analytical prediction
In the catering industry, one major challenge is the unknown short-term demand for dish portions. Meeting these demands is important for the industry but predicting future sales is a challenging task.
This data set presents sales of food portions from a canteen in absolute numbers of dish portions per day. In particular, the columns include text-based extractions of ingredients and a date. The data set is intended to be used for forecasting/predicting the food portions on a daily level.
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The paper introduces an analytical approach to predict the no-load flux density spatial repartition of inner permanent magnet tubular-linear synchronous machines (IPM T-LSMs). It considers a trapezoidal waveform whose maximum value is predicted using a simple magnetic equivalent circuit of an elementary part of the machine. Then, the accuracy of the proposed approach is enhanced by the incorporation of a mover permeance function that accounts for the PM luxconcentrating arrangement.
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