40 results about "Sigmoid activation function" patented technology

The embodiment of the invention relates to a heart beat classification method and device for multi-label labeling of electrocardiosignals, and the method comprises the steps: obtaining a heart beat time sequence, carrying out the data cutting of the heart beat time sequence, and obtaining a plurality of groups of data segments of multi-lead heart beat data; inputting the data fragments into a trained multi-label classification model; wherein the multi-label classification model comprises a feature extraction module, a full connection layer and a Sigmoid activation function layer; the feature extraction module performs feature extraction on the sent data fragments to obtain high-dimensional embedded information including information of heart beat types corresponding to each group of data fragments; sending the high-dimensional embedded information into a full connection layer, and outputting the high-dimensional embedded information as a one-dimensional vector [1, N], wherein N is the number of heart beat categories of the data fragments; inputting the one-dimensional vector [1, N] into a Sigmoid activation function layer, and outputting probability values of a category number and heart beat categories; and determining whether the probability value exceeds a threshold value of a set probability, and outputting the heart beat category of which the probability value exceeds the threshold value of the set probability as a heart beat classification result.

The invention relates to a power distribution network probabilistic load flow obtaining method considering wind power uncertainty, and the method comprises the following steps: S1, constructing a network structure of a bidirectional generative adversarial network: employing an artificial neural network of a full connection layer for the network structures of an encoder, a generator and a discriminator, and employing a LeakyReLU activation function for the full connection layer, wherein an output layer of the generator uses a Tanh function, an output layer of the discriminator uses a Sigmoid activation function, a Dropout layer is added behind a full connection layer of the discriminator, and a batch standardization layer is added before each layer of input of the encoder, the generator andthe discriminator; S2, training the bidirectional generative adversarial network in the step S1; S3, after the bidirectional generative adversarial network is trained, intercepting a generator as a generation model, and inputting one-dimensional random noise obeying Gaussian distribution to obtain wind power data conforming to original data probability distribution; S4, inputting the node load and the obtained wind power data into a probabilistic load flow calculation model, and calculating output node voltage and branch power. According to the method, the result of probabilistic load flow calculation is obtained more accurately in the environment of considering the uncertainty of wind power output.

The invention discloses a communication network performance fault cause and characteristic parameter correlation analysis method, and relates to the technical field of communication network fault diagnosis. The method obtains the characteristic parameter data of the communication network performance fault to be analyzed, adopts a communication network performance fault analysis model of a transfinite learning machine established in advance, and processes the characteristic parameter of the communication network performance fault to be analyzed, thereby analyzing the communication network performance fault types corresponding to the characteristic parameter. The invention realizes the effective and accurate analysis of the communication network performance fault types, and enhances the generalization ability of the traditional transfinite learning machine model and improves the model precision by selecting two different activation functions, the sigmoid activation function and the Gaussian radial basis function.

The invention provides a character string recognition method based on a named entity model, comprising: the input layer of the named entity model receives the character string input by the user, converts the character string into a word index array and outputs it to the word embedding layer, and the word embedding layer Each element in the word index array is converted into a multi-dimensional word vector and output to the bidirectional long short memory network layer. When the sigmoid activation function unit or tanh activation function unit of the bidirectional long-short memory network layer receives input data, it generates a sigmoid lookup request or a tanh lookup request, and calls a preset function interface, and uses the preset function interface for different lookup requests Using different table lookup methods to look up corresponding data in the same preset lookup table, and use the found data as the output result of the corresponding activation function unit. The bidirectional long-short memory network layer logically processes the output of the activation function unit and then outputs it to the fully connected layer, and the fully connected layer adds entity labels to the output result data. The scheme of the invention can effectively improve the data processing efficiency of the activation function.

The invention proposes a SAR image target classification method based on a deep convolutional neural network, which is used to improve the classification accuracy of the SAR image target. The implementation steps are: obtain the training sample set and the test sample set containing the SAR target image; remove the background clutter of each SAR image in the training sample set and the test sample set; construct the deep convolution including the transformed sigmoid activation function to form the Enhanced-SE layer Neural network model; train the deep convolutional neural network model; use the trained deep convolutional neural network model to classify the test sample set. The present invention fuses the edge gap of the target area and fills the internal defects of the target area when removing the background clutter in the SAR target image through the morphological closed operation method, effectively retaining the shape characteristics of the target area; forming Enhanced-SE by transforming the sigmoid function layer, inhibiting the automatic extraction of redundant features by the deep convolutional network, and improving the accuracy of SAR image target classification.

The present invention provides an audio-visual recommendation system and method based on a bilinear perceptual graph neural network model, which calculates the similarity between the obtained program vector and the pre-acquired user embedding vector through the similarity function; and then uses the predictive activation function (sigmoid activation function ) to limit the range of similarity to the preset interval, so that the value in the preset interval can be used as the possibility evaluation score of the user's audio-visual choice, and it is constructed as a personalized audio-visual recommendation model based on the knowledge map - a dual Linear knowledge-aware graph neural network model system; the system captures the second-order feature interaction information of neighbor nodes in the knowledge graph by adding a bilinear collector on the basis of the graph neural network, obtains the program knowledge vector, and expresses it through the program text The module obtains the program text vector, and then obtains the recommendation data based on the program knowledge vector and the program text vector. In this way, the accuracy of audio-visual recommendation is improved, the operation effect of audio-visual recommendation is improved, and user stickiness is improved.

The invention relates to a diagnosis method based on the linear interpolation type fuzzy neural network. The method comprises the following steps that a BP neural network is trained by utilizing neural network learning knowledge in which the possibility theory is fused with Dempster & Shafer evidence theory information; and linearization is carried out on a sigmoid activation function, and the possibility of a device fault state is predicted to determine the fault state of a device. The diagnosis method is used to effectively discover the relation between the fuzzy characteristic parameter and the device fault type.

The invention discloses a service data protection method, which comprises the following steps of: providing a service data protection method based on a trusted measurement model to ensure the security of network communication data, and improving a communication security model through a key technology Whirlpool method of the model; secondly, providing a binary code interpretable vulnerability management and control method based on an attention mechanism aiming at vulnerabilities appearing in the model, enabling an input layer as an output vector of a word embedding layer to pass through two convolution layers, enabling output obtained by multiplying the two convolution layers to pass through a ReLU function activation layer, and then obtaining a feature weight through an attention module; multiplying with the feature map before being input into the attention module, and further passing through a global maximum pooling layer; and finally, through the two fully connected layers, carrying out classification probability prediction by using a sigmoid activation function. According to the invention, safe network credible transmission and communication data protection of the industrial internet can be ensured, and the control precision and the overall safety of the industrial internet are improved.

The invention discloses an image saliency target detection method combined with edge information, which comprises the following steps of: firstly, performing feature extraction on an input image by using an encoder network to obtain features of five different scales; in the decoder step, an edge information module is used for processing the feature information of the first layer, the second layer and the third layer to obtain an edge prediction map; processing the features of the fourth layer and the fifth layer, and adding the features with the edge prediction map; then splicing is carried out along the channel dimension, the channel is compressed through conv processing, and a sigmoid activation function is used to finally obtain a prediction saliency map of the network; and finally, optimizing the whole model network by using a loss function. According to the method, the accuracy of image salient target detection can be effectively improved. And by using the edge information extraction module, the integrity of the salient target can be effectively improved.

The invention relates to a workpiece length measuring method and system, and belongs to the field of workpiece size measurement. The method comprises the following steps: constructing a coding path; constructing a feature splicing path comprising an ECA module and two down-sampling modules; an activation layer of the ECA module adopts a sigmoid activation function to activate a feature vector input into the activation layer, and an element-level multiplication layer is used for multiplying a feature map output by the first coding network by the feature vector output by the activation layer to obtain a cross-channel interactive feature map; constructing a decoding path, training the constructed image segmentation network, inputting a to-be-detected workpiece image into an image segmentation network model, carrying out skeleton refinement on a workpiece area in an output segmentation feature map, and determining the length of a to-be-detected workpiece according to pixel parameters and camera calibration parameters of the to-be-detected workpiece image. Labor and time cost can be saved, the efficiency of the whole workpiece length measurement process is improved, and meanwhile the accuracy of the measurement result is improved.

The invention discloses a feature fusion method of a multi-modal deep neural network. In the multi-modal deep three-dimensional CNN, by using a compression excitation (squeeze and excitation, S&E) module on the deep learning feature domain, the information about the mode can be obtained. Channel attention mask between modalities, that is, in all modalities, give greater attention to those channels that significantly help the task goal, thus explicitly establishing the weight of the multimodal 3D depth feature map on the channel distribution; then, using four-dimensional convolution and Sigmoid activation function calculation, the spatial attention mask between the modalities can be obtained, that is, in the three-dimensional feature map of each modality, which positions in space need to be given greater attention , thereby explicitly establishing the spatial correlation of the multimodal 3D depth feature map, and giving greater attention to the positions with important information in the modality, channel, and space, thereby improving the diagnosis of the multimodal intelligent diagnosis system efficacy.

The invention relates to a diagnosis method based on the linear interpolation type fuzzy neural network. The method comprises the following steps that a BP neural network is trained by utilizing neural network learning knowledge in which the possibility theory is fused with Dempster & Shafer evidence theory information; and linearization is carried out on a sigmoid activation function, and the possibility of a device fault state is predicted to determine the fault state of a device. The diagnosis method is used to effectively discover the relation between the fuzzy characteristic parameter and the device fault type.

The invention relates to the technical field of fire detection, in particular to an attention method for reducing smoke and fire monitoring calculation amount and electronic equipment, and the methodcomprises the following steps: firstly obtaining a to-be-detected image, generating a color feature channel through a color feature network, and respectively extracting texture features and edge features through a texture feature network and an edge feature network; converting and merging the texture features and the edge features into comprehensive features by using a large convolution kernel, and then using a sigmoid activation function to generate a one-dimensional weight feature map; embedding time attention between encoders and decoders of a recurrent neural network RNN, measuring contribution of the ith hidden state Eti of an encoder E and the D state Dt1 of the decoder to Dt firstly, adjusting the weight of each historical encoder continuously, and subjecting only frames with the decoder Dt exceeding a threshold value to key detection. According to the invention, only high-possibility space-time further strict detection is carried out, and the consumption of computing power by full-width and full-time monitoring can be greatly reduced.