Quantifying wintertime O3 and NOx formation with relevance vector machines

Olson, David A.; Riedel, T. P.; Offenberg, John H.; Lewandowski, Michael; Long, Russell; Kleindienst, Tadeusz E.

doi:10.1016/j.atmosenv.2021.118538

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…Relevance vector machine has a better generalization ability than support vector machine due to the less support vectors of RVM than those of SVM, and less training parameters need to be determined [12,13].In order to improve the generalization ability of relevance vector machine,an incremental relevance vector machine algorithm is presented in this paper. In incremental relevance vector machine algorithm ,the original formulation of RVM is extended to design incremental RVM.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Gearbox by Multivariate Extended Variational Mode Decomposition-Based Time-Frequency Images and Incremental RVM Algorithm

Nao

Wang

2023

Preprint

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A novel detection method based on multivariate extended variational mode decomposition-based time-frequency images and incremental RVM algorithm(MEVMDTFI-IRVM) is presented for fault detection of gearbox. The time-frequency images are constructed by multivariate extended variational mode decomposition. Compared with single-variable modal decomposition method, multivariate extended variational mode decomposition not only has an accurate mathematical framework, but also possible to better align the common frequency mode of the signal. The incremental RVM algorithm is presented for fault detection of gearbox based on the time-frequency images constructed by multivariate extended variational mode decomposition. The testing results demonstrate that the detection results of MEVMDTFI-IRVM for gearbox are stable, in addition, the detection results of MEVMDTFI-IRVM for gearbox are better than those of variational mode decomposition-based time-frequency images and incremental RVM algorithm (VMDTFI-IRVM), variational mode decomposition- RVM algorithm(VMD-RVM),and traditional RVM algorithm.

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Section: Introductionmentioning

confidence: 99%

Fault Detection of Gearbox by Multivariate Extended Variational Mode Decomposition-Based Time-Frequency Images and Incremental RVM Algorithm

Nao

Wang

2023

Preprint

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“…To reduce the complexity of meteorological and environmental variations some studies focus on developing ML models specifically for predicting wintertime NOx peaks. 21 Applying autoregressive integrated moving average (ARIMA) models to univariate NOx time series can avoid the use of additional input variables and provide short-term predictions achieving moderate accuracy. 22–24 Typically, ARIMA predictions can be improved upon by applying ML and/or deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes

Wood

2023

Environ. Sci.: Adv.

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“…Relevance vector machine has a better generalization ability than support vector machine due to the less support vectors of RVM than those of SVM, and less training parameters need to be determined 14 , 15 . In order to improve the generalization ability of relevance vector machine,an incremental relevance vector machine algorithm is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm

Nao

Wang

2023

Sci Rep

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A novel detection method based on multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm (MEVMDTFI–IRVM) is presented for fault detection of gearbox. The time–frequency images are constructed by multivariate extended variational mode decomposition. Compared with single-variable modal decomposition method, multivariate extended variational mode decomposition not only has an accurate mathematical framework, but also has good robustness to non-stationary multi-channel signals with low signal-to-noise ratio. The incremental RVM algorithm is presented for fault detection of gearbox based on the time–frequency images constructed by multivariate extended variational mode decomposition. The testing results demonstrate that the detection results of MEVMDTFI–IRVM for gearbox are stable, in addition, the detection results of MEVMDTFI–IRVM for gearbox are better than those of variational mode decomposition-based time–frequency images and incremental RVM algorithm (VMDTFI–IRVM), variational mode decomposition–RVM algorithm (VMD–RVM), and traditional RVM algorithm.

show abstract

Quantifying wintertime O3 and NOx formation with relevance vector machines

Cited by 7 publications

References 40 publications

Fault Detection of Gearbox by Multivariate Extended Variational Mode Decomposition-Based Time-Frequency Images and Incremental RVM Algorithm

Fault Detection of Gearbox by Multivariate Extended Variational Mode Decomposition-Based Time-Frequency Images and Incremental RVM Algorithm

Machine learning for hours-ahead forecasts of urban air concentrations of oxides of nitrogen from univariate data exploiting trend attributes

Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm

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