9results about How to "Guaranteed generalization ability" patented technology

This invention relates to the field of scene text segmentation, specifically a scene text segmentation method based on an improved SAM visual segmentation large model. Based on the SAM visual large model, this invention extracts text content perception features through an image content perception module and text edge perception features through a text edge perception module. Furthermore, the text feature fusion module extracts and calculates text edge perception feature maps, which are then added to the vectors requiring attention calculation before each self-attention calculation in the SAM encoder. This improves the accuracy of SAM in text segmentation and shortens the model training time while maintaining generalization.

This invention discloses a method for constructing a parameter extraction model for active ingredients in traditional Chinese and Tibetan medicines. The method involves acquiring multi-source sample data of traditional Chinese and Tibetan medicines and constructing key derived features from the preprocessed multi-source sample data. A random forest model is used to generate a corresponding importance score for each key derived feature, forming a feature importance vector. The feature importance vector and the key derived features are weighted and summed to obtain a fusion feature, and the variance and correlation coefficient matrices corresponding to the fusion feature are calculated. The average values ​​of the variance and correlation coefficient matrices are weighted and summed to obtain a comprehensive score. The depth and width of the parameter extraction model to be trained are determined based on the comprehensive score. The number of fully connected modules and neurons in the parameter extraction model is determined by the depth and width to obtain the corresponding target architecture. The parameter extraction model under the target architecture is trained using the fusion features and extracted parameter data to obtain a trained parameter extraction model.

The application discloses an electrochemical noise corrosion state identification and early warning method and system, and belongs to the technical field of corrosion monitoring and early warning. The method collects the potential noise signal and the current noise signal of the target component through an electrochemical workstation and calculates the noise resistance, adopts db4 wavelet for multi-layer wavelet decomposition and threshold denoising processing, extracts the kurtosis feature, the skewness feature and the wavelet coefficient feature, and constructs a corrosion state identification and early warning data set after feature screening; a fusion model including an LSTM time sequence feature extraction layer and an Attention attention weight distribution layer is constructed for training, and a corrosion state identification model is obtained; the real-time collected and processed feature parameters are input into the model for identification, the proportion of a specific corrosion state in a preset time period is counted, and a blue, orange or red graded early warning is triggered. The application realizes high-precision identification and accurate graded early warning of the corrosion state, the identification accuracy is above 85%, and reliable technical support is provided for industrial component corrosion protection.

The invention relates to a multi-target parameter self-adaptive noninvasive tibial nerve stimulation method and system, and aims to solve the problem that an existing nerve regulation device cannot perform on-demand treatment according to dynamic change of symptoms of a patient. The induced foot sole neural signals are collected, and the F wave peak value amplitude and the incubation period meeting the stability condition are extracted in a preset time window to serve as real-time feedback signals. And establishing an individualized target interval based on the F-wave characteristic, calculating a dynamic deviation of a current F-wave peak value amplitude relative to the interval, and iteratively adjusting parameters such as stimulation intensity, frequency, pulse width and a double-target stimulation intensity proportion by taking a mean square error of the dynamic deviation as a loss function and adopting a gradient descent method until the F-wave characteristic is converged into the target interval. The dynamic change of the bladder function is self-adapted, and multi-target closed-loop self-adaptive stimulation of the tibial nerve is realized.

This invention proposes a test-time optimized method for super-resolution reconstruction of UAV aerial infrared images, belonging to the fields of image processing and computer vision. A test-time optimization framework based on unfamiliar infrared degradation perception is constructed: a teacher-student model fusion mechanism is introduced to synthesize degraded reference images, and the optimization amplitude is dynamically controlled to prevent catastrophic forgetting; a frequency domain degradation estimation network calculates the blur kernel and noise parameters to generate pseudo-label images to guide model optimization; a multi-scale linear layer parameter optimization strategy is designed, updating only linear layer parameters to achieve rapid adaptation; and a joint constraint optimization process using reconstruction loss and feature consistency loss ensures pixel-level reconstruction accuracy and the preservation of pre-trained feature extraction capabilities. This method effectively solves the problem of super-resolution quality degradation caused by unknown degradation conditions in UAV aerial photography scenarios, significantly improves generalization ability under complex degradation migration conditions, and is suitable for applications such as nighttime search and rescue, security patrol, and agricultural assessment.

The application discloses a hot shock cooling rock rotary cutting damage prediction method, solves the technical problem that the prior art cannot realize high-precision and high-generalization prediction of hot shock cooling rock rotary cutting damage parameters while ensuring physical mechanism consistency. The method first acquires hot shock cooling rock rotary cutting sample data under different initial temperature conditions, constructs a hot shock cooling rock rotary cutting failure criterion RCFC considering thermal damage evolution and temperature-dependent fracture mechanics characteristics, and then embeds the RCFC as a hard physical constraint into a physical information neural network model, and outputs the prediction results of key damage parameters such as rotary cutting energy and rotary speed after training. The application realizes the deep fusion of data driving and rock cutting damage physical mechanism, significantly improves the prediction accuracy and physical consistency, and still has good robustness under the condition of sparse data, and can provide technical support for drilling parameter design and construction safety control of deep high-temperature rock mass engineering.

The present application relates to the technical field of detection of Dendrobium polysaccharide content, and discloses a system and method for rapid nondestructive detection of Dendrobium polysaccharide content, which comprises the following steps: collecting spectral reflection data in the 600 nm-1700 nm band by using a FLA6800 spectrometer, and performing data preprocessing, feature extraction, machine learning model training and prediction to rapidly and accurately predict the Dendrobium polysaccharide content, and finally generating a visual report and performing grading. The present application combines near-infrared spectroscopy technology with a machine learning model to realize efficient, accurate and nondestructive detection of the Dendrobium polysaccharide content, significantly improves the detection speed and accuracy, and provides real-time and reliable quality control basis.

This invention discloses a data forgetting method for dynamically allocating pruning ratios for heterogeneous resources in industrial equipment. The method includes: when a server detects a target device failure, the server allocates an adaptive pruning ratio to each industrial device based on its resource information; each industrial device prunes its local model according to the allocated pruning ratio, removing parameters related to the abnormal data of the target device, thus forgetting the target data; other industrial devices update their pruned local models, obtaining updated local models and uploading them to the server; the server aggregates the updated local models to obtain a global model that guarantees generalization performance and forgets the abnormal data of the target device. This invention can adaptively allocate appropriate pruning ratios to heterogeneous computing and communication resources, thereby reducing the waiting time of all devices, improving system resource utilization, and enhancing the execution efficiency of the forgetting task.

This invention discloses a method for intraday dynamic prediction of cyanobacterial blooms based on meteorological and water quality synergy, relating to the field of water environment monitoring. This method, based on satellite data, identifies hourly cyanobacterial bloom area results images using a multi-index consensus method and calculates the actual coverage area of ​​the cyanobacterial bloom. Using joint data of meteorological factors and water quality parameters as input and the actual coverage area of ​​the cyanobacterial bloom as output, a BP neural network model is constructed and optimized. The optimized model is then used to predict the estimated coverage area of ​​the cyanobacterial bloom in the future hourly, and the efficiency coefficient between the estimated and actual coverage areas of the cyanobacterial bloom is calculated. This invention fully utilizes the high temporal resolution advantage of geostationary meteorological satellites to identify cyanobacterial blooms and establish a prediction model, achieving intraday prediction of the cyanobacterial bloom area.