scholarly journals Simple simulation training system for short-wave radio station

2018 ◽  
Author(s):  
Xianglin Tan ◽  
Zhichao Shao ◽  
Jianhua Tu ◽  
Fuqi Qu
Keyword(s):  
Simulation Training ◽  
Short Wave ◽  
Training System ◽  
Radio Station ◽  
Wave Radio ◽  
Simple Simulation

scholarly journals Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96055-96064 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei ◽  
Hao Xiong
Keyword(s):  
Short Wave ◽  
Radio Station ◽  
Wave Radio

scholarly journals Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4320
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Short Wave ◽  
Radio Station ◽  
Communication Behavior ◽  
Communication Behaviors ◽  
Training Time ◽  
Wave Radio ◽  
Bispectral Estimation ◽  
Model Algorithm

This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

scholarly journals Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4270 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Short Wave ◽  
Recognition Algorithm ◽  
Radio Station ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Radio Stations ◽  
Real Samples ◽  
Adversarial Network ◽  
Wave Radio ◽  
Training Samples

It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.

scholarly journals Research on Simulation Training System of Immersive Substation Based on Virtual Reality

2019 ◽  
Vol 486 ◽  
pp. 012112
Author(s):  
Xiaohui Liao ◽  
Hao Wang ◽  
Jinliang Niu ◽  
Jingbo Xiao ◽  
Chuan Liu
Keyword(s):  
Virtual Reality ◽  
Simulation Training ◽  
Training System

Research and Design of SWET Equipment for Engineering Machinery Company

2014 ◽  
Vol 971-973 ◽  
pp. 646-649
Author(s):  
Qing Song Zhao
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Work Environment ◽  
Simulation Training ◽  
Assembly Line ◽  
Training System ◽  
Assembly Lines ◽  
Structural Framework ◽  
Research And Design ◽  
Disassembly Line

The structural framework for the car’s assembly line simulation training system of the SWET(Simulated Work Environment Training) is designed overall, including two automatic car assembly lines and two manually run the disassembly line. The automatic control system of the car’s assembly line simulation training system is designed with the knowledge of electrical and electronic, SCM principles, counts the number of the car, automatically pause and open the line with alarm and automatic recovery control.

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