Capacitance

There are four scripts in this repository in the "code" folder.The"fft RF-AM-MLP.py","fft-MLP.py","Linear static.py" are the training and testing procedures of "RF-AM-MLP","MLP" and "static linear model" respectively. And after training all the three models are saved to remain there parameters unchanged.The "fft plot figures.py" compares the three models using different validation sets which are 30rpm,60rpm,100rpm and variable rates.And different figures are drawn to show the prediction results and error distribution of the three models.

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In my datasets, the "training data" is the data for the model training,the "static" is the data where peristaltic pump speed at 10pm. And others are the validaiton data of variable rates level descent and  three constant rates level descent respectively as their title says

. For every set of data, there are 3 columns. The first is time, the second is capacitance and the third is liquid level.

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The law of conservation of energy has yet to be disproven. However, an inability to disprove this law is no proof of its validity. Energy creation was modeled with a lagging current and a quasi-sinusoidal variance in capacitance. A quasi-sinusoidal variance here is a sinusoidal waveform whose mean is larger than its median. The stator’s quasi-sinusoidal variance in capacitance is formed by the rotors’ electrostatic induction within its conductive segments, which is induced by the stator’s charge.

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