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Based on the point cloud data of BMW X1prototype measured by the CMM(three coordinates measuring instrument) , specific geometries of car body is achieved.
Reference data are obtained for the parameters in the formula: m = 1685kg, V '= 0km / h, V = 48km / h, t = 80ms.
Fig. 12 Data of three beams after the lightweight Fig. 13 The number of iterations vs. the mass of the beams Fig. 14 The number of iterations vs. the axial stress of the beams Table 3 Data of three beams Serial number a b S1 0.06m 0.006796m S2 0.06m 0.006109m S3 0.06m 0.003348m Fig. 15 The shape of beam cross-section On the basis of ensuring the performance of the car body, we can observe from the data in Fig. 13 that the weight before is 5.6 times that after the lightweight.
Conclusion To the characteristic of chassis layout and load distribution for electric car, this paper mainly conducted an optimization of the topology and size with car body, most of which includes: (1) Use CMM to collect data about key points of car body.
We obtain the CAD model and the original data of the bodywork, with reverse engineering techniques and the software Imageware

The quantitative data is mainly obtained from this province’s survey by Tsinghua University.
Qualitative data is primarily obtained by experts and the score is presented by five scales: high, higher, middle, lower, low.
The corresponding values expressed in the form: High:= 0.9, higher: = 0.7, Middle: = 0.5, lower: = 0.3, low: = 0.1, data acquisition results in Table 3.
Effective implementation of this program is to determine the assumptions to obtain qualitative and quantitative evaluation data.
However, in the renewable energy policy, the predicted data is often uncertain at the same time, policy-makers to judge is usually vague, it is difficult to provide criteria for decision-makers accurate data.

Step 1: Experiment data generation.
Select testing points in terms of different load masses within the range, and conduct swept frequency test at each point to acquire FRF data set.
Using to represent the true parameter sets of the system, the noisy FRF data can be described as (7) where is the frequency of the sinusoidal input signal, is the measured frequency response, and is the number of frequencies.
The experiment FRF data (solid blue line) and the computational FRF data calculated by the identified LPV model set (dashed red line) are compared in Fig. 2(b).
Model reduction for control system design.

ICP performs well if the two data sets are close to each other.
To speed up the search for K nearest neighbors, the kd tree data structure can be used to organize the data [4].
Fig. 7(a) shows the reduction in the number of data points to be used for fine alignment.
It is clear that the use of higher order neighborhood leads to a significant reduction in data size.
(a) Reduction in the No. of points (b) Alignment error using different neighborhoods Fig. 7 Results of fine alignment using reduced data sets Conclusion and future work PCA is quick way of arriving at an intelligent guess for fine alignment.

KP 19, Microbiologically Influenced Corrosion, Nitrate reduction Abstract.
The XRD raw data was interpreted using the program DIFFRAC.EVA version 3.2, by performing subsequent search and match scan from reference library derived from crystallography open database (COD) to trace the peak pattern.
Results and Discussion Microbial Nitrate Reduction.
The data indicated that Cronobacter sp.
Figure 1: Comparison of nitrate reduction by Cronobacter sp.

Nanoparticles Stabilized Carbon Dioxide Foams in Sandstone and Limestone Reservoir Azlinda Azizi1,a, Hazlina Husin1,b,NurulAimi Ghazali1,c, Muhammad Kamil Khairudin1,d, Arina Sauki1,e, NurHashimah Alias1,f and TengkuAmranTengku Mohd1,g 1Fakulti Kejuruteraan Kimia, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia aAzlinda68@salam.uitm.edu.my, bhazlina858@salam.uitm.edu.my, cnurulaimi@salam.uitm.edu.my, dlimak_555@yahoo.com, earina_sauki@salam.uitm.edu.my, and gamran865@salam.uitm.edu.my Keywords:Retention, absorption, porosity reduction Abstract.The use of nanoparticles with carbon dioxide foams has been proposed for enhanced oil recovery due to their robust chemical stability in harsh environment.
Limestone showed the higher porosity reduction at the end of experiment compared to sandstone with the porosity of 7.56% on limestone and 12.49% on sandstone respectively.
The initial porous medium condition was set up all over again to ensure that a significant oil recovery data could be collected at the end of the experiment.
The residual oil contacted with CO2 foam slugs was not properly solubilized into the micro emulsion with less capillary pressure reduction between grains and oil cannot be removed from the sandpack.

Top gear reduction of the system is iH=nT/n0=20.8 and low gear reduction is iL=TT/ηn0=50.6. η stands for low gear efficiency, take η=75%.
According to single stage spur gear reduction ratio, the stage of the top gear reduction and low gear reduction is 2-4 and 3-5 respectively.
System structure The system is designed to be with minimum radial and axial dimension on the basis of each reduction meets the involute cylindrical gears reduction gear ratio.
To simplify the reduction between top gear and low gear take i34=i56, because i34*i56=iL/iH, so i34=i56=1.6.Reduction i78=iH/i12=5.2.
So the system works as below: The controller calculates the output torque and speed by sampling data of motor currency and encoder output data.

L10-CoPt Bit Patterned Media with Tilted Easy Axis for Ultrahigh Areal Density over 2.5 Tb/in2 Arkom Kaewrawang1,a* 1Magnetic Materials & Data Storage Research Laboratory, Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand aarkom@elec.kku.ac.th Keywords: tilted easy axis; bit patterned media; L10-CoPt; switching field Abstract.
In this work, we propose the one of alternative method to increase data density and reduce Hsw of the media by using tilted easy axis technology for bit patterned media (BPM) at areal density beyond 2.5 Tb/in2.
The BPM with tilted easy axis perform clearly the reduction of Hsw below perpendicular media and available writing head field.
Thereby, BPM with tilted easy axis have high potentiality to improve Hsw of media at ultrahigh data density.
In this work, the Hsw reduction of BPM with tilted easy axis of L10-CoPt materials is proposed.

The dynamic field parameters calculated with the HBM are used as input data for the computation of the SIF through the VCCT.
In the proposed procedure, the dynamic forces and displacements calculated with the HBM at specific nodes of the ROM of the system are used as input data for the computation of the SIF through the VCCT.
As shown in Fig.3 and according to the value reported in Table 1, the HBM permits to achieve very accurate displacement and force fields with a drastic reduction of the computational time.
Implicit solver ABAQUS HBM+VCCT Reduction ratio Computational time [s] 93600 158 ~600 Calculation of SIF is then performed with the VCCT, starting from the displacement and force fields obtained with the HBM.
It was found that a reduction factor of about 600 in terms of computational time can be achieved with the proposed procedure with no loss of accuracy.

The purpose of this work is to develop an experiment applied to milling process cutting parameters optimization based on data collection from the factory shop floor.
Hardened steel dies currently used for forging process were milled to generate data to be used during the experiments.
From data in Table 5 is possible to compare the cut parameters found during the experiences with the cut parameters previously the optimization procedure.
Cutting conditions before and after the experiences Data before the Experiences Data after the Experiences Cutting Speed [m/min] 155 162 Depth of Cut [mm] 0.3 0.6 Feed Rate [mm/rot] 0.1 0.1 The consequence of the adoption of these new cut conditions was an increase of 64 % in the process efficiency.
It is important to note that the use of both procedures can occur in the factory shop floor using the same data collected during the machining process.