Application of the Monte Carlo method to estimate the uncertainty of air flow measurement

Sediva, Sona; Uher, Miroslav; Havlikova, Marie

doi:10.1109/carpathiancc.2015.7145124

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…A set of data was generated based on MCS to train the NF network. MCS is based on iterated random samplings and it is widely used to evaluate difficult mathematical problems, such as complex integration [25]. In this study, the generation of training data was carried out through bivariate normal distribution function as shown in Equation 8with two-step transformation.…”

Section: Training Based On Monte Carlo Simulationmentioning

confidence: 99%

Transformers Health Index Assessment Based on Neural-Fuzzy Network

et al. 2018

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In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis based on all 73 transformers reveals that 62% of transformers have the same assessments between NF trained by MCS data and scoring methods.

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Section: Training Based On Monte Carlo Simulationmentioning

confidence: 99%

Transformers Health Index Assessment Based on Neural-Fuzzy Network

et al. 2018

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“…Using the same scenarios (NDn and NDnC) and comparing at plot-level the mean error estimate, resulting in an overestimation of 2% of the GUM Method (p<0.01, Tables VIII-14 and VIII-16 in Appendix IV). Assessments in instrumentation, and material quality controls, report a range of 8 -21% of overestimate of GUM Method results (Mahmoud & Hegazy, 2017;Sana Sediva et al, 2015;Sona Sediva & Havlikova, 2013). However, when we applied the best-fit distribution with the MCM (scenarios MCBD & MCBDC) the uW per plot was not significantly different from the GUM Method results (scenarios NDn and NDnC) (p≥0.614, Table VIII-16 in Appendix IV).…”

Section: V35 Stand-level MCM (Non-sampling and Sampling Errors)mentioning

confidence: 89%

“…MCM has been applied to different fields of science to solve many of the problems associated. Error limits in accidentology (Martínez, 2003), risk estimation (Azofeifa, 2005), uncertainty in flow measurement (Basil et al, 2001), evaluation of measurement uncertainty of pharmaceutical certified reference material (Rocha & Nogueira, 2012), estimate the uncertainty of airflow measurement (Sediva et al, 2015). In natural resources, the uncertainty assessment has been applied in ecosystem budget calculations (Yanai et al, 2010), individual tree volume estimation (McRoberts, Tomppo, et al, 2015;McRoberts & Westfall, 2014, plot-based estimates of carbon stock (Holdaway et al, 2014), among others.…”

Section: I43 Monte-carlo Simulation Methods For Error Propagationmentioning

confidence: 99%

Quantifying the uncertainty caused by sampling, modeling, and field measurements in the estimation of AGB with information of the national forest inventory in Durango, Mexico

Trucíos-Caciano¹

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Aboveground biomass (AGB) is related to the carbon content of the forest and forest carbon is a core variable for contemporary forest management and policy decisions. A credible and precise estimate of AGB is a prerequisite for the scientifically proper implementation of commitments made regarding the reduction of forest carbon emissions. With datasets of the Mexican National Forest Inventory (MNFI), this thesis estimates AGB in the temperate forests of Durango, a federal state of Mexico, evaluating the uncertainty of the estimate of total AGB and how different error sources contribute to the overall error. In addition to AGB, also the basal area was analyzed which is closely correlated to AGB but does not have the error source of coming from the application of allometric models. SummaryVI almost be ignoredalways. However, assuming that measurement and model errors are random errors and biases are absent.Applying re-measurements was an efficient way to estimate and describe the measurement errors in DBH and TH. Through the application of the GUM Method, error propagation is decomposed into sources and processes, and it is better understood how uncertainties are combined. The Monte-Carlo simulation Method (MCM) also proves to be an effective, practical and reliable way to approximate the total AGB uncertainty estimate with acceptable ranges of probable error at scales of the MNFI. The results in the error propagation by the GUM Method and by the MCM are equivalent.We found that TH measurement errors have a greater contribution than DBH measurement errors at the tree-level. Furthermore, when the main contribution to the uncertainty at tree-level comes from uAM instead of uMes, then the total estimate of uNS at stand-level is proportional to the number of the trees. However, a ratio of uMes>uAM produces a total uNS estimate at stand level that is proportional to the contribution according to the size of the tree. Therefore, in this last relation, a greater contribution to the total NS estimate is made by the trees with the largest AGB estimated.

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