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Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences.

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Machine Learning Techniques for Increasing Efficiency of the Robot's Sensor and Control Information Processing.

Yuriy Kondratenko1, Igor Atamanyuk2,3, Ievgen Sidenko1

  • 1Intelligent Information Systems Department, Petro Mohyla Black Sea National University, 68th Desantnykiv Str. 10, 54003 Mykolaiv, Ukraine.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study explores machine learning for robot sensor and control systems, enhancing object recognition accuracy with advanced neural network architectures. The findings aid in selecting efficient design approaches and machine learning methods for robotic applications.

Keywords:
canonical decompositionclassificationcontrol systemfuzzy logicmachine learningneural networkpattern recognitionreal-time systemroboticssensor

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Area of Science:

  • Robotics and Control Systems
  • Machine Learning Applications
  • Artificial Intelligence in Automation

Background:

  • Real-time systems are crucial for industrial applications like process control, automation, and robotics.
  • Robot mission efficiency relies heavily on sensor and control systems for tasks such as trajectory planning and object recognition.
  • Machine learning (ML) offers advanced methods for processing sensor and control information in robotic systems.

Purpose of the Study:

  • To analyze approaches for real-time sensor and control information processing using machine learning.
  • To investigate successful ML applications in synthesizing robot sensor and control systems.
  • To evaluate the performance of different ML models and techniques for object recognition in robotics.

Main Methods:

  • Analysis of existing approaches for real-time sensor and control data processing.
  • Implementation of machine learning models, including YOLOv2 and ResNet34 architectures, for object recognition and classification.
  • Utilizing Swift programming language and CreateML framework for control system design and neural network creation.
  • Application of fine-tuning technology and dataset size impact analysis with ResNet34 architecture.

Main Results:

  • Expanded intelligent control system capabilities to recognize five geometric object classes (cube, cylinder, sphere, pyramid, cone).
  • Achieved 100% validation accuracy for object recognition using CreateML (YOLOv2).
  • Demonstrated superior training (98.02%) and testing (98.0%) accuracy with Torch (ResNet34) compared to CreateML, with reduced training time.
  • Attained high training (99.75%) and testing (99.2%) accuracy using Torch (ResNet34) with fine-tuning.
  • Analyzed the impact of dataset size on recognition accuracy with ResNet34 and fine-tuning.

Conclusions:

  • Machine learning significantly enhances the capabilities of robot sensor and control systems.
  • ResNet34 architecture with fine-tuning offers efficient and accurate object recognition for robotic applications.
  • The findings provide guidance for selecting optimal design approaches, ML methods, and computer technologies for robotic system development.