11results about How to "Short training time" patented technology

The application provides a wheelchair control system based on motor imagery electroencephalogram signals and surface electromyogram signals, and relates to the technical field of brain-computer control, comprising a data acquisition module, a system mode selection module, a signal processing module and a decision module; the data acquisition module is used for acquiring physiological signals of a wheelchair user and sending the physiological signals to the system mode selection module; the system mode selection module is used for selecting a processing mode of the physiological signals; the signal processing module is used for pre-processing the physiological signals and extracting signal features for identification; the signal processing module sends an identification result to the decision module; the decision module is used for fusing multiple instruction information and converting the instruction information into a final wheelchair control instruction, and outputting the control instruction to the wheelchair. The application can improve the operation accuracy of the control system, reduce the fatigue of the user when performing motor imagery behavior, improve the user experience and the application performance of the wheelchair control system, and expand the user range of the brain-controlled wheelchair.

The invention discloses a social network link prediction-oriented time sequence diagram network parallel training acceleration method. The method comprises the following steps: firstly, calculating a redundancy score and an old score for each user interaction edge in a training set, and calculating a comprehensive information loss score according to the redundancy score and the old score; secondly, according to a preset core set retention proportion alpha, selecting all user interaction side comprehensive information loss scores and upper alpha quantiles of repetitiveness as threshold values, and discarding user interaction with the comprehensive information loss scores lower than the threshold values, so that a simplified core set is obtained through single-time preprocessing; then carrying out adaptive batch division on the obtained core set, and dynamically determining an acceptable maximum user interaction number in each batch according to a redundancy and old comprehensive information loss score; and finally, training the time sequence diagram neural network based on the divided batches to realize social network link prediction. The method not only improves the training efficiency, but also greatly improves the precision of the model.

The application provides a wall-penetrating target behavior recognition method based on channel state information, which comprises the following steps: preprocessing the collected CSI signals; performing PCA data dimension reduction on the preprocessed CSI signals, removing redundant signals and irrelevant information, and extracting optimal subcarriers; performing first-order difference processing on the signals extracted after PCA dimension reduction, and then using a method based on a buffer sliding window to segment effective feature signal segments; converting the effective feature signal segments into feature images with time-frequency domain features through STFT, and inputting the feature images into a pre-trained SE-ResNet18 convolutional neural network for behavior recognition classification. The method can penetrate the wall to realize the behavior recognition of the human target behind the wall. Compared with the traditional deep learning network, the method adopts a small sample transfer learning method combined with a pre-trained model, and the recognition accuracy can reach 91.67% under the conditions of fewer training times, fewer iteration times and shorter training time, so that the behavior recognition classification task can be effectively completed.

This invention relates to a prediction-based robotic arm path planning method, apparatus, electronic device, and medium. The method involves real-time acquisition of hand position data and storage in a raw dataset, which also includes the current position data of the robotic arm, target position data, and actual obstacle position data. Based on the raw dataset, an Informer model is trained to obtain model weights and output predicted hand position data. The predicted hand position is used as a virtual obstacle, and a predicted repulsive potential field is obtained based on the predicted hand position data. Based on the predicted repulsive potential field, the repulsive potential field of the actual obstacle, and the gravitational potential field of the target position, the current resultant force is calculated to optimize the robotic arm's trajectory, control the robotic arm's movement, and perform sorting. The target position is the endpoint of the robotic arm's movement. Compared with existing technologies, this invention has advantages such as higher prediction accuracy, faster calculation speed, and higher human-machine collaboration efficiency.

This invention relates to a remote sensing extraction method for pear orchard areas based on the Re-UNet model, which overcomes the shortcomings of inaccurate classification results and low efficiency in pear orchard area extraction from remote sensing images compared with existing technologies. The invention includes the following steps: acquiring a remote sensing image dataset; constructing the Re-UNet pear orchard area extraction model; training the Re-UNet pear orchard area extraction model; acquiring and preprocessing the remote sensing images of the pear orchard areas to be segmented; and obtaining the remote sensing extraction results for the pear orchard areas. Based on the UNet semantic segmentation model, this invention solves the overfitting problem that easily occurs in small datasets. It also incorporates spatial and channel attention mechanisms and a residual module, further enhancing the feature transfer and cumulative integration characteristics of pear orchard areas in high-resolution remote sensing images, effectively reducing the "salt and pepper" phenomenon and misclassification, and improving the overall segmentation accuracy.

The present disclosure discloses a training method, device and system of a large model-based risk identification system, relating to the technical field of artificial intelligence such as machine learning and natural language processing. The specific implementation scheme comprises: adopting a first training sample, fine-tuning a pre-trained large language model, so that the large language model learns to identify the intent information of the query sequence of the user; adopting a second training sample, training a risk identification system including the fine-tuned large language model and a risk identification model, so that the risk identification system learns to identify the risk information of the user; wherein during the training process, only the parameters of the fine-tuning layer in the fine-tuned large language model are adjusted.

The application provides a medical image segmentation method and system based on a CNN-Transformer parallel encoder, and relates to the technical field of medical image segmentation, and the specific steps comprise: inputting a standardized medical image into a parallel encoder, extracting local texture and global semantic feature maps, and aligning the resolution and spatial position through a spatial correlation matrix; screening lesion features based on an anatomical structure prior feature set, dynamically allocating weights to obtain fusion features; taking a lesion gold standard mask as a label, obtaining an initial lesion probability feature map through a decoder, and calculating a bias value; iteratively optimizing a parameter combination to output an optimal configuration parameter combination, and obtaining a final lesion probability feature map, which is classified and activated, thresholded, and outputted as a segmentation mask. The application effectively avoids the local optimal solution of parameter optimization, combines a bias value threshold-driven rapid screening mechanism, fine-tunes parameters for different modal images without full retraining, and effectively breaks through the bottleneck of poor adaptability of the prior art.

The invention provides a WOA-GRU-based hydrogen atomic clock fixed parameter control method and device. The method comprises the following steps: acquiring and preprocessing a historical operation parameter time sequence of a hydrogen atomic clock; constructing a parameter time sequence prediction model taking a gating circulation unit as a core; performing global automatic optimization on the hidden layer size, the discard rate and the learning rate of the GRU network by adopting a whale optimization algorithm; training a GRU model by using the optimized hyper-parameters; and finally, obtaining a group of collaborative optimal fixed parameter values capable of enabling the hydrogen atomic clock to stably operate for a long time by utilizing the trained model in a rolling prediction mode. The GRU network with a more concise structure is adopted to replace a traditional LSTM network, intelligent global search of WOA is combined, the training efficiency and the model stability are remarkably improved while the prediction precision is guaranteed, and the method is particularly suitable for a hydrogen atomic clock control scene with limited data volume and high parameter noise and has good application prospects. Accurate, stable and intelligent fixed control of internal parameters of the hydrogen atomic clock is realized.

The invention discloses a wind turbine planetary gearbox fault diagnosis method based on multi-modal feature fusion, and the method comprises the four steps: S1, collecting a vibration signal, and carrying out the noise suppression through the combination of variational mode decomposition and wavelet threshold denoising; s2, classifying signal types by using a random forest model through extracting time-frequency domain features; s3, dynamically extracting physical characteristics according to the signal type, and combining with CNN time-frequency graph characteristics for fusion; s4, carrying out fault classification by adopting a Bayesian optimized LightGBM model; through the multi-modal feature fusion and intelligent classification technology, the fault diagnosis precision and real-time performance under the complex working condition are remarkably improved, the problems that a traditional method is high in noise sensitivity and insufficient in feature expression capacity are effectively solved, and reliable guarantee is provided for intelligent operation and maintenance of the planetary gearbox of the wind turbine.

The application discloses a kind of based on twin network's smart contract reentrant vulnerability detection method, comprising, collate original smart contract sample, form the initial sample set, and to the initial sample set is handled, generate the first time processing sample set;Through Word2vec model to the first time processing sample set embedding vector and matrix composition, obtain the second time processing sample set;Let the positive sample and negative sample quantity in the second time processing sample set be consistent, obtain the third time processing sample set, and utilize the third time processing sample set to make dataset;Data set is respectively input neural network A and neural network B, to extract feature A and feature B, and calculate the similarity between feature A and feature B;The similarity of feature A and feature B is compared with threshold value respectively, complete detection;The application can accurately detect smart contract reentrant vulnerability, and expand the size of dataset.

The application discloses a kind of video behavior dual-flow identification method, system, equipment and medium, comprising: the one-dimensional image block vector of the video to be identified is obtained by processing the video to be identified, and is divided, and visual flow data set and action flow data set are obtained;Position coding is carried out to two-dimensional image frame and is embedded into the one-dimensional image block sequence in visual flow data set, to obtain the visual flow data set containing position coding information;The visual flow data set containing position coding information is extracted using the first space-time separation Transformer network model unit, to obtain visual flow feature extraction result;Action flow data set is extracted using the second space-time separation Transformer network model unit, to obtain action flow feature extraction result;Visual flow feature extraction result and action flow feature extraction result are integrated, to obtain the dual-flow identification result of video;The application has higher identification efficiency, meets the identification requirement of high-resolution video.