ANOVA-Based Approach for DAC Diagnostics

D’Arco, Mauro; Liccardo, Annalisa; Pasquino, Nicola

doi:10.1109/tim.2011.2182251

Cited by 25 publications

(7 citation statements)

References 17 publications

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“…The performance of the two new hybrid models for flood modelling was assessed by parametric and non-parametric statistical tests 3 . If the data follows a normal distribution with an equal variance, parametric methods are applied 53 . Although non-parametric tests are free from any statistical assumptions, they are safer and their results are stronger than those of parametric tests, since the former do not assume normal distribution or homogeneity of variance 54 .…”

Section: Methodsmentioning

confidence: 99%

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Bui

Panahi²,

Shahabi

et al. 2018

Sci Rep

147

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Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.

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Section: Methodsmentioning

confidence: 99%

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Bui

Panahi²,

Shahabi

et al. 2018

Sci Rep

147

View full text Add to dashboard Cite

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“…All classifiers were evaluated using the same random samples. For this reason, in this study, Freidman [109] and Wilcoxon [110] signed rank tests were used to compare the significant differences between the treatments of models. The null hypothesis for them is that there are not any differences between the performances of the landslide models at the significance level of α = 0.05 (or 5%).…”

Section: Statistical Tests (Friedman and Wilcoxon)mentioning

confidence: 99%

Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

et al. 2018

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Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

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“…Concluding, the sensor response of conductometric gas sensors is a complex function of many quantities and parameters, that are only partially known. This paper proposes an approach to search the optimal working conditions of YCoO

sensors by applying statistical methods, in particular the design of experiment and the Response Surface Methodology (RSM); in this direction, see also [ 12 ]. The proposed approach involves an ad hoc planning of a split-plot design as an RSM second order experimental design and the application of RS models with random effects, e.g., mixed RS models, in order to expound and to improve the contribution achieved through a previous study [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

Bertocci

Fort

Mugnaini

et al. 2017

Sensors

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Eight different types of nanostructured perovskites based on YCoO3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo0.9Pd0.1O3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312∘C for CO (284 ppm, from design) and response equal to −14.17% at 185∘C for NO2 (16 ppm, from design). Analogously, for NO2 (16 ppm, from design), the material type YCo0.9O2.85+1%Pd (Mt8) allows for optimizing the response value at −15.39% with a working temperature at 181.0∘C, whereas for YCo0.95Pd0.05O3 (Mt3), the best response value is achieved at −15.40% with the temperature equal to 204∘C.

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ANOVA-Based Approach for DAC Diagnostics

Cited by 25 publications

References 17 publications

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

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