Visible to the public Biblio

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2018-05-27
M. A. Suresh, R. Stoleru, E. M. Zechman, B. Shihada.  2013.  On Event Detection and Localization in Acyclic Flow Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 43:708-723.
M. A. Suresh, L. Smith, A. Rasekh, R. Stoleru, M. K. Banks, B. Shihada.  2014.  Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems. 2014 IEEE 28th International Conference on Advanced Information Networking and Applications. :673-680.
M. Suresh, U. Manohary, A. G. Ry, R. Stoleru, M. K. M. Sy.  2014.  A cyber-physical system for continuous monitoring of Water Distribution Systems. 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :570-577.
2018-05-25
S. Munir, J. A. Stankovic.  2014.  FailureSense: Detecting Sensor Failure Using Electrical Appliances in the Home. 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems. :73-81.
H. T. Yang, K. S. Liu, J. Gao, S. Lin, S. Munir, K. Whitehouse, J. Stankovic.  2017.  Reliable Stream Scheduling with Minimum Latency for Wireless Sensor Networks. 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). :1-9.
2018-05-17
G. Greenwood, M. Podhradsky, J. Gallagher, E. Matson.  2015.  A Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle. 2015 IEEE Symposium Series on Computational Intelligence. :1073-1080.

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

2018-05-14
Y. Cui, R. Kavasseri, S. Brahma.  2016.  Dynamic state estimation assisted posturing for generator out-of-step protection. 2016 IEEE Power and Energy Society General Meeting (PESGM). :1-5.