Statistics for Engineering and the Sciences Student Solutions Manual

Mendenhall, William M.; Sincich, Terry; Boudreau, Nancy

doi:10.1201/9781315382494

Cited by 53 publications

(58 citation statements)

References 0 publications

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“…This black box model relates the dependant variable and the measurement parameters. Both, ANOVA and RSM, are well established statistical methods [32].…”

Section: Methodsmentioning

confidence: 99%

“…As it was stated in section 2, 33 experimental points were obtained. In order to get a significant model fitting using a response surface method, it is recommended to have at least 10 experimental points more than parameters considered in the model [32]. Therefore, in this case, there are enough experimental points to obtain a significant fitting.…”

Section: Regression Modelmentioning

confidence: 99%

“…Adding centerpoints to the experimental design is useful for detecting possible curvatures in the output variables [31]. This experimental design strategy is much more efficient than the traditional "one after one" experimental strategy since it requires fewer experiments to analyse the same number of input factors and allows studying the interaction between these factors [32].…”

Section: Experimental Designmentioning

confidence: 99%

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Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination

Giner-Sanz

Ortega

Pérez-Herranz

2015

Electrochimica Acta

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ElsevierGiner Sanz, JJ.; Ortega Navarro, EM.; Pérez-Herranz, V. (2015). Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination. Electrochimica Acta. 174:1290Acta. 174: -1298Acta. 174: . doi:10.1016Acta. 174: /j.electacta.2015 Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination Keywords: Electrochemical Impedance Spectroscopy; measurement parameters; optimization; factorial experimental design; PEMFC. AbstractCurrently, electrochemical Impedance Spectroscopy (EIS) is a widely used tool for the study of electrochemical systems, in general; and fuel cells, in particular. A great effort is typically invested in the analysis of the obtained spectra; whereas, little time is usually spent optimizing the measurement parameters used to obtain these spectra. In general, the default settings provided by the control software used to perform the measurements, or the parameters used in similar systems available in literature, are selected to carry out the measurements. The goal of this work is to determine the optimal measurement parameters for obtaining impedance spectra of a commercial PEM fuel cell. In order to achieve this, a 2 5 factorial design was considered. Five factors were considered, the five impedance spectroscopy measurement parameters: maximum integration time; minimum number of integration cycles; number of stabilization cycles; maximum stabilization time; and minimum cycle fraction. For each factor combination envisaged in the experimental design, the cell spectrum was obtained in given operation conditions, for which the reference spectrum of the system was known, since it had been determined in previous works. The experimentally obtained spectra were fitted to the reference electric equivalent circuit. The mean square error between the experimental data fitting and the reference spectrum fitting was determined in each case, and was used as the dependant variable for the experimental design analysis. An analysis of the variance was performed in order to determine which measurement parameters have a significant effect on the dependant variable; and a model relating the dependant variable and the measurement parameters was built. This model was used in order to obtain the optimal value of each one of the measurement parameters that minimized the mean square error of the fit obtained from the experimental data with respect to the reference fit.

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“…This black box model relates the dependant variable and the measurement parameters. Both, ANOVA and RSM, are well established statistical methods [32].…”

Section: Methodsmentioning

confidence: 99%

Section: Regression Modelmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination

Giner-Sanz

Ortega

Pérez-Herranz

2015

Electrochimica Acta

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“…Firstly, each circuit element was assigned a random variable value, normally distributed around the fitted value of that model parameter and with a standard deviation equal to the fitting error for that model parameter. The normality assumption is a good hypothesis when no a priori information of the distribution is available [56]. Therefore, the random vector of input parameters of the model, _`> , is generated according to the following distribution:…”

Section: Montecarlo Error Propagationmentioning

confidence: 99%

Montecarlo based quantitative Kramers–Kronig test for PEMFC impedance spectrum validation

Giner-Sanz

Ortega

Pérez-Herranz

2015

International Journal of Hydrogen Energy

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Electrochemical Impedance Spectroscopy (EIS) is a very powerful tool to study the behaviour of electrochemical systems. At present, it is widely used in the fuel cell field in order to study challenging cutting edge issues as membrane drying or gas diffusion layer flooding amongst others. The proper analysis of impedance data requires the fulfilment of four fundamental conditions: causality, linearity, stability and finiteness. The non compliance with any of these conditions may lead to biased, or even misguided, conclusions. Therefore it is critical to verify the compliance of these conditions before accepting any analysis performed on an experimental spectrum. This is even more important in a fuel cell experimental spectrum analysis, since fuel cells are markedly non stationary systems. The aim of this work is to establish an impedance spectrum quantitative validation technique to validate the whole experimental spectrum and to identify the individual points within a spectrum that do not comply any of the four conditions, in order to remove these inconsistent points from the analysis. The designed validation method consists in a Kramers-Kronig (KK) validation test, by equivalent electrical circuit fitting, coupled with a Montecarlo error propagation method. In a first step, the experimental spectrum is fitted to a particular electrical equivalent circuit, which satisfies the KK relations. Then, in a second step, a statistical Montecarlo method is used in order to propagate the model fitting parameter uncertainty through the model. Using this approach, a consistency region is built for a given confidence level: the experimental points inside this region are considered consistent for the given confidence level, whereas the outside points are rejected. The method was used on PEMFC experimental impedance spectra; and it successfully managed to identify inconsistent points, associated to no stationarities.2

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“…Clearly, if the CU has a depth of zero then an averaging is not required. In [21], the stepwise regression procedure is described using the following steps. In the first step, the procedure tests all possible one-predictor regression models in an attempt to find the predictor that has the highest correlation with the response variable.…”

mentioning

confidence: 99%

Saliency detection in MPEG and HEVC video using intra-frame and inter-frame distances

Shanableh

2015

SIViP

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Statistics for Engineering and the Sciences Student Solutions Manual

Cited by 53 publications

References 0 publications

Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination

Optimization of the electrochemical impedance spectroscopy measurement parameters for PEM fuel cell spectrum determination

Montecarlo based quantitative Kramers–Kronig test for PEMFC impedance spectrum validation

Saliency detection in MPEG and HEVC video using intra-frame and inter-frame distances

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