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Consequently, reduction in the coating's hardness and wear resistance were discovered.
The amount of TiB2 in the coating is interesting, and there is also little data studied, especially in the application of high velocity oxygen fuel (HVOF) coatings.
In addition, transfer films also influenced on the reduction of abrasive wear in the tests.

SRAM uses bistable latching circuitry to store data bits, avoiding to be refreshed periodically.
Each cache bank, including data and tag, occupies 2.84mm2.
Cache size When a core in the NoC needs to read or write data in the memory, it firstly checks whether the data is in the cache (L1 and L2 etc.).
If the data is found in the cache, a cache hit has occurred.
These data are gathered from the trace file of the application from our simulator.

In the fuzzy clustering, the fuzzy c-means (FCM) proposed by Dunn [2] and Bezdek [3] plays an important role in unsupervised data analysis.
Let be a finite unlabeled data set composed by patterns for which every .
The objective function of FCM is given by (1) where () denote the cluster prototypes of the data set .
Feil, Fuzzy clustering and data analysis toolbox, Department of Process Engineering, University of Veszprem, Hungary, 2005
Chen, Clustering incomplete data using kernel-based fuzzy c-means algorithm, Neural Process.

The data was spectrally and statistically analysed.
The sounds resulting from the equal impacts to the plates were transmitted to the data acquisition system through a microphone, and next transferred to computer in order to begin the data processing phase.
The output audio data of the amplifier is transmitted to the computer at a sampling rate of 0.00001 seconds via Advantech 1716L Multifunction PCI card and data analysis is performed using Matlab (Fig. 2).
For a block of data of length N samples the transform at frequency mDf is given by . (6) Where Df is the frequency resolution and Dt is the data-sampling interval.
Taylor, Statistical Techniques for Data Analysis, Lewis Publishers, 1990.

PC-1 and PC-2 is the orthogonal vectors, so a new space is they propped up by them, and the data can be expressed using the mapping of original data set in this space.
This space is actually a feature space of the original data set.
In Fig.2, the ellipse represents the data distribution; PC-1 and PC-2 were the two principal components and corresponding to the two feature vectors of data covariance matrix.
So the whole chart formed a data matrix.
The new data matrix is.

Method and Data Definition of Product Category.
This paper use CML2002 as LCIA methodology Table 1 Data inventory of ready-mixed concrete production Item Substance Unit Quantity Consumption of raw materials Cement t 0.252 Coal ash t 0.045 Mineral powder t 0.054 Sand t 0.841 Gravel t 1.065 admixture t 0.004 Energy consumption Power kWh 7.885 Transportation Road transport t*km 2.261 (3) Collection of inventory data and analysis on results This study collects data about on-site production of several ready-mixed concrete enterprises in China, and obtains the inventory as shown in Table 1.
The background data for the purpose of this study is mainly from the public database at home and abroad, the background process data about raw and auxiliary materials, power and road transportation is mainly from the database of Beijing University of Technology [8] and CLCD database of Sichuan University [9], the background process data about the admixture is mainly from Eco-invent database [10].
Process excluded 1 Cement product production 1 Coal ash production 2 Admixture production 2 Mineral powder production 3 Sand production 3 / 4 Gravel production 4 / 5 Power 5 / 6 Road transport 6 / (2) Determining enterprise’s on-site process data Table 4 Collection of enterprise’s on-site process data Item Substance Unit Remarks Consumption of raw and auxiliary materials Cement t/m3 Mode and distance of transportation Admixture t/m3 Mode and distance of transportation Sand t/m3 Mode and distance of transportation Gravel t/m3 Mode and distance of transportation Energy consumption Power kW·h/ m3 / Environmental emission Particulate matter t/m3 / Based on the above background process included, the enterprise shall collect corresponding on-site process data and raw material consumption data.
Collection range of the enterprise’s on-site process data as finalized is shown in Table 4

These terminals uninterruptedly collect data and information to related objects, and promptly upload to the network layer.
Network layer includes WEB, application servers, and data servers.
Next, the vehicle exhaust in north China is to be monitored and tracked, meanwhile the data should be collected and coped timely.
Comprehensive monitoring system that is the combination of fixed data collection points and movable data collection points can be used during the IOT establishment of air pollution monitoring.
By deepening the data layer and evaluating comprehensively, we can build a more unified center for data monitoring and a service platform for data sharing of atmospheric environment in north China.

Due to the space limitation, the measurement result data within a period of time before and after starting the SRE suppression circuit are taken, and the distribution in the time domain is shown in Fig.2. 10-4 Fig.2 The measurement data in time domain distribution 3.4 Experiment data analysis 3.4.1 Time domain analysis The experimental data in time domain distribution is shown in Fig.13.
The data before and after starting the SRE suppression circuit for about 8 minutes are intercepted for the data analysis.
Although the experimental data in time domain display 1-7Hz alternating electric field strength from 2.86×10-4V/m down to 0.72×10-4V/m, approximately 75% reduction.
Where, figure a represents the experimental data of frequency domain distribution when the SRE suppression circuit is not activated, and figure b represents the experimental data of frequency domain distribution when the SRE suppression circuit is activated.
In addition, the significant frequency of 6.6Hz AC component is contained in the ship electric field measurement data.

Through research statistics, sort the data based on the use of maximum entropy method forecasting model construction land transfer price forecast.
In a typical related analysis, we can assume that the dependent variable Y and X for data set, to study the relationship between respectively we can extract typical components F1 and G1, In the conditions that original variable data is standardized ，it should meet: (1).
A dependent variable is Y , the raw data of the indicators in Table 1.
The data in the table is standardization, receiving standard data, and by using the data of the standardized after the extraction of composition and the effectiveness of the cross and judgment, finally establish the regression equation: The maximum entropy solution of the model.
To the land of the earnings estimates and assumptions KaiFaFa reduction method of theory analysis.

Suppose there are sub factors (x1, x2...x) associated with main factors (x0), which are at least N raw data year by year, this series of value constituting the sequence.
The Grey GM (1, n) model is: (5) The simulation value for x1(0)(k) is: (6) 2.2 Analysis of Grey GM(1,n)in the application of imported power modeling .According to the list of the 2000-2009 China's total import battery x1(0) and relevant influence sequence: gross x2(0), disposable income x3(0).According to the calculation method of GM (1, n) model, and considering the changes in order to weaken the fluctuation of data sequence, reduce the randomness, adjust the change trend of data sequences, meet or close to the decision-making needs, on the data adopted in advance, a smooth processing method, namely x(0)(k)=[x(k-1)+2x(k)+x(k+1)]/4.
Then get the problems of GM (1, 3) model, as follows: before the first k - 1, k, k + 1, x (0) the first k (k) after processing the data
Table 4 Simulation value of x1(1) 1 2 3 4 5 6 7 8 9 10 15.5 32.07 63.77 102.33 147.62 201.53 265.19 341.75 430.76 525.72 To cumulative decrease and reduction x1(1), get the Simulation value of x1(0).Calculation results and the residual error are calculated as follows: Table 5 The simulation value and residual table k residual error % 1 15.5 15.5 0 0 2 18.0 16.57 1.4 0.079237 3 23.0 31.70 -8.7 -0.37822 4 29.8 38.56 -8.8 -0.2938 5 34.0 45.29 -11.3 -0.33204 6 50.1 53.92 -3.8 -0.07615 7 53.9 63.66 -9.8 -0.18098 8 42.5 76.57 -34.1 -0.80156 9 38.4 89.01 -50.6 -1.31793 10 60.1 94.96 -34.9 -0.57999 As shown in table 5, if you ignore the data in 2007 and 2008 cannot meet the needs of the grey system theory, the relative error is still large, and the data deviation to one side.
(4) In the GM(1,n)modeling, According to previous Grey correlation analysis, selecting high correlation factors and pre-processing of the data smoothing, to make the budget has high accuracy result