Critical infrastructure operation process related to climate-weather change process including extreme weather hazard

• Autorzy: Kołowrocki K. , Soszyńska-Budny J. , Torbicki M.
• Języki publikacji: EN

Abstrakty

EN

The operation process of the critical infrastructure is considered and its operation states are introduced. The semi-Markov process is used to construct a general probabilistic model of the critical infrastructure operation process. The semi-Markov process is used to construct a general probabilistic model of the climate-weather change process for the critical infrastructure operating area.

Słowa kluczowe

EN

critical infrastructure; operation; prediction; climate-weather change

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2017
• Tom: Vol. 8, No. 2
• Strony: 25--40
• Opis fizyczny: Bibliogr. 15 poz.

Twórcy

autor

Kołowrocki K.

• Maritime University, Gdynia, Poland

autor

Soszyńska-Budny J.

• Maritime University, Gdynia, Poland

autor

Torbicki M.

• Maritime University, Gdynia, Poland

Bibliografia

• 1. Barbu V., Limnios N., Empirical estimation for discrete-time semi-Markov processes with applications in reliability. Journal of Nonparametric Statistics, Vol. 18, No. 7-8, 483-498, 2006
• 2. EU-CIRCLE Report D2.1-GMU1, Overview of existing climate and weather models that can be utilized for assessing the climate related hazards influence on the operation and safety of critical infrastructures, 2016
• 3. EU-CIRCLE Report D2.1-GMU2, Modelling outside dependences influence on Critical Infrastructure Safety (CIS) - Modelling Critical Infrastructure Operation Process (CIOP) including Operating Environment Threats (OET), 2016
• 4. EU-CIRCLE Report D2.1-GMU4, Modelling outside dependences influence on Critical Infrastructure Safety (CIS) - Designing Critical Infrastructure Operation Process General Model (CIOPGM) related to Operating Environment Threats (OET) and Extreme Weather Hazards (EWH) by linking CIOP and C-WCP models, 2016
• 5. Ferreira F., Pacheco A., Comparison of level- crossing times for Markov and semi-Markov processes. Statistics and Probability Letters, Vol. 7, No 2, 151-157, 2007
• 6. Glynn P.W., Haas P.J., Laws of large numbers and functional central limit theorems for generalized semi Markov processes. Stochastic Models, Vol. 22, No 2, 201-231, 2006
• 7. Grabski F., (2002) Semi-Markov Models of Systems Reliability and Operations Analysis. System Research Institute, Polish Academy of Science, 2002 (in Polish)
• 8. Kołowrocki K., Reliability of Large and Complex Systems, Amsterdam, Boston, Heidelberd, London, New York, Oxford, Paris, San Diego, San Francisco, Singapore, Sidney, Tokyo, Elsevier, 2014b
• 9. Kołowrocki K., Soszyńska J., Methods and algorithms for evaluating unknown parameters of operation processes of complex technical systems. Summer Safety & Reliability Seminars. Journal of Polish Safety and Reliability Association, Issue 3, Vol. 1, 2, 211-222, 2009d
• 10. Kołowrocki K., Soszyńska-Budny J., Reliability and Safety of Complex Technical Systems and Processes: Modeling - Identification - Prediction - Optimization, London, Dordrecht, Heildeberg, New York, Springer, 2011
• 11. Limnios N., Oprisan G., Semi-Markov Processes and Reliability. Birkhauser, Boston, 2005
• 12. Limnios N., Ouhbi B., Sadek A., Empirical estimator of stationary distribution for semi-Markov processes. Communications in Statistics-Theory and Methods, Vol. 34, No. 4, 987-995 12, 2005
• 13. Macci C., Large deviations for empirical estimators of the stationary distribution of a semi-Markov process with finite state space. Communications in Statistics- Theory and Methods, Vol. 37, No. 19,3077-3089, 2008
• 14. Mercier S., Numerical bounds for semi-Markovian quantities and application to reliability. Methodology and Computing in Applied Probability, Vol. 10, No. 2, 179-198, 2008
• 15. Rice J. A., Mathematical statistics and data analysis. Duxbury. Thomson Brooks/Cole. University of California. Berkeley, 2007

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).