Special Issue "Modeling and Analysis of Autonomous Intelligence"

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


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.

• 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
  • Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene
  • Abstract The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability. Localization of mobile robot is increasingly important for the printing of buildings in the construction scene. Although many available studies on the localization have been conducted, only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes. To realize the accurate localization of mobile robot in designated stations, we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map. Then, the… More
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  • Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model
  • Abstract In this paper, we propose an improved deep residual network model to recognize human actions. Action data is composed of channel state information signals, which are continuous fine-grained signals. We replaced the traditional identity connection with the shrinking threshold module. The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components. We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action. To verify the validity of the experiment more accurately, we collected action data… More
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  • Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion
  • Abstract Traffic flow statistics have become a particularly important part of intelligent transportation. To solve the problems of low real-time robustness and accuracy in traffic flow statistics. In the DeepSort tracking algorithm, the Kalman filter (KF), which is only suitable for linear problems, is replaced by the extended Kalman filter (EKF), which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient (HOG) of the target. The multi-target tracking framework was constructed with YOLO V5 target detection algorithm. An efficient and long-running Traffic Flow Statistical framework (TFSF) is established based on the tracking framework. Virtual lines are set up… More
  •   Views:504       Downloads:617        Download PDF

  • Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron
  • Abstract Autonomous intelligence plays a significant role in aviation security. Since most aviation accidents occur in the take-off and landing stage, accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely. In this study, an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron. Firstly, based on channels of color histogram, the pre-estimated object probability map is employed to reduce searching computation, and the optimization of the disturbance suppression options can make good resistance to similar areas around the object. Then… More
  •   Views:505       Downloads:463        Download PDF

  • Method for Collision Avoidance in Spacecraft Rendezvous Problems with Space Objects in a Phasing Orbit
  • Abstract As the number of space objects (SO) increases, collision avoidance problem in the rendezvous tasks or re-constellation of satellites with SO has been paid more attention, and the dangerous area of a possible collision should be derived. In this paper, a maneuvering method is proposed for avoiding collision with a space debris object in the phasing orbit of the initial optimal solution. Accordingly, based on the plane of eccentricity vector components, relevant dangerous area which is bounded by two parallel lines is formulated. The axises of eccentricity vector system pass through the end of eccentricity vector of phasing orbit in… More
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  • 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
  •   Views:777       Downloads:664        Download PDF