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Automatic parameter classification for dimension reduction as basis for robust parameter identification D.
Numerical approximation methods of high dimensional data suffer from the curse of dimensionality [1].
Dimension Reduction.
The dimension reduction is performed user-controlled.
These parameter classification results are used for a parameter space reduction.

During the field research the facts were observed and taken data collection.
Following, it was made the analysis and interpretation of the collected data.
In Figure 7 there are partial data from the implementation of LEED in the work of Maracanã referring to February 2011.
This is the result of a first data collection of points aimed at sustainability in the construction site of Maracanã.
In Figure 8 are the final certification process data from LEED, dating from May 2013.

Generalization of the results of experimental studies and thermodynamic modeling made it possible to obtain new data on the influence of the basicity and content of B2O3 in the slag of the CaO-SiO2-B2O3-MgO-Al2O3 system on the interphase distribution of sulfur and boron.
The shift of the formed slags to the area of ​​increased basicity up to 5.0 shows a high degree of boron reduction from slag to metal.
In addition, when boron oxide is used in the slag, the process of boron reduction occurs [14].
In this case, along with the high desulfurization of the metal, the development of boron reduction from the boron-containing basic slag is observed, the concentration of which approaches equilibrium within 10 minutes, reaching 0.017%.
Experimental content of boron and sulfur in the metal under the slag of the investigated compositions Summary Theoretical and experimental studies of sulfur and boron behavior made it possible with minimal time and material costs to obtain new data on the equilibrium distribution of sulfur and boron between the of CaO–SiO2–B2O3–25%Al2O3–8%MgO slag system, in a wide range of chemical composition and between the metal, containing 0.2% C, 0.35 Si and 0.028% Al, at 1600°C.

The phase purity and chemical purity of hafnium chloride has immense influence on the following process namely, Kroll’s Reduction.
The data leading to optimised process parameters for the production of phase pure material has been presented.
Hafnium oxide is converted to hafnium chloride using a reductive chlorination process prior to reduction.
The HfCl4 is then sent to Reduction Section for further processing.
Fig.3 Process Flow Chart for Carbo-Chlorination Table 4 Experimental data on elemental analysis Element Test Results (in %) Batch 1 Batch 2 Batch 3 1 Iron 1.13 0.17 0.6 2 Calcium 0.009 0.003 0.9 3 Aluminium 1.96 0.41 0.3 4 Titanium 7.46 1.0 0.4 5 Nitrogen 0.082 0.095 0.02 6 Silicon 0.094 0.012 0.009 7 Chromium 0.024 0.041 0.021 8 Magnesium 0.034 0.008 0.23 9 Nickel 0.082 0.095 0.022 10 Zirconium 0.87 0.29 0.5 11 Hafnium 80.44 85.45 89 X-ray diffraction (XRD) Studies Phase purity is confirmed by analysing XRD and diffraction patterns of Hafnium Chloride powder in the Fig. 4, which shows that no impure elements were present.

The hot-rolled plates were symmetrically cold-rolled to 65-95% reduction in thickness and then asymmetrically warm-rolled to 20-40% reduction by one pass at 473 K on a condition of roll speed ratio of 1.5.
To reveal a change in texture during solution treatment, the area fraction of representative orientations were determined from EBSD data by using the samples annealed at 813 K for some short periods within 90 s and immediately water-quenched.
In addtion, the microstructure in an as-rolled state before solution treating was observed by a transmission electron microscope (TEM) and the misorientation in deformed structure was determined from EBSD data of the as-rolled sample.
Results and Discussion Effect of rolling reduction on recrystallization texture.
The near-{111}<110> recrystallization texture, which has a peak in the center of a {111} pole figure, can be observed at relatively low reductions of AWR, while the {111} texture components hardly exist at all CR reductions on a condition of 40% AWR reduction and the principal component is expressed by {013}<631>.

The two dimensional IDDM algorithm is checked herein through numerical simulations, using the FE model of a plate and modeling local reductions of stiffness through a reduction of the elastic modulus of the material of one or more elements of the model.
Furthermore, as shown in reference [3][5], useful information on damage is lost when data, in a frequency range away from resonance, are discarded (as occurs in the modal analysis process).
If a reduction of stiffness occurs at a certain location, the operational shapes change in the region close to that location.
Bi-cubic interpolation is a cubic interpolation in two dimensions and calculates the value of a given function at a point P(xl, yp) by considering 16 data points in the neighborhood of P.
The interpolation damage index The drawback of bi-cubic interpolation for the application of the IDDM is that it can be applied only to data point lying on a two dimensional regular grid.

., initial wire dimensions, height reduction and lubrication conditions.
The plastic regime also involved material strain hardening, as described by eq. 1 in which the values of σy0 (static yield stress), the strength coefficient K and the hardening exponent n were opportunely calculated by fitting the experimental data obtained by tensile tests conducted on the samples before drawing.
Factors and respective levels utilized in the simulation campaign Initial Wire diameter (mm) Height Reduction (%) Working Rolls Diameter (mm) 5.3 10 40 4.6 20 80 30 120 40 160 50 Results Experimental data were analyzed by means of statistical techniques such as analysis of mean (ANOM) and of variance (ANOVA) to assess the influence of each factor on the experimental response, namely final width of wire.
The points in the plot are the means of the response variable at the various levels of each factor, with a reference line drawn at the grand mean of the response data.
Initial diameter, Lubrication and Height reduction.

At about 420˚C, the temper brittleness occurred and obvious properties change happened of the samples, such as impact value, reduction area and elongation percent.
To attain the optimal mechanical properties and avoid accidental failure in use, it is necessary to make effort on its temper temperature in order to fully exert its potential capability, which could offer experimental data for the manufacturing production.
Fig.5 Effect of temper temperature on the hardness of samples ˚C ˚C Fig.3 Effect of different temper temperature on the tensile strength, yield strength and impact toughness Ak ˚C Fig.4 Effect of temper temperature on the reduction area and elongation percent of samples The effect of temper temperature on the reduction area and elongation percent of samples is showed in figure 4.
While compared with the close data around 420 ˚C, the reduction area percent as about 4% and the elongation percent as 11% declined, but at the same time the change of the reduction area percent becomes larger.
Considering the result showed in figure 3, it is corresponding to the indicators of impact toughness, which can be described that with toughness declined, the reduction area and elongation percent will drop.

The position on solidification end point which obviously effect on soft reduction was predicted and verified by nail—shooting technique.
Soft reduction technology is an effective method to minimize centreline segregation and eliminate porosity [2].
Its parameters, such as reduction zone, reduction amount and reduction rate, were determined by laboratory experiments or plant trials.
The result showed that the reduction zone, the position on solidification end point was obviously effect on soft reduction [3].
Experimental procedure The experimental data were obtained by using Nail—shooting system located at selected position between seven section and eight section，distance from the meniscus surface 18.339m.

As FRF is sensitive to the changes of the structural physical parameter, it is often used as the original data for structural damage identification.
Wu[1] uses the former 200 data of FRF from experiment as input to the BP neural network to identify the damage of a 3-layer building.
Using all available vibration transmissibility data, let us form matrix [H(w)]M×N which has M rows of vibration transmissibility, each with N frequency points.
Structural damage detection using artificial neural networks and measured FRF data reduced via principal componet projection.
Imregun combined neural network and reduced FRF techniques for slight damage detection using measured response data.