Interpretation and Gas Distribution Net Threat Attack Markov models

Oleg Syrovatsky

doi:10.1051/e3sconf/202022103008

All issues

Volume 221 (2020)

E3S Web Conf., 221 (2020) 03008

References

Open Access

Issue		E3S Web Conf. Volume 221, 2020 Energy Systems Environmental Impacts (ESEI 2020)


Article Number		03008
Number of page(s)		7
Section		Power Engineering
DOI		https://doi.org/10.1051/e3sconf/202022103008
Published online		17 December 2020

URL: https://ggf.bsu.edu.ru/Conferences/Conf_2011/Materials/Gribanov.htm (Date 12/10/2020) [Google Scholar]
Ejofodomi, O., & Ofualagba, G. Automated volume measurement, adulteration detection, and tracking ofpetroleum products. Paper presented at the Society of Petroleum Engineers SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020. [Google Scholar]
Ofualagba, G., & Ejofodomi, O. Automated oil and gas pipeline vandalism detection system. Paper presented at the Society of Petroleum Engineers SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020. [Google Scholar]
Visalakshi, P., Rohith, M., & Surya, C. Fuel theft protection and management system. International Journal of Advanced Science and Technology, 29(6), 2020, pp. 2606-2612. [Google Scholar]
Yang, X., Yi, X., Chen, S., Ruan, S., Zhang, J., Zheng, Y., & Li, T. You are how you use: Catching gas theft suspects among diverse restaurant users. Paper presented at the International Conference on Information and Knowledge Management, Proceedings, 2020. pp. 2885-2892. doi:10.1145/3340531.3412751 [Google Scholar]
Akimoto, T., Barkai, E., & Radons, G. Infinite invariant density in a semi-markov process with continuous state variables. Physical Review E, 101(5), 2020. doi:10.1103/PhysRevE.101.052112 [Google Scholar]
Kouřim, T., & Volf, P. Discrete random processes with memory: Models and applications. Applications of Mathematics, 65(3), 2020. pp. 271-286. doi:10.21136/AM.2020.0335-19 [CrossRef] [Google Scholar]
Ortu, M., Conversano, C., Marchesi, M., Tonelli, R., Counsell, S., & Destefanis, G. Describing software developers affectiveness through markov chain models. Electronic Journal of Applied Statistical Analysis, 13(1), 2020. pp. 96-127. doi:10.1285/i20705948v13n1p96 [Google Scholar]
Xu, Z., Ni, H., Reza Karimi, H., & Zhang, D. A markovian jump system approach to consensus of heterogeneous multiagent systems with partially unknown and uncertain attack strategies. International Journal of Robust and Nonlinear Control, 30(7), 2020. pp. 3039-3053. doi:10.1002/rnc.4923 [CrossRef] [Google Scholar]
Yang, W., Deng, M., Tang, J., & Jin, R. On the use of markov chain models for drought class transition analysis while considering spatial effects. Natural Hazards, 103(3), 2020. pp. 2945-2959. doi:10.1007/s11069-020-04113-6 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] URL: https://ggf.bsu.edu.ru/Conferences/Conf_2011/Materials/Gribanov.htm (Date 12/10/2020) [Google Scholar]

[2] Ejofodomi, O., & Ofualagba, G. Automated volume measurement, adulteration detection, and tracking ofpetroleum products. Paper presented at the Society of Petroleum Engineers SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020. [Google Scholar]

[3] Ofualagba, G., & Ejofodomi, O. Automated oil and gas pipeline vandalism detection system. Paper presented at the Society of Petroleum Engineers SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020. [Google Scholar]

[4] Visalakshi, P., Rohith, M., & Surya, C. Fuel theft protection and management system. International Journal of Advanced Science and Technology, 29(6), 2020, pp. 2606-2612. [Google Scholar]

[5] Yang, X., Yi, X., Chen, S., Ruan, S., Zhang, J., Zheng, Y., & Li, T. You are how you use: Catching gas theft suspects among diverse restaurant users. Paper presented at the International Conference on Information and Knowledge Management, Proceedings, 2020. pp. 2885-2892. doi:10.1145/3340531.3412751 [Google Scholar]

[6] Akimoto, T., Barkai, E., & Radons, G. Infinite invariant density in a semi-markov process with continuous state variables. Physical Review E, 101(5), 2020. doi:10.1103/PhysRevE.101.052112 [Google Scholar]

[7] Kouřim, T., & Volf, P. Discrete random processes with memory: Models and applications. Applications of Mathematics, 65(3), 2020. pp. 271-286. doi:10.21136/AM.2020.0335-19 [CrossRef] [Google Scholar]

[8] Ortu, M., Conversano, C., Marchesi, M., Tonelli, R., Counsell, S., & Destefanis, G. Describing software developers affectiveness through markov chain models. Electronic Journal of Applied Statistical Analysis, 13(1), 2020. pp. 96-127. doi:10.1285/i20705948v13n1p96 [Google Scholar]

[9] Xu, Z., Ni, H., Reza Karimi, H., & Zhang, D. A markovian jump system approach to consensus of heterogeneous multiagent systems with partially unknown and uncertain attack strategies. International Journal of Robust and Nonlinear Control, 30(7), 2020. pp. 3039-3053. doi:10.1002/rnc.4923 [CrossRef] [Google Scholar]

[10] Yang, W., Deng, M., Tang, J., & Jin, R. On the use of markov chain models for drought class transition analysis while considering spatial effects. Natural Hazards, 103(3), 2020. pp. 2945-2959. doi:10.1007/s11069-020-04113-6 [CrossRef] [Google Scholar]