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According to the above analysis, the numbers of reduction furnaces are not enough, and the increase of number, the rate of operation in reduction furnaces can be improved effectively, thus increasing capacity.
Through analyzing the data, the number of reduction furnace is A, the number of is B, and the optimized capacity is X, so the following relation can be satisfied.
B=2A X=78A There should be 64 distillation furnaces matching reduction furnaces.
As for different number of reduction furnaces before and after optimization, the capacity of distillation is showing in table 4: Table 4 The corresponding capacity of different matching of reduction furnace and distillation furnace Reduction furnace Distillation furnace Reduction: distillation capacity 32 47 1:3 2053 32 64 1:2 2496 The Influence of Assembling Time.
Keep 32 reduction furnaces, and add distillation furnaces to 64.

The total reduction for these passes is 55%, calculated according to the thickness change.
This result is corresponding to the data obtained in the intermediate bar.
Secondly, the total reduction below temperature Tnr is not big enough, which is just 55%.
After experiencing 55% rolling reduction, the Sv increases to a range of 175~45mm-1.
The size distribution data of the precipitates is summarized in Fig.9.

Compared to this reference approach, a makespan reduction and a setup time reduction can be observed.
Future research will focus on expanding the model and verifying its applicability on a larger data-set.
The proposed approach results in both a makespan reduction and a reduction in the number of setups.
For this mathematical model, good results can be obtained as far as makespan reduction, setup time reduction and reduction of the number of toolchanges is concerned.
On average a makespan reduction of 6% is observed.

The threshold voltage (VT) was calculated using the saturation extrapolation technique [3], from the data shown in Figures 2 and 3.
This technique was used as it gives the same order of magnitude for DIT, in comparison with the conduction technique [8] and, as this was an initial study, was only used to identify trends in the data.
The data for the nFET is shown in Figure 6.
This reduction in conductance indicates a reduction in interface trap density (DIT).
The total charge in the oxide layers (QT) is calculated from the observed flat band shift of the capacitance data from the theoretical values, giving -22 μC/cm2 for the nFET and 11.5 μC/cm2 for the pFET at room temperature.

In particular, the nano-silica modified epoxies showed only very little reduction in the glass transition temperature (Tg).
Fracture toughness data from a parallel study [2] for the unmodified Araldite-F epoxy and modified epoxy of 5 wt% nano-silica are also shown in the figure.
In particular, the nano-silica modified epoxies showed only little reduction in Tg, measured using DMA with a single cantilever beam mode.
Table 1: Tensile and flexure properties of nano-silica modified epoxies Tension Three-point flexure Content of nano-silica (%) Modulus [GPa] Strength [MPa] Modulus [GPa] Strength [MPa] Tg* [o C] 0 2.76±0.05 67.1±0.3 3.06±0.08 125.5±4.2 101.5 2 3.03±0.06 74.0±0.9 3.01±0.04 121.3±1.7 101.0 3 2.92±0.04 68.9±0.7 3.09±0.08 122.3±4.6 100.4 5 2.99±0.13 69.6±2.3 3.22±0.05 124.9±3.0 99.1 7 3.03±0.02 70.6±0.8 3.21±0.11 120.5±7.2 97.6 *Derived from tan δ versus temperature relationship of DMA data 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 �� �� �� �� �� �� �� �
� Content of nano-silica [wt%] KIC [MPa.M 1/2] Post-cured As-cured Data from [2] Data from [2] Figure 2: Mode-I fracture toughness of nano-silica modified epoxies Effects of nano-silica on fracture behaviour of CF/EP Composites modified with nano-silica.
A summary of DCB and ENF as well as transverse tensile data for three groups of unidirectional CF/EP laminates are shown in Table 2.

This paper describes the structure and hierarchical analysis of granularity briefly, and then details the construction algorithms currently, makes an emphasis on the merits of the various construction algorithms, at the last summarizes applications in the rule extraction, attribute reduction, cluster analysis, optimization theory, neural networks and fuzzy control and other aspects.
Applications of Hierarchical Algorithm Hierarchy, having the advantages of reducing the computational complexity, is applied to rule extraction, attribute reduction, cluster analysis, optimization theory, neural networks, fuzzy control, and knowledge discovery and so on.
Attribute Reduction Granularity reduction algorithms imitate the granularity principle of human understanding for things changing, so that the knowledge representation changes from a single-level to a multi-level with all of the attributes in information system or decision-making system; correspondingly, hierarchy reduction changes from single-granularity to multi-granularity on the problem-solving.
Hierarchical attribute reduction problem is simplified by the hierarchy and has strong applications [12].
"data mining algorithm based on classification granularity", Taiyuan Univer- sity of Technology, Master Degree Thesis, (2007)

Next, multi-scale LGRPH is constructed for dimensionality reduction.
Introduction Synthetic aperture radar (SAR) image recognition remains as a hard task, while feature extraction plays an important role in it, which reduces the dimension of the data set while preserves the information useful for recognition.
Furthermore, for dimensionality reduction, we construct multi-scale LGRPH (i.e.
In this section, we selected real images with a 17 degree incident angle of BMP2-C21, T72-132 and BTR70-C71 as training data.
Retrieving forest stand parameters from SAR backscatter data using a neural network trained by a canopy backscatter model.

Rough Set Theory Poland Z.Pawlak professor, it is a data analysis tool.
However, the two problems: the best reduction is not unique and reduction computation time.
Rough set theory in data mining because of its classification ability of the tolerable limit Β.
Rough set theory is in order to analyze the facts hidden in the data without the need for any additional information about the data.
The basic algorithm can be obtained all of the reduction, but only suitable for very small data sets.

Therefore, the data reported by different authors are frequently not compatible and cannot be compared.
These data indicate that the incorporation of fluorine into TiO2 results in reduction of the band gap.
This effect, however, is not consistent with the data reported by Alhakimi [22].
Data Compatibility The data reported by different authors may be compared only when these data are well defined in terms of the quantities that have an effect on photocatalytic properties, such as: 1.
Therefore, the data can be compared only for the same light source and the same light intensity.

It is done the analytical calculation of eccentrically compressed reinforced concrete columns under different thermo powers conditions, there are shown the results of calculations and experimental data.
This article presents a calculation of eccentrically compressed reinforced concrete element [5] under fire impacts by using experimental data [6].
compressed concrete elements under static loading in fire conditions determines by Eq. (2): (2) Load bearing capacity of eccentrically compressed concrete elements under dynamic loading in fire conditions determinesby Eq. (3):: (3) Depending on the heating temperature and the eccentricity of the applied load, the height of the compressed zone of eccentrically compressed element determines: when (4) (5) When (6) (7) whereand characterize the reduction
When from [8] we find The temperature in the center section of the column: The temperature of fire at : The temperature of reinforcement bars at four-face heating: According to [8], the reduction of static design resistance of reinforcing steel [9,10] at 736 ° C is the reduction of dynamic strength of reinforcing steel equal to: The results of the load bearing capacity of eccentrically compressed reinforced concrete columns at different thermopower conditions defined by analytical and experimental ways are shown in the table.