Internet online monitoring technology has been widely used in the temperature monitoring of high-voltage transmission
lines. However, due to the influence of data volume and other factors, it is possible for multiple lines to have delay at a
certain time and for a certain line to repeatedly appear abnormal. Manual processing speed is slow and low efficiency.
To improve the data transmission efficiency, make the line temperature prediction faster and grasp the line temperature
more accurately, comparing and analyzing the accuracy and training time of circuit temperature under the existing
several neural networks. Then, a temperature prediction method based on LSTM-ELM network under broadband and
narrowband fusion is proposed to unify the broadband and narrowband structure and transmit data of different sizes
through a unified frequency band. Establish the LSTM-ELM network, extract data features, analyze temperature data,
and realize the rapid prediction of the line temperature trend. The experimental results show that the network prediction
accuracy based on LSTM-ELM reaches 92.02% while the prediction time is greatly reduced to 863.68s, compared with
the traditional LSTM network, the predicted time is improved by nearly 2000%, which can provide a reliable basis for
background management in engineering practice.
In this paper, we propose a workflow and a deep learning algorithm for recognizing Quadrature amplitude modulation signal(QAM), this design adopts a convolutional neural network (CNN) and Extreme Learning Machine (ELM) as the core,leverage the powerful feature extraction of CNN and fast classification learning of ELM. The spectrogram image features of the signal obtained by short-time Fourier transform (STFT) are input to the CNN-ELM hybrid model, the modulation mode of the QAM signal is finally recognized by ELM. This algorithm surmounts the shortcomings of traditional methods well, Simulation results also verify the superiority of the proposed system whose classification accuracy is beyond 99.86%.
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