7results about How to "Improve feature learning ability" patented technology

The application relates to the technical field of microwave remote sensing, and proposes an adaptive wet troposphere path delay inversion method and system fusing wind speed overlapping partition modeling, multi-scale collaborative sample balancing and model soft fusion. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data to obtain to-be-inverted samples; according to a preset threshold, the full-range wind speed of the to-be-inverted samples is divided into a plurality of wind speed subintervals with overlapping transition zones; the to-be-inverted samples are input into trained inversion submodels, the output results of the inversion submodels are weighted and summed according to final fusion weights, and finally, wet troposphere path delay inversion results are obtained; when abnormal wind speed or invalid submodel prediction output is detected, the output result of a pre-trained global unified model is taken as the final inversion result. While guaranteeing overall inversion precision and stability, the application can significantly suppress systematic deviation under sea conditions of extremely low and extremely high wind speed.

An automatic extraction method for complex event patterns based on BiGRU-CNN and Gaussian Mixture Model (GMM) includes: preprocessing the raw time series data from sensors to obtain structured time series feature data; constructing a BiGRU-CNN feature extraction model; supervised training of the BiGRU-CNN feature extraction model; extracting bottleneck features from the unlabeled sample set and generating pseudo-labels with confidence through Gaussian Mixture Model clustering; merging the high-confidence pseudo-label sample set with the labeled sample set to form an expanded training set for retraining the BiGRU-CNN feature extraction model; automatically generating CEP event pattern rules; and automatically outputting complex event detection results. This invention solves the problem of insufficient feature representation capabilities in traditional models, significantly reducing the computational overhead of subsequent GMM clustering and CEP rule matching, allowing the model to possess both strong feature learning capabilities and maintain lightweight computational characteristics, meeting the computational resource requirements of different scenarios.

This application discloses a method, system, device, and medium for long-term power consumption prediction based on an attention mechanism and frequency enhancement. A deep network structure model is constructed based on the attention mechanism and frequency enhancement. The deep network structure model includes an encoder, a frequency-enhanced hybrid attention module, and a decoder connected sequentially. The frequency-enhanced hybrid attention module includes a self-attention module and a frequency-enhanced channel attention module. Power consumption is predicted based on this deep network structure model. The power consumption prediction method provided by this invention incorporates an attention mechanism into the Transformer neural network to address the characteristics of power consumption data, making the model's prediction results more stable. The frequency-enhanced hybrid attention module enhances the model's noise resistance in the frequency domain, thereby improving the model's feature learning ability and the accuracy of long-term prediction of sequence data. An iterative prediction strategy is adopted, making multiple predictions to achieve long-term prediction.

A transmission tower defect identification method based on an improved YOLOv11 algorithm belongs to the technical field of defect identification, and designs an improved YOLOv11 network model, firstly designs and adds a HorNet high-order interactive recursive gating network module in a Backbone network, and through gating convolution and recursive structures, fuses information of different widths, and improves the capability of accurately identifying target defects; secondly, a Conv standard convolution module in the Backbone network is replaced by a GhostConv ghost convolution module, so that the calculation amount and the parameter amount are greatly reduced; and finally, a RepViTblock re-parameterization visual transformation module is added in the Neck neck network design, each pixel position in the input image is mapped to a vector space, and the precision and efficiency of target detection are improved. The method is high in identification accuracy and high in identification efficiency, and fully guarantees the safe and stable operation of the power transmission line.

This invention discloses a method, system, and device for estimating the area of ​​the eye muscles and the backfat thickness of live pigs based on deep learning. The method includes: acquiring and constructing a dataset of live pig ultrasound images; selecting regions of interest (ROIs) in the ultrasound images, preprocessing them, and standardizing the input size; inputting the preprocessed ultrasound images into a ReAMS-UNet neural network for semantic segmentation of the eye muscle region; filtering the image contours to remove false positives and false negatives in the segmentation results; determining the upper and lower boundaries of the backfat thickness through image binarization; calculating the eye muscle area and backfat thickness, and outputting the calculation results. Based on a large-scale, highly diverse dataset of pig ultrasound images, this invention utilizes ReAMS-UNet to integrate residual learning for stable training, a hybrid attention mechanism for adaptive feature optimization, multi-scale fusion for contextual and spatial information fusion, and auxiliary supervision for enhanced gradient propagation. It achieves high segmentation accuracy, fast inference speed, accurate trait estimation, and results that reflect true carcass traits. The process is automated and suitable for high-throughput analysis applications.

This application provides a temporal sampling method and apparatus, and a speech recognition method and apparatus, relating to the field of artificial intelligence technology. The temporal sampling method includes: acquiring target speech data; and determining the temporal sampling result of the target speech data using a sampling model based on the target speech data. The sampling model includes an l-layer downsampling network and an l-layer upsampling network. Each downsampling network layer includes parallel skip modules and downsampling modules, and each upsampling network layer includes parallel skip modules and upsampling modules. The sampling model is used to match the optimal sampling path in the sampling model for the target speech data based on the skip modules and downsampling modules of each downsampling network layer and the skip modules and upsampling modules of each upsampling network layer. l is a positive integer greater than 1. The temporal sampling method in this application can improve the learning ability of acoustic features of speech data and the adaptability of modeling granularity.

The application relates to a double-layer data-driven ship trajectory prediction method and system based on a GRU network, and relates to the technical field of trajectory prediction.The application mainly obtains an observation sequence X with a length of g from an AIS data set g , inputs the observation sequence X g into a first machine learning model to obtain an intermediate feature state output with a length of l, simultaneously inputs the observation sequence X g into a second machine learning model to obtain an intermediate feature state output with a length of l, splices the intermediate feature states and to form composite training data as the input of a third machine learning model, and obtains a prediction result.The application solves the problems of insufficient multi-dimensional feature data mining and low prediction accuracy in the prior art.