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We compare energy storage characteristics of ITO-GO and Cu-GO using cyclic voltammogram data.
Energy storage characteristics of the films was examined from the energy density estimated from Cyclic Voltamogram (CV) data.
The CV curves of both samples do have oxidation and reduction peaks.
Cheng, The reduction of graphene oxide, Carbon 50(9) (2012) 3210-3228
Nugroho, Thermal reduction study of graphene oxide paper, IOP Conf.

Compared with the fuzzy sets and probability and statistics, RS merely use the data itself without using the priori knowledge [5, 6].
Before processing the data using the rough set theory, firstly, the data should be discredited, see table 2.
Research and Implementation on Reduction Algorithm of Rough Set [J].
[6] Li Liangbao, Wang Yaowu.Study on Data Standard and Data Exchange[J].
Data Reduction Based on Rough Set Theory and HierarchicAnalysis[J].Journal of Northeastern University (Natural Science), 2008 Vol.29 No.1：22-24.

There are various strategies to improve Breimans’ RF: (1) through data [7] (2) using clustering [8] (3) with Boosting [9] (4) weighted voting and dynamic data reduction [10,11] Decision Tree (DT).
Data Preparation for ML.
The percentage distributions of atoms in each of these data sets are the same as the percentage distributions of atoms in the original data. 75% of data is used as training data and 25% of data are used as test data.
Dalkilic: “A New Set of Random Forests with Varying Dynamic Data Reduction and Voting Techniques”.
Dalkilic: “Red-rf: Reduced random forest for big data using priority voting & dynamic data reduction”.

Too broad width is negative for the road noise reduction and smoothness improvement.
Therefore, in the limited technical conditions and economic situation, equidistant groove is recommended. ②The greater the equal spacing, SFC and TD are smaller, the anti-sidle and drainage performance is worse, noise reduction effect is better. ③The greatest impact on the noise reduction is groove trend, followed by groove spacing.
The influence of groove form, groove depth and groove width on noise reduction is very small.
It corresponds with too groove width having negative effect on noise reduction and smoothness, so the6mm groove width is abandoned.
The flexural strength of no groove specimen is 6.13Mpa, the average compressive strength is 1050.1KN, and they are significantly greater than the data of groove specimens.

Data acquired from this sensor are compared with data received from the position sensor of the drive.
CompactRIO collects and process measured data from both sensors.
By this approach the collected data are evaluated synchronously.
FPGA part of cRIO allows the evaluation of the data with high sampling rates.
Measured data are evaluated in accordance with standard ČSN ISO 230 – 2.

In addition, providing better security of data through layer-to-layer protection strategy as well as ensuring the original benefits of cloud storage, such as data flexibility, cost reduction and highly automated, can be achieved by DDFM.
This layer implements a variety of features, such as data encryption, data backup, data compression and distributed file-based system.
Data Fragment Algorithm based on Granular Computing The data fragment algorithm based on granular computing can give the data localization a suitable priority.
For every single piece of data, it’s meaningless even if the data piece has been picked up by attackers during the data transfer or scanned by Trojan.
When a tenant uploads a data, he first sends the data to web server (step1).

Many ideas were put forward to solve this problem, weight reduction seems to be the most feasible solution [1, 2].
Material replacement is the most direct way to realize weight reduction, which uses light metals like Al to take place heavy materials like steel.
Engineering stress-strain curves were got after tensile tests, then transformed engineering stress-strain data into true stress-strain data by calculating.
Table 2 RMSEs between fitting result data and experiment data Strain rates [s-1] 0.0003 0.003 0.03 Temperature [℃] 360 430 500 360 430 500 360 430 500 RMSE 1.70 0.23 0.10 2.48 0.44 0.15 7.41 3.15 0.26 Conclusions In order to investigating flow stress behavior of material Al A5005 at high temperature, twelve hot tensile tests at temperatures 360℃, 430℃, 500℃ and strain rates 0.0003s-1, 0.003s-1, 0.03s-1 were set up.
A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures[J].

The main factor of plasticity for optimal warm temperature selection from examined temperature interval is value of reduction of area that was determined by tensile test.
On the basis of thermal course of plasticity characteristics (reduction of area Z, ductility A) we are able to observe reduction of area decline at the temperature 750 °C.
Fig. 3 Courses of graphic relations of parameters resulted from the tensile test For the purpose of optimal warm temperature selection from examined temperature interval the crucial indicator of steel 16MnCr5 plasticity is value of reduction of area Z.
As reduction of area Z achieves its maximum value at the temperature 700 °C, the same will be recommended as optimal temperature of steel 16MnCr5 for warm forming.
For starting a simulation of spur gear it is necessary to properly define the input data – these data were determined as follows: · process - closed die forging · material of billet DIN 17210 (1.7131) · material of the tool ASTM A 681 (H13) · temperature of billet 700 °C · temperature of the tool 250 °C Fig. 4 Closed die model and correct material flow in closed die cavity Computer simulation results of warm forging at the recommended temperature 700 °C describes fig. 4. where it is possible to see correct plastic flow and flawless filling of closed die cavity.

The crystalline phases were identified by the JCPDS data bank.
Fig. 1 shows the XRD patterns of samples prepared at reduction temperatures of 45 °C and 50 °C respectively.
These peaks, according to JCPDS data bank (06-0344), are the fingerprints of CuCl.
At the reduction temperature of 50 °C, the prepared CuCl powder starts to sinter (Fig. 2b). 
When the reduction temperature is increased, the surface area and pore volumes become smaller.

Compounds 1 display dramatic reductions in both their optical and electrochemical band gaps in comparison to thienylpyrroles 2.
S N TCNE / DMF / rt 2 1 a R1 = n-propyl b R1 = phenyl c R1 = naphthyl d R1 = 4-methoxyphenyl e R1 = 2,4-dimethoxyphenyl f R1 = 3,5-dimethoxyphenyl g R1 = 3,4,5-trimethoxyphenyl S N R1 R1 CN NC CN Table 1- Yields, IR and UV-visible data of pyrroles 2 and tricyanovinyl-thienylpyrroles 1.
The tricyanovinyl-substituted thienylpyrroles display two reduction processes.
An obvious cathodic shift of reduction peak potentials was observed with the increase of capacity of the donor group in the aryl moiety of pyrroles 1.
Table 3- Electrochemical data for thienylpyrroles 2 and 5-tricyanovinyl-substituted thienylpyrroles 1 at a glassy carbon electrode.a Thienylpyrroles Tricyanovinyl-substituted thienylpyrroles Compound Oxidation Compound Oxidation Reduction band gap b Epa (V) Epa (V) 1E1/2 (V) 2Epc (V) (eV) 2a 0.57 1a 1.11 0.92 1.61 1.95 2b 0.53 1b 0.95 1.00 1.70 1.89 2c 0.54 1c 0.96 1.02 1.73 1.91 2d 0.48 1d 0.94 1.14 1.80 2.01 2e 0.45 1e 0.92 1.05 1.75 1.88 2f 0.48 1f 0.95 1.01 1.78 1.88 2g 0.46 1g 0.94 1.06 1.72 1.94 a Solution approximately 1.5 mM in each compounds in acetonitrile 0.10 M [NBu4][BF4] was used, and the scan rate was 100 mV s -1, potentials versus the ferrocinium/ferrocene-couple.