6results about How to "Reduce computing consumption" patented technology

This invention relates to the field of weld defect type identification technology. Existing weld defect detection methods fail to achieve intelligent and efficient detection due to the diverse types of weld defects. This invention provides a low-cost self-learning neural network design method for ultrasonic detection of weld defects. It performs high-dimensional spatial domain feature representation on the initial one-dimensional ultrasonic signal, enriches the feature expression of weld defect data, selects the feature domain with better performance, constructs an adaptive scaling network for weld defect detection, and uses a multi-objective, training-free network search and evaluation method to iteratively search and evaluate candidate networks for weld defect detection, obtaining a balanced solution on both network classification accuracy and parameter quantity. Ultimately, it achieves the selection of a weld defect type detection network with superior overall performance without any training.

The present application relates to the technical field of pedal data processing, and particularly relates to a method and system for replacing pedal data in a playing file. The method comprises: obtaining a playing file of a piece of music; obtaining a score file corresponding to the piece of music; generating an insertion node according to the score file and a pedal insertion rule, and / or according to the playing file, wherein the pedal insertion rule comprises a chord rule; selecting replacement data of a pedal in response to a selection signal of a user, wherein the selection signal comprises a specified style or a specified pedal file; and inserting the replacement data into the insertion node to form a new playing file. The present application can meet the demand of users who want to reference pedal data of others by replacing pedal data, and greatly improves the efficiency of music production, teaching assistance, performance analysis and the like.

The present application relates to the technical field of three-dimensional reconstruction, and in particular to an object three-dimensional reconstruction system based on deep learning, which introduces an adaptive cost aggregation method with visibility perception for cost volume aggregation, acquires the visibility of pixel points in the view through a network, and can improve the reconstruction integrity of the occluded area; a variance-based method is used to predict the disparity range of each pixel, and a spatially-varying depth hypothesis surface is constructed for depth estimation in the next stage, and a residual and channel attention guided fusion depth map optimization module is proposed in the last stage to obtain an optimized depth map; an improved depth map fusion algorithm is used to combine the pixel point and 3D point re-projection error for consistency checking to obtain a dense point cloud. Quantitative and qualitative comparison results of the present application and other methods on the DTU dataset show that the present application can reconstruct a scene with better details, and achieves the purposes of reducing GPU memory consumption and computation time.

The invention discloses a three-dimensional geologic structure modeling method and system based on SH-LDM and a medium, and relates to the technical field of deep learning and geologic modeling crossing, and the three-dimensional geologic structure modeling method based on SH-LDM mainly comprises the steps: carrying out the preprocessing of original data, and obtaining a training data set and a test data set; training the multi-branch self-encoding network by using the first-stage loss function, and training the multi-condition diffusion network by using the second-stage loss function; and according to soft data and hard data of a real scene, utilizing the trained multi-branch self-encoding network and the multi-condition diffusion network to obtain a three-dimensional geologic structure generation image. By implementing the SH-LDM-based three-dimensional geologic structure modeling method and system and the medium provided by the invention, high-precision and efficient modeling of a complex non-stationary underground structure can be realized.

This invention discloses a cross-domain authentication method for trusted data spaces based on digital certificates, belonging to the field of trusted data space authentication technology. The method includes S1, constructing a trusted data space cross-domain authentication system; S2, system initialization; S3, trusted data space and user registration; S4, intra-domain two-way authentication between the trusted data space and the user; and S5, cross-domain authentication and key negotiation between the trusted data space and the user. This invention combines a lightweight algorithm to achieve lightweight intra-domain authentication and secure cross-domain communication. Addressing the single point of failure problem in traditional authentication, it designs a blockchain distributed verification mechanism to avoid the risk of system paralysis by a centralized trusted party. During the authentication process, the identity information of the trusted data space and the user is anonymized through pseudo-identity and hash processing. Simultaneously, location privacy and communication security are protected through timestamps and digital signatures. The blockchain stores a permission table, simplifying the cross-domain verification steps for authenticated entities and reducing system computation and communication costs.

This invention discloses a method, apparatus, device, medium, and product for optimizing computing power node scheduling. The method includes quantifying the allocable computing power of each node in a computing power cluster; obtaining a set of nodes that meet the computing power threshold requirements based on the quantified cluster computing power matrix and a preset computing power threshold matrix; and using a greedy binary search algorithm based on the node set and target business requirements to find a subset of nodes corresponding to each type of computing power that meets the target business requirements, thereby optimizing computing power node scheduling. This method can quickly filter the remaining computing power matrix using the computing power quantification method and calculate each node that meets each type of computing power requirement based on the greedy binary search algorithm, reducing the computational cost and algorithm complexity of computing power node scheduling.