An Information-Geometric Approach to Learning Bayesian Network Topologies from Data

Availability is an economically important metric used to assess the design of marine energy farms. Nowadays, three typical concepts of energy delivery network topologies have been proposed for marine energy conversion systems. Few research works assessing the availability of marine energy conversion systems have been published. Their methodology is only based upon time-consuming Monte Carlo simulations and only covers one maintenance strategy. The objective of this study is to consider different maintenance strategies and quantitatively assess the time-dependent availability of these typical energy delivery network topologies by investigating the working philosophy of these topologies and modelling the logic dependencies of them in a Bayesian network (BN). The working philosophy of each topology is investigated to obtain the logic dependencies of the units in the energy delivery network, by means of qualitative system analysis. A table-like data structure, called hierarchy, is introduced to store the information on the logic dependencies and serves as a basis for establishing the corresponding BN models. A logic gate in the hierarchy can be represented by a conditional probability table in the BN model. The availability of these topologies, as a function of time, can be estimated through the BN models. The optimal topology can be selected, based upon the time-dependent availability.

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An evolutionary algorithmic approach to learning a Bayesian network from complete data

10.1117/12.542371 ◽

2004 ◽

Author(s):

Ferat Sahin ◽

Jason Tillet ◽

Raghuveer Rao ◽

T. M. Rao

Keyword(s):

Bayesian Network ◽

Complete Data ◽

Algorithmic Approach ◽

Approach To Learning

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Learning Bayesian-Network Topologies in Realistic Medical Domains

Medical Data Analysis - Lecture Notes in Computer Science ◽

10.1007/3-540-45497-7_46 ◽

2001 ◽

pp. 302-307 ◽

Cited By ~ 5

Author(s):

Xiaofeng Wu ◽

Peter Lucas ◽

Susan Kerr ◽

Roelf Dijkhuizen

Keyword(s):

Bayesian Network ◽

Network Topologies

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An Online Distributed Boundary Detection and Classiﬁcation Algorithm for Mobile Sensor Networks

REV Journal on Electronics and Communications ◽

10.21553/rev-jec.158 ◽

2017 ◽

Author(s):

Hung Pham Duy

Keyword(s):

Sensor Networks ◽

Geometric Approach ◽

Mobile Sensor Networks ◽

Boundary Detection ◽

Detection Algorithm ◽

Classification Algorithm ◽

Mobile Sensor ◽

Mobile Nodes ◽

Network Topologies ◽

Error Correction Algorithm

We present a novel online distributed boundary detection and classiﬁcation algorithm in order to improve accuracy of boundary detection and classiﬁcation for mobile sensor networks. This algorithm is developed by incorporating a boundary detection algorithm and our newly proposed boundary error correction algorithm. It is a fully distributed algorithm based on the geometric approach allowing to remove boundary errors without recursive process and global synchronization. Moreover, the algorithm allows mobile nodes to identify their states corresponding to their positions in network topologies, leading to self-classiﬁcation of interior and exterior boundaries of network topologies. We have demonstrated effectiveness ofthis algorithm in both simulation and real-world experiments and proved that the accuracy of the ratio of correctly identiﬁed nodes over the total number of nodes is 100%.

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