Real-Time Systems Publications of Kenneth Paul Baclawski

1. M. Kokar and K. Baclawski. Control theory based dynamic scan scheduler. Northeastern University. (2000)

2. M. Kokar and K. Baclawski. Control theory based distributed dynamic scan scheduler. Northeastern University. (2000)

3. Y. Xun, M. Kokar and K. Baclawski. Using a Control Architecture for Real-Time Dynamic Resource Allocation. Northeastern University, College of Engineering. (2002) [pdf]

4. Y. Xun, M.M. Kokar and K. Baclawski. Control based sensor management for a multiple radar monitoring scenario. Information Fusion: An International Journal on Multi-Sensor, Multi-Source Information Fusion 5(4):49-63. Elsevier Science Publishers. (2004) [pdf]

5. Y. Xun, M. Kokar and K. Baclawski. Using a Task-Specific QoS for Controlling Sensing Requests and Scheduling. In The 3rd IEEE Int. Sympos. Network Computing and Applications 269-276. (2004) [pdf]

6. K. Baclawski, E.S. Chan, D. Gawlick, A. Ghoneimy, K. Gross, Z.H. Liu and X. Zhang. Framework for Ontology-Driven Decision Making. Applied Ontology 12(3-4):245-273. IOS Press, The Netherlands. https://bit.ly/2LYPszt (2017) [pdf]

7. K. Baclawski, K. Gross, E.S. Chan, D. Gawlick, A. Ghoneimy and Z.H. Liu. Self-Adaptive Dynamic Decision Making Processes. In IEEE Conference on Cognitive and Computational Aspects of Situation Management . http://bit.ly/2fOG9G2 (2017) [pdf]

8. K. Gross, K. Baclawski, E.S. Chan, D. Gawlick, A. Ghoneimy and Z.H. Liu. A Supervisory Control Loop with Prognostics for Human-in-the-Loop Decision Support and Control Applications. In IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA 2017) . http://bit.ly/2fPlG45 (2017) [pdf]

9. M. Liu, P. Sonderegger, K. Baclawski, D. Gawlick, A. Chystiakova, G. Wang, Z.H. Liu, H. Balasubramanian and K.C. Gross. Optimizing a Prognostic-Surveillance System to Achieve a User-Selectable Functional Objective. United States Patent and Trademark Office. (February 2, 2023) [pdf]

10. J. Courtney, K. Baclawski, D. Gawlick, K.C. Gross, G. Wang, A. Chystiakova, P. Sonderegger and Z.H. Liu. Machine Learning Traceback-Enabled Decision Rationales as Models for Explainability. United States Patent and Trademark Office. (July 28, 2022) [pdf]

11. J. Rohrkemper, P. Sonderegger, A. Chystiakova, K. Baclawski, D. Gawlick, K.C. Gross, Z. Liu and G. Wang. Root Cause Analysis for Deterministic Machine Learning Model. United States Patent and Trademark Office. (March 2, 2023) [pdf]

12. J. Rohrkemper, K. Baclawski, D. Gawlick, K.C. Gross, G. Wang, A. Chystiakova, P. Sonderegger and Z. Liu. Recommendation Generation Using Machine Learning Data Validation. United States Patent and Trademark Office. (May 18, 2023) [pdf]

13. P. Sonderegger, K. Baclawski, G. Wang, A. Chystiakova, D. Gawlick, Z.H. Liu and K.C. Gross. Automatically adapting a prognostic-surveillance system to account for age-related changes in monitored assets. United States Patent and Trademark Office. (October 10, 2023) [pdf]

14. M. Gerdes, K. Baclawski, D. Gawlick, K.C. Gross, G. Wang, A. Chystiakova, P. Sonderegger and Z.H. Liu. Dependency Checking for Machine Learning Models. United States Patent and Trademark Office. (November 23, 2023) [pdf]

15. K. Ru, K. Baclawski, P. Sonderegger, D. Gawlick, A. Chystiakova, G. Wang, M. Gerdes and K.C. Gross. Bias Detection in Machine Learning Tools. United States Patent and Trademark Office. (August 1, 2024) [pdf]