Randomized flow model and centrality measure for electrical power transmission network analysis

Zio, Enrico; Piccinelli, Roberta

doi:10.1016/j.ress.2009.11.008

Cited by 63 publications

(29 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When studying faults and fault propagation in physical networks, complex networks such as smart grid, communication, highway, traffic networks, centrality plays a somewhat different role than in social networks [4].…”

Section: Introductionmentioning

confidence: 99%

Structural and functional analytics for community detection in large-scale complex networks

Chopade

Zhan

2015

Journal of Big Data

View full text Add to dashboard Cite

Community structure is thought to be one of the main organizing principles in most complex networks. Big data and complex networks represent an area which researchers are analyzing worldwide. Of special interest are groups of vertices within which connections are dense. In this paper we begin with discussing community dynamics and exploring complex network structural parameters. We put forward structural and functional models for analyzing complex networks under situations of perturbations. We introduce modified adjacency and modified Laplacian matrices. We further introduce network or degree centrality (weighted Laplacian centrality) based on modified Laplacian, weighted micro-community centrality. We discuss its robustness and importance for micro-community detection for social and technological complex networks with overlapping communities. We also introduce 'k-clique sub-community' overlapping community detection based on degree and weighted micro-community centrality. The proposed algorithms use optimal partition of k-clique sub-community for modularity optimization. We establish relationship between degree centrality and modularity. This proposed method with modified adjacency matrix helps us solve NP-hard problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Structural and functional analytics for community detection in large-scale complex networks

Chopade

Zhan

2015

Journal of Big Data

View full text Add to dashboard Cite

show abstract

“…As for CIM of network structures, Barker et al (2013) have proposed a resilience-based network CIM method. Centrality measure is used to identify the most important elements in an electrical power transmission system (Zio and Piccinelli 2010). In the case of manufacturing systems, network modeling is quite different from that of communication networks or power transmission systems, on account that manufacturing processes are relatively fixed if process planning is determined before.…”

Section: Vulnerabilities Identification By Using Cimmentioning

confidence: 99%

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Zhang

et al. 2015

J Intell Manuf

View full text Add to dashboard Cite

Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods. CNCComputer numerical control DNC Distributed numerical control ERP Enterprise resource planning FT Failure times MES Manufacturing execution system MPT Machine processing time MTBF Mean time between failure MTTF Mean time to failure MTTR Mean time to repair OEE Overall equipment effectiveness RFID Radio frequency identification WDN Weighted and directed network WIP Work-in-process t, T Sequence number of time intervals (number of all intervals is T ) i, I t Sequence number of products items (number of all items is I t ) j, J t Sequence number of processes during t (J t is the maximum value) k, K Sequence number of equipment (number of all equipment is K ) G t WDN during t M k Equipment k r k Failure rate of M k F T tk Failure times of M k during t T total Total time V ti Volume size of item i during t B ti j Processing time of item i for the jth process during t x ti jk Be equal to 1 if M k is used for machining the jth process of item i during t, 0 otherwise MT B F k MTBF of M k MT T R k MTTR of M k C k Average maintenance cost for every repair process of M k 123 J Intell Manuf P L tk Production losses for M k during time period t C f k

show abstract

“…Zio and Piccinelli [19] have highlighted the importance of considering the actual service capacities of nodes as well as other parameters such as the probabilities of node failures and the fact that the flows between network nodes are not restricted to direct, shortest paths as typically assumed. For these reasons, Zio and Piccinelli extend the topological concept of betweenness centrality to account for random flow propagation across a network.…”

Section: Related Workmentioning

confidence: 99%

Using Centrality Measures in Dependency Risk Graphs for Efficient Risk Mitigation

Stergiopoulos

Theocharidou

Kotzanikolaou

et al. 2015

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Randomized flow model and centrality measure for electrical power transmission network analysis

Cited by 63 publications

References 19 publications

Structural and functional analytics for community detection in large-scale complex networks

Structural and functional analytics for community detection in large-scale complex networks

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Using Centrality Measures in Dependency Risk Graphs for Efficient Risk Mitigation

Contact Info

Product

Resources

About