4 results about "Simulation noise" patented technology

The application discloses a 12-lead electrocardiosignal denoising method and system based on a dual generative adversarial network, which comprises the following steps: obtaining time-adjacent clean electrocardiosignal segments and noisy electrocardiosignal segments; constructing a noise generative adversarial network, wherein the generator takes a random noise vector as input and simulates noise as output, the discriminator takes the noisy electrocardiosignal segment as a positive sample and a synthetic signal composed of the clean electrocardiosignal segment and the simulated noise as a negative sample for adversarial training, and a noise generator snapshot library is generated; performing dynamic noise injection on the clean electrocardiosignal segment to obtain 'noisy-clean' paired data; constructing a denoising network comprising a denoiser; obtaining the 12-lead electrocardiosignal containing noise and inputting it into the denoiser to output the denoised 12-lead electrocardiosignal. The application aims to overcome the defects in the prior art, such as the lack of real 12-lead electrocardiosignal denoising paired data and the poor model generalization ability caused by excessive dependence on the Gaussian noise assumption.

Automatic exposure with simulated histograms may include obtaining a noise-blur exposure duration value in accordance with a minimal simulated noise-blur cost value obtained in accordance with simulated noise data and simulated blur data, obtaining a saturation exposure value in accordance with a minimal simulated saturation cost value obtained in accordance with simulated black saturation data and simulated white saturation data, comparing the noise-blur exposure duration value and the saturation exposure value, and obtaining a target gain value and a target exposure duration value based on the comparison, wherein the comparing may include obtaining the target exposure duration value in accordance with a minimal simulated blur-saturation cost value obtained in accordance with the simulated blur data and the simulated black saturation data or in accordance with a minimal simulated noise-saturation cost value obtained in accordance with the simulated noise data and the simulated white saturation data.

The application provides an electroencephalogram data synthesis method, device and equipment and a storage medium, and belongs to the field of biological signals. The method comprises the following steps: in response to an electroencephalogram data synthesis task, loading corresponding head model information, wherein the head model information is biophysical basic information; synthesizing electroencephalogram scalp signals according to the head model information and the electroencephalogram data synthesis task to obtain scalp signal data, and outputting electroencephalogram signal causality during the synthesis of the electroencephalogram scalp signals to obtain causality feature data; generating simulated noise for electroencephalogram extraction according to the head model information and the electroencephalogram data synthesis task to obtain noise signal data; and synthesizing electroencephalogram data according to the electroencephalogram data synthesis task, the scalp signal data, the noise signal data and the causality feature data. By adding the electroencephalogram signal causality and the simulated noise for electroencephalogram extraction into the electroencephalogram data, the generated electroencephalogram data has a causal true value and is more accurate.

This invention discloses a method and apparatus for grading the sugar content of kiwifruit by combining multi-scale feature extraction and global inference. Near-infrared spectral and sugar content data from two batches of kiwifruit samples spanning two years are collected as independent datasets. Abnormal spectral data and corresponding sugar content data entries are removed. The remaining spectral data is preprocessed and mixed with simulated noise. Sugar content is graded and used as label values ​​for training the model. A multi-scale attention residual convolutional network is used to extract fine spectral features related to sugar content. A parallel long-distance correlation encoding and decoding model is used to capture the long-pathway dependence of sugar content traits implicit in the spectral data. The trained and tested stable model is deployed to the kiwifruit grading device for online non-destructive testing and automatic grading. This overcomes the problems of low grading accuracy and easy confusion of critical sugar content grades caused by the limited local receptive field in existing technologies, which makes it difficult to capture fine spectral morphology and lacks modeling of long-pathway global dependencies.