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Data fusion usually has three links: Data acquisition, feature extraction and reasoning decision.
It has tree three levels: Data level, feature level and decision level.
It provides data reduction to feature level.
In feature level, data of images can be classified in computer.
The data processing efficiency of decision level can be improved by this step.

The essence of this research work is to determine individual stiffness of 3D space frame members using reduction member method.
By applying reduction member method, the individual stiffness of all member can be determined.
Fig. 2 : Flow process to find individual stiffness of 3D space frame joint members 2.1 Experimental Data An experiment for 3D space frame structural joint is carried out to find the relationship between force and displacement.
By applying reduction member method, the individual stiffness of all members can be calculated.
Figure 6: Load versus deformation for experimental and simulation Fig. 7: Equivalent stiffness of all members 4.0 Conclusion From this research work, reduction member method is proposed to predict individual stiffness for 3D space frame thin walled structural joint.

Powder metallurgy has been used for titanium based implants fabrication due to advantages such as the production of more complex shapes and reduction of machining operation.
According to this data, the Ti powders were subjected to standard powder metallurgy operations: uniaxial compacting at 730 MPa, with expected relative density of 79%.
b a Fig. 2 – General aspect of rolling process: (a) sample with compacted powders in a machined recipient; (b) the same specimen after 50% thickness reduction rolling with indication of sample cutting.
The data indicated that the Ti powders are according to ASTM F67-06 grade 2 for oxygen (O), carbon (C) and iron (Fe) contents.
However, when all the results are compared, it is evident that 300 to 400°C can be considered the best temperature range for rolling this material, since it allows obtaining low porosity but with significantly higher hardness, comparable to the reported data obtained with bulk titanium processed by ECAP at this same temperature [16].

The main train of thought of Method 1 is, the measured data at hub height is set to be Data Set 0, increase each wind speed value in Data Set 0 by 0.1m/s to form Data Set 1, increase each wind speed value in Data Set 0 by 0.2m/s to form Data Set 2, …, increase each wind speed value in Data Set 0 by 1.0m/s to form Data Set 10.
The main train of thought of Method 2 is, multiply each wind speed value in Data Set 0 with coefficient c1 to form Data Set 1 so that the MWS of Data Set 1 is larger than Data Set 0 by 0.1m/s, multiply each wind speed value in Data Set 1 with coefficient c2 to form Data Set 2 so that the MWS of Data Set 2 is larger than Data Set 1 by 0.1m/s, …, multiply each wind speed value in Data Set 9 with coefficient c10 to form Data Set 10 so that the MWS of Data Set 10 is larger than Data Set 9 by 0.1m/s.
Tab. 2 Fitting Results of Wind Farm A at 80m Under DPM 1 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01047 2.263 0.9981 Data Set 2 0.00951 2.296 0.9989 Data Set 3 0.008585 2.333 0.9990 Data Set 4 0.007742 2.369 0.9992 Data Set 5 0.007045 2.402 0.9994 Data Set 6 0.006565 2.425 0.9988 Data Set 7 0.00591 2.462 0.9988 Data Set 8 0.005322 2.498 0.9987 Data Set 9 0.004784 2.535 0.9987 Data Set 10 0.004306 2.57 0.9986 Tab. 3 Fitting Results of Wind Farm A at 80m Under DPM 2 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01207 2.195 0.9970 Data Set 2 0.01148 2.201 0.9938 Data Set 3 0.01100 2.204 0.9921 Data Set 4 0.01076 2.202 0.9930 Data Set 5 0.01022 2.211 0.9914 Data Set 6 0.00972 2.222 0.9926 Data Set 7 0.01008 2.193 0.9963 Data Set 8 0.00946 2.209 0.9950 Data Set 9 0.00910 2.207 0.9923 Data Set 10 0.00861 2.220 0.9922 In Tab. 2 and Tab. 3, R2 is known as square value of fitting correlation coefficient, and if this value is close to 1, it is illustrated
For easy of comparison and analysis, the combined reduction coefficient is set to be 0.667, which is corresponding to 1950.61h of EAD.
According to the method above, PG and EDA of Data Set 0-10 can be obtained, which is shown in Tab. 5, where denotes EAD corresponding to theoretical PG and denotes EAD with combined reduction coefficient of 0.667.

A schedule was designed in order to maintain an approximate reduction of 20% between each rolling pass, allowing achieving a final thickness of 0,2 mm for a total reduction of 98% after 12 passes, without any interpass annealing.
Fig. 2(b) further depicts the average values of the effective shear strain (the data scatter bars represent the standard deviation) as a function of the imposed asymmetry ratio.
Despite the wide range of R values (from 1 to 2) initially considered, the data demonstrate that, for the present alloy and for the selected reduction per pass of 20%, R values exceeding 1,6 were inappropriate since the increased shear strain could not be totally transferred to the processed material.
From these experimental data it can be observed that alternate asymmetric rolling is more effective in increasing the crystallite misalignment with respect to unidirectional ASR.
Comparison between unidirectional and alternate ASR for the 5083 (a) and 6082 (b) alloys rolled by asymmetry ratio of 1,2 From the data gathered in figures 6 and 7, it can be stated that hardness significantly increases for both alloys after the first rolling passes (the data point at equivalent strain of 0,77 corresponds to the third pass) whereas, when approaching the highest strain levels, hardness saturates at about 90 and 160 HVn for the 6082 and 5083 alloy, respectively.

The temperature-programmed reduction (H2-TPR) experiment was carried out for 50 mg of catalyst.
In addition to the TPR result, the X-ray diffraction data also showed the existence of Mn3O4.
At low temperature, the manganese reduction peak is observed at 578 oC in Mn-Ce/TiO2 catalyst.
The addition of CeO2 to the Mn/TiO2 showed manganese reduction peaks at 561 oC.
Wu, Low-temperature selective catalytic reduction of NO on MnOx/TiO2 prepared by different methods.

Methodology According to the carbon balance method, a model is build to calculate the gasoline consumption of light-duty GVs using the emission data firstly.
Data source.
Fig. 1 The driving cycle The data collected by EV includes time, vehicle speed, battery working current and battery voltage et al.
The information collected by GV under the same driving condition contains time, vehicle speed and emission data (hydrocarbon, carbon dioxide and carbon monoxide emission rates) et al.
For GVs, gasoline consumption rates can be estimated by using the vehicle emission data.

Measurement of reduction rate of enamel hardness.
Table 2 Enamel surface hardness (kg/cm2) before and after demineralization Materials Before demineralization After demineralization Reduction rate% GIC 334.07±36.24 279.97±30.92 17.72±2.50a Fuji II LC 361.46±34.42 236.79±37.01 35.06±3.46b Compoglass F 379.58±41.11 203.78±26.75 48.12±4.23c Beautifil 382.80±42.26 182.94±36.78 48.08±4.34c Charisma 390.34±41.43 111.55±33.19 71.36±4.67d Experimental resin 380.32±35.12 201.12±32.25 47.11±5.13c The data mark with the same letter demonstrated no significant difference ( p>0.05).
Table 3 The depth of enamel demineralization along the surface and the interface (μm) Materials Along the surface Along the interface GIC 17.86±2.45a 23.13±5.65a Fuji II LC 68.37±10.19b 70.79±12.88b Compoglass F 127.61±22.15c 111.94±6.38c Beautifil 136.95±28.43c 123.52±34.10c Charisma 172.46±23.38d 173.62±37.90d Experimental resin 113.61±17.32c 108.34±9.62c The data mark with the same letter demonstrated no significant difference ( p>0.05).
GIC showed the least reduction, followed by Fuji II LC.
Compoglass F, Beautifil and Experimental resin showed a comparable reduction and Charisma the most reduction ( p<0.01).

Results and discussion Fig.1 shows the microstructures and scattered data pole figures of AZ31 alloys after ECAP and annealed.
The results of scattered data pole figures also indicate that the basal plane texture component is the primary orientation for small grains.
Fig.1 Microstructures and scattered data pole figures of as-impact samples(a-1pass，b-4passes) The relative reduction of the height at rupture was considered as a measure of the impact ductility of the material [8].
The sample (impacted along TD) possesses the highest impact reduction (about 0.3) at the strain rate of 1681S-1.
At this high reduction, the maximum flow stress reaches nearly as high as 436MPa.

The activation energy for annealing behavior was calculated using DSC data.
The plates, 8mm in thickness, were rolled with the reduction of 85% at cryogenic temperature.
Since the DSC peak position depends on the heating rate [6], the measured data of the peak positions for the different heating rates of 1, 2, 4, 8, 16, 32℃/min were used.
The measured data for the first peak due to the precipitation, the second peak due to recovery and third peak due to recrystallization are listed in Table 1.
The present result showed that the Q value for precipitation coincided with the calculated data by Picu et al. [9].