4results about How to "With technical effect" patented technology

The application belongs to the technical field of signal transmission, and discloses a method for eliminating analog signal transmission error through digital compensation, which specifically comprises the following steps: in addition to the original signal transmitting circuit part, a signal detecting part composed of MCU, ADC and a communication module is arranged at the signal transmitting end; a signal detecting part composed of MCU and ADC communication module and a control part capable of controlling the signal at the output end to be offset and gain varied are arranged at the signal receiving end; the signal at the receiving end is collected by the MCU to compare with the data of the transmitting end communication module, and the difference between the two data is calculated to obtain the correction value of the amplitude and offset; the gain and offset of the signal are changed by controlling the offset and gain circuit part at the receiving end, so that the transmission error is eliminated, and the gain and offset control method is realized by using DAC, PGA and other components; the deviation of the frequency response can also be calculated according to the requirement, and is corrected by the gain and offset control circuit part.

This invention belongs to the field of artificial intelligence algorithm technology and discloses an intelligent transfer adversarial method and system for electromagnetic signal recognition under incomplete information. First, multiple models of alternative target networks with incomplete information are constructed in parallel, and these alternative models are trained using an evolutionary strategy based on a prior knowledge matrix. Then, an ensemble network model is generated based on the trained alternative models, and a fully connected layer and a layer with feature-level weighted fusion are designed. Finally, an optimized adversarial method for electromagnetic spatial signal recognition tasks is designed, generating adversarial samples under the ensemble model, and transferring these adversarial samples to the target network to achieve adversarial capabilities against the target network. This invention can effectively achieve adversarial capabilities against the target network when the target network information is unknown, and it still exhibits good transfer adversarial performance under the same signal recognition task and different unknown target network conditions.

The application belongs to the field of computer vision, and discloses a weakly supervised semantic segmentation method, system, device and medium based on random combination, which comprises the following steps: training classification networks N1, N2 and N3 respectively by using a training data set, a slice training data set and a slice training data set randomly combined, so that each network can extract different active regions in the picture, and the learning results of the other two networks are learned by using the mutual supervision training mode; finally, the prediction results of the three networks are combined to obtain the final semantic segmentation result, which is used as a semantic segmentation training data set to train a semantic segmentation model to predict the final semantic segmentation result. The application effectively utilizes the different perception areas of the network for the randomly combined slice pictures, and utilizes the different classification networks to perceive the categories of the same picture, thereby improving the semantic segmentation ability and prediction accuracy of the semantic segmentation model through the semantic segmentation data set obtained by combining the results of the three classification networks.

The application belongs to the technical field of natural language processing, and discloses a federated learning optimization method and system for entity extraction, a device and a terminal. In the shared data meta information transmission stage, the server sends a shared data request to each selected client participating in federated learning, each client calculates shared data set meta information, and uploads the meta information to the server. In the approximate IID entity annotation data construction stage, the server constructs an approximate IID entity annotation data index set according to the meta information uploaded by the client, and requests the corresponding client for data corresponding to the index set. In the model training stage, the server is regarded as a client participating in federated learning training, and the approximate IID entity annotation data set is used for model training to obtain an approximate centralized model W IID The application reduces the difference between the model parameter spaces of the clients, improves the accuracy and convergence speed of the global model, and reduces the training communication cost.