@inproceedings{abab031119474341b1bb111f0cd3d532,
title = "On the Reliability of Dynamical Stochastic Binary Systems",
abstract = "In system reliability analysis, the goal is to understand the correct operation of a multi-component on-off system, i.e., each component can be either working or not, and each component fails randomly. The reliability of a system is the probability of correct operation. Since the reliability evaluation is a hard problem, the scientific literature offers both efficient reliability estimations and exact exponential-time evaluation methods. In this work, the concept of Dynamical Stochastic Binary Systems (DSBS) is introduced. Samaniego signature provides a method to find the reliability and Mean-Time-to-Failure of a DSBS. However, we formally prove that the computation of Samaniego signature belongs to the hierarchy of # P -Complete problems. The interplay between static and dynamic models is here studied. Two methodologies for the reliability evaluation are presented. A discussion of its applications to structural reliability and analysis of dependent failures in the novel setting of DSBS is also included.",
keywords = "Computational complexity, Crude Monte Carlo, Network optimization, Reliability, Samaniego signature",
author = "Guido Lagos and Pablo Romero",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 ; Conference date: 19-07-2020 Through 23-07-2020",
year = "2020",
doi = "10.1007/978-3-030-64583-0_46",
language = "English",
isbn = "9783030645823",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "516--527",
editor = "Giuseppe Nicosia and Varun Ojha and {La Malfa}, Emanuele and Giorgio Jansen and Vincenzo Sciacca and Panos Pardalos and Giovanni Giuffrida and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science - 6th International Conference, LOD 2020, Revised Selected Papers",
}