Data compression, data fusion and Kalman filtering in wavelet packet sub-bands of a multisensor tracking system

1998 ◽  
Vol 145 (2) ◽  
pp. 100 ◽  
Author(s):  
K.M. Wong ◽  
Z.Q. Luo ◽  
Q. Jin ◽  
É. Bossé
Keyword(s):  
Data Fusion ◽  
Data Compression ◽  
Kalman Filtering ◽  
Tracking System ◽  
Wavelet Packet ◽  
Compression Data

Rock hardness identification based on optimized PNN and multi-source data fusion

2021 ◽  
pp. 095440622110420
Author(s):  
Ying He ◽  
Muqin Tian ◽  
Jiancheng Song ◽  
Junling Feng
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Differential Evolution Algorithm ◽  
Vibration Signal ◽  
Optimization Method ◽  
Rock Wall ◽  
Rock Hardness ◽  
Source Data

To solve the problem that it is difficult to identify the cutting rock wall hardness of the roadheader in coal mine, a recognition method of cutting rock wall hardness is proposed based on multi-source data fusion and optimized probabilistic neural network. In this method, all kinds of cutting signals (the vibration signal of cutting arm, the pressure signal of hydraulic cylinders and current signal of cutting motor) are analyzed by wavelet packet to extract the feature vector, and the multi feature signal sample database of rock cutting with different hardness is established. To solve the problems of uncertain spread and complex network structure of probabilistic neural network (PNN), a PNN optimization method based on differential evolution algorithm (DE) and QR decomposition was proposed, and the rock hardness was identified based on multi-source data fusion by optimizing PNN. Then, based on the ground test monitoring data of a heavy longitudinal roadheader, the method is applied to recognize the cutting rock hardness, and compared with other common pattern recognition methods. The experimental results show that the cutting rock hardness recognition based on multi-source data fusion and optimized PNN has higher recognition accuracy, and the overall recognition error is reduced to 6.8%. The recognition of random cutting rock hardness is highly close to the actual. The method provides theoretical basis and technical premise for realizing automatic and intelligent cutting of heading face.

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