7results about How to "Reduce model size" patented technology

The invention belongs to the technical field of image recognition, discloses a multi-category industrial image anomaly detection method and device based on a three-tower knowledge distillation architecture, and designs a three-tower network architecture covering a local context student network, a global context student network and a pre-trained teacher network. For multi-class normal industrial images used for training, features output by two student networks are restrained to be consistent with a teacher network through feature distillation loss, and output features between the two student networks using local and global context region prediction center features are restrained to be consistent through feature alignment loss. The ability of learning multi-class normal image feature representation by the model is improved, and it is guaranteed that the model performs high-precision detection and positioning on multi-class abnormal samples in a combined anomaly detection scene; the method has high real-time performance and small model scale, and can be applied to industrial anomaly detection scenes with high real-time performance requirements.

The present invention discloses a method and system for super-resolution reconstruction and recognition of faces used in video surveillance, and belongs to the field of deep learning super-resolution reconstruction of faces. The method of the present invention first obtains the original video stream collected in real time and preprocesses it, then locates the face position of the preprocessed video stream in the target area, then inputs the low-resolution face image into the trained face super-resolution reconstruction network for reconstruction, outputs the reconstructed super-resolution face image, and finally performs face recognition on the reconstructed super-resolution face image, outputs the face recognition result and visualizes it, and stores the recognized personnel for later auditing. The present invention aims to efficiently and accurately restore high-quality face images from low-quality face images through the powerful learning ability of the face super-resolution reconstruction network, thereby providing a clearer and more reliable input for the face recognition system, and improving the overall recognition accuracy and robustness.

The invention discloses a multi-component intelligent fault diagnosis method and system for a low-rotating-speed mechanical transmission system, belongs to the field of mechanical fault diagnosis, and adopts improved robust local binarization operation to convert a vibration signal into a feature enhanced gray texture image to highlight component feature information in original vibration data. A vibration gray texture image noise reduction model based on an auto-encoder is constructed, and the model has a good noise reduction effect on different degrees of noise. By optimizing the width of the convolutional neural network model, the light weight of the fault diagnosis model is realized, and the fault diagnosis efficiency is improved while the fault diagnosis accuracy is ensured. According to the method, the problem of low accuracy of a fault diagnosis model caused by weak vibration signal energy and noise interference of a traditional mechanical system in a low-rotating-speed and noise environment is solved, and an effective solution is provided for intelligent fault diagnosis of each part of the mechanical transmission system in the low-rotating-speed and noise environment.

The invention discloses a nuclear reactor neutron transport calculation method and device, and the method comprises the steps: carrying out the space merging of a fine network multi-group neutron transport model in each time of source iteration in a process of calculating the fine network multi-group neutron transport model through a source iteration mode, and obtaining a coarse network multi-group neutron transport model; correcting the first coarse mesh multi-group neutron standard flux through the coarse mesh multi-group correction factor to obtain a second coarse mesh multi-group neutron standard flux, and solving the coarse mesh multi-group neutron transport model to obtain a third coarse mesh multi-group neutron standard flux; performing iterative solution on the coarse mesh multi-group neutron transport model to obtain a target coarse mesh multi-group neutron standard flux so as to determine a fine mesh multi-group correction factor; and correcting the initial fine-net multi-group neutron standard flux through the fine-net multi-group correction factor to obtain a target fine-net multi-group neutron standard flux, and calculating the fine-net multi-group neutron transport model through the target fine-net multi-group neutron standard flux to improve the efficiency of nuclear reactor neutron transport calculation.

This application discloses a method, system, device, and storage medium for intelligent simulation modeling of PCBs for new energy charging piles. The method includes: acquiring target modeling region data; determining thermal conductivity contribution values; classifying the thermal via array according to the thermal conductivity contribution values ​​to obtain at least an explicit modeling level, a cluster equivalent modeling level, and a peripheral diffusion modeling level; establishing explicit via models for the thermal vias in the explicit modeling level; dividing the thermal vias in the cluster equivalent modeling level into at least one via cluster according to a preset aggregation rule, and establishing an equivalent thermal bridge model for at least one via cluster; establishing a continuous equivalent diffusion model for the thermal vias in the peripheral diffusion modeling level; coupling the explicit via model, the equivalent thermal bridge model, and the continuous equivalent diffusion model to obtain a local hybrid simulation sub-model of the target modeling region; and embedding the local hybrid simulation sub-model into the charging pile PCB simulation model to perform thermal simulation.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

This disclosure relates to an equivalent modeling method, an electrical component selection method and apparatus, equipment and medium. The equivalent modeling method includes: obtaining the electrical component parameters of electrical components in an electrical device; determining a three-dimensional constant cross-sectional configuration for replacing the electrical component and a three-dimensional joint configuration for the electrical component's joints, and equivalently replacing the electrical component to obtain a simplified electrical component model; determining the equivalent parameters of the electrical component based on the electrical component parameters and the simplified electrical component model, and assigning the equivalent parameters to the simplified electrical component model to obtain an equivalent electrical component model; and establishing a thermoelectric coupling simulation equivalent model of the electrical device based on the equivalent electrical component model. This effectively improves the efficiency of equivalent modeling for thermoelectric coupling simulation of electrical devices.