Ivan Okhotnikov's Projects
Bidding keywords with machine learning
Training and inference code for the claim veracity checker built on Longformer-4096 tuned to PUBHEALTH
Training and tuning of xgboost to classify customers with synthetic campaign and mortgage data
Web app displaying properties of the test rig data with pandas-profiling and streamlit
Introduces two classes: hydro-static transmission (HST) and regression models (RegressionModel). The former serves to initilize an HST object for a certain displacement and other design pararmeters. Included methods allow to size, to calculate efficiencies as well as perfromance characteristics for a given operational regime and to create, print and save an efficiency map - a contour or a surface plot for ranges of speeds and torques required. The latter class loads the collected catalogue data of displacements, speeds and masses of axial-piston machines from the data.csv file. Then it fits regression models to the data in order to provide inter- and extrapolating predictions. The data and the regression models could then be printed and saved.
Effmap_demo is the python library, which demonstrates the use of the hydro-static transmission (HST) and regression model (Regressor) classses intriduced in effmap.
Calculates and prints the efficiency map of a hydrostatic transmission with a certain displacement Vd, max swash angle sa within a speed (nmin, nmax) and pressure (pmin, pmax) ranges.
HSU performance dashboard
Multi-objective optimization of hydrostatic transmission performance with NSGA-II
scikit-learn: machine learning in Python
The repository contains the splines calculator in `splines` module and its tests in `test_splines`. The calculator uses the sizes calculation methodology described in ISO 4156-1:2005. The `test_splines` implements the tests of the calculations module by asserting compliance between the sizes computed with the `splines` module and the sizing calculation examples in appendices to ISO 4156-1:2005.
The repository contains the calculator module for compression and torsion springs implemented according to BS EN 13906.
Test rig condition monitoring and predictive maintenance. Training
Local training, tuning, experimentation on the test rig data
Test rig condition monitoring and predictive maintenance. Serving
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