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F is also time dependent; at the time of step penetration it equals kinematical dilation, but anneals away non-linearly so that a length scale can be established over which downstream effects of a velocity step on the dispersion extends.
Zamba and Hawkins (2006) presented a model to estimate the time of step change in the mean vector of multivariate processes in situations where the parameters are unknown.
The results show that the proposed ANN-based methodology outperforms the MLE in estimating the time of step change in the process variability with multivariate-attribute quality characteristics.
Doğu and Kocakoc (2011) estimated the time of step change in the covariance matrix of multivariate normal processes, in which a multivariate control chart based on sample covariance is used for receiving out-of-control signals.
Then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ANN for estimating the time of step change in the multivariate-attribute process variability.
As one of the first methods in change point estimation of univariate processes, Samuel et al. (1998) investigated the time of step changes in the X-bar chartol chart.
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Fig. 8 Thermopower of Fe-Cu (4:1) precursor specimens rolled with relative deformation of ε Σ = 70 (1, 3, 5) and 95 (2, 4, 6) % treated in the planetary mill during 20 (1, 2), 60 (3, 4) and 120 (5, 6) minutes against the time of step-by-step annealing at the temperature values given on the figure.
It's a time of stepping up and beginning again.
In this two steps, Δ t 1 is the time step of step 1. Δ t 2 and Δ t 3 represent the two time steps of step 2. In step 1, we obtain ϕ n+1/2 and then utilize it in step 2, where Δ ϕ n+1/2 represents the Laplacian operator that acts on the level set function.
Therefore, the time complexity of step 2 is O(NΔ2).
The real time of each step can be calibrated by historical data.
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