SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network

This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical.

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Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments

Knights,

Petrovska,

Prchkovska

2024

Navigating the Internet of Things in the 22nd Century - Concepts, Applications, and Innovations [Working Title]

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The integration of Internet of Things (IoT) technology has profoundly transformed urban life, particularly in the realm of parking management. Smart parking systems harness the capabilities of IoT to optimize parking space utilization, alleviate congestion, and elevate user experience. This chapter delves into the intricate process of data collection within IoT-enabled smart parking environments, with a specific emphasis on the seamless integration of machine learning and artificial intelligence (AI) techniques. By conducting a comprehensive analysis of various data sources, machine learning algorithms, and AI technologies, this chapter elucidates how smart parking systems leverage intelligent data collection and analysis to enhance operational efficiency and effectiveness. Through the convergence of IoT, machine learning, and AI, smart parking systems are poised to revolutionize urban mobility and drive sustainable urban development.

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SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network

Cited by 3 publications

References 35 publications

Diagnostics in Neurotribe Identification: Case Studies

Diagnostics in Neurotribe Identification: Case Studies

Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications

Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments

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