Monte Carlo Network Reliability Ranking Estimation

Hui, Kin-Ping

doi:10.1109/tr.2006.890898

Cited by 16 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the two networks are similar, the noise can become so significant that it may impair the accuracy of the comparisons. Hui et al proposed a coupled approach to estimate reliability difference of two similar networks with high confidence [17]. In this section we demonstrate how a similar concept can be used and specialized to compare many similar networks very effectively.…”

Section: Reliability Rankingmentioning

confidence: 98%

“…The concept of Edge Relocated Networks [17] refers to networks having the same number of edges with matched link reliabilities 1 . The differences between the networks are thus restricted to a few links being reconnected to different nodes.…”

Section: Edge Relocated Networkmentioning

confidence: 99%

“…Since the edge relocated networks share the same edge reliability vector p, it is proven [17] that their reliability can be compared very efficiently. The idea is to sample the edge states and observe them in different edge relocated networks.…”

Section: Coupled Samplingmentioning

confidence: 99%

“…When comparing reliability of networks, the Coupled CP proposed in [17] is very efficient in finding the reliability difference of two networks. It can achieve 10 11 times speedup over the best known independent sampling scheme.…”

Section: Coupled Samplingmentioning

confidence: 99%

See 3 more Smart Citations

Applications of the Cross-Entropy Method in Reliability

Kroese

Hui

2007

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Section: Reliability Rankingmentioning

confidence: 98%

Section: Edge Relocated Networkmentioning

confidence: 99%

Section: Coupled Samplingmentioning

confidence: 99%

Section: Coupled Samplingmentioning

confidence: 99%

See 2 more Smart Citations

Applications of the Cross-Entropy Method in Reliability

Kroese

Hui

2007

Studies in Computational Intelligence

Self Cite

View full text Add to dashboard Cite

“…MC methods are also key for node ranking and prioritisation of assets to further management and rehabilitation plans. For instance, Hui [72] proposed MC methods as a criterion for prioritising network assets in order to approach a reliability ranking for maintenance issues. The following bullet points gather some main features of node raking in networks.…”

Section: Node Ranking In Complex Networkmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

View full text Add to dashboard Cite

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

show abstract