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Intelligence and Control

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InfoBeyond conducts research on automation, intelligence, learning, and system control for cyber physical system.

Humans can participate in the processing of in cyber-physical systems to improve the system accuracy. However, such human-cyber-physical systems could be inefficient as humans need to manually examine a large amount of data. The existing approaches offer limited capabilities to systems with heterogeneous data; efficient seamless human-machine interactions, and testing and verifications. To address the existing issues, InfoBeyond advocates accurate, robust, and visual-supported machine learning information analytics for human-cyber-physical systems with heterogeneous data as illustrated in the below figure.

InfoBeyond's research contributes in three fold:

  • Semantic machine learning: Our system offers a semantic-enabled machine learning capability to provide an efficient and accurate information extraction and integration on the massive heterogeneous data. Information trustworthiness is also taken into account when performing data fusion.
  • Real time interaction: We conduct research to enable iteratively real-time interactions between humans and cyber systems for an in-depth information analytics. Human can observe process and instruct the information fusion engine via interactively visual interface and peripherals.
  • Testing and verification: We design approach to offer capability for a deep reasoning via hypothesis generation and testing algorithm. The primary tests of our implementation demonstrates the ability to save up to 41% and 76%, respectively, in storage space and processing time compared with the existing approaches.

  


 
 
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