6results about How to "Strong generalization ability" patented technology

The invention provides a breast cancer adjuvant therapy pathology complete remission prediction method and electronic device.The breast cancer adjuvant therapy pathology complete remission prediction method comprises the steps that at the first stage, based on multi-time-point and multi-parameter MRI images of a patient before and after adjuvant therapy, the breast cancer adjuvant therapy pathology complete remission prediction result is obtained; training an image feature encoder with a tumor dynamic change identification capability through a dynamic supervision pre-training mode; and a second stage of constructing a multi-modal deep fusion network based on the image feature encoder to integrate the image features, radiomics features and clinical pathological features of the patient, and outputting a prediction result of complete pathology remission, the radiomics features being extracted from the MRI image. And the purposes of high precision and strong generalization capability are achieved.

This invention provides a method for modeling a cylinder motion system based on a physical information neural network, comprising: acquiring time-series data of the cylinder motion system, including time-series control quantities and system state quantities; constructing a physical information neural network model with a dynamic feature input layer and a physical parameter input layer, receiving the time-series control quantities and physical parameter vectors respectively; performing a nonlinear transformation on the physical parameter vectors through a physical parameter encoding network to obtain encoded physical feature vectors; fusing the time-series control quantities and physical feature vectors to form a joint input vector; inputting the joint input vector into the backbone neural network, synchronously predicting the system state quantities through multi-layer nonlinear transformations; constructing a composite loss function including a data loss term and a physical loss term; training the model using time-series data, updating the model parameters by minimizing the composite loss function until the model converges, thereby achieving high-precision modeling using only single motion data.

This invention discloses a navigation method and system for offshore wind power booster stations based on multimodal information interaction, belonging to the field of artificial intelligence and robot navigation technology. It achieves visual-language modality alignment through the CLIP model, constructing an end-to-end navigation parameter generation model. This model can adaptively generate AGV motion parameters based on natural language commands and scene images, enabling efficient and accurate inspection of offshore wind power booster stations. By establishing a multimodal dataset and associating language commands with AGV motion parameters, this invention solves the error accumulation problem caused by multi-stage processing in traditional methods, improving navigation accuracy and adaptability. Furthermore, this invention employs a multi-task loss function optimization model, including contrastive loss and motion parameter regression loss, enabling the AGV to adaptively adjust motion parameters according to environmental changes, thus improving the efficiency and safety of offshore wind power booster station inspections.

The application provides a breast cancer adjuvant therapy pathological complete remission prediction method and an electronic device, wherein the breast cancer adjuvant therapy pathological complete remission prediction method comprises the following steps: in the first stage, based on the multi-time point and multi-parameter MRI images of a patient before and after adjuvant therapy, an image feature encoder with tumor dynamic change recognition ability is trained through a dynamic supervised pre-training mode; in the second stage, a multi-modal deep fusion network is constructed based on the image feature encoder, so as to integrate image features, radiomics features and clinical pathological features of the patient, and output a prediction result of pathological complete remission, and the radiomics features are extracted from the MRI images. The purpose of high precision and strong generalization ability is achieved.

The invention discloses a fast regional self-adaptive ACM method based on meta-learning, and belongs to the technical field of low-orbit satellite communication. Aiming at the technical problems of dependence on a large amount of local data, long debugging period, unstable performance and the like during cross-region deployment of the existing adaptive coding modulation technology, the method comprises the following steps of: constructing a meta-training task set covering various global climate characteristics, and training to obtain a meta-initial model with strong generalization ability; when a ground station is deployed in a new region, an optimized localized ACM strategy can be quickly adapted through several steps of gradient updating by using a very small amount of initial communication data collected by the station. Simulation results show that the method only needs 30 samples and 2-minute fine tuning to achieve the approximate optimal performance, the spectrum efficiency is improved by 35.8% compared with a traditional fixed threshold value method, the sample demand is reduced by 99% and the deployment time is shortened by more than 98% compared with a supervised learning method, the regional self-adaption problem in global rapid deployment of the satellite communication system is effectively solved, and the method has good application prospects. And the operation cost is obviously reduced.

The invention relates to a skeleton point-based shielding scene behavior recognition method and system, and the method comprises the steps: firstly extracting time sequence skeleton point data through a human body detection and posture estimation algorithm, and constructing five types of structured inputs through a mask module; then, the multi-stream GCN learns local spatio-temporal features of different body areas respectively; in the fusion process, the model can adaptively reinforce the feature contribution of a region which is highly related to the current action or is not shielded, and meanwhile, the interference caused by a weak correlation region and a missing part is effectively inhibited. Meanwhile, three types of regularization items of diversity, sparsity and consistency are designed, so that the weights are kept different among the types, are kept compressed on branches and are kept stable in the types. The scheme provided by the invention is obviously superior to the existing method in complex scenes such as random shielding, arm shielding, leg shielding and the like, has high robustness and strong generalization ability, and is suitable for the fields of intelligent monitoring, human-computer interaction, edge calculation behavior analysis and the like.