Linear, interaction, and high-order nonlinear impacts are all identified via statistical tests and stepwise backward removal parameter screening.
To assess the magnitude of the variation created by removal of one parameter, the variation index was computed as: {text{Var}}_{i} = left( {frac{{V_{{i - Vx_{i} }} }}{{V_{text{i}} }}} right) times 100 (3)where, Var i is variation index of the map removal parameter; and V i and V xi represent vulnerability index for the ith sub-area in two different conditions as mentioned above.
Removal parameters such as contact time, pH, and sorbent dosage were optimized.
The aim of the work was to quantify deposition and removal parameters, based on the influence of surface energy properties on the type of deposit.
Baseline demographic and clinical characteristics, sheath removal parameters, and subsequent outcomes were collected prospectively.
The characteristic removal parameters such as retention capacity, removal percent and unused bed length were experimentally determined at different operating conditions.
Due to the very different illumination conditions in the indoor and outdoor scenarios, the learning rate and the shadow removal parameters were chosen separately for indoor sequences and outdoor sequences; see Table 2. Detection results for the indoor sequences Shopping_Mall and Airport_Hall can be seen in Figure 9 while detection results for outdoor scenarios are shown in Figure 10.
Entry parameters were set at (F < 0.05), and removal parameters were set at (F > 0.10).
Usually, it is better to remove such a difference in advance to further processes; without the removal, a parameter value suitable for image A is not suitable for image B. In addition, there is a risk that they are recognized as very different images just because of their brightness difference.
Myelin sheaths of nerve fibres in peroneal nerves at F14 and the following time-points of the consolidation phase were significantly thinner on average than in intact nerves especially at F37, but, after fixator removal, the parameter recovered.
In the correlation method, the removal of parameter kb7 occurs in the last iteration and results in a clear increase of the smallest eigenvalue by a factor of about 100 (from 3.3·10-5 to 2.2·10-3, see Figure 1).
