Automatic radar antenna scan type recognition based on limited penetrable visibility graph

To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type,a recognition method for radar antenna scan types based on limited penetrable visibility graph(LPVG)is proposed.Firstly,seven types of radar antenna scans are analyzed,which include the circular scan,sector scan,helical scan,raster scan,conical scan,electromechanical hybrid scan and two-dimensional electronic scan.Then,the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network,and the feature parameters are extracted.Finally,the recognition result is obtained by using a support vector machine(SVM)classifier.The experimental results show that the recognition accuracy and noise resistance of this new method are improved,where the average recognition accuracy for radar antenna type is at least 90%when the signalto-noise ratio(SNR)is 5 dB and above.

A novel complex network-based deep learning method for characterizing gas-liquid two-phase flow

Gas-liquid two-phase flow widely exits in production and transportation of petroleum industry.Characterizing gas-liquid flow and measuring flow parameters represent challenges of great importance,which contribute to the recognition of flow regime and the optimal design of industrial equipment.In this paper,we propose a novel complex network-based deep learning method for characterizing gas-liquid flow.Firstly,we map the multichannel measurements to multiple limited penetrable visibility graphs(LPVGs)and obtain their degree sequences as the graph representation.Based on the degree distribution,we analyze the complicated flow behavior under different flow structures.Then,we design a dual-input convolutional neural network to fuse the raw signals and the graph representation of LPVGs for the classification of flow structures and measurement of gas void fraction.We implement the model with two parallel branches with the same structure,each corresponding to one input.Each branch consists of a channel-projection convolutional part,a spatial-temporal convolutional part,a dense block and an attention module.The outputs of the two branches are concatenated and fed into several full connected layers for the classification and measurement.At last,our method achieves an accuracy of 95.3%for the classification of flow structures,and a mean squared error of 0.0038 and a mean absolute percent error of 6.3%for the measurement of gas void fraction.Our method provides a promising solution for characterizing gas-liquid flow and measuring flow parameters.