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On integrating the gray relational analysis and Taguchi method, the shrinkage behavior in tooth thickness, addendum and dedendum circles of moulded gear is investigated via optimization of process parameters.
We show that using a single iteration on a single scale, the two methods can be made equivalent by the choice of the nonlinearity which controls each method: the shrinkage function, or the diffusivity function, respectively.
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We call our proposed methods the shrinkage regression-based methods (see Figure 1).
The drying behaviour of one single granule, a porous particle, can be described using the continuum approach, the pore network modelling method and the shrinkage of the diameter of the wet core approach.
Our shrinkage LLSimpute associates the LLSimpute method with the shrinkage estimator to impute the missing values.
By researching the signal information extraction feature map of locally adaptive linear minimum mean square-error estimation (LALMMSE) method, the proposed shrinkage function is produced.
This study aims to propose a method to reduce the shrinkage error of shell molds in the injection molding process.
In general, the cores of threshold shrinkage are as follows: the method of shrinkage and the selection criterion of the threshold.
In both the methods, the % shrinkage of lignite ash was measured at different temperatures while increasing the temperature.
In general, the key points of threshold shrinkage are the following: the method of shrinkage (e.g., soft-threshold method) and the selection criterion of the threshold.
These classification techniques include Recursive Partitioning, Figueiredo's method, the "least absolute shrinkage and selection operator" or LASSO, and Logistic Regression on Principal Components.
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