Real-Time Intelligent Healthcare Monitoring and Diagnosis System Through Deep Learning and Segmented Analysis

2019 ◽  
pp. 15-25 ◽  
Author(s):  
Edward B. Panganiban ◽  
Wen-Yaw Chung ◽  
Wei-Chieh Tai ◽  
Arnold C. Paglinawan ◽  
Jheng-Siang Lai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Diagnosis System ◽  
Monitoring And Diagnosis ◽  
Healthcare Monitoring ◽  
Intelligent Healthcare

scholarly journals Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning

2021 ◽  
Vol 23 (4) ◽  
pp. 57-62 ◽  
Author(s):  
Amjad Rehman ◽  
Tariq Sadad ◽  
Tanzila Saba ◽  
Ayyaz Hussain ◽  
Usman Tariq
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Diagnosis System ◽  
X Ray ◽  
Time Diagnosis

scholarly journals Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7246
Author(s):  
Sungmok Hwang ◽  
Cheol Yoo
Keyword(s):  
Wind Turbine ◽  
Real Time ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Vertical Axis ◽  
Power Production ◽  
Operating Conditions ◽  
Vertical Axis Wind Turbine ◽  
Diagnosis System ◽  
Monitoring And Diagnosis

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.

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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