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Exposure time per dataset was 15 min and the CCD-detector was used for automated data acquisition.
A fit of the data from 1 to 39 hours with a diffusion limited precipitation model [7] results in the solid curve shown in Fig.4.
The model function represents the data well up to 33 hours, i.e. to the end of the first holding step.
Such behavior is found qualitatively in our data.
Fig.3: Part of the data (intensity) from Fig.2 in the range from 2 to 10 hours.

Thus cluster heads closer to the base station can preserve energy for the inter-cluster data forwarding.
However, in the steady phase member node sends data to the corresponding cluster head, then the cluster head aggregate data and forward them to parent node, till to root node like this.
Each member node sends L bits data to the cluster head in a round.
When=4,=0.1, the relation between number of data received at BS and time is shown in Fig.3.
In the data transmission phase, routing tree can balance cluster heads’ energy consumption.

This provides a reduction in error of the algorithm after each iteration [9].
The feature of creating such observer is data preprocessing because the input data for processing are the stator currents and voltages.
Data preprocessing Data preprocessing performs conversion of currents, voltages and their delays to the polar coordinate system [12, 13]:: (3) In Fig. 1,a are presented the vector of current and its delay in the polar coordinate system, as in Fig. 1,b – principle of data conversion for learning of the neuroemulator according to equations (3).
For the neural network as input data used data from the preprocessing of unit, as well as the speed feedback delay.
Advanced Mathematics: Precalculus with Discrete Mathematics and Data Analysis / Andrew M.

Each tree is constructed using a different bootstrap data set with randomly chosen instances, and each node is split using the best among randomly selected features.
These two kinds of randomness make random forest more robust against model over-fitting and data noise.
Given data on a set of n sequences for training, D = {(X1, Y1), . . ., (Xn, Yn)}, where Xi, i=1, . . ., n, is an observed input vector of features and Yi is numerical outcome, the training algorithm is as follows: (1)From the training data of n sequences, draw a bootstrap sample
Specifically, performance for a regression algorithm should be evaluated using a large independent test data set that was not used in the training.
In practical application when the data is limited, some type of cross-validation has to be done, which, in some cases, could be computationally cumbersome.

It also send out all kinds of video images, sound, and the data stored on the SD card or NAND Flash.
In Fig. 5, The pins SPCE3200 associated with the LCD module consist of LCD_CLK (clock signal), LCD_ACT (data enable signal), LCD_VS (vertical sync signal), LCD_HS (horizontal sync signal), LCD_Data [15: 0] (data bus).
This design uses the file system interface function to read data collected by camera, the data appears in the LCD.
Last, the procedure calls the function read () can read the images data collected by the camera.
Because the image data need to display in the LCD screen the function lcd _drawrgb (() is called.

High spectral acquisition test All kinds of nitrogen level lettuce leaves is collected in the growth period, and sent to the indoor promptly to get the blade spectral data.
The spectral reflection map of lettuce in three different nitrogen levels Feature bands extraction The spectral data contains a large number of redundant data and noisy data, so it is necessary to reduce dimension and deal with the noise.
First of all, 350-450 nm and 2000-2500 nm spectral data are eliminated, and then to process the rest of the data.
Principal component analysis performs dimensionality reduction by projecting the original m-dimensional data onto the （k<data covariance matrix.
Given an input data matrix (Ndata（）.

The data points were sampled from the design space of process parameters via the Latin hypercube method.
Aluminium alloys can help mass reduction and energy saving meanwhile improves the performance of the vehicle.
Matrix and objective function values of Latin Hypercube Design Run BHF/x1 [kN] Stamping speed/x2 [mm/s] Min. thickness/f1 [mm] Rupture distance/f2 Distance /f3 [mm] 1 7 48 1.6854 -0.0671 0.5067 2 11 96 1.5314 0.0315 0.5335 3 17 160 1.1992 0.2766 0.5033 4 9 144 1.5708 0.0044 0.5365 5 1 128 1.7510 -0.1037 7.5046 6 5 176 1.6711 -0.0648 0.5234 7 3 64 1.7194 -0.0856 1.4154 8 19 192 1.4036 0.1163 0.5223 9 13 80 1.5143 0.0432 0.5733 10 15 112 1.3129 0.1861 0.6905 The response surface can be initialized by using datum from the above method.

Using the theoretical equations, the stresses on the die surface have been calculated from the data of sensor resistances.
From the lattice data, the local stress stresses and all stress components can be computed.
Using the measured data and appropriate theoretical equations, the stresses differences on the die surface have been calculated.
Electrical wires were soldered to the PCB pads for data gathering.
Using the measured data and appropriate theoretical equations, the room temperature die surface stresses induced by the die attachment process have been calculated.

According to the conclusion from the statistical analysis for the questionnaires' data, the relationship between the environmental attitude and environmental behavior of the city dwellers is explored and the environmental issues of living place in Shenzhen are found out in the end.
The original data of questionnaire are analyzed by the IBM SPSS Statistics 19.0 with frequency analysis, relation coefficient analysis, cross table analysis, statistical analysis and other related statistics.
The data analysis has confirmed the effect of environmental attitude is positively related to environmental behavior.
However, the analysis data also indicated dwellers with poor environmental knowledge almost tend not to have the habit of planting.
In addition, the questionnaire data are not screened for dimension reduction in detail; we just test the variables for one to one correlation coefficient by Spearman rank correlation coefficient test, so the questionnaire validity needs to be improved.

One-hot encoding is used to convert IP, categorical to numerical data.
IP being a nominal categorical factor, one-hot coding is necessary to convert categorical data to numerical data from Gyroid, Line, and Tri-hexagon to 1, 2, and 3 respectively.
This error represents how well the model fits the training data.
This reflects the model’s predictive performance on new data.
Plot distribution of (a) training, (b) testing, and (c) checking data vs prediction of FIS.