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Published Articles and Papers

  • T. Kurtoglu, O.J. Mengshoel, and S. Poll "A framework for systematic benchmarking of monitoring and diagnostic systems," International Conference on Prognostics and Health Management (PHM-2008), pp. 1-13, October 2008.
  • O. J. Mengshoel, D. C. Wilkins, and D. Roth, “Controlled Generation of Hard and Easy Bayesian Networks: Impact on Maximal Clique Tree in Tree Clustering”. Artificial Intelligence, 170(16–17), October 2006, pp. 1137–1174.
  • O. J. Mengshoel, “Designing Resource-Bounded Reasoners using Bayesian Networks: System Health Monitoring and Diagnosis”, In Proc. of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  • O. J. Mengshoel, “Macroscopic Models of Clique Tree Growth for Bayesian Networks”. In Proc. of the 22nd National Conference on Artificial Intelligence (AAAI-07). July 2007, Vancouver, Canada, pp. 1256-1262.
  • S. Poll, A. Patterson-Hine, J. Camisa, D. Garcia, D. Hall, C. Lee, O. J. Mengshoel, C. Neukom, D. Nishikawa, J. Ossenfort, A. Sweet, S. Yentus, I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, “Advanced Diagnostics and Prognostics Testbed”, In Proc. of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  • O. J. Mengshoel, A. Darwiche, K. Cascio, M. Chavira, S. Poll, and S. Uckun, “Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft”, In Proc. of the Twentieth Innovative Applications of Artificial Intelligence, Conference (IAAI-08), Chicago, IL, 2008.
  • O. J. Mengshoel, A. Darwiche, and S. Uckun, “Sensor Validation using Bayesian Networks”, In Proc. of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08), Los Angeles, CA, 2008.
  • W. B. Knox and O. J. Mengshoel, “Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study”. Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges , 2009
  • O. J. Mengshoel, S. Poll, and T. Kurtoglu. “Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft”. Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges , 2009
  • B. W. Ricks, and O. J. Mengshoel. “ The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems”. Proc. of the 20th International workshop on Principles of Diagnosis (DX-09) Stockholm, Sweden, 2009
  • O. J. Mengshoel, M. Chavira, K. and Cascio, and S. Poll, and A. Darwiche, and S. Uckun. “Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. IEEE Transactions onSystems, Man, and Cybernetics, Part A, 2009.

Other Relevant References

  • R. L. Bickford, T. W. Bickmore, and V. A. Caluori, “Real-Time Sensor Validation for Autonomous Flight Control”, In Proc. 33rd Joint Propulsion Conference and Exhibit Seattle, WA, July 1997.
  • T. W. Bickmore, “A Probabilistic Approach to Sensor Data Validation”, In Proc. 28th Joint Propulsion Conference and Exhibit, Nashville, TN, July 1992.
  • R. M. Button and A. Chicatelli, “Electrical Power System Health Management”, In Proc. 1st International Forum on Integrated System Health Engineering and Management in Aerospace, November 2005, Napa, CA.
  • M. Chavira and A. Darwiche, “Compiling Bayesian Networks Using Variable Elimination”, In Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), January 2007, pp. 2443 – 2449.
  • S. Ferrari and A. Vaghi, “Demining Sensor Modeling and Feature-Level Fusion by Bayesian Networks”, IEEE Sensors Journal, Vol. 6, No. 2, April 2006.
  • M. Chavira, M. and A. Darwiche, “Compiling Bayesian Networks with Local Structure”, In Proc. of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), 2005, 1306-1312.
  • S. Lauritzen and D. J. Spiegelhalter, “Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems (with Discussion)”, Journal of the Royal Statistical Society series B, Vol. 50, No. 2, 1988, pp. 157-224.
  • U. Lerner, R. Parr, D. Koller, and G. Biswas, “Bayesian fault detection and diagnosis in dynamic systems”, In Proc. of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), 2000, pp. 531–537.
  • E. Liu and D. Zhang, “Diagnosis of Component Failures in Space Shuttle Main Engines using Bayesian Belief Networks: A Feasibility Study”, In Proc. 14th IEEEE International Conference on Tools with Artificial Intelligence (ICTAI-02), 2002.
  • W. A. Maul, K. J. Melcher, A. K. Chicatelli, and T. S. Sowers, “Sensor Data Qualification for Autonomous Operation of Space Systems”, In AAAI Fall Symposium on Spacecraft Autonomy: Using AI to Expand Human Space Exploration, Arlington, VA, October 2006.
  • A. Darwiche, “A Differential Approach to Inference in Bayesian Networks”, Journal of the ACM, Volume 50, Number 3, pp. 280-305, 2003.
  • J. D. Park and A. Darwiche, “Complexity Results and Approximation Strategies for MAP Explanations”, Journal of Artificial Intelligence Research (JAIR), Vol. 21, 2004, pp. 101-133.
  • F. Figueroa and J. Schmalzel, “Rocket Testing and Integrated System Health Management”, In Condition Monitoring and Control for Intelligent Manufacturing, W. Gao (ed), Springer Verlag, 2006, pp. 373-392.
  • D. Koller and X. Boyen, “Exploiting the Architecture of Dynamic Systems,” In Proc. of the 16th National Conference on Artificial Intelligence (AAAI-99), July 1999, pp. 313-320.
  • J. Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference”, Morgan Kaufmann, 1988.
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