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

The invention provides a method for manufacturing a hollow mandril of a continuously rolling seamless steel pipe. 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 invention relates to an improved LSTM‑MLP-based fault prediction method for small generators, which belongs to the technical field of artificial intelligence and machine learning. The method includes two models based on the improved LSTM-MLP and multi-layer perceptron, and predicts the input by inputting the data collected by the DTU module of the small generating set. The improved LSTM network in the network is responsible for the small generating set as the front network. For state prediction, the multi-layer perceptron is responsible for the fault classification of small generator sets and outputs the results of the entire system as a post-network. The present invention improves the LSTM network structure and introduces the covariance of the previous timing state and the current timing state into the gating structure of the network as a weight coefficient, further improves the accuracy of LSTM network prediction, and increases the reliable use of small generators time, and reduce the labor cost required for the operation and maintenance of the generating set.

The invention discloses a PMU primary frequency modulation load prediction method based on an associated fully connected neural network and LSTM, which specifically includes selecting training data, verifying data, establishing a joint neural network model, training the joint neural network model, and inputting the prediction sample set into the trained joint neural network. Network model; the method of the present invention considers the correlation between the historical data of load and power in the ultra-short-term electric load forecasting, adopts the structure associated with the LSTM neural network and the fully connected neural network, and effectively solves the long-term dependence problem ; The present invention also has the advantages of relatively simple algorithm, short running time and high prediction accuracy, which provides a solid guarantee for the stable operation of the power grid.

The present invention provides an auxiliary diagnosis model for childhood pneumonia and a training method thereof. The training method includes acquiring medical images of children with pneumonia and corresponding medical diagnosis sentences, the medical images being used as a training image set, and the medical diagnosis sentences being used as training sentences ; Extract the image depth feature vector from the image training set data through CNN neural network to obtain a depth feature atlas, and perform word vector training on the training sentence through the word2vec model to obtain a depth feature vector word set; The feature fusion of the deep feature vector word set and the deep feature vector word set is carried out, and then the LSTM neural network is used for training, that is, a trained child pneumonia auxiliary diagnosis model can be obtained. The present invention trains the existing medical images of children with pneumonia and the corresponding medical diagnosis sentences, so that the model obtained by training can be used as a tool for doctors to learn diagnosis or provide effective reference opinions for doctors' clinical diagnosis.