4results about How to "Reduce data dependence" patented technology

The application provides an industrial equipment fault diagnosis model construction method and related equipment, and relates to the technical field of industrial equipment fault diagnosis. The method comprises the following steps: obtaining a physical mechanism model of the industrial equipment; the physical mechanism model comprises a physical constraint equation; constructing a deep learning network; the deep learning network comprises a composite loss function constructed according to the physical constraint equation; collecting full-time series state data of the industrial equipment during operation; the full-time series state data comprises specified time series state data collected when the industrial equipment is in a healthy state; based on the full-time series state data, the deep learning network is trained for parameter optimization to obtain a final available industrial equipment fault diagnosis model. The method of the application aims to solve the problems of weak early fault identification ability, poor interpretability, lack of physical meaning and strong data dependence in the existing industrial equipment fault diagnosis technology, and effectively improves the intelligent level and operation and maintenance efficiency of the industrial equipment fault diagnosis.

This invention relates to the interdisciplinary fields of biomedical engineering, artificial intelligence, and neuromodulation, and provides a cognitive fatigue assessment and electrical stimulation intervention system based on a combined EEG-behavioral AI model. The system includes: a data acquisition and preprocessing module for simultaneously acquiring and preprocessing the user's EEG signals and behavioral data; a real-time fatigue assessment module for extracting and fusing features from the preprocessed EEG signals and behavioral data using the combined AI model, outputting the user's real-time fatigue assessment results; the combined AI model is constructed based on the Transduction Information Maximization (TIM) algorithm to achieve cognitive fatigue assessment in scenarios with few samples; and a closed-loop electrical stimulation intervention module for dynamically adjusting transcranial electrical stimulation parameters and performing targeted intervention based on the real-time fatigue assessment results. This invention achieves real-time, accurate detection and personalized targeted intervention of cognitive fatigue states through multimodal data fusion and closed-loop intervention.

This invention discloses a remote sensing image target detection method based on single-sample contrast feature changes. Belonging to the field of target detection technology, the method includes: acquiring an original remote sensing image set; preprocessing the original remote sensing image set to obtain an augmented image set; establishing a multi-head contrastive learning (MBR) training feature encoding network; inputting the augmented image set into the MBR training feature encoding network to obtain a trained feature extraction network; acquiring a remote sensing image to be tested and a conditional image; constructing a conditional target detection framework, wherein the conditional target detection framework includes the trained feature extraction network; and inputting the remote sensing image to be tested and the conditional image into the conditional target detection framework to obtain several targets in the remote sensing image that share the same interest category as the conditional image. This invention can extract same-category and rotation-invariant features from the remote sensing image and the conditional image, reducing the model's data dependency and improving its transferability.

This invention relates to a method for calculating the residual magnetism of a converter transformer based on inrush current, belonging to the field of power systems. Based on obtaining the deep saturation excitation characteristic curve of the converter transformer, this method uses the per-unit value of the effective inrush current generated by no-load closing at the peak voltage of the converter transformer as a parameter to calculate the theoretical per-unit value of magnetic flux. Using the starting point for calculating the per-unit value of residual magnetism, the ratio of the rated voltages of the primary and secondary sides of the converter transformer, and a weighting constant, a superposition weighting formula for the residual magnetism of the converter transformer and the rated voltage magnetic flux is determined. Finally, the residual magnetism of the converter transformer core is calculated using the calculated per-unit value of the theoretical magnetic flux corresponding to the inrush current and the superposition weighting formula for the residual magnetism and the rated voltage magnetic flux. The method proposed in this invention is simple to implement, highly operable, and has minimal impact on the converter transformer, providing an effective verification strategy for calculating the residual magnetism of converter transformers and evaluating the effect of residual magnetism elimination.