Moreover, the datasets also influence model performance but are not as significant as the label distribution.īackground: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Neural network is relatively robust to the distribution of labeled data. In addition, they show that the distribution of labels significantly impacts the model’s performance when using support vector regression and Gaussian regression. The findings are verified using a data-driven virtual flow rate sensor to observe the leakage. In addition, the effect of the number of datasets and label distribution on the performance of the virtual flow sensor were systematically studied. The performance of neural network, support vector regression, and Gaussian regression methods for developing the virtual flow rate sensor was systematically investigated. This work developed a data-driven virtual flow rate sensor for monitoring the leakage of cradle bearings in axial piston pumps under different operating conditions and recess pressures. However, due to assembly limitations, it is not always feasible to observe the leakage of each tribological contact individually with a flow rate sensor. Leakage observations can be used to optimize the pump design and monitor the behavior of the tribological contact. The leakage of the tribological contact in axial piston pumps significantly impacts the pump efficiency. LHS is more efficient than simple random sampling in a large range of conditions.Keywords:Latin hypercube sampling uncertainty analysis sensitivity analysis rank correlation hurricane loss projection uncertainty importance The values of the stratified sampling scheme can be paired to ensure a desired correlation structure among the k input variables. If an input variable is not important, then the method of sampling is of little or no concern. By sampling over the entire range, each variable has the opportunity to show up as important, if it indeed is important. This means that a single sample will provide useful information when some input variable(s) dominate certain responses (or certain time intervals), while other input variables dominate other responses (or time intervals). Latin hypercube sampling (LHS) uses a stratified sampling scheme to improve on the coverage of the k-dimensional input space for such computer models. In addition, the model response is frequently multivariate and time dependent. Such models are usually characterized by a large number of input variables (perhaps as many as a few hundred), and usually, only a handful of these inputs are important for a given response. The results of the LHS/PRCC sensitivity analysis are used to assess the biological significance of the parameters in relation to each compartment of the model to further understand its biological implications.This chapter discusses the use of computer models for such diverse applications as safety assessments for geologic isolation of radioactive waste and for nuclear power plants loss cost projections for hurricanes reliability analyses for manufacturing equipment transmission of HIV and subsurface storm flow modelling. This sensitivity analysis is applied to ordinary differential equation models describing the interactions of various biological components in the healing of a diabetic foot ulcer. For montonic, non-linear relationships, the correlation between the outputs and parameters can be understood by performing a Partial Rank Correlation Coefficient procedure. Latin hypercube sampling divides the parameter space into equiprobable regions and sample without replacement, producing a global, unbiased selection of parameter values. Through this analysis, the uncertainty of the parameters and therefore the variability of the model output in response to this uncertainty can be observed. Latin hypercube sampling and Partial Rank Correlation Coefficient procedure (LHS/PRCC) can be used in combination to perform a sensitivity analysis that assesses a model over a global parameter space.
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