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The determination method of deviation source diagnosis is obtained. 3-D scanner is used to measure the key data.
We introduce the diagnosis matrix H[9],where H is symmetric and given as (7) Traditional three-coordinate measuri- ng data is difficulty, It is easy and reliable to acquire data by 3-D scanner, sticking labels for key points.In different stages of assembly, data collection is convenient.
Experimental data obtained here reflects the rapidity.
We use the fixture data for comparison to optimize the fixture.
Dimensional variation reduction for automotive body assembly[J].

Theoretical approach and experimental setup were validated by using the actual data from thermal decomposition of limestone.
This reaction is well known in the industry and real data on this process are available for comparison with simulation data obtained from proposed approach.
Data of DHf, DGf and Cp for the chemical species present in this reaction are given in Table 2 [3].
Table 2: Data of DHf ,DGf and Cp(*) of CaSO4, CaO , SO2 and O2.
Experimental data from calcination of phosphogypsum were also obtained.

Then carry out the fuzzy cluster analysis of the sample data
(3) Get new data matrix through deleting condition attribute from condition attribute set .
Use the law of range transform to standard basic data
“A fuzzy clustering algorithm based on fuzzy distance norms for asynchronously sampled data.”
“New heuristic attribute reduction algorithm based on rough set.”

Different rotational speed of disc brake was selected and the experiments squeal noise data was collected and analyzed using the Fast Fourier Transformation (FFT) analyzer.
Data Acquisition System (DAQ) was used to collect information from the transducer, and process it to suitable data for display or storage outcomes.
Quaglia, “Disc Brake Squeal: Mechanism, Analysis, Evaluation and Reduction/Prevention”, SAE-Society of Automotive Engineers, 2006 [2] M.

The FEA results provided some valuable data for the engineering application of C/SiC composites bolt.
He investigated the high temperature cycle effect occurring during atmospheric re-entry on the reduction of the tightening torque of the CMC fasteners.
Stress response in different loading conditions Conclusions From the data presented in this paper, the following results are concluded: (1) Friction coefficient between screw bolt and screw nut has serious influence on the stress response and minor influence on the total displacement under pressure loading

During the implementation process, numerical results must be verified by comparing them with, for example, analytical solutions or experimental data.
Conforming to [13], in terms of execution time, the performance of the CUDA API is superior to that of OpenACC, and in terms of data exchanged, OpenACC is more affected when compared to CUDA.
Moreover, to determine the values in i ± 1/2, j using the known data in i, j, and i ± 1, j, a harmonic mean is applied to calculate the apparent permeability and area.
Therefore, it is evident that we parallelize the operations involving updating vectors, matrix-vector products and scalar products using the OpenACC API.Algorithm 1: Parallelized Conjugate Gradient Method 1 Provide the maximum number of iterations nmax and the tolerance tol; Calculate the residual r0 = b − Ax0 for the initial estimate x0; Let p0 = r0. 2 #pragma acc data copy for k = 0, 1, ...nmax do 3 Calculate Apk // #pragma acc parallel loop private(i,k) reduction(+:sum) gang num_workers(numWorkersACC) vector_legth(VectorLenACC) 4 end 5 #pragma acc parallel loop for k = 0, 1, ...nmax do 6 Calculate < pk, Apk > // #pragma data copy(a[:N], b[:N]) #pragma acc parallel loop reduction(+:sum) 7 end 8 #pragma acc parallel loop for k = 0, 1, ...nmax do 9 Calculate < rk, rk > // #pragma data copy(a[:N], b[:N]) #pragma acc parallel loop reduction(+:sum) 10 end 11 #pragma acc parallel loop for k = 0, 1, ...nmax do 12 Calculate αk = < rk, rk > < pk, Apk > 13 Calculate xk+1
Zhang, A comprehensive model for production data analysis in unconventional reservoirs of hydrocarbons.

Design Maps and Shape Optimization of a Prototype Vehicle Chassis Murshed, M.R.a, ud Duja, B.b, Ranganathan, S.I.c,1 and Darras, B.M.d Department of Mechanical Engineering American University of Sharjah, Sharjah, UAE, P.O. 26666 aridwan.murshed@gmail.com, bbadar.bd@gmail.com, csranganathan@aus.edu, dbdarras@aus.edu Keywords: optimization, section modulus, chassis, cross-section, weight reduction, section efficiency, design maps Abstract.
Weight reduction is an important area of study in the automotive industry.
Table1: Comparison of frame analysis tool data with the analysis provided in this paper Max.

Therefore, restraining the formation of the skeleton textune rhombic hematite during sintering is the basic measure to reduce reduction degradation and enhance the strength of sinter.
After respectively granulating, the permeability of the sinter layers improved, the time of sintering became shorter, he vertical sintering speed became faster, judging from the datas, its RDI+3.15 increased 24.24%, the RDI improved significantly.
Rhombus titanium hematite that caused low temperature reduction degradation reduced significantly.
First of all, rhombus titanium hematite in Vanadium titanium sinter had major effect on the reduction degradation.
Respectively granulating Vanadium-titanium magnetite sinter greatly reduced the major facters that caused reduction degradation.

It is generally believed that the urea selective catalytic reduction (SCR) technology is one of the most promising methods to reduce NOX emissions of vehicle diesel engine [2].
Timely correction of urea injection can not only ensure the substantial reduction of NOX emissions with the purpose of meeting the national regulations, but also avoid the leakage of NH3.
In general, each datum drops a little after installing SCR system.
Progress of Research on Reduction NOX Emission of Diesel Engine with NH3-SCR Technology [J].
[2] Selective Catalytic Reduction (Final Report) the most promising technology to comply with the imminent Euro Ⅳ and Ⅴemission standards for HD vehicles [R].

Sensor registration is a prerequisite for successful multi-sensor data fusion [2].
A least squares fusion of multiple radar data.
An exact maximum likelihood registration algorithm for data fusion.
New least squares registration algorithm for data fusion.
Ill-condition and registration of system error estimation in data fusion systems.