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  •   Ubiquitous

    1. Motivation

    Why intelligent techniques for ubiquitous systems?

    • Ubiquitous environments are highly dynamic, noisy and unpredictable.
    • Inference of high-level contexts from various information sources (sensors, user profile, web information) is needed because the high-level contexts are not directly sensed
    • Predefinition of service for all configurations is impossible and generation of high-level services from low-level components in real-time is very important

    Figure 1. Context-Awareness between human and ubiquitous robot

    The goals of this research are as follows.

    • Developing an adaptation scheme in the context of middleware and applications
    • Developing appropriate information fusion methods for ubiquitous systems
    • Developing context-aware system for ubiquitous systems
    • Developing basic theories and algorithms of the advanced intelligent models for ubiquitous environment
    2. Proposed Methods
    • Building Bayesian networks for context-awareness of service robots
    • Developing advanced methods for Bayesian networks such as refinement of Bayesian networks, decision networks, and dynamic Bayesian networks
    • Writing our own customized Bayesian network API (learning and inference)
    • Detection of system faults (system crush, malfunction of applications ...) before they happen using probabilistic models
    • Flexible service generation mechanism based on behavior-based approach

    Figure 2. Proposed context-awareness system based on multiple Bayesian networks and rule-based selection scheme


    3. Experimental Results
    • Scenario: Greeting service of service robot
    • Building 15 different Bayesian networks for a number of different situations

    Figure 3. Greeting scenario in the early morning

    Figure 4. Bayesian networks for inference of user's intention in the early morning


    4. Publications
    [1] S.-J. Han, and S.-B. Cho, "Evolutionary neural network for anomaly detection based on program's behavior," IEEE Transactions on Systems, Man and Cybernetics-Part B, 2005.

    [2] K.-J. Kim and S.-B. Cho, "Robust inference of Bayesian networks using speciated evolution and ensemble," Lecture Notes in Artificial Intelligence, vol. 3488, pp. 92-101, 2005.

    [3] K.-J. Kim and S.-B. Cho, "Construction of Bayesian networks for context-aware service robot," The 2nd International Conference on Ubiquitous Robots and Ambient Intelligence, 2005.

    [4] J.-O. Yoo, K.-J. Kim and S.-B. Cho, "Inference of high-level context using fuzzy Bayesian network for music recommendation of service robot," The 2nd International Conference on Ubiquitous Robots and Ambient Intelligence, 2005.

    [5] M.-C. Jung, and S.-B. Cho, "A novel method based on behavior network for web service composition," IEEE International Conference on Next Generation Web Services Practices, 2005.

      Last update : 2019.07.23 @ Softcomputing lab info : we at

      Soft Computing Laboratory,Dept. of Computer Science, Yonsei University,
      50 Yonsei-ro, Seodaemoon-Gu, Seoul, 03722, Korea
      Telephone : +82-2-2123-3877