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PCA reduces the dimension of the data without sacrificing valuable information.
PCA is defined by linear transformation matrix P that transforms matrix of input data C in a new group of orthogonal data Γ [14].
After transformation, original data can be represented by a smaller number of principal components due to redundancy in the data.
Every set of data consists of 28 capacitance values.
There are 28 capacitance values in each set of data.

Fig. 5 B analyzes the log([HCB]) and log(rate) data, confirming the kinetics observed.
[20] Safety Data Sheet of Pentachlorobenzene, Agilent. https://www.agilent.com/cs/library/msds/RCP-030_NAEnglish.pdf
[21] Safety Data Sheet of 1,2,4-Tetrachlorobenzene, Fischer Scientific. https://www.fishersci.com/store/msds?
[22] Safety Data Sheet of 1,2,3,5-Tetrachlorobenzene, Agilent. https://www.agilent.com/cs/library/msds/RCP-028_NAEnglish.pdf
[24] Safety Data Sheet of 1,3,5-Trichlorobenzene, Agilent. https://www.agilent.com/cs/library/msds/RCP-026_NAEnglish.pdf

The main train of thought of Method 1 is, the measured data at hub height is set to be Data Set 0, increase each wind speed value in Data Set 0 by 0.1m/s to form Data Set 1, increase each wind speed value in Data Set 0 by 0.2m/s to form Data Set 2, …, increase each wind speed value in Data Set 0 by 1.0m/s to form Data Set 10.
The main train of thought of Method 2 is, multiply each wind speed value in Data Set 0 with coefficient c1 to form Data Set 1 so that the MWS of Data Set 1 is larger than Data Set 0 by 0.1m/s, multiply each wind speed value in Data Set 1 with coefficient c2 to form Data Set 2 so that the MWS of Data Set 2 is larger than Data Set 1 by 0.1m/s, …, multiply each wind speed value in Data Set 9 with coefficient c10 to form Data Set 10 so that the MWS of Data Set 10 is larger than Data Set 9 by 0.1m/s.
Tab. 2 Fitting Results of Wind Farm A at 80m Under DPM 1 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01047 2.263 0.9981 Data Set 2 0.00951 2.296 0.9989 Data Set 3 0.008585 2.333 0.9990 Data Set 4 0.007742 2.369 0.9992 Data Set 5 0.007045 2.402 0.9994 Data Set 6 0.006565 2.425 0.9988 Data Set 7 0.00591 2.462 0.9988 Data Set 8 0.005322 2.498 0.9987 Data Set 9 0.004784 2.535 0.9987 Data Set 10 0.004306 2.57 0.9986 Tab. 3 Fitting Results of Wind Farm A at 80m Under DPM 2 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01207 2.195 0.9970 Data Set 2 0.01148 2.201 0.9938 Data Set 3 0.01100 2.204 0.9921 Data Set 4 0.01076 2.202 0.9930 Data Set 5 0.01022 2.211 0.9914 Data Set 6 0.00972 2.222 0.9926 Data Set 7 0.01008 2.193 0.9963 Data Set 8 0.00946 2.209 0.9950 Data Set 9 0.00910 2.207 0.9923 Data Set 10 0.00861 2.220 0.9922 In Tab. 2 and Tab. 3, R2 is known as square value of fitting correlation coefficient, and if this value is close to 1, it is illustrated
For easy of comparison and analysis, the combined reduction coefficient is set to be 0.667, which is corresponding to 1950.61h of EAD.
According to the method above, PG and EDA of Data Set 0-10 can be obtained, which is shown in Tab. 5, where denotes EAD corresponding to theoretical PG and denotes EAD with combined reduction coefficient of 0.667.

The effects of cooling rate and austenite structure on bainite formation was investigated by means of electron backscatter diffraction analysis and processing of obtained orientation data.
The data on local orientations were treated using MTEX software [8].
To choose the OR, which corresponds better to the data of the present investigation, the relationships obtained in Refs. [7, 10] were compared.
Austenite deformation results in the packet size reduction.
Note that the packet refinement does not imply a reduction of the effective grain size.

Thus to meet mid-to long-term CO2 reduction targets, cost-effective CO2 separation technologies are key issue.
Table 2 summarizes literature data on the pros and cons of CO2 separation techniques [4-7].
The methods to calculate the costs for each of the cost categories and the entire data are drawn from them (Table 4).
The economic data of MA case are superior to MEA case contributed to the favorable operation and low solvent losses, meaning that using membrane gas absorption process can save the capture investment and reduce CO2 avoided cost.
Compared the data revealed in Fig. 3 and 4, effect of regeneration energy requirements on CO2 avoided cost is more sensitive than that of capital cost.

Tensile test shows that the yield strength and tensile strength of domestic 7050-T7451 aluminum alloy are both higher than imported 7050-T7451 aluminum alloy, however, the material elongation and the reduction of area are both lower than imported material.
From tensile fracture analysis, it concluded that the domestic 7050-T7451 aluminum alloy has large grain size, low structural homogeneity, and little toughness characteristic of fracture, and those characteristics cause low elongation and low reduction of area of domestic 7050-T7451 aluminum alloy.
Mechanical properties of aluminum alloys Material Orientation Yield strength [MPa] Tensile strength [MPa] Elongation [%] Reduction of area [%] Imported 7050-T7451 L 467 513 12.6 51.9 ST 442 521 11.0 15.5 Domestic 7050-T7451 L 481 538 10.5 26.9 ST 475 530 8.0 10.5 It can be seen that in the L and ST direction of the material, the yield strength and tensile strength of domestic 7050-T7451 aluminum alloy are both higher than imported 7050-T7451 aluminum alloy, however, the elongation and the reduction of area of the material are both lower than imported 7050-T7451 aluminum alloy, especially the reduction of area varies considerably.
Conclusion Differences between the domestic and imported 7050-T7451 aluminum alloy were compared based on microstructure and tensile fracture analysis, and findings can be summarized as below. 1) The yield strength and tensile strength of domestic 7050-T7451 aluminum alloy are both higher than imported aluminum alloy, however, the elongation and reduction of area are both lower than imported aluminum alloy. 2) In the microstructure of domestic 7050-T7451 aluminum alloy, the recrystallization and anisotropy degree is higher than imported 7050-T7451 aluminum alloy, and analysis shows that the improper control of rolling deformation and improper control of alloying element are reasons of above phenomenon. 3) The domestic 7050-T7451 aluminum alloy has large grain size, low structural homogeneity, and little toughness characteristic in the tensile fracture, which will cause low elongation and low reduction of area of domestic 7050-T7451 aluminum alloy.
Issues on the mean stress effect in fretting fatigue of a 7050-T7451 Al alloy posed by new experimental data, J.

The results obtained by diffraction of X-rays showed that complete reduction and carburization of APT have been took place resulted in pure WC formation.
The decomposition, reduction and carburizing reactions were done at 850 °C with heating rate of 10 °C / min with 120 minutes isothermal heating.
Obtained with deviation of S = 2.22 from the data of literature.
Table 1 presents some of the key data acheived from refinement of X-ray diffractogram of WC.
Table 1 Refinement data of tungsten carbide from Maud Software.

An additional data acquisition system is also available to log the dynamic behaviour of the system.
On the basis of the input-output data recorded during the experiments a linear model was constructed according to [3], 1 1 1 ( ) ( ) ( ) ( ) ( ) ( ), A z y kT B z u kT C z z kT − − − = + (1) where 1 1 1 1 1 0 1 1 1 1 ( ) 1 , ( ) , ( ) 1 .
Experimental Results Encouraged by the results obtained from preliminary simulation studies, the regulator and estimator algorithms were programmed using MATLAB/Simulink ® and implemented in the data acquisition and control board dSPACE ® DS1401 for further experimental investigations.
Both AVR systems have achieved a significant vibration reduction in the first eigenmode, which is important for riding comfort.
With this in mind, more effort devoted to the dynamic model reduction of distributed structures and to the development of adaptive regulation algorithms is required.

Table 1 – Characteristics of the products studied in this work (manufacturer data).
The features of the diffractograms indicate a reduction in the peak intensity of Co due to the increase in milling time.
Conclusions The results indicated a reduction of the particles size and spheroidization. as function of increasing of milling time, in a ball/powder ratio of 6:1.
The metal chips were reduced particle size, with respective reduction of the crystallite size on the order of 50% in 120 minutes of grinding.
Pensilvânia: Swarthmore, International Centre for Diffraction Data., 2010

Rough set theory (RST) as a new data mining method, can deal with uncertainty and incompleteness in data analysis.
Intelligent control model Rule Acquisition User Interface Data Inspection Data Discretization Attribution Reduction Reasoning Machine Fig.1 RST-based intelligent control model architecture Quality Rule Base Rules Composing Spinning Process Raw materials Yarn Evaluation of yarn quality Keys: Data Flow Process Flow Considering a mass of data from textile production and inspection, as well as the variety of knowledge and experience from domain experts, this paper presents an intelligent control model for spinning process.
The rule acquisition is made up of four components (enveloped by broke line), namely, data inspection, data discretization, attribution reduction and rule composing, those are basic steps within mining control rule process.
It integrates learning-from-example techniques, extracts rules from a data set of interest, and discovers data regularities.
The next aim of this stage is to assess the requirements for the model complexity. 4.2 data discretization All attribute values are regarded as qualitative data for the RST based on symbol, so the quantitative spinning data must be changed into qualitative data by generating a partition via discretization.