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Performance evaluation based fault tolerant control with actuator saturation avoidance

, Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2006, Beijing, China , pp. 1303-1308. Jiang, J. and Zhang, Y. (2002). Graceful performance degradation in active fault tolerant control systems, Proceedings of the 15th IFAC World Congress b'02, Barcelona, Spain . Jiang, J. and Zhang, Y. (2006). Accepting performance degradation in fault-tolerant control system design, IEEE Transactions on Control Systems Technology 14(2): 284

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Multilayered Autoscaling Performance Evaluation: Can Virtual Machines and Containers Co–Scale?

References Abedi, A. and Brecht, T. (2017). Conducting repeatable experiments in highly variable cloud computing environments, Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE’17, L’Aquila, Italy , pp. 287–292. Al-Dhuraibi, Y., Paraiso, F., Djarallah, N. and Merle, P. (2017). Autonomic vertical elasticity of docker containers with elasticdocker, 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA , pp. 472–479. Bauer, A., Herbst, N. and Kounev, S. (2017). Design and

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High-performance simulation-based algorithms for an alpine ski racer’s trajectory optimization in heterogeneous computer systems

Dynamics 21(2): 193-207. Brodie, M. (2009). Development of Fusion Motion Capture for Optimisation of Performance in Alpine Ski Racing, Ph.D. thesis, Massey University, Wellington. Byrski, A., D˛ebski, R. and Kisiel-Dorohinicki, M. (2012). Agent-based computing in an augmented cloud environment, Computer Systems Science and Engineering 27(1): 7-18. Ceriotti, M. and Vasile, M. (2010). MGA trajectory planning with an ACO-inspired algorithm, Acta Astronautica 67(9-10): 1202-1217. Crauser, A., Mehlhorn, K., Meyer

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Novel fault detection criteria based on linear quadratic control performances

This paper proposes a new approach to designing a relatively simple algorithmic fault detection system that is potentially applicable in embedded diagnostic structures. The method blends the LQ control principle with checking and evaluating unavoidable degradation in the sequence of discrete-time LQ control performance index values due to faults in actuators, sensors or system dynamics. Design conditions are derived, and direct computational forms of the algorithms are given. A simulation example subject to different types of failures is used to illustrate the design process and to demonstrate the effectiveness of the method.

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Analysis of performance bottleneck of P2P grid applications

References ANDRADE, N., AND COSTA, L., AND GERMOGLIO, G., AND CIRNE, W. 2005. Peer-to-peer Grid Computing with the OurGrid Community. In Proceedings of the SBRC, 2005. CALHEIROS, N., AND FERRETO, AND T., ROSE, C. D. 2008. Scheduling anf management of virtual resources in grid sites: the site resource scheduler. In Parallel Processing Letters, 2008. FOSTER, I., AND KESSELMAN, C., AND TUECKE, S. 2001. The anatomy of the grid: Enabling scalable virtual organizations. In International Journal of High Performance

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On Explainable Fuzzy Recommenders and their Performance Evaluation

-Fuzzy Systems: Structures, Learning and Performance Evaluation , Kluwer Academic Publishers, Boston, MA/Dordrecht/London. Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques , Springer, Berlin. Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P. and Nielek, R. (2018). Towards interpretability of the movie recommender based on a neuro-fuzzy approach, 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, Zakopane, Poland, pp. 752–762. Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P

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The performance profile: A multi–criteria performance evaluation method for test–based problems

, Part I , Lecture Notes in Computer Science, Vol. 3102, Springer-Verlag, Berlin/Heidelberg, pp. 501–512. Chong, S.Y., Tiño, P., Ku, D.C. and Xin, Y. (2012). Improving generalization performance in co-evolutionary learning, IEEE Transactions on Evolutionary Computation 16 (1): 70–85. Chong, S.Y., Tiño, P. and Yao, X. (2008). Measuring generalization performance in coevolutionary learning, IEEE Transactions on Evolutionary Computation 12 (4): 479–505. Chong, S.Y., Tiño, P. and Yao, X. (2009). Relationship between generalization and diversity in

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Assessment of the GPC Control Quality Using Non–Gaussian Statistical Measures

, Wiley, Hoboken, NJ, Chapter 6. Harris, T. (1989). Assessment of closed loop performance, Canadian Journal of Chemical Engineering 67(5): 856-861. Hill, I.D., Hill, R. and Holder, R.L. (1976). Algorithm AS 99: Fitting Johnson curves by moments, Journal of the Royal Statistical Society C: Applied Statistics 25(2): 180-189. Horch, A. and Isaksson, A.J. (1998). A modified index for control performance assessment, Proceedings of the 1998 American Control Conference, Philadelphia, PA, USA, Vol. 6, pp. 3430

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A multivariable multiobjective predictive controller

45 (12): 2823-2830. Ben Abdennour, R., Ksouri, M. and Favier, G. (1998). Application of fuzzy logic to the on-line adjustment of the parameters of a generalized predictive controller, Intelligent Automation and Soft Computing 4 (3): 197-214. Berro, A. (2001). Optimisation multiobjectif et strat’egies d”evolution en environment dynamique , Ph.D. thesis, Université des Sciences Sociales Toulouse I, Toulouse. Boussaid, B., Aubrun, C., Abdelkrim, M.N. and Ben Gayed, M.K. (2011). Performance evaluation based fault

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Performance evaluation of MapReduce using full virtualisation on a departmental cloud

.com,skynet.rubyforge.org Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G. and Kozyrakis, C. (2007). Evaluating MapReduce for multi-core and multiprocessor systems, 13th International Conference on High-Performance Computer Architecture (HPCA-13 2007), Phoenix, AZ, USA , pp. 13-24. Robertazzi, T.G. (2003). Ten reasons to use divisible load theory, Computer 36 (5): 63-68. Sandholm, T. and Lai, K. (2009). MapReduce optimization using regulated dynamic prioritization, in J.R. Douceur, A.G. Greenberg, T. Bonald, J. Nieh (Eds

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