An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

Wan, Chengpeng; Yan, Xinping; Zhang, Di; Qu, Zhuohua; Yang, Zaili

doi:10.1016/j.tre.2019.03.011

Cited by 184 publications

(97 citation statements)

References 57 publications

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“…FTA/ETA and ER models are tree-structured model and cannot express the correlations among different factors. Bayesian Belief Networks (BBN) [32] are another widely used tools in modelling maritime accidents. A comprehensive literature review is performed in [33], in which the authors highlighted its advantages in expressing complex, weak and uncertain relationships in maritime safety and risk assessment.…”

Section: Collision Avoidance Failure Predictionmentioning

confidence: 99%

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2020

brod

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Qinzhou Port is one of the most important ports in the "Beibu Gulf" of China. It is also the main hub port of the "21st century maritime silk road" strategy. Based on a basic collision risk assessment approach, an Event Sequence Diagram (ESD) model that explains the fourstage collision avoidance decision-making procedure is proposed from the perspectives of perception, cognition, decision, and execution. Using the historical data derived from collision accident reports from the Qinzhou Port waters from 2013 to 2017, as well as the data elicited from expert knowledge, a quantitative evaluation of probability distributions of different collision failure modes is performed. The results are also compared with relevant results from other types of navigation waters to analyse collision risk level of Qinzhou waters. At the same time, the main failures paths of collision avoidance decision making are identified. The proposed model can provide with an overall collision risk picture from a macro perspective.

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Section: Collision Avoidance Failure Predictionmentioning

confidence: 99%

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2020

brod

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“…where ρ is the equilibrium factor of the equation. By using the Lagrange multiplier method [32,33], the constraint P m j¼1 u ij ¼ 1 can be introduced into the Lagrange multiplier λ, and the Eq. (9) can be transformed to…”

Section: Clustering Objective Functionmentioning

confidence: 99%

Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Yin

2019

J Wireless Com Network

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Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node's detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet modifications, multidimensional trust indicators are derived from communication between adjacent sensor nodes, and direct and indirect trust values will be estimated based on the corresponding behaviors of those sensor nodes. In order to improve the validity of trust quantification and ensure the objectivity of evaluation, the entropy weight method is applied to determine the proper value of the weight. Finally, the indirect trust value and direct trust value are synthesized to obtain the overall trust. Experimental results show that the proposed scheme performs well in terms of the identification of malicious node.

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“…This technology is commonly used in channel allocation in wireless networks to avoid thousands of interference. However, in the case of intensive deployment, due to the lack of sufficient channel resources, it may not be possible to apply coloring technology to effectively solve the same layer interference problem [9][10][11][12]. At the same time, due to the relatively small coverage of the base station cell, the same-layer interference often appears as a localization phenomenon, and CR technology can play a key role in obtaining such local interference information, including perception, processing, decision-making, and so on.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

Meng

Liu

2019

J Wireless Com Network

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Aiming at the research of resource allocation and interference management in multi-layer wireless networks in heterogeneous cognitive networks, this paper mainly discusses the resource allocation and interference management in heterogeneous cognitive networks and proposes our proposed cluster-based cooperative interference management scheme. This scheme minimizes the cross-layer interference of multiple base station cells to primary users and avoids the same-layer interference between base station cells through joint channel allocation and power allocation. Through cognitive perception or joint scheduling, the available channel can be obtained by the home base station cell, and then the channel, power and other resources can be allocated according to the related algorithms proposed by us, so as to ensure the harmonious coexistence of the home base station cell and the authorization system such as macrocell. Finally, we validate the proposed algorithm. The simulation results show that the algorithms are effective in reducing interference, but the degree is different.

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An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 184 publications

References 57 publications

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Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

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