed, and adaptive region growth based on queue priority is executed, to generate initial river center line;Markov random field model is introduced, global structure optimization and topological connectivity correction are carried out using graph cut algorithm, to obtain optimized river center line;Direction and radiation characteristic constraint is applied again, morphological dilation is carried out, and finally complete river mask is generated.

This invention provides a method for suppressing and enhancing infrared image defects and thermal noise, relating to the field of image enhancement. The method aims to construct thermal target protection weights and data fidelity weights by normalizing, performing local statistics, and estimating gradient and fringe principal directions on the original infrared image. The observed image is decomposed into structural components, point-like defective pixel components, structured thermal noise components, and low-frequency thermal bias components. Through a thermal consistency-guided defective pixel weight generation mechanism, direction-aware structured thermal noise separation, background-gated low-frequency thermal bias separation, anisotropic edge-preserving enhancement for thermal target protection, and multi-scale local thermal contrast preservation, a joint optimization solution is achieved, and the enhanced image is output after local thermal contrast remapping. This method effectively suppresses defective pixels, fringe noise, and background thermal bias, reduces the risk of accidental deletion of small thermal targets, maintains the target outline and the thermal difference between the target and the background, and improves the clarity, recognizability, and engineering applicability of infrared images.