Special Issue "Modeling and Analysis of Autonomous Intelligence"

Submission Deadline: 31 December 2021
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Guest Editors
Prof. Shiping Wen, University of Technology Sydney, Australia
Prof. Yin Yang, Hamad Bin Khalifa University, Qatar

Summary

For intelligent control, the implication is that, without a similar brain-body-environment triumvirate, self-driving cars, drones and agile robots will be forever limited to environments they have been programmed to navigate. Currently, progress in autonomy for these artificial agents is constrained by the available learning algorithms and design methods, most of which only work in static environments. As a result, they exhibit crippling fragility in unstructured and changing environments. Therefore, this topic aims to promote the development of autonomous control methods to be adapted to dynamically changing tasks and environments in real-time. Therefore, this topic is suitable for a special issue of CMES.


Keywords
• Autonomous intelligence
• Model design of general deep networks
• Neurodynamical analysis and application
• Dynamic analysis of deep neural networks
• Efficient training analysis for deep learning
• Deep neural network based control method
• Deep neural networks for image processing
• Robotic system modeling and its application
• Mathematical analysis of deep neural networks
• New meta-heuristic algorithm and its application
• Deep neural network based algorithms in smart grids
• New model of memristor-based system and its application
• Novel deep network architecture for emerging nano-devices
• Plug-in Electric Vehicle (PEV) management via learning systems
• The other related topics

Published Papers
  • Remote Sensing Monitoring Method Based on BDS-Based Maritime Joint Positioning Model
  • Abstract Complicated sea conditions have a serious impact on ship navigation safety and even maritime accidents. Accordingly, this paper proposes a remote sensing monitoring method based on the Beidou Navigation Satellite System (BDS) maritime joint positioning model. This method is mainly based on the BDS and multiple Global Navigation Satellite Systems (GNSS) to build a data fusion model, which can capture more steady positioning, navigation, and timing (PNT) data. Compared with the current Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) mandatory used by the International Maritime Organization (IMO), this model has the characteristics of more accurate positioning data… More
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