Noise-Induced Resonance and Particle Swarm Optimization-Based Weak Signal Detection

2018 ◽  
Vol 38 (6) ◽  
pp. 2677-2702 ◽  
Author(s):  
Sumit Kumar ◽  
Rajib Kumar Jha
Keyword(s):  
Particle Swarm Optimization ◽  
Signal Detection ◽  
Particle Swarm ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Swarm Optimization

Accurate Detection of Fault Signal in Large-Scale Communication

2014 ◽  
Vol 989-994 ◽  
pp. 3802-3805
Author(s):  
Na Shu
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Detection ◽  
Signal Detection ◽  
Communication System ◽  
Communication Systems ◽  
Large Scale ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Mining Methods ◽  
Reaction Speed

Deep data-mining methods of fault signal in large-scale communication system are researched. Although with the characteristic of frequency uniformity as signals distribute in each reaction zone, common method of fault signal detection based on shortwave dispersing is invalid employing in large-scale communication system, which presents the absence or instability of fault signal. For this reason, a method based on particle swarm optimization is proposed for fault signal detection in large-scale communication system. As reaction speed and activity scope within the whole particle swarm are replaced, accurate results are achieved. Taking particle swarm optimization, it is detected that whether there is a fault in communication systems. The experimental results show that proposed method in signal fault detection process can greatly increase accuracy of signal fault detection, as plays a greater role in future.

Particle Swarm Optimization of Compression Measurement for Signal Detection

2011 ◽  
Vol 31 (3) ◽  
pp. 1109-1126 ◽  
Author(s):  
Yongjie Li ◽  
Rongfang Song ◽  
Wennai Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Signal Detection ◽  
Particle Swarm ◽  
Swarm Optimization

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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