5results about How to "Guaranteed classification effect" patented technology

A base classification method comprises: acquiring a fluorescence image to be identified; identifying a position of each DNA nanoball in the fluorescence image; extracting a brightness data feature of the fluorescence image, the brightness data feature comprising brightness data of M dimensions corresponding to the position of each DNA nanoball; and inputting the extracted brightness data feature into a preset base identification model to obtain a base category corresponding to the fluorescence image. The application also provides a gene sequencer and a computer readable storage medium. The application can realize end-to-end classification from brightness data of a fluorescence image to a base category.

This invention discloses an adversarial defense method for a speaker recognition system based on F-ratio adaptive masking. The method first extracts features from the input speech to obtain an amplitude spectrogram; then, it denoises the amplitude spectrogram to obtain a denoised amplitude spectrogram; F-ratio is used to statistically analyze the high-relevance and low-relevance frequency band sets in the denoised amplitude spectrogram to distinguish the speaker; next, masking thresholds for the high-relevance and low-relevance frequency bands are calculated separately; the amplitude spectrogram is then masked to obtain a reconstructed amplitude spectrogram; the reconstructed amplitude spectrogram is transformed using librosa.griffinlim to obtain the corresponding waveform signal as the reconstructed speech; finally, a batch of clean samples is used for speech reconstruction, and the reconstructed speech is used for SRS fine-tuning training to ensure the classification performance of SRS. This invention exhibits significant defensive advantages, and the average defense capability demonstrates the versatility of this scheme against different attacks. Furthermore, because this invention does not involve additional data and training, it possesses low cost.

The low-power pulse wave signal arrhythmia classification method and system of the application comprises the following steps: using a large convolution kernel to extract the bottom local features of the original signal, and reducing the sequence length by half through pooling; adopting a leaky integral-discharge neuron activation to make the data pulsed; using a large convolution kernel and a hollow convolution to expand the receptive field and capture longer-range local timing features; dimensionally reducing the output features, cooperating with batch normalization, leaky integral-discharge neurons and maximum pooling, and aggregating the local features; calculating self-attention in each subsequence, combining a linear mapping layer, focusing the model on the feature correlation in the local subsequence through the aggregation of the sub-attention map, and extracting the waveform dependence in the short time window of the pulse wave signal; calculating QKV interaction on the whole sequence, combining a linear layer and a pulse neuron, capturing long-distance global feature correlation, and mining the waveform dependence in different time periods of the pulse wave signal. The application can achieve a classification performance equivalent to that of a deep artificial neural network.

The invention discloses a breath sound classification detection method based on an improved convolutional neural network, and the method comprises the steps: firstly carrying out the preprocessing and feature extraction of a breath sound signal collected in real time, and constructing a breath sound cepstrum feature matrix; and then the breath sound features are input into an improved convolutional neural network comprising a convolution module, a channel attention mechanism module and a lightweight feature extraction module for training and classification, and accurate recognition of breath sound categories is realized. The method realizes real-time, efficient and high-precision classification detection of breath sound, has the advantages of low calculation complexity, strong real-time performance and high identification accuracy, and is suitable for the field of biomedical signal processing.

The application discloses an encrypted traffic classification model and method based on a dual-mode hierarchical encoder, constructs a dual-domain hierarchical flow representation (DFHR) matrix, extracts and fuses traffic byte and timing depth features through a dual-mode hierarchical encoder (DMHE), completes self-supervised pre-training by using a hierarchical mask multi-task mask autoencoder (HM-MAE), and outputs a classification result through an adaptive gating classification head (AGCH) after supplementing features through a traffic feature enhancement module (TFEM). The application solves the problems of insufficient single-mode feature extraction capability, small-size matrix information loss, large deviation between pre-training and downstream tasks, and poor generalization of the classification head of the prior art, greatly reduces the model training time, maintains high classification accuracy and robustness in small sample and lightweight scenarios, and adapts to the deployment requirements of edge resource-limited devices.