scholarly journals Real-Time Implementation of Self-Adaptive Salp Swarm Optimization-Based Microgrid Droop Control

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185738-185751 ◽  
Author(s):  
M. A. Ebrahim ◽  
Reham Mohamed Abdel Fattah ◽  
Ebtisam Mostafa Mohamed Saied ◽  
Samir Mohamed Abdel Maksoud ◽  
Hisham El Khashab
Keyword(s):  
Real Time ◽  
Droop Control ◽  
Swarm Optimization ◽  
Self Adaptive

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.

Sign in / Sign up

Export Citation Format

Share Document