Two-terminal reliability analysis for multi-phase communication networks

Lu, Jiming; Innal, Fares; Xiao-yue, WU; Liu, Yiliu; Lundteigen, Mary Ann

doi:10.17531/ein.2016.3.14

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Methods for analyzing network reliability are usually based on Boolean algebra, error tree, diagram, and BDD. In the 1990s, a binary decision diagram (BDD) became increasingly popular for network analysis [2]. Lee and Akers first introduced the general concept of BDDs but later spread by Bryant, where it turns out that, according to the European Interactive Digital Advertising Alliance (EDAA) order and reduction rules, BDDs are a standard form of representation.…”

Section: Binary Decision Diagramsmentioning

confidence: 99%

Case studies for binary decision diagram for 5G mobile networks optimization

Panagiotis Leliopoulos,

Athanasios Drigas

2024

Int. J. Sci. Res. Arch.

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In this paper, we study the binary decision diagrams and mobile network optimization. As we see about the binary decision diagrams, Lee and Akers first introduced the general concept of binary decision diagrams but later spread by Bryant. It turns out that, according to the European Interactive Digital Advertising Alliance (EDAA) order and reduction rules, binary decision diagrams are a standard form of representation. After that, we study the spanning trees and binary decision diagrams. An advantage of binary decision diagrams-based methods is that binary decision diagrams provide a concise and separate description of system success or failure. The effectiveness of binary decision diagram methods is measured by the size of them, which depends heavily on the selected series.

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Section: Binary Decision Diagramsmentioning

confidence: 99%

Case studies for binary decision diagram for 5G mobile networks optimization

Panagiotis Leliopoulos,

Athanasios Drigas

2024

Int. J. Sci. Res. Arch.

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“…In [10], a matrix is used to represent the direct trust relations among users of a social network, and a breadth-first search is performed to update users' trustworthiness in an iterative manner. In [4], a binary decision diagrams-based method [29]- [31] was proposed to evaluate the trust between two parties, but it is only applicable to the binary trust model.…”

Section: Related Workmentioning

confidence: 99%

Multivalued Decision Diagrams-Based Trust Level Analysis for Social Networks

et al. 2019

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Interactions among different parties within social networks are greatly dependent on trust. Therefore, trust analysis is significant for solving social network related problems such as privacy protect, and rumor tracking and containment. This paper makes advancements in the trust analysis by proposing a reliability model-based algorithm for assessing the trust level of any two parties within a social network. Particularly, a multi-level trust model with the probability distribution is proposed and a multivalued decision diagrams (MDD)-based method is suggested for assessing the trust level of two parties that may be connected through multiple indirect or direct links. These connection paths may be correlated due to sharing a common party or link. Further, the MDD-based method is extended for performing a trust sensitivity analysis with the aim to pinpoint which direct link contributes the most to the trust relationship between two non-directly connected parties within the social network. Dynamics in trust are also investigated. Examples are provided to illustrate the proposed probabilistic MDD-based method for trust and sensitivity analyses. Performance of the proposed method is evaluated through experiments and comparisons with existing trust evaluation methods. INDEX TERMS Multivalued decision diagram, reliability model, sensitivity analysis, trust analysis.

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“…The power generation system deteriorates over time and thus its reliability is time dependent. Binary Decision Diagram (BDD) has been proposed to address a multi-phase network system as discussed by Lu et al [26]. The industrial gas turbine system operation may also be considered as phased-mission system (PMS) since their operation mode usually consists of numerous repetitive phases.…”

Section: Introductionmentioning

confidence: 99%

Industrial gas turbine operating parameters monitoring and data-driven prediction

Pawełczyk¹,

Fulara²,

Sepe³

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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The article reviews traditional and modern methods for prediction of gas turbine operating characteristics and its potential failures. Moreover, a comparison of Machine Learning based prediction models, including Artificial Neural Networks (ANN), is presented. The research focuses on High Pressure Compressor (HPC) recoup pressure level of 4th generation LM2500 gas generator (LM2500+G4) coupled with a 2-stage High Speed Power Turbine Module. The researched parameter is adjustable and may be used to balance net axial loads exerted on thrust bearing to ensure stable gas turbine operation, but its direct measurement is technically difficult implicating the need to indirect measurement via set of other gas turbine sensors. Input data for the research have been obtained from BHGE manufactured and monitored gas turbines and consists of real-time data extracted from industrial installations. Machine learning models trained using the data show less than 1% Mean Absolute Percentage Error (MAPE) as obtained with the use of Random Forest and Gradient Boosting Regression models. Multilayer Perceptron Artificial Neural Networks (MLP ANN) models are reviewed, and their performance checks inferior to Random Forest algorithm-based model. The importance of hyperparameter tuning and feature engineering is discussed.

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Two-terminal reliability analysis for multi-phase communication networks

Cited by 7 publications

References 41 publications

Case studies for binary decision diagram for 5G mobile networks optimization

Case studies for binary decision diagram for 5G mobile networks optimization

Multivalued Decision Diagrams-Based Trust Level Analysis for Social Networks

Industrial gas turbine operating parameters monitoring and data-driven prediction

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