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Principal component analysis principles and steps 1.1 The principal component analysis of the basic principles The basic idea of principal component analysis is able in the case of no loss of the original information in a set of variables to find the amount of variance and covariance matrix of characteristics of multiple variables through dimensionality reduction into a few variables Statistics methods of analysis[2,3].
Tectonic sample matrix Data matrix as a set of sample observations： (1) Among： represents the n-th sample in the indicators of the j-th observation. 2.
With coal indicator of blending coal correlation coefficient matrix calculations First with coal quality characteristic data for statistical analysis, to standardize the data processing based on the correlation coefficient matrix in Table 1, which can be analyzed on the correlation between the various factors.
The cumulative contribution rate of close to 75% in the three main ingredients, and be able to explain most of the data.
[2] Wang, Haijuan ; Han, Lixin ; Multi-population Principal Component Analysis Based on Spectral Graph Technique for Data Analysis, Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on , Publication Year: 2011, pp.1191 – 1195 [3] Pyatykh, S. 

As the distance of roller is reduction, the rubber shear stress increases.
The sampled data must be processed by regulation for the guarantee astringent.
It makes all data between the [0,1] in the network.
Table 1 Part of training sample Serial number Roller Temperature °C Roller distance mm Transmission ratio 1:n Time s Mooney ML+d100°C viscosity 1 35 0.5 1：1.05 180 61 2 45 0.7 1：1.10 240 65 3 55 0.9 1：1.15 300 63 4 65 1.1 1：1.20 360 50 5 70 1.2 1：1.25 420 55 6 75 1.3 1：1.30 480 53 7 80 1.5 1：1.35 540 50 8 90 2.0 1：1.40 600 57 In order to confirm the network the accuracy, it takes the part empirical data as the examination sample to confirm the network.
Table 2 is the contrast list of the empirical data and the network output.

Introduction The increasing need for high data rate transmission over wireless fading channels has spurred much research effort into new communication techniques, which have been resistant to multipath effects [1].
(3) where yi (i= 1,. . . ,4) are data received at a given receiving antenna over four consecutive time slots.
Then we can change the signal back to , where are complex “base” symbols representing the data.
Conclusion In this work, we introduced a systemic procedure by a simple linear decoupling process to demodulate the data of QO-STBC to facilitate the fast ML detection.
This leads to a significant reduction in the search space of the ML detector and furthermore to the computation time of the receiver.

The first proximity sensor attached to the rear left arm measures the rotational speed of wheel as an input data for calculation of vehicle speed (Fig. 4).
The current module measures the electric motor current which is used as input data for microcontroller.
The data was recorded in the memory of MCU and sent to personal computer using a serial communication every 200ms.
The data contains information of rotational speed of wheel, rotational speed of engine, vehicle speed, and the amount of current flows in both electric motors.
Van Mierlo: Data Acquisition System for Optimization of Series Hybrid Propulsion Systems, World Electric Vehicle Journal Vol. 3 (2009).

Then, forecasting a volume of clinker between 2015-2036 and use following data to analysis amount of CO2 emissions.
From PDP2015’s data, the demand of electricity is higher and consistent with economic expansion that needs to construct more the power plant especially coal-fired power plant to be sufficient and security in energy section.
Chai Jaturapitakkul and Mae Moh Technology’s data found that the fly ash about 47% is used in cement industrial sector.
Furthermore, considering the technical recommended data for the clinker substitution proportion 30: 70; thus, the CO2 emissions could mitigate in 2036 about 34.39 M TonCO2 from the 17.40 M Ton of fly ash utilized, shows in Fig. 3.
Bragança: submitted to Journal of Cleaner Production (2016) [7] Cement Road Map, Cement Technology Roadmap 2009 Carbon emissions reductions up to 2050, Report of the International Energy Agen and World Business Council for Sustainable Development, 2009

This reduction is not instantaneous [14,15] and the effect is termed "conditioning".
For this purpose, it is necessary to develop a method for the analysis of the data which allows to highlight conditioning and memory using pulses instead of continuous waves, as usually done.
Experimental set up and data analysis We have studied the behavior of a cylindrical (16 cm long, 6 cm diameter) concrete sample, equipped with two identical piezoelectric transducers (acting as emitter and receiver), with diameter of 4 cm (see Fig.1a).
The receiving transducer was connected to an oscilloscope for data acquisition (see Fig.1c).
The analysis of the recorded data has been performed using the Scaling Subtraction Method - SSM [15] to highlight the nonlinearity in the response of the sample.

Moreover,the growth in data services in virtually all customer sectors, as well as the accompanying increase in the required bandwidth, is substantial and continues at a steady rate.
As end users have become obsessed with sending content such as video, digital pictures, and voice-over-IP, the need for bandwidth and high-speed data transport has dramatically increased over the past decade [2].
To cope with these rapidly increasing demands on data traffic, 100-Gb/s Ethernet standards are being debated by many vendors [3].
Such passive optical network (PON) access solutions currently come in different flavors, including Ethernet-based PON (EPON), or PON based on the framing of voice, data, and video to the GPON ITU standard.
As for electronic devices,the monolithic integration of ADC, DSP and FEC in a single CMOS chip provides good cost reduction from high functionality concentrated into a single chip.

Fired bulk density, water absorption and Vickers hardness data are presented below.
Table 1 gives representative chemical composition data.
The corresponding XRD data is given in Fig.1a.
The highest value of 4.50GPa was obtained for the sample prepared with 40wt% addition of K2CO3·1/2H2O.From these data, it can be concluded that materials obtained from CFA with 20wt% and 30wt% addition of K2CO3·1/2H2O are porous and the properties of the obtained materials are influenced by the amount of total porosity.
Meanwhile, the reduction in density and Vikers hardness that occurs is clearlyassociated with the formation of a significant volume ofpores around the obtained grains that can be seen in Fig. 4(b).

Plus, the wave front and the wavefront aberration can be achieved by data fitting i.e. radial basis function fitting method and Zernike polynomial fitting method.
This paper employs the radial basis function of Gauss type to fit the local data.
It adopts the feed rates i.e. 0.01mm/rpm, 0.015mm/rpm, 0.02mm/rpm and 0.025mm/rpm and the wavelet analysis method is applied to achieve surface error data by separating the surface topography data.
PSF of XZ center section diagram PSF of YZ center section diagram Fig.3 PSF of the center section diagram and its enlargement From Fig.3, we know that with the increment of surface error, the reduction speed of the center range of PSF increases and its value decreases.
References [1] Xiaotong Li, Zhaofeng Cen: Geometrical Optics· Aberration·Optical Design [M] , Zhejiang University Press(2003)(in Chinese) [2] Naiguang Lv: Fourier Optics [M], Machinery Industry Press(2006)(in Chinese) [3] Shuben Sun: Mathematical Foundations of Optical Transfer Function [M], Science Press(1980)(in Chinese) [4] Zongmin Wu: Radial Basis Function·Scattered Data Fitting and Numerical Solution of Non-network Partial Differential Equations [J], Journal of Engineering Mathematics(2002), p. 1-12(in Chinese) [5] Jing Xu: Wavefront Reconstruction Method of Measuring Optical Part by Shearing Interferometry, Harbin Institute of Technology (2010) (in Chinese) [6] Williams, C.S., O.A.

The measured data suggests that this wind farm is uncompetitive.
The real capacity factor (CFR) determined using measured data is 14.90%.
Research Methodology Data Collection Operational data of the two turbines was collected from 1st May 2009 to 30th April 2010.
The data was taken for only one year.
However, these tests rely on the accuracy of the manufacturers’ data.