Data for our study was collected from the study area located in northwestern China.
#Evasi0n 7 version 1.0.2 series
The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian process regression.
In particular, the displacement forecasting problem is cast as a time-series regression problem with limited training samples and must be solved by statistical inference. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards. Landslide hazards are complex nonlinear systems with a highly dynamic nature. Therefore, the amendment based on the prediction of trained models is available. A planar spiral spring for a powered ankle–foot prosthesis is designed and manufactured to verify further, of which the predicted value possesses a 3.25% error compared with the measured stiffness. The simulation of five spring arms and five planar spiral springs with arbitrary dimensional parameters verifies that the absolute values of errors between the predicted stiffness and the stiffness from FEA are reduced to be less than 0.5% and 2.8%, respectively. The latter engages 180 double-arm springs’ data sets, including widths instead of wrap angles. For the former, 216 spring arms’ data sets, including different spiral radiuses, pitches, wrap angles and the stiffness from FEA, are employed for training. Two Gaussian process regression models are trained to amend this term in the stiffness calculation of spring arm and complete spring. It deems that the errors arise from the spiral length term in the calculation formula. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element analysis (FEA). Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators.
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