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    Bayesian Estimation of A Periodically-Releasing Biochemical Source Using Sensor Networks

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    Date
    2018
    Author
    Hu, Liang;
    Su, Jinya;
    Hutchinson, Michael;
    Liu, Cunjia;
    Chen, Wen-Hua
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    Abstract
    This paper develops a Bayesian estimation method to estimate source parameters of a biochemical source using a network of sensors. Based on existing models of continuous and instantaneous releases, a model of discrete and periodic releases is proposed, which has extra parameters such as the time interval between two successive releases. Different from existing source term estimation methods, based on the sensor characteristic of chemical sensors, the zero readings of sensors are exploited in our algorithm where the zero readings may be caused by the concentration being below the threshold of the sensors. Two types of Bayesian inference algorithms for key parameters of the sources are developed and their particle filtering implementation is discussed. The efficiency of the proposed algorithms for periodic release is demonstrated and verified by simulation where the algorithm with the exploitation of the zero readings significantly outperforms that without.
    Description
    Citation : Hu, L. et al. (2018) Bayesian Estimation of A Periodically-Releasing Biochemical Source Using Sensor Networks, UKACC 11th International Conference on Control (CONTROL), 2018.
    URI
    http://hdl.handle.net/2086/16287
    DOI
    https://doi.org/10.1109/control.2018.8516751
    Research Group : DIGITS
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2970]

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