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Reliability based rehabilitation of water distribution networks by means of Bayesian networks

References BOUDALI H., DUGAN J.B. 2005. A discrete-time Bayesian network reliability modeling and analysis framework. Reliability Engineering and System Safety. Vol. 87. No. 3 p. 337-349. DOI 10.1016/j.ress.2004.06.004. BOZORG-HADDAD O., GHAJARNIA N., SOLGI M., LOÁICIGA H.A., MARIÑO M.A. 2017. Multi-objective design of water distribution systems based on the fuzzy reliability index. Journal of Water Supply: Research and Technology - Aqua. Vol. 66. Iss. 1 p. 36-48. DOI 10.2166/aqua.2016.067. GHEISI A., FORSYTH

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Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development

.1109/incos.2010.99 [3] S. W. Ambler and M. Lines, Disciplined Agile Delivery: A Practitioner’s Guide to Agile Software Delivery in the Enterprise, Indianapolis: IBM Press, 2012. [4] M. Perukusich, G. Soares, H. Almeida and A. Perkusich, “A procedure to detect problems of processes in software development projects using Bayesian networks,” Expert Systems with Applications , vol. 42, pp. 437–450, Jan. 2015. http://dx.doi.org/10.1016/j.eswa.2014.08.015 [5] S. Mohanarajah and M. A. Jabar, “An Improved Adaptive and Dynamic Hybrid Agile Methodology to

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Use Of Bayesian Networks And Augmented Reality To Reliability Testing Of Complex Technical Objects

/2010, s. 135-152. [5] Matuszak Z.: Components validity evaluation in a complex technical structure, Zeszyty Naukowe Akademia Morska w Szczecinie, nr 32/2012, s. 115-122. [6] Młynarski S.: Problemy określania niezawodności w eksploatacji maszyn i pojazdów, Problemy Eksploatacji, nr 2/2003, s. 165-174. [7] Gregory P.: Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, Cambridge 2005. [8] Jensen F., Nielsen T.: Bayesian Network and Decision Graphs, Springer, USA 2009.

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Application of Bayesian Networks for Forecasting Future Model of Farm

. Agricultural Engineering, 3 (159), 149-156. Grzegorek, J. (2012). Miejsce Polski w Europie i Świecie według wybranych danych statystycznych. Polska Akademia Nauk, Tom III , 286-297. Jongsawat, N., Tungkasthan, A., Premchaiswadi, W. (2010). Dynamic Data Feed to Bayesian Ne twork Model and SMILE Web Application. Bayesian Network , ISBN 978-953-307-124-4. Kusz, A., Marciniak, A., Skwarcz, J.(2015). Implementation of computation process in a bayesian network on the example of unit operating costs determination. Eksploatacja i Niezawodność – Maintenance and

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Bayesian reliability models of Weibull systems: State of the art

References Agena (2011). Website, http://www.agenarisk.com. Almond, R.G. (1992). An extended example for testing graphical-belief, Technical report , Statistical Sciences Inc., Seattle, WA. Anderson, M., Anderson, R. and Wheeler, K. (2004). Filtering in hybrid dynamic Bayesian networks, International Conference on Acoustics, Speech and Signal Processing, Montreal, Canada , Vol. 5, pp. 773-776. Andrews, J.D. and Moss, T.R. (1993). Reliability and Risk Assessment , Longman Scientific & Technical

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Dynamic Bayesian Network for Reliability of Mechatronic System with Taking Account the Multi-Domain Interaction

mapping fault trees into Bayesian networks. Reliability Engineering and System Safety, 71,3, 2001. 7. Deleuze G., Quatrain R., Jouanet F., Talbourdet D., Lucet F.: A Method for the Assessment of Common Cause Failures of Digital I and C Hardware. The annual European Safety and Reliability Conference ESREL, Finland, Helsinki 2012. 8. Demri A., Guerin F., Bigaud D.: Mechatronic system reliability evaluation using Petri networks and phi2 method. Proceeding Int. Conf. European Safety and Reliability Conference ESREL, Czech Republic, Prague 2009. 9. Habchi G

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Benefits of a dance group intervention on institutionalized elder people: a Bayesian network approach

construct a simple Bayesian network (BN) that will help us to illustrate graphically the relationships between the variables. It permits us to provide an alternative quantification of the strength of causation of the intervention on the test variables. With both approaches, we not only confirm that the intervention had a positive impact, but we also provide further information to explain it. 2 Matherial and Methods BNs permit to handle uncertainty according to probability theory. We first introduce some preliminaries on BN modeling and the proposed structure for

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Dosimetric study of the protection level of the bone marrow in patients with cervical or endometrial cancer for three radiotherapy techniques - 3D CRT, IMRT and VMAT. Study protocol.

Tengg-Kobligk H, et al. 3D printing based on imaging data: review of medical applications. Int J CARS. 2010;5(4):335-341. [13] Jensen FV, Nielson TD. Bayesian Networks and Decision Graphs. New York: Springer-Verlag; 2007. [14] Cowell RG, Dawid AP, Lauritzen AL, Spiegelhalter DJ. Probabilistic Networks and Expert Systems. New York: Springer; 1999. [15] Murphy K. The bayes net toolbox for matlab. Computing Science and Statistics. 2001;3(2)3:1024-1034. [16] Van Esch A, Tillikainen L, Pyykkonen J, et al. Testing of the analytical anisotropic

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Learning the naive Bayes classifier with optimization models

References Asuncion, A. and Newman, D. (2007). UCI machine learning repository, http://www.ics.uci.edu/mlearn/mlrepository. Campos, M., Fernandez-Luna, Gamez, A. and Puerta, M. (2002). Ant colony optimization for learning Bayesian networks, International Journal of Approximate Reasoning 31(3): 291-311. Chang, C. and Lin, C. (2001). LIBSVM: A library for support vector machines, http://www.csie.ntu.edu.tw/cjlin/libsvm. Chickering, D.M. (1996). Learning Bayesian networks is NP-complete, in

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The Concept Of Fuzzy Modeling Of Safety In Collective Water Supply Systems Using Bayesian Network

Abstract

The paper presents the use of fuzzy Bayesian network in safety modeling with regard to collective water supply system (CWSS). The theoretical basis of Bayesian networks and fuzzy modeling were presented. The paper presents failure events threatening the CWSS safety. The probability of the risk of lack of water supply to the city was designated. The model allows to determine the fuzzy probability of the risk at a given level.

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