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Establishment and Verification of Prediction Model Data normalization.
The transfer function of the neural network has a certain limit on the input, and the units and the magnitude of the original data varies.
Therefore, it is necessary for the original data to be normalized.
This paper chooses MATLAB mapminmax function, and all the data are in the range of [-1,1].
The function can be expressed as: (7) Where and are respectively the maximum and minimum values of the variables in all operating conditions, is the normalized data, and is the original data.

A special class that covered the concept of specific functional areas, such as separation, drag reduction, etc.
Information representation based on XML has many features, such as: 1) XML data can be delivered to the desktop for local computation or remote computation. 2) It can provide users with the correct structure of the data view. 3) It allows the integration of different sources of structured data. 4) Because XML is extensible, so it can be used to describe the data from a variety of applications, even without the built-in data description, and can receive and process data. 5) It can improve performance through granular updates.

The practical application in Jibei power grid indicates that this model can meet the compiling requirements of day-ahead generation schedule, realizes the prior acceptance of wind power and contributes to the energy saving and emissions reduction in power field under the power grid security constraint. 1.
According to the former analysis on historical data of wind power integration and operation control, it is found that the insufficient acceptance of wind power is due to the weak power grid structure as well as the irrational power layout, which cannot meet the requirements of powering reliability after the input of high-permeability wind power[5].
By taking a practical data construction case in a typical winter day, it checks and analyzes the constructed module.

After cold storage at 4 o C for 24 h, presence of OS rice starch reduced retrogradation of rice starch mixtures as indicated by reduction of measured retrogradation parameters based on the three abovementioned rheological properties. 1.Introduction Modification of starch with octenyl succinic anhydride(OSA) was patented by Caldwell andWurzburg [2].
Data were statistically analyzed by an analysis of variance (ANOVA) test procedure and differences identified by Tukey's HSD test (p , 0.05) using SPSS 12.0 for Windows (SPSS Inc., IL, USA). 3 Results and Discussion 3.1 Degree of substitution DS of the octenyl succinate starch used in the experiment was 0.016. 3.2 Pasting property RVA pasting curves of the normal rice starch, octenylsuccinate starch and blends of native and OS starch are shown in Fig (not shown) and the RVA parameters are listed (Table not shown).
These experimental data fitted well with power law and Casson's model (r 2of 0.95-0.98 and 0.94-0.99,respectively).

The facilities offered by the research centers – database, software - led to rapid selection and configuration solutions, demonstrating achieving research productivity growth and a substantial reduction of times redesign.
The locating mode for inserts Radiating insert located on the inner cylindrical surface; tangential insert placed on the inner cylindrical surface, radiating insert located on the front surface, tangential insert placed on the front surface; Selecting Constructive Solutions The data resulting from the previous heuristic analysis represents a starting point for determining the morphological space, which in this case is compound of nine components (E, F, G ) each having between 2 and 4 configurations ( E1, E4, F1, F2, F3, F6, G1, G2, G4 ), a total of 24 combinations (2x4x3).
Databases contain both technical data specific to industrial design activity, knowledge base, but also specialists and customer base.

In order to find out the variation in slope stability when the reservoir water level changes at different rate, the stability calculation model was established adopting the Seep module and Slope module of Geoscience software GeoStudio, and calibrated with a long sequence of real-time monitoring data, based on the landslide survey data and test data.
Besides, most of the previous studies have concentrated on the numerical simulation and general mechanic calculation means with a model that is not refined enough and is calibrated with macro phenomena or little monitoring data lack of the long-term continuous multi-monitoring data.
Therefore, this paper is conducted by collecting various parameters more accurately, establishing a relatively refined model and adopting the multiple real-time continuous monitoring data as the basis for model calibration to ensure the overall accuracy of the simulation results and provide the effective data to further study on the impact of seepage field change on the landslide stability.
Based on the previous survey date and test data, the Seep model was built with the Seep module in the geosicence software GeoStudio and calibrated with long sequences of real-time monitoring data of water content and pore water pressure.
Fig.4 Contrasting pattern of the SF_LJP_1 simulation result and monitoring data Fig.5 Contrasting pattern of the VW_LJP_1 simulation result and monitoring data (a) Soil-water characteristic curve (b) Seepage curve Fig.6 The seepage curve and soil-water characteristic curve As demonstrated in Fig.4 and Fig.5, the simulation results of the water content and pore water pressure are relatively close to monitoring data in terms of the overall trend and the individual value in the long time, indicating that the model is calibrated quite well and is reliable to do the stability simulation under different conditions.

Structured Sparsity and Coding Complexity The minimum description length (MDL) principle is based on the following insight: any regularity in a given set of data can be used to compress the data, i.e. to describe it using fewer symbols than needed to describe the data literally.
In fact, the structured sparsity has the same principle, it utilizes the potential regularity in raw data to obtain data structure, then force structured sparsity to reduce the coding complexity of data, thereby improve performance in accuracy and speed.
This part will illustrate the coding complexity of structured data.
In this case, the data has the block coding complexity of . …… Fig.2.
It can be used to represent abnormal data, such as noise or occlusion.

The noise reduction coefficient (NRC) of sample D is 38.26% while for sample C is 37.42%.
But there are opposite result as shown in Fig. 4 which is foam thickness from D, E and F sound absorption increases but sometime fluctuating data found at the lower frequency.

For example, Cheng Shaoming etc. processed the data by using LDA method, and distinguished these seeds with BP neural networks.
Its goal is to get the best optimization based on present data instead of some samples which sizes tend to infinite big.
This experiment obtained a total of 178*8 sample data, from which we selected 89 groups as train sample, and the remaining 89 groups as test sample.
The mapping of normalization was as follows: (x, y∈，=min(x)， = max(x) ) The normalized raw data is structured to [0, 1].
A SVM based classifier with previous PCA processed the data signals, and got a high recognition effect.

From the collected data and results of the fatigue crack growth measurements the exponent (n) and the constant (C) of the Paris-Erdogan equation [3] have been determined: .
Both data for steels investigated in this study and for aluminium alloys [12-15] and one superalloy [16-17] were summarized.
Both data for steels investigated in this study and for aluminium alloys [13, 18-19] were summarized.
Seeger: Materials Data for Cyclic Loading.
Seeger:, Materials Data for Cyclic Loading.