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Experimental Design and Data Acquisition As introduced, an experiment has been conducted under a humid tropical climate.
All of these probes are connected to a data logger (CR3000), directly or through a multiplexer (AM25T).
Figure 2: Synoptic of the data acquisition.
But, this is not the only phenomenon that is able to lead to a heat gain reduction.
The data manipulation allows to get the VCP resistance Rvcp=0.302±0.016 m².K.W-1, then the substrate (lava-rock) and vegetation (Zosya Tenuifolia) layer conductivities: λsub=0.319±0.017 W.m-1.K-1 and λveg= 0.775±0.046 W.m-1.K-1.

Optimization through vibration reduction of amplitude and frequency followed the idea that for the human body very important are the vibrations transmitted in the direction OY, so acceleration and vibration intensity has to be reduced to acceptable values​​.
Due to the vibration intensity reduction, a noise sound pressure diminishing is also expected​​.
Centralized data for vibration characteristics using initial and optimized rim geometry Proper vibration mode Initial supporting structure Optimized supporting structure Amplitude [mm] Frequency [Hz] Vibration intensity [vibrars] Amplitude [mm] Frequency [Hz] Vibration intensity [vibrars] LAeq reduction [%] First proper vibration mode 1.04 245.76 88.65836 2.573333 51.84 57.08882 36.22 Second proper vibration mode 1.1 252.48 89.25343 2.53 55.68 57.94609 34.58 Third proper vibration mode 1.17 302.72 91.88579 1.413333 100.48 63.10874 38.47 Fourth proper vibration mode 6.14 366.4 101.573 1.963333 131.52 68.0436 39.52 Fifth proper vibration mode 7.31 488.32 106.073 4.633333 214.72 78.15902 37.65 Table 2.
Centralized data for vibration intensity using initial and optimized rim geometry Vibration intensity measured using initial rim geometry [vibrars] Vibration intensity measured using optimized rim geometry [vibrars] Degree of diminishing [%] OX direction OY direction OZ direction OX direction OY direction OZ direction OX direction OY direction OZ direction Medium value 99.03 91.93 84.92 67.13 61.45 55.69 32.21 33.16 34.42 Minimal value 83.83 85.79 58.05 53.83 56.19 38.74 35.78 34.51 33.27 Maximal value 104.4 97.33 97.54 69.27 64.95 64.31 33.66 33.27 34.07 Conclusions By optimizing the support structure of sorting and washing stations of mineral aggregates, which can be easily applied in practice by any producer of such equipment, it was obtained a noise reduction (expressed as LAeq [dB], the parameter describing year sound level with the same energy content as the varying acoustic signal measured) of approx. 37.28%.
The vibration intensity measured over the orthogonal axes shows that maximum intensity reduction gained along the of axis, stipulated in the standards as the most dangerous propagation direction of vibrations for the human body, the value of the vibration intensity is 32.38 vibrars, that represents 33.27% reduction.

AQDS stimulated bacterial iron reduction and Fe 2+ concentration in solution was higher than Fe 3+.
The reduction of Fe(III) for incorporation into biomolecules is assimilatory iron reduction.
Fe(III) reduction in samples was tested in media with and without 0,1mM AQDS and 2mM NTA.
X-ray and EDS analysis (data not shown) showed that quartz sands sample composed a brown ultra-fine smectite phase and amorphous iron oxide coatings over quartz grains.
[7] Lovley, D.R., Dissimilatory Fe(III) and Mn(IV) reduction.

Rough set theory is a kind of analysis data mathematical theory and proposed by Poland mathematician Z.
This theory has been widely applied in data mining, artificial intelligence, pattern recognition and other cognitive areas.
Through the experiment based on UCI data set, this paper compares C4.5 algorithm based on information entropy and the generation algorithm of decision tree based on weighted average roughness [3].
All the attributes reduction of C. all the intersections of C attribute reduction in is called the core and written as Core (C).
the comparison of two generation algorithm in UCI data set Data set Object number Attribute number (C/D) C4.5 CCF accuracy leaf accuracy leaf Breast 699 9/2 95 6 96 5 Diabetes 768 8/2 74 14 78 12 Lymph 148 18/4 77 7 72 5 Iris 150 4/3 76 5 76 5 Primary tumor 339 3/21 34 167 59 135 From the table, it can be seen that comparing to C4.5 based on information entropy method, the methods based on classification contribution has better average classification accuracy.

An analysis of X-ray diffraction, XRD data using refinement method shown both x = 0 and x = 0.02 samples were in single phased and crystallized in rhombohedral structural with Pnma space group.
The experimental resistivity data for metallic and insulating regions are further analysed by fitting with the scattering and hopping models, respectively, and the data are presented to elucidate the possible mechanism involved on the observed resistivity behaviour.
The phase purity and crystal structure of samples were confirmed by Rietveld refinement of XRD data using the GSAS-EXPGUI software.
The solid line plotted on the graph, which represents the SPH model, fits the resistivity data, thus indicating that the electrical behaviour of La0.7Ba0.3Mn1-xFexO3 (x = 0 & 0.02) is governed by the SPH mechanism.
The solid line indicates the best fit SPH model to the resistivity data The Ea values obtained from the slope of the ln (ρ/T) vs. 1000/T are found to increase from 66.61 meV (x = 0.0) to 80.68 meV (x = 0.02) under applied current of 10 mA due to Fe substitution.

Mechanical properties of TC16 were measured by tensile experiments and use the BP neural network model to train and simulate the experimental data.
The 6 sets of data are obtained by the tensile experiment.
Use 4 sets for training, 1 set for verification, 1 set for test data.
It could be seen from the figure, the training data of regression analysis R is 0.99448, the validation data of regression analysis R is 0.99239, the test data of regression analysis R is 0.99982 and all data f regression analysis R is 0.99478.
Table 2 gives the errors analysis between the experimental data and forecast data.

In this paper, a new load forecasting approach is proposed based on big data technologies using smart meter data.
Big data architecture can handle large amount of data and computation efforts.
Traditional data architecture have been unable to assume such a large amount of data calculation work.
Dimension reduction mapping from input space (n-d) to output plane (2-d) is achieved.
All data removed noise (some sampling point to null) treats as experimental data.

The server take out the corresponding data from the data queue of receiving and to extract In accordance with the key rules, then to reduce of information in corresponding positions.
The data structure of DS is a two byte DS= (A, R), where A is a finite set of data nodes, R is a finite set A relationship.
Linear table is a data structure, it can be expressed as DS= (A, R), A is a collection of n nodes with a0，a1，……，an-1, R has only one relation , (N={(ai-1ai) |1Definition 4: Data structure of compressed storage.There are two main types methods of compressing data matrix in traditional: random sparse matrix compression and special shape matrix (symmetric matrix and diagonal matrix) compression.
Corresponding to this,the server take out the corresponding data from the received data queue,in accordance with the rules and to extract key,then the reduction of information in corresponding positions,finally realizes the identity information authenticity, integrity verification of communication subject.Can effectively prevent man in the middle attack and reduce the amount of data transmission, greatly reduce the transmission time for the information.

Therefore reduction inspection field, tracking inspection process and the cause of the accident are especially important.
Application of mobile data acquisition technology can make the whole  lightning inspection data of highway be traced back. 
Data transmission.After field data acquisition,  it should be return through the wireless network or USB storage device. 
The mobile data acquisition terminal is used to collect data. 
Then fill the data real-time after inspection.

Automatic parameter classification for dimension reduction as basis for robust parameter identification D.
Numerical approximation methods of high dimensional data suffer from the curse of dimensionality [1].
Dimension Reduction.
The dimension reduction is performed user-controlled.
These parameter classification results are used for a parameter space reduction.