31results about How to "Solve the problem of insufficient training samples" patented technology

The invention discloses an underwater acoustic signal modulation mode inter-class identification method based on an improved dense neural network. The method comprises the following steps: firstly receiving a to-be-identified underwater acoustic modulation signal, and extracting the features of the underwater acoustic modulation signal; carrying out dimension reduction and denoising on underwater acoustic modulation signal features by using a principal component analysis method; carrying out normalization and dimension change; based on the dense neural network, removing the pooling layer to obtain an improved dense neural network, and training the neural network; and inputting the processed underwater acoustic modulation signal features into the trained improved dense neural network, and finally completing modulation mode inter-class identification. Low-delay and high-accuracy inter-class recognition of the underwater acoustic signal modulation mode is finally realized, and the recognition method is high in anti-interference capability, low in calculation cost and high in recognition accuracy.

The invention discloses a method for identifying organs in a medical image, comprising: acquiring a medical image to be processed, splitting the medical image into several two-dimensional images in the directions of X, Y, and Z axes, and Set the size of the detection window; use the detection window to traverse and detect the two-dimensional image according to the set detection step, and obtain the detection results in the X, Y and Z axis directions; perform result fusion on the detection results , keep the pixel points that are detected as positive in the three directions of the X, Y and Z axes, so as to determine the boundary of the target organ. The method for identifying organs in medical images of the invention can accurately and quickly identify areas where target organs are evenly located, determine the boundaries of target organs, and has strong self-adaptive ability. In addition, the present invention also provides a method for segmenting organs in medical images.

The invention discloses an audio event classification method based on stack-based sparse representation and a computer device. The method comprises the following steps: at the training stage, first, creating audio dictionaries of all kinds of audio events; then, constructing a large-scale dictionary by stacking the audio dictionaries of the all kinds of audio events; at the testing stage, extracting a sparse representation coefficient of a tested audio sample according to the large-scale dictionary constructed at the training stage, and mapping the sparse representation coefficient through a softmax function; and finally, constructing the confidence degree of the tested audio file on the all kinds of audio events according to the mapped coefficient, and carrying out classified judgment according to the magnitude of the confidence degree. According to the method, the large-scale dictionary is constructed through the stacking base innovatively, and then the sparse representation coefficient of the sample is obtained; the extracted sparse representation coefficient can well represent the audio event sample, the inter-class difference of the samples is increased, the intra-class difference is reduced, and the classification accuracy is improved.

A 3D face recognition method based on Bayesian multivariate distribution feature extraction, including 3D data preprocessing, feature extraction and recognition classification. The advantages of the present invention are: to overcome the shortcomings of the large amount of calculation in the prior art, the present invention uses a three-dimensional face depth map for recognition, which can reduce the amount of calculation and improve the recognition efficiency; and solve the problem of insufficient training samples in the single-sample recognition problem , using the block method to increase the training samples; on this basis, a feature extraction method based on Bayesian analysis is proposed, so that the obtained features have the smallest intra-class distance and the largest inter-class distance, that is, the best separable and use the classification method based on the Mahalanobis distance to obtain the optimal recognition classification. Experimental data proves that the method of the present invention has better three-dimensional face recognition results.

The present invention relates to a method for estimating air quality in urban areas based on collaborative training. The present invention makes full use of regional spatial features, such as traffic conditions and road network structures in the area, and is a semi-supervised learning method based on multi-classifier collaborative training. Model the feature vector of the region to learn multiple classifiers; then pruning these classifiers to form the final combined classifier; use the pruned combined classifier model to perform air monitoring for areas without air monitoring stations. quality rating estimates. This method can estimate the air quality level according to the spatial differences between the areas with air monitoring stations and the areas without air monitoring stations under the condition of limited air monitoring stations, and the estimation results are accurate.

The invention proposes a method for evaluating the frequency modulation capability of an air conditioner user based on a generative confrontation network. According to the frequency regulation mechanism of air conditioner users, select the measurement data of the frequency regulation capability and the strong correlation factors affecting the frequency regulation capability, and construct a small sample training set, a small sample generation set and a test sample set for the frequency regulation capability evaluation model; improve the generator of the generative adversarial network algorithm model, and use the small sample training set to train the improved generative adversarial network model to obtain the improved generative adversarial network model after training, and further use the small sample generation set to generate a synthetic sample set, and construct the training set of the frequency modulation capability evaluation model; The multi-layer feedforward neural network model is trained by using the training set of the frequency modulation capability evaluation model, and the trained multi-layer feedforward neural network model is obtained as an air conditioner user frequency modulation capability evaluation model. The invention improves the accuracy of an air conditioner user's frequency modulation capability evaluation model.