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The comparison of experimental data with simulations allows deriving the pressure distribution and flow velocities inside the adhesive layer.
First, the surface of the blanks is measured and digitalized and based on this data a 3D model is generated.
The data gathering is made complicated because of different disturbances (as vibration of the assembly devices, shadows of the tools), therefore the Point-Tracking was developed.
In the second step, the extracted data is transformed into boundary conditions acting on each connection region of the global 3D multi-point model.
Experimental data is used to validate the numerical simulations.

Multiple clamping is characterized by a series of economical advantages, due to the reduction of total machining time.
The measurement equipment consists of: force transducer type S9 made by Höttinger Baldwin Messtechnik; inductive displacement transducer type Wl/ 10mm made by Höttinger Baldwin Messtechnik; multi channel electronic PC measurement unit made by Höttinger Baldwin Messtechnik; and computer using Catman Easy/AP, versions 2.2 data acquisition software (Fig. 1).

After the signals de-noising, combining the envelope demodulation analysis with the spectrum zoom technology, which can effective extract the weak fault characteristic frequency The result of simulated signals and experimental signals shows that the proposed method has better noise reduction with a higher signal noise ratio (SNR) and lower mean square error (MSE), and it can successfully extract the fault characteristic frequency of rotating machinery early AE signals.
Samples of the input data are divided into odd sample sequenceand even samples sequence (1) (2) Predict.

With the reduction of temperature, liquid phase separation phenomenon is more obvious.
The potentials were fit to the following set of experimental data: elastic constants, vacancy formation energy and cohesive energy for Cu, Co as well as the mixing enthalpy and defect properties for alloy compound CuCo.

For simplicity, iterative times or continuously without optimization iterations (Iteration_number) can be used as the end of the iteration process. 3.3 AIA-CARP algorithm processes and steps The algorithm process is shown in Fig.2. 4 experiment results and analysis Artificial Immune Algorithm proposed in this paper was used to test the data set in the literature [4].
Based on the data of different scales, the algorithm test results are shown in table 1: Table 1 CARP experiment data Experiment data set Vehicle number results （km） Runtime(s) Data set Data size Node in test 5t 8t 10t Data1 9vertex10arc 1,2,4,5,6,7,8,25,29 1 1 1 19.597 4 Data2 16vertex18arc 1,2,4,5,6,7,8,9,20,23,24,25,26,29,36,37 2 2 1 32.043 9 Data3 21vertex27arc 1,2,4,5,6,7,8,9,10, 14,17,27,21,20,23, 24,25,26,29,36,37 2 2 2 49.310 25 Data4 37vertex51arc Full graph 2 3 3 67.391 202 Testing results show that, base on different sets of data, all the total travelling mileage have different degrees of reduction.
Acknowledgments This work was supported in part by “Data Structure and Algorithm” project, included in the national bilingual education model curriculum in 2009, in part by Chongqing application technology research projects (cstc2011pt-gc70007, cstc2012gg-yyjs40019 ), in part by Chongqing municipal education commission research projects ( kjzh11221), in part by Research Project of Chongqing University of Science and Technology (CK2013B15), in part by Teaching & Research Program of Chongqing Education Committee (KJ131401), in part by Natural Science Foundation Project of CQ CSTC (cstc2012jjA90011).

Thus, there is high potential for stimulating ridership intent, increasing public transportation system efficiency, and achieving urban carbon reduction goals.
This system uses real-time bus route, bus stop, and dynamic bus data XML documents to inquire into real-time dynamic bus data. 1) Bus Route Data:Bus stop name, bus stop number, route number. 2) Bus Stop Data:Bus stop latitude and longitude, bus dynamic data, bus stop name, route number. 3) Bus Dynamic Data:Bus license plate number, bus stop latitude and longitude, bus dynamic data, route number.
[6] Rastogi, R. and Rao, K.V.K., “Modelling Transit Access Behaviour using Revealed Preference and Stated Preference Information”, Proceedings of Choice Modeling and Revealed and Stated Preference Data at 9th WCTR Meeting (2001), Seoul Korea

(1) Assuming UID label length k bit, due to the dynamic binary search algorithm like binary search algorithm for each tag and reader are sent complete UID length k bit, dynamic binary search algorithm tag and reader communication to send commands length parameter of the two : 1) the first is the start time of the paging cycle of each round, all commands sent 2k bit data to be transmitted; data length 2) for further communication with the paging command to the tag reader needs to be transmitted to k bit.
Which is based on reducing tag reader communicates with the amount of each transfer of data to improve the binary search algorithm is not based on reducing the number of reader and tag communication excellent binary search algorithm improvements in the transmission time.
Secondly, dynamic binary search algorithm, for example, analyze the reader every time a binary search algorithm and data tags based communication transmission reduction by comparing the binary search algorithm simulation and dynamic binary search algorithm paging time, finally to return to the binary search algorithm, for example, based on a binary search algorithm to reduce the number of reader and tag communication is analyzed, and Matlab simulation by comparing the three algorithms paging time.
Provide theoretical support for the improved binary search algorithm, it can be concluded that the improved algorithm to reduce the number of reader and tag communication than the start of each tag communication with the amount of data transmission to reduce the reader to proceed from the better.
Hatamlou: In search of optimal centroids on data clustering using a binary search algorithm, Pattern Recognition Letters, Vol.33 (2012), p.1756-1760 [3] A.

e) Physical and chemical property data storage of hazardous materials.
Applicable to store physical and chemical property data of the hazardous materials to provide transfer support for necessary data information during the process of hazardous material discharge estimation and gas three-dimensional dispersion forecasting.
Utilize high space-time resolution atmospheric circulation numerical forecasting model to calculate the meteorological data, then transfer and extract program to extract and convert the meteorological data.
e) Provide the estimation result and simulation parameter document of meteorological data and hazardous material discharge to the core computer module, transfer the physical and chemical property data of the hazardous materials to obtain physical and chemical property data and operate gas three-dimensional forecasting model.
f) Distinguish and screen out effective data of the dispersion simulation result and convert the forecasting data into the standard data of the equitime spatial grid interval coordinates.

Data from [8] for 0.78 C/ 0.63 Mn steel and [10] for steel 0.81 C/ 0.02 Mn (weight %).
The elevation of the eutectoid temperature (A1), at first, would indicate a reduction of the interlamellar spacing by the Zener equation [11]: So = 2 σα/Fe3C [1 / (1 -T/TE)].
Silicon is a ferrite stabilizer and increased TE to 765 ºC, increasing So for temperatures above 630 ºC in relation to steel without silicon and with subsequent reduction of So for temperatures lower than 630 ºC.
In the niobium steel, the hardness was higher due to the precipitation of niobium carbide, and it decreases with time, indicating the coalescence of such carbides, with a reduction in the precipitation hardening effect.
Both the refining of the ferritic grain and the reduction of the interlamellar spacing of the pearlite increase the mechanical strength and toughness (reduction of the brittle to ductile fracture transition temperature – FATT).

On the other hand, it is important to note the reduction in the intensity of this absorbance band after heat treatment, i.e., for the sample SNHC.
This is due to the reduction of moisture in the treated material.
The SNHC infrared spectrum shows that the combination of the two treatments has led to a chemical modifications traduced by bond scissions resulting in the reduction of NH groups’ characteristic absorbance bands around the pyranosic cycle overlapped at about 1000 and 1100cm-1.
Comparison obtained data’s with other studies at 25 °C Study Dye pH Contact time Material Q(mg/g) Choy et al.* [11] Blue (RAWL) - 21 days Charcoal 100.9 Red (AR114) 21 days Charcoal 101.0 Yellow (AY) 21 days Charcoal 128.8 Tsai et al. [12] Orange 51 - 10 hours Activated bleaching earth 8.45 This study Nylosane Red (F3GL) 5.5 5 hours S 117.65 SNHC 294.12 *at (20±2) °C Thermodynamic parameters (Table 3) indicate that the adsorption is enhanced by increasing the temperature.