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Firstly, it creates MCU (Minimum Coding Unit) of the colored JPEG image from the DU (Data Unit) of the greyscale image by the construction matrix randomly.
Introduction Image compression algorithms are used to reduce the amount of data needed to represent a digital image and the basis of reduction process is the removal of spatial and visual redundancies.
MCU construction is to construct a new MCU which contains four Data Units of, one DU of and one DU of. 8x8 block shuffle is to shuffle all the DUs randomly.

Except for the PMLSM, the platform consists of “data collection”, “data processing”, and “control output”.
“Data processing” works in PC based on LabVIEW according to the algorithm of FOC.
Fig.13 shows the curves of speed versus the number of data under various speeds.
In order to get excellent drive performance, one data is got in one PWM period.
The speed versus the number of data under various speed settings.

Combined with the same time period monthly precipitation data of the regional meteorological stations, we analysis spatial-temporal variation trend of water storge change in Tangnaihai basin for nearly 6 years.
The satellite can obtain high accuracy and high time resolution data of spatial and temporal variations of the gravity field of the Earth by month, which can be used to retrieve water storage change [2].
In this paper, water storage change was calculated using GRACE time-variable gravity field data from 2003–2008.
Fig 5.The distribution of water storage trends in the upper Yellow River basin above the Tangnaihai Station during 2003−2008 CONCLUSION This article first GRACE been Tang Naihai gravity satellite data watershed 2003-2008 of land water storage changes, taking into account the meteorological stations in 31 countries monthly precipitation data, analysis of the spatial and temporal distribution of water reserves in the region changes and trends.
NASA are thanked for making data available at http://grace.jpl.nasa.gov.

In n-type semiconductor (figure 1a) the oxidation and reduction take place at the semiconductor anode and counter electrode (eg.
In electrochemical study, the energy level of the material is referred to the hydrogen reduction potential (H+/H2).
Therefore, a bias potential is required to allow for the reduction reaction.
In fact, from IPCE data, the researchers can estimate the STH of the system but only in zero bias potential.
It is not a valid estimation of STH if the data is measured under bias potential.

It include multi-base such as part information, green material, performance data of resource and environment, equipment resource, green process, cutting parameter and other data etc.
Data Source.
Data Evaluation and Certification.
Therefore, the data collection is difficult and complex.
The system adopts these new technology can reduce the resource and energy consumption or short the development cycle and cost reduction.

Principal component analysis (Principal Component Analysis, PCA for short)[6] is an important feature extraction method, with it high-dimensional data is projected onto low-dimensional space via linear transformations, so as to achieve the purpose of noise reduction and redundancy.
If the cumulative contribution ratio is greater than 85%, then the first m principal components can represent most of the information of the original data, not only reduces the number of dimensions, but also minimize the loss of original data information.
Composition of the experimental data sets Data sets name Number of the samples Number of the categories Original dimension Dimension after PCA Iris 150 3 4 2 Weld_defect 200 4 10 4 In this paper, using Principal Component Analysis technology to reduce the dimension of data, so as to achieve the effect of data compression to reduce the amount of computation, set the parameter to explain the degree in threshshold=90.
In order to verify the effectiveness and feasibility of the KNN algorithm, respectively do the classification study of the standard Iris data set and welding defect ultrasonic signal data set, as shown in “TAB.
Wherein, Iris data set is the feature vectors constituted by the three kinds of different types of irises.

The climatic and geographic characteristics in the Yunnan basin are introduced and characteristics of precipitation，runof and silting in the river basin are analyzed．The regulation of rainstorm and flood within the river basin is analyze according to the statistic data of previous rainstorm and flood.
Season assignment of Rainstorm: According to statistical data, the rainstorm mainly appears in May to October, Occurs by June to August rainstorm frequently.
References [1]The Yunnan provincial government, Geography(C), http://www.yfao.gov.cn/ewindows/Geography.html, 2011 [2]China eTours Travel service, Yunnan Weather(C), http://www.etours.cn/china_city_guide/yunnan_travel_guide/weather,2011 [3]The Yunnan provincial government, Climate(C), http://www.yfao.gov.cn/ewindows/Climate.html,2011 [4]Yunnan Province water department, The Yunnan Province storm runoff looks up calculates practical guide(M), Yunnan Province water department, 1992 [5]Si-lin He, Rainstorm flood characteristic analysis of Qujing(J), CHINA WATER RESOURCES,2008.13 [6]Zhenghong Qi, Xishuangbanna Lancang River basin rainstorm flood characteristic and disaster reduction countermeasure(J), PEARL RIVER,2008.5

The data was analysed in the raw and corrected state.
Figure 1 presents the corrected XRD data plotted for comparison with the neutron diffraction data and associated error measured at the four electro-polished locations.
This shows the XRD data to be in good agreement with the neutron diffraction data, although due to the penetration depth of the neutron beam, limited near surface comparison is possible.
Strain data was analysed using the Integral Method.
In many cases the strain levels relieved in any single depth increment are a few µε, thus meticulous experimental practice, accurate strain measurement and appropriate data reduction techniques must be used to avoid large errors and uncertainty in the measurements.

The model was validated by comparison with experimental data of compression tests of the 6xxx series aluminium alloys and a reasonable agreement of the simulated and measured flow stress curves was found.
The material softening is described by reduction of dislocation density through spontaneous annihilation of dislocations and thermally activated dislocation climb [3]: dρdt=2BdannbρMφ+2CDGb3kBTρ2-ρeq2, (2) where B and C are calibration parameters, kB is the Boltzmann constant and ρeq is the equilibrium dislocation density (1011 m-2).
The obtained results were verified by the experimental data measured for AA6061 and AA6082 using transmission electron microscopy (TEM).
One can note a reasonable agreement of the simulated data and measured flow stress curves.

However, hardly any data are available for these types of materials.
Data reduction The global force-displacement data and the actual crack length values were used to calculate the tearing energy values.