Lindsay Allen and John Broderick Receive Best Application Paper Award

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Lindsay Allen  John Broderick

Lindsay Allen (PhD EE:S ’10) and John Broderick (EE:S PhD Candidate) received the Best Application Paper Award at the 2011 IEEE International Conference on Automation Science and Engineering (CASE) for their paper, “Anomaly Detection without a Pre-Existing Formal Model: Application to an Industrial Manufacturing System,” co-authored by Prof. Dawn Tilbury. The QSI (Qualtech Systems Inc.) Best Application Paper Award is awarded to the paper that best represents applied advances in engineering science toward high-impact applications.

Dr. Allen and Broderick applied Allen's Anomaly Detection method, described in her PhD thesis, to data received from a Ford Motor Company manufacturing line with the goal of detecting any problems in the line. The Anomaly Detection method uses knowledge of the events and resources in a discrete event system to create models and detect non-normal behavior. Using this method, they were able to identify the anomaly in the data provided from Ford, as well as another anomalous event preceding the anomaly in question, which indicated a possible cause for the problem.

Dr. Allen is a recent EE:Systems alum who graduated in December 2010, with a PhD in Control Systems. She conducted her research under the direction of Prof. Tilbury. Her PhD dissertation was on verification and fault detection of event-based manufacturing systems, with research areas of interest including control systems and machine learning. Dr. Allen is now an engineer with Creare, a research and development company in Hanover, New Hampshire. She is working on projects related to inspection during machining, hearing assessment, and measuring space weather from Cube satellites. 

Broderick is a second-year EE:Systems PhD student working in the GRRC (Ground Robotics Reliability Center), advised by Profs. Dawn Tilbury and Ella Atkins. He is interested in looking at improving the energy efficiency in area coverage missions for ground robots.

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The IEEE Conference on Automation Science and Engineering (CASE) represents the flagship automation conference of the IEEE Robotics and Automation Society (RAS) and constitutes the primary forum for cross-industry, multidisciplinary research in automation. The goal of IEEE CASE is the broad coverage and dissemination of foundational research in automation among researchers, academics, and industry practitioners. The focus is on scientific methods for automated machines and systems operating in structured environments over long periods, and also on the explicit structuring of environments.


Posted: September 16, 2011 by
Catharine June
EECS/ECE Communications Coordinator
cmsj@umich.edu or 734-936-2965