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The weight decrease on time, presented in Fig. 4 (the data is processed by methods of linear approximation) was fixed.
Under the received data the values of reactionary ability corresponding to these temperatures under the formula (14) are calculated as: For processing of experimental data, the mathematical procedure offered in the works [3, 16] was used
There is no data about equivalent or close correlation connection of the indicators CRI/CSR with technical and economic indicators of the blast furnace smelting.
In a certain zone of the furnace at a reduction stage of magnetite the reaction of wustite reduction tends to a thermodynamic equilibrium.
In this zone so-called zonal reduction realizes when the processes of reduction of various iron oxides combine in time.

aemail:hn61886@163.com, bemail:chennsh@imde.ac.cn Keywords: Debris Flow, Wen-chuan Earthquake, Disaster Prevention and Reduction, Economic loss Abstract: Debris flow disaster occurred frequently, at the same time it covered a wide area in China.
Through statistics analyzing debris flow disaster data from 1914 to 1999, obtain that debris flow hazards mainly distributed in the east longitude 100-110°, 25-30°north latitude and 30-35°north latitude in China.
Fig10 Total geological disasters in our country from 2005 to 2010 Fig 11 Total debris flow disasters in our country from 2005 to 2010 Fig 12 The percentage of debris flow in all geological disasters Based on the data we could predict that due to the earthquake the origin structure of the slope material become loose, which in turn provide abundant clastic-sediment for debris flow, accompany with intense precipitation and steep topography debris flow will occurred frequently, plus unreasonable human activity, debris flow disasters will become more serious.
At the same time, we could use these data to test the area which is prone to debris flow, and give risk hierarchies for different areas, in order to give much better information for debris flow disaster prevention and mitigation.
ct=503316480&z=&tn=baiduimagedetail&word [6] Zhixing Zhou “ debris flow disasters in downstream of Jinsha river” [7] Gansu debris flow data

It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data.
Manifold learning algorithms are kinds of novelly promoted nonlinear methods for dimension reduction, which can effectively find out the intrinsic low dimensional structure from high dimensional data.
Fig.1 The framework of KIsomap-LSSVM 3.2 Data Selection The data diesel oil solidifying point was obtained at Soutwest Research Institute (SWRI) on a project sponsored by the U.S.
In this article we select freezegatest data as research objective.
These 201 samples of pretreated were randomly divided into training data and test data.

H2-Temperature-programmed-reduction (TPR) profiles.
Genarally the reduction of iron oxides take place in two steps.
Table 1: H2-TPR data of the catalysts Catalyst 5wt%/Al2O3 Reduction temperature (◦C) Peak 1 Peak 2 Peak 3 Co/ Al2O3 507 650 731 70Co:30Fe/Al2O3 447 501 667 50Co:50Fe/Al2O3 328 412 614 30Co:70Fe/Al2O3 456 458 669 100Fe/ Al2O3 454 635 716 Bimetallic nanocatalysts showed different reduction patterns than monometallic nanocatalysts.
Total H2-consumption and degree of reduction of the catalysts.
Fig. 3: Representative spectrum of CO-Chemisorption at 250 ◦C for Co/Al2O3 Table 3: CO-Chemisorption data for 5wt%/Al2O3 catalysts Catalyst CO-Adsorbed (μmol/g.cat) Co/Al2O3 0.41 70Co:30Fe/Al2O3 0.50 50Co:50Fe/Al2O3 1.77 30Co:70Fe/Al2O3 0.57 Fe/Al2O3 0.27 X-ray diffraction analysis.

Results and Discussion Reaction Mechanism of Catalytic Reduction of Nitrate.
Fig. 1 Hypothetical mechanism of catalytic nitrate reduction（*--the state of adsorbed phase） NO is the key intermediate on the selectivity of catalytic nitrate reduction [10].
Kinetics of Catalytic Nitrate Reduction.
Then equation (2) is simplified as： (3) Considering given conditions：when t=0, and , Equation (3) was processed by Laplace transform, then nitrate concentrations over time can be calculated by computer simulation as following: (4) In order to test for the proposal dynamic model, the experiment results from Fig.3 were plotted according to Eq. (1) and Eq. (4) to see whether a straight line passing through data can be obtained.
Kinetics of nitrate reduction in monolith reactor.

Fig.5 Relationship between variation of cylindrical void dimension and reduction (inter- mediate section) As indicated by the data shown in Fig. 5, the first stage closure of cylindrical void at different locations in ingot has identical process.
Fig.7 shows the relationship between void dimension variation and reduction.
(a) spherical void (b) tetrahedral void Fig.8 Distribution of isoline of effective strain in ingot (ε=42%) Also, cylindrical, spherical and tetrahedral void of different dimension from 1 to 5 mm were used to carry out analysis of influence on the rate of void closure induced by different dimension. 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0 1 2 3 4 5 6 void dimension c r i t i c a l r e d u c t i o n spherical void tetrahedral void cylindrical void (intermediate section) Fig.9 Relationship between void dimension and critical reduction (central porosity) The data in Fig. 9 show that void of different dimension is almost closed up with identical reduction, which is theoretically confirmed by elasticity analytical solution of void issue.
Tetrahedral void has the maximal values of critical reduction on void closure during forging.
Tetrahedral void has the maximal values of critical reduction on void closure during forging.

And the third is the index layer: energy efficiency indexes related to statistical data.
The assessment of the single index refers to the process of observing the change curve or data change of the single index value and finding the abnormal data.
Such indexes can be established with the data in the energy information warehouse.
l To classify and screen the huge historical data collected and construct various kinds of data bases step by step to facilitate directly the modeling of the urban development research model system, including the basic data base of the basic building information, the business data base of the real-time building energy consumption data base, the service data base of the building energy consumption analysis results, and the model data base of the statistical and operation models.
Energy Data Modeling and Analysis for Improved Energy Management Planning and Performance.

Fig. 1 shows the data collection field.
Fig. 1 The field of data collection Fig. 2 3-D data obtained by the laser tracker Cloud Data Processing It is unavoidable of the existence of noise points and background disturbance points in the cloud data collected with 3-D measurement [5].
Noise reduction and smoothing of cloud.Some noise points are involved in the data collected with T-Scan for the influence of field condition, light source and reflection characteristic of blade surfaces.
Noise reduction and smoothing comprises the following two aspects: (1) Noise reduction and smoothing for disordered cloud: For the disordered part, feasible projection angles can be selected according to the characteristics of blade surface, 3-D data points will be reflected on the vision plane, and data cloud will be filtered to remove noise based on experience threshold values. (2) Noise reduction and smoothing for ordered cloud: For ordered part, since the cloud is a kind of scanning line data, smoothly standard Gaussian filter method.
There are 19,053 original 3-D data points, only 8,958 of whom are kept after noise reduction and downsize (as shown in Fig. 3).

The two main problems in data-based SHM are therefore: (1) If supervised learning is necessary, how does one acquire data corresponding to damage states of the structure
Basically, a statistical model of the healthy system is created, based on normal data and then subsequent data are tested to see if they are statistically consistent or inconsistent with the normal data.
The first SVM (labelled SVM0) seeks to separate damage-state data from normal-state data.
Confusion matrix for Classifier 2 applied to testing data.
Also concerned with the problem of sourcing data, a classifier has been presented that has generalised from single-site damage data to multi-site data.

Figure 1 a) Schematic of PPT experimental set-up and b) thermal decay data of surface temperature after heating over defect and non-defect areas In PPT the surface IR data is processed into phase data by using a FFT.
The current work focusses on optimising the processing of the IR thermal data into the FFT phase data to maximise the contrast between defective and non-defective regions and to ensure the repeatability of such data.
All data was collected at a frame rate of 383 Hz in order to allow maximum sampling of the decay period with enough data for analysis.
From this point the camera manufacturer’s software was used to process the thermal data into phase data.
Figure 3 Flow diagram illustrating data processing routine from thermal IR data to PPT phase data Datum location for FFT.