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Your search keyword '"Yuan, Chenghao"' showing total 2 results
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2 results on '"Yuan, Chenghao"'

1. Bimetallic ammeter: a novel method of current measurement

Author

Uy Robert Frederik, Miao Qiaozi, and Yuan Chenghao

Subjects
bimetallic ammeter, current measurement, thermal expansion, heating effect of current, mechanical displacement, Physics, QC1-999, Mathematics, QA1-939
Abstract

An electric current flowing through a bimetallic coil heats it up, and due to thermal expansion, the coil either unwinds or winds depending on the direction of net heat transfer and the specific heat capacities of the metals used. This means that by relating a certain measure of its mechanical displacement with current, the bimetallic coil can be used as an ammeter. Thus, a mathematical model relating the current to the time taken by the bimetallic coil to unwind a fixed displacement was developed and verified through experiments, which show a good agreement between theoretical and experimental values.

Published
2020
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2. Mid-long Term Load Forecasting Model Based on Principal Component Regression

Author

Yu Zhiyong, Yuan Chenghao, Xia Xinmao, and Zhang Zengqiang

Subjects
Estimation, Multivariate statistics, Multicollinearity, Principal component analysis, Econometrics, Principal component regression, Predictive modelling, Regression, Mathematics, Term (time)
Abstract

Most of the factors influencing multivariate regression prediction models are macroeconomic indicators, and indicators tend to have strong correlations. The regression prediction model has strong multicollinearity, which results in distortion or inaccurate estimation of model estimates. To solve this problem, this paper first analyzes the principal components of the indicators, extracts the two principal components, eliminates the multicollinearity between the indicators, and considers the policy background of the “Electrification of Xinjiang”, and finally establishes a mid-long term load forecasting model based on principal component regression. The principal component regression prediction model has a better prediction effect. After inspection, the prediction accuracy of this model is ideal.

Published
2019

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