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Methodology Data.
Energy consumption data are expressed in million tonnes of Oil Equivalent.
Based on the availability of the data, the analysis in this study uses annual time series data for the period 1981-2010.
Energy conservation and emission reduction policies for the electric power industry in China.
CO2 emissions, energy consumption and economic growth in China: A panel data analysis.

Using this data of weight difference and surface area of ​​the sample, the average depth of metal removed by oxidation (X) can be determined.
(5) Results and discussion Fig 3 gives the experimental data on the depth of steel removed by oxidation as a function of time and temperature of exposure to oxidizing environment.
These data are substituted in Eq (4) to evaluate KC , which, as expected, varies exponentially with temperature as shown in Fig 4.
Eq (6) is a functional relationship applicable to this steel arrived at on the basis of experimental data.                    
From the experimental data, it was observed also that for temperatures below 850 °C decarburization was not significant even with long times of soaking, as high as 48 hr.

DATA SET The study site was the city of ChengDu, China.
The incident angle is 28° and the data is single HH-polarized.
The index value decreases with correlation, and -1 means strong negative spatial autocorrelation, 1 strong positive spatial autocorrelation and 0 spatially uncorrelated data.
Within the past few years, SVM has become a popular and widely-used classifier for sparse sampling, non-linear, high-dimensional data classification in many scientific areas.
Roth, “TerraSAR-X: a new perspective for scientific use of high resolution spaceborne SAR data,” in Proc. 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Berlin,Germany, May 22–23, 2003, pp. 4–7

However, the data ranges obtained by MOPSO do not match exactly with the data range obtained by RSM.
The data range given by MOPSO for f1 is from 0.5169 to 0.5178 as shown in Fig. 3b, while the RSM gives values from 0.5169 to 0.5175 for f1 (see Fig. 3a).
Similarly, the data range given by MOPSO for f3 is from -3.9048e-5 to -3.5951e-5 and for RSM for f3 is from 3.5205e-5 to 3.603e-5.
This shows the efficiency of the MOPSO in providing more data range for cortical density than RSM.
This increasing of data range is interesting for the FGM dental implant design and shows that the MOPSO has surpassed the RSM in terms of cortical density function.

On the other hand, in the trajectory learning the robot utilizes the sensors data to be able to reproduce the target trajectory.
The iPhone sensors data are transmitted wirelessly to the control PC which converts them to the robot motion.
The iPhone sensors data are converted to the robot motion by the control PC (Fig. 1).
As the end-effector gets near to the picking and goal location, the sensors data are small resulting in a slow robot.
(b) iPhone sensor data.

(3) Reduction in resource utilization.
For the TCP data stream, which is to enter slow-start phase of a signal, so as to reduce the network load and ease the congestion.
And increase the TCP data stream delay.
When the data stream burst, the queues were already booked, and then have to discard large amounts of data packets.
In this way, the congestion starts, slowing down rate is gradual and, therefore, load of data is also gradually reduce, This will also minimize the impact of TCP connections, and avoid the Global synchronization.

Then the trained model is applied to determine the sense category of an ambiguous word in test data.
In unsupervised WSD methods, sense-labeled data is not required.
Mitesh uses the bilingual bootstrapping method for WSD, where a model trained based on the seed annotated data of L1 is used to annotate the untagged data of L2 and vice versa using parameter projection.
It is a particularly important step in analyzing the data from many experimental techniques.
The lower dimension helps to reduce data sparse and the computational complexity.

Cluster centers obtained by the fuzzy c-means method and data can be divided into k fuzzy sets.
Suppose a factor is divided into k fuzzy sets, and the data number belong to the fuzzy set is mi, then the weight of fuzzy set i is wi=mii=1kmi (2) The more data belong to the fuzzy set, the greater weight of this fuzzy set, otherwise the smaller the weight.
Because the membership of each data attribute for its fuzzy partition is in [0,1], the domain of membership function of impact level is [0,1].
Each factor were selected to the 1000 historical data, do the evaluation to the impact degrade of airport noise environment.
Select the affecting factors data to be assessed as X = (50, 200, 32, 20, 150, 80).

Data analysis Sleep EEG data used in this paper were from the PhysioBank the MIT-BIH Polysomnographic Database.
Records in the Database is a multi-parameter sleep data, including EEG, ECG, EOG, EMG etc.
The data sampling frequency is of 250Hz.
It was randomly extracted 5 wake stage signals and 5 NREM I stage signals from subjects slp41 with data length 2000.
For average energy dissipation, random multi-samples data was verified and the results were shown in Fig. 2.

Introduction Silicon content is an important index to describe hot metal quality, and silicon in blast furnace comes from the reduction of raw materials[1].
Flow chart of genetic algorithm optimizes BP neural network initial weights model Comparison of Calculation and Application A 220 h continuous production data (220 groups) was used in calculation, including 150 sets of data used for training the network, another 70 sets of data for off-line prediction.
With the blast furnace production running, the required real-time production data of each hour was imported into the system and the model did training again.
At the same time, the first set of data in the group of samples was out according to the FIFO principle.
Two kinds of model were trained by 150 sets of data and finished off-line prediction through 70 sets of data.