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Smart Systems for Industrial Applications
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Страница 1
Table of Contents
Guide
List of Illustrations
List of Tables
Pages
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1
AI-Driven Information and Communication Technologies, Services, and Applications for Next-Generation Healthcare System
1.1 Introduction: Overview of Communication Technology and Services for Healthcare
1.2 AI-Driven Communication Technology in Healthcare
1.2.1 Technologies Empowering in Healthcare
1.2.2 AI in Diagnosis
1.2.3 Conversion Protocols
1.2.4 AI in Treatment Assistant
1.2.5 AI in the Monitoring Process
1.2.6 Challenges of AI in Healthcare
1.3 AI-Driven mHealth Communication System and Services
1.3.1 Embedding of Handheld Imaging Platforms With mHealth Devices
1.3.2 The Adaptability of POCUS in Telemedicine
1.4 AI-Driven Body Area Network Communication Technologies and Applications
1.4.1 Features
1.4.2 Communication Architecture of Wireless Body Area Networks
1.4.3 Role of AI in WBAN Architecture
1.4.4 Medical Applications
1.4.5 Nonmedical Applications
1.4.6 Challenges
1.5 AI-Driven IoT Device Communication Technologies and Healthcare Applications
1.5.1 AI’s and IoT’s Role in Healthcare
1.5.2 Creating Efficient Communication Framework for Remote Healthcare Management
1.5.3 Developing Autonomous Capability is Key for Remote Healthcare Management
1.5.4 Enabling Data Privacy and Security in the Field of Remote Healthcare Management
1.6 AI-Driven Augmented and Virtual Reality–Based Communication Technologies and Healthcare Applications
1.6.1 Clinical Applications of Communication-Based AI and Augmented Reality
1.6.2 Surgical Applications of Communication-Based on Artificial Intelligence and Augmented Reality
References
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2
Pneumatic Position Servo System Using Multi-Variable Multi-Objective Genetic Algorithm–Based Fractional-Order PID Controller
2.1 Introduction
2.2 Pneumatic Servo System
2.3 Existing System Analysis
2.4 Proposed Controller and Its Modeling
2.4.1 Modeling of Fractional-Order PID Controller
2.4.1.1 Fractional-Order Calculus
2.4.1.2 Fractional-Order PID Controller
2.5 Genetic Algorithm
2.5.1 GA Optimization Methodology
2.5.1.1 Initialization
2.5.1.2 Fitness Function
2.5.1.3 Evaluation and Selection
2.5.1.4 Crossover
2.5.1.5 Mutation
2.5.2 GA Parameter Tuning
2.6 Simulation Results and Discussion 2.6.1 MATLAB Genetic Algorithm Tool Box
2.6.2 Simulation Results
2.6.2.1 Reference = 500 (Error)
2.6.2.2 Reference = 500
2.6.2.3 Reference = 1,500
2.6.2.4 Analysis Report
2.7 Hardware Results
2.7.1 Reference = 500
2.7.2 Reference = 1,500
2.8 Conclusion
References
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