34results about How to "Solve long-term dependence" patented technology

The invention discloses a multi-dimension labelling and model optimization method for audio and video. The method specifically comprises the following steps: first, carrying out sample management andsorting, carrying out de-duplication aiming at sample data of an input system, carrying out numbering, and establishing a sample labelling task library; at the preprocessing stage of audio data, carrying out audio extraction on video data of the task library, and completing the preprocessing operation for the audio data; at the audio content analysis and feature extraction stage, after the audio preprocessing is completed, carrying out deep analysis according to a labelling standardized system configured at the background, and outputting label data; S304, at the video content analysis and feature extraction stage, carrying out image analysis on the video content, and carrying out deep analysis according to the labelling standardized system configured at the background, and outputting the label data; S305, carrying out feature fusion and label generation, namely, fusing the recognition features and label information, and outputting a label result of the sample; carrying out manual rechecking and model optimization, wherein the label result data generated by the system can be subjected to artificial re-check conformation.

The invention provides an end-to-end classification method of a large-scale news text based on Bi-GRU and a word vector. The end-to-end classification method comprises the following steps: S1. word Embedding word-level semantic feature representation is performed; S2. the attention weight Bi-GRU word level sentence feature coding model is constructed; S3. the Bi-GRU sentence level feature coding model based on the attention weight is established; S4. hierarchical Softmax is applied to realize end-to-end classification implementation. According to the method, the dimension of the vector can bereduced and the problem that the features are too sparse can be effectively prevented. The final output vector is optimized and the effectiveness of model feature coding is enhanced. The problem thatthe model is difficult to train because of the high dimension can be avoided and the additional semantic information can also be provided. The feature extraction model and various common classifiers can be flexibly combined so as to facilitate replacement and debugging of the classifiers. The computational complexity is reduced from | K | to log | K | in comparison with that of Softmax.

The invention provides a rolling bearing remaining life prediction method based on a long-short term memory network. The rolling bearing remaining life prediction method comprises the steps that characteristics of abrasion signals of the rolling bearing is extracted; principle component analysis is conducted on the extracted characteristics to obtain fusion characteristics; normalization processing is conducted on the fusion characteristics; cyclic overlapped interception is conducted on fusion characteristic data in a set step-size to obtain short sequences; the short sequences are divided into a training set and a prediction set; an LSTM deep learning network is constructed; the LSTM deep learning network is trained through the training set; the LSTM deep learning network is verified through the prediction set; and training results and prediction results are subjected to inverse normalization processing and output. According to the rolling bearing remaining life prediction method based on the long-short term memory network, an LSTM prediction model is provided based on the field of deep learning and has high applicability and accuracy in fault time sequence analysis, and the problem of long-term dependence in time sequence is solved.

The invention discloses a residual network and attention mechanism-based drug relationship extraction method. The method comprises the following steps of: S1, carrying out vector representation on words in a drug entity relationship data set; S2, carrying out time-series modeling on a drug relationship statement by utilizing a two-layer bidirectional long-short term memory model neural network; S3, importing residual connection into the constructed two-layer bidirectional long-short term memory model neural network; S4, decomposing a deep semantic meaning automatically obtained by the two-layer bidirectional long-short term memory model neural network into a memory space and an attention space, fusing memory information and attention information, and inputting the fused information into aSoftmax classifier to extract a drug relationship. According to the drug relationship extraction method disclosed by the invention, dependency relationships between long-distance words are effectivelysolved, gradient diffusion is overcome, model overfitting is prevented, the model robustness is good and the classification effect is good.

The invention discloses a cement finished product specific surface area prediction method and system based on a long-term and short-term memory network. The method comprises the following steps: sorting training input data in a training input set according to a time sequence; inputting the sorted training input set into a pre-constructed long-term and short-term memory network model to obtain a cement finished product specific surface area prediction value at each moment; calculating a node error term of each neuron by adopting a time-based back propagation algorithm according to the trainingoutput set and the cement finished product specific surface area prediction value, wherein the node error term comprises a forgetting gate error term, an input gate error term and an output gate errorterm; training the to-be-trained parameters by adopting a random gradient descent method according to the node error term to obtain a trained long-term and short-term memory network model; and inputting the to-be-tested input set into the trained long-short-term memory network model to obtain a to-be-tested cement finished product specific surface area prediction value. The accuracy of cement finished product specific surface area prediction can be improved.

The invention provides a manufacturing method for continuously rolling a seamless steel pipe by using a hollow mandril. The method comprises the following steps of: designing the steel grade and the specification of the hollow mandril; performing continuous casting, cogging, and forging; rolling a mandril material, performing tempering heat treatment; and detecting the performance of the mandril material, and processing and connecting the mandril material. The method has the advantages that: the problem that the retained mandril depends on import for a long time is solved, so that the production localization of the retained madril is realized; the service life of the mandril is close to or the same as that of the solid mandril of the same specification which is imported from foreign countries or bought in China; and the quality of the inner and outer surfaces of a seamless steel pipe product in the production process is good, 1.5 to 1.7kg of steel is consumed for one ton of mandrils, the cost of one ton of steel is 30 yuan which is only one third the original cost, and the production cost is greatly reduced. Due to the development and use of the hollow mandril, the consumption cost of a tool is reduced, and the market competitiveness of a steel pipe product is improved; the technology promotion of the mandril manufacturing industry in China is driven, and the method has a profound and lasting significance for the top potential and consumption reduction of metallurgical industry.

The present invention discloses a PMU primary frequency modulation load forecasting method based on the associated fully connected neural network and LSTM, in particular comprising the following steps: selecting training data, verifying the data, establishing a joint neural network model, training the joint neural network model, and inputting the forecasting sample set into the joint neural network model trained. The method of the invention considers the correlation between the historical data of the load and the power in the ultra-short-term load forecasting, adopts the structure of the LSTMneural network and the fully connected neural network, and effectively solves the problem of long-term dependency. The invention also has the advantages of simple algorithm, short running time and high prediction accuracy, and provides a solid guarantee for the stable operation of the power network.

The invention provides a remote sensing image semantic description method based on multistage feature fusion, and belongs to the field of remote sensing image processing and computer vision, and the method comprises the following steps: obtaining a high-resolution remote sensing image, and constructing a remote sensing image semantic description data set; training a semantic classification model of the image by using the semantic annotation data set, extracting word description from the image and encoding to obtain semantic features; training a target detection model by using the target detection data set, extracting region-level features of the image and encoding the region-level features to obtain visual features; aggregating the obtained semantic and visual features, namely splicing the two groups of features together; and taking the aggregated multi-level features as the input of Transform, and training an image natural language generation model. The semantic and visual features of the image are utilized, the extracted information comprises the scene information, the regional visual information and the semantic relation of the object, and the generated image semantic description is high in readability and high in accuracy.

The invention discloses a news website general crawler design method based on a GRU neural network. HTML data preprocessing is carried out on HTML page content, target data is constructed, charactersare marked, a character dictionary is constructed, the HTML content is converted into a digital vector, and finally batch filling is carried out; establishing a GRU neural network, using a Cross Entropy as a loss function, and using a pre-trained character vector to train and predict the GRU neural network by an Embedding layer; and based on the Scrapy crawler framework, constructing a whole-station crawling crawler. According to the method, after a crawler crawls HTML content of any news page, the HTML content is transmitted into the model trained by using the neural network algorithm designed by the invention, so that news texts can be automatically extracted, and customized time and manpower are saved.

The invention relates to deep learning in the field of artificial intelligence, in particular to learning of a system call sequence. The method comprises: data collection and processing, model construction, model training, model evaluation and sequence generation. The data collection and processing comprises the following steps: firstly, collecting a system call sequence with parameters and a sequence in a trace format, and then coding the sequences into input data suitable for model training. The model construction comprises: selecting RNN and LSTM neural network models, and determining a network structure as an input layer, a hidden layer and an output layer. Model training includes batching input data, initializing network parameters, calculating a value of a loss function to adjust the network parameters. Model evaluation includes calculating a normalized edit distance between test sequence data and a prediction sequence. The sequence generation comprises the following steps: randomly selecting initial system call and sequence length, generating an integer sequence according to a model obtained by training, and decoding the integer sequence into a system call sequence. And the generated sequence is used as the input of the kernel fuzzy test, so that the vulnerability mining efficiency is improved. The process is shown in Figure 1.

The invention relates to an LSTM-based optical cable manufacturing equipment fault remote prediction system. The system comprises a detection node and a data processing node, wherein the detection node comprises a microprocessor, a data acquisition module, a communication module, an analog-to-digital conversion module and a power supply module, the data processing node comprises an upper computerand a display module. When the system works, the microprocessor in the detection node controls the sensor of the data acquisition module to acquire and detect typical fault process parameter data of an optical cable production line, the microprocessor processes the acquired and detected data and wirelessly transmits the processed data to the data processing node through the communication module ofthe microprocessor, and the upper computer receives a data signal and sends the data signal to the communication module of the microprocessor; the trained LSTM network is called to analyze and calculate the data, and finally the equipment operation state model is outputted to a display screen to complete fault prediction. The system can prevent problems of the optical cable production line in thecase of sudden faults, reduce operation and maintenance cost and improve the coping capability of the production line for the sudden faults.

The invention relates to a haze prediction method based on a global attention mechanism, and belongs to the technical field of artificial intelligence information prediction. The method comprises thefollowing steps: firstly, acquiring haze data of an environment monitoring point, processing the acquired haze data, training a haze prediction model based on a global attention mechanism, and outputting a final prediction result by using the haze prediction model. In a haze prediction task, a global attention mechanism is introduced, different influence factors are endowed with different weights,and the problem that the information transmission distance is too long is effectively solved. A bidirectional gating recurrent neural network is introduced, the influence of previous moment data in training data on subsequent moment data is introduced, the association of the subsequent moment data and the previous moment data is analyzed, the problem of long-term dependence in haze prediction data is solved, and the haze data at the future moment can be accurately predicted. The method has good expansibility, the network structure can be dynamically changed according to the data characteristics of different regions, and the haze prediction method suitable for the local region is obtained.

The invention discloses a Chinese statement simplification method and device. The method comprises the steps of carrying out the word segmentation processing on a received text sequence to obtain a word segmentation sequence; performing encoding processing on the word segmentation sequence through a trained encoder to generate a semantic vector; and decoding the semantic vector through the traineddecoder to generate a simplified output sequence, wherein the encoder and the decoder are both LSTM models. According to the scheme provided by the embodiment of the invention, the combination of LSTM is selected for encoding and decoding in the model training process because the LSTM can effectively solve the problem of long-term dependence in sequence prediction.

The invention discloses an electric vehicle speed reducer remaining service life prediction method based on LSTM. The method comprises the steps of: preprocessing full-life-cycle vibration data of an electric vehicle speed reducer; constructing degradation characteristics of reducer vibration signals on a time domain, a frequency domain and a time-frequency domain, thereby constructing a degradation characteristic data set of each electric vehicle reducer, and constructing a residual life prediction model by using an LSTM neural network; normalizing the degradation characteristic data set input into the prediction model; carrying out segmentation processing on the normalized data; dividing the degradation characteristic data set into a training set and a test set, and taking training data as the input of the whole prediction network model; and performing network parameter adjustment by using a single variable method, and outputting an optimal life prediction result. The prediction method which is more reliable and higher in generalization is provided for analysis of the remaining service life of the speed reducer of the electric vehicle.

The invention discloses a microgrid load prediction method based on a long and short term neural network model, and relates to the technical field of microgrid load prediction, and the method comprises the steps: obtaining the weight of each influence factor when the clustering interval is maximum, and extracting the feature vector of each influence factor in a current group through a double-layer convolutional neural network; optimizing the long-short-term neural network through an optimizer, and carrying out feature vector screening through the optimized long-short-term neural network; weighting processing is carried out on the screened feature vectors through attention optimization, and optimized feature vectors are obtained; and connecting the optimized feature vectors through a full connection layer and obtaining the predicted load of the current group. According to the invention, the optimized feature vectors are connected through the full connection layer, the predicted load of the current group is obtained, and the long-term and short-term neural network is adopted to solve the problem of long-term dependence in the training process.