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According to this, dielectric material has well conducting grains separated by highly resistive grain boundaries.
On the application of electric field, space charge accumulates at the grain boundaries and voltage drops mainly at grain boundaries [42].
Koops proposed that grain boundary effect is more at low frequencies [42].
Moreover, the dielectric loss tangent also depends on a number of factors such as stoichiometry, Fe2+ content, and structural homogeneity which in turn depend upon the composition and sintering temperature of the samples [46].
Makino, ‘Core losses and magnetic properties of Mn-Zn ferrites with fine grain sizes’, J.

First, the odd numbered outer dies and inner dies press the trial tube.
Second, the even number outer dies and inner dies press the trial tube return to the starting position.
On the other hand, the test machine of manufacturing method 2 modifies the number of dies (Fig. 4).
The number of outer dies is four and the number of inner dies is two.
The reason to prepare for two types of machine is to investigate how the number of dies has an effect on strain and thickness of the tube.

Angella et al. [2] conducted hot torsion experiment and proposed an analytical model to describe the flow behavior of the 316L based on the unusual grain structure evolution.
The number of test data conforms to the general rule [5] that it should be 10-20% of the training data set to check the performance of the network.
Up to now, no theoretical guidance to the determination of the number of hidden layers in the neural network and the number of neurons in each hidden layer can be followed, while the accuracy of the predicted results varies greatly with different structures of the ANN.
For the choice of the number of neurons in each hidden layer, the principle is to choose a number as low as possible to simplify the network on the basis that the data from the input-to-output is mapped well.
The optimum architecture of the ANN is 3-5-6-1: the input includes , T and , the output is , the number of hidden layer is two with 5 neurons in first layer and 6 neurons in second layer.

Fig.1 Principle of the special spiral rods grinding process i i F l T = (1) Subject to the constraints: bPP0 << (2) max a RR0 << (3) maximin FFF << (4) In Eq.1, i is the section number of numerical control grinding interpolation program; il is the interpolation length of number i section which is only relevant to the workpiece outline and can not be changed; Fi is the feed rate of number i interpolation section.
The wheel cut depth ai of number i section is related to feed rate Fi as shown in Eq.5, where V is the principal axis velocity.
In the course of parameter optimization, the grinding power is the most important constraint, and can be calculated as follows [7]: ε/2 e εε-1ε)/2(1 i -2ε )(dVvakbωP − − = (8) Where ε is an empirical exponential, k is a constant, b the grinding width, ω the interval of the abrasive grain, de=(dw+ds)/dwds the equivalent diameter, dw the diameter of the workpiece and ds the diameter of the wheel, v the workpiece linear velocity and V the wheel linear velocity.
Numerous constraints and number of passes make the machining optimization problem more complicated.
Choose a maximum allowable number tmax.

The following results are observed: The number of the micro inclusions is 11.37 piece /mm2 under unsteady casting.
The number of the macro inclusions is 890.18 mg/(10 kg), and 275.4 mg/(10 kg) under steady casting.
The ratios of their respective share of the total number are tested: 6:3:1.
The number of micro inclusions is1.38 times comparing to that under steady casting.
The number of the macro inclusions is 3.23 times comparing to that under steady casting

Optical observation windows are provided on either sides of the experimental model so to take pictures of the test images and grains.
Mach numbers of 2.5 and 3.0 in the experiment should correspond to Mach numbers of 4.1 and 5.3 in flight status.
This paper only compares and analyze the data generated from numerical simulations at Mach number 2.5.
Figure 6 shows the Mach number distribution under various turbulence models.
Figure. 6 Mach number distribution of various turbulence models All turbulence model computations predict very well the development trend of the shock wave system along the tunnel.

However, danger of contention increases when the mesh is very small compared to the number of threads operating on it.
Thus it needs to elastically adjust thread number according to mesh size and the number of 3D feature points so that it can get the highest speedup.
The whole points cloud is divided into several regions according to the number of processors of the platform then assign each region to each processor.
To solve this problem, the platform is designed to elastically decide parallel grain.
From our experiments, when the number of points assigned to one processor is less than 2,000, time waste of thread operation cannot be ignored.

The available literature data indicate only that the superposition of high frequency low amplitude vibrations on LCF loading may be both negative and positive as regards the number of LCF cycles to failure.
The average grain size determined by linear intercept method was 3 ± 0.5 mm.
However, light microscopy observation revealed number of casting defects on metallographically polished surface.
The fatigue life is expressed in terms of the number of HCF cycles spent at the mean stress of 300 MPa.
Strong effect of casting defects on the fatigue lifetime resulting in large scatter of number of cycles to fracture was observed. 3.

If it could predict the general deformation of a given point based on the current condition, inevitably reduce the number of approach times and system response time, reduce vibration, improve dynamic performance of the system.
The first layer is input layer, in this layer each node directly connecting each component xi of input vector. the input features value or the unitary value of the characteristics, if the parameters have n, then, the number of nodes on the first floor for n.
So the number of nodes in the layer is N = 3.
The third layer is rules layer[5].Each node of the third layer which is on behalf of a fuzzy reasoning rules, its role is to match the fuzzy rules, calculate each of the applicable rules, each nerve represent a fuzzy rule, so the number of nodes in the layer is N = 12.This layer introduce "and" operate. )()( )3()3( xxIO i i B A ii µµ •== (3) For the ith rule,x1 have m input expressions, x2 have n input expressions, then )2( 2 )2( 1 )3()3( nm ii OOIO •== (4) The fourth layer is anti-fuzzy layer, which express the fuzzy reasoning output with a specific method, which is clear output, the number of nodes of this layer equal to the number of the output.
Third International Conference on, 2010,pp:447 - 450 [4] Jianjun Wu，The Research and Design of the Grain Monitor and Control System Based on Fuzzy Neural Network，IT in Medicine and Education, 2008.

In large enterprise applications, in order to manage access to resource precisely, a group of resources for completing one business will be divided into several fine grained resources.
Role set (RS): It is a set combined by a finite number of roles, described as {Role1，Role2，……}.
RSV plus or minus RV represents role number increase or decrease in RS.
When there had no RSV, assume that the number of resource which user could access was n.
It avoids performance bottleneck produced by number increase of roles and users.