5results about How to "Accurate feature extraction" patented technology

The invention discloses a bolt pre-tightening force monitoring system and method based on ultrasonic waves. The system comprises an ultrasonic probe, a data acquisition and processing analyzer and the like, a probe array is uniformly mounted on the surface of a bolt, an improved depth residual network is arranged in the data acquisition and processing analyzer and can process a flight time difference signal, and an audible and visual alarm can trigger a three-level alarm according to pre-tightening force deviation. The data remote transmission device provides data for the digital twin system, the display unit displays a pre-tightening force distribution atlas in real time, and a digital twin system platform generates a three-dimensional visual dynamic model and the like. According to the method, bolt pre-tightening force monitoring is achieved through the system. The system is high in measurement precision, convenient to install, capable of achieving real-time early warning and intelligent operation and maintenance, and suitable for various industrial fields.

This invention relates to a method for NAION prediction and acute phase differentiation based on deformable convolution and multi-site OCTA, belonging to the field of ophthalmic disease diagnosis and medical image processing technology. This invention acquires OCTA images of ocular samples and extracts data from key sites. Using the OCTA deformable convolution feature extraction module as the core feature extraction backbone network, its optimal performance is verified through single-site experiments, and a multi-site joint diagnostic framework is constructed. Radiomics features are introduced, and deep fusion of deformable convolution features and radiomics features is achieved through a bidirectional cyclic feature interaction module. Finally, a hybrid expert module is added to the three-site joint model to decouple NAION prediction from the acute phase differentiation task of NAION / ON, improving diagnostic accuracy. This invention effectively solves the problems of difficult accurate differentiation between the acute phases of NAION and ON and insufficient early prediction of NAION, providing reliable technical support for ophthalmic clinical diagnosis.

The invention relates to the technical field of image display, in particular to a metal metallographic structure image classification display method and system. The method comprises the following steps: preprocessing a metal metallographic structure image, calculating and evaluating an image feature index Tzls, quantifying the overall quality of the metal metallographic structure image, automatically screening low-quality and repeated images, and reducing stored and processed invalid data; classifying the metal metallographic structure images by using two characteristics of structural factors and defect factors of the metal metallographic structure; the metal metallographic structure image is classified and graded by calculating and evaluating a structure characteristic index Jtzs and a defect characteristic index Qtzs; and all grade division results are transmitted to the image classification display module, so that timely and accurate data transmission is ensured, a user searches and filters according to a structure grade or a defect grade, a required image is quickly found, and the working efficiency is improved.

The invention relates to the technical field of power transformers, and provides a transformer voiceprint detection method and system based on edge intelligence and deep learning, and the method comprises the steps: S1, collecting an original voiceprint signal, and carrying out the preprocessing and voice enhancement, and obtaining a voiceprint signal; s2, carrying out real-time analysis and feature extraction on the voiceprint signal by the edge computing node by utilizing a preliminary analysis deep learning model, and judging whether voiceprint abnormity exists or not; s3, when the edge computing node judges that abnormity exists, the voiceprint signal and related feature data are uploaded to a cloud big data platform; s4, collecting massive voiceprint data, constructing and continuously updating a standard voiceprint library, and optimizing to generate a deep analysis model; s5, issuing the optimized depth analysis model or model parameters to an edge computing node, and updating the preliminary analysis model; and S6, identifying the type and severity of the defect, carrying out fault positioning, and triggering an early warning and decision instruction. According to the invention, the voiceprint of the transformer can be well detected.

A difference guided multi-directional complementary remote sensing land use classification mapping method comprises the following steps: S1, acquiring a public remote sensing image dataset and performing cropping; S2, constructing a multi-class pixel-level classification model for land use classification and mapping, and training the model by using the cropped remote sensing image dataset; S3, processing a remote sensing image to be classified and mapped by using the trained multi-class pixel-level classification model to obtain a land use classification map; the model adjusts the attention distribution of aggregated features by using difference information to enhance the difference between classes with similar textures; meanwhile, four sub-bands obtained by wavelet decomposition are used to extract edge features in different directions, and texture information is used to position edge details to retain the contour and boundary information of small-area ground objects. The present application can perform pixel-level classification on multi-class ground objects in a remote sensing image and generate a land use classification map.