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The base data of power plants are shown in table 1.
Tab.1 Base data of power plants Plant Net coal consumption rate/(g/kWh) auxiliary power ratio /% SO2 mg/m3 NOx mg/m3 EAF Net loss/% Water consumption t/h plant 1 340 6.2 390 380 0.90 6.7 800 plant 2 330 6.1 380 350 0.91 6.9 820 plant 3 310 7.2 61 310 0.92 7.1 200 plant 4 320 7.1 55 120 0.91 7.2 80 plant 5 303 7.5 65 250 0.90 7.3 350 The matrix of the original data B is: The B’ after standardized treatment is: The indexes of energy-saving and emission reduction and the comprehensive evaluation indexes are shown in table 2.
In order to compare the effect of energy-saving and emission reduction, the basic data of power plant 3, 4, 5 should be calculated.
Tab.3 Calculation data of power plants Plant Coal consumption t/h Flue gas/(m3/h) SO2 kg/h NOx kg/h Water consumption t/h Order Power plant 3 186 1116000 68 245.5 300 2 Power plant 4 192 1152000 57.6 138.2 80 1 Power plant 5 181.8 1090800 81.8 272.7 350 3 As is shown in table 3, although the coal consumption of power plant 5 is low, compared to power plant 4, the SO2 and NOx emission per hour is higher.
According to the comparison of the computational data, the proposed evaluation method of this paper can fully reflect the situation of energy-saving and emission reduction.

As shown in Fig.1, after performing mapping and serial-to-parallel(S/P) conversion to the information bits of an arbitrary user (indexed by ), a data symbol is replicated into parallel copies.
We should notice IFFT number is a multiple of the spreading sequence length and data symbols length .
Hence, the estimation of this detector is (13) which is just the decoupled data plus a noise term.
After detecting as (15) we can further transform them to by using and recover efficient data symbol by .
Wang, Lattice-reduction-aided receivers for MIMO-OFDM in spatial multiplexing systems, Proc. of IEEE PIMRC2004, 2004, pp.798-802

The effects of reduction temperature and reduction time on the removal rate of magnesium were investigated.
Therefore, the garnierite was treated by carbothermal reduction in vacuum in this paper, and the effects of reduction temperature and reduction time on the removal rate magnesium were studied.
The data of the first 120min was fitted by the least squares according to Fig. 3, and the reaction rate constant K and its correlation coefficient R2 at different temperatures are shown in Table 4.
By introducing the experimental data into the expression of the zero-order reaction kinetic model, the kinetic equation for the removal of magnesium by carbothermal reduction in vacuum from the garnierite can be obtained: 1-(1-α)1/3=(-22850.1/T+2.6296)t (3) Table 4 Reaction rate constant K and model correlation coefficient (R2) at different temperatures Model Number 1623K 1673K 1723K 1773K 1823K K(10-6) R2 K(10-6) R2 K(10-6) R2 K(10-6) R2 K(10-6) R2 D1 11.50 0.7678 14.00 0.7818 25.30 0.8006 36.00 0.9834 122.00 0.9899 D2 6.43 0.7502 7.95 0.7617 15.00 0.7725 22.60 0.9711 105.00 0.9826 D3 1.60 0.7320 2.01 0.7407 4.03 0.7424 6.43 0.9819 47.90 0.9125 D4 1.48 0.7441 1.84 0.7546 3.56 0.7623 5.46 0.9652 29.50 0.9661 D5 -2.62 0.7621 -3.21 0.7743 -5.93 0.7873 -8.70 0.9763 -39.20 0.9829 R1 13.70 0.9362 14.40 0.9276 20.90 0.9276 24.00 0.9843 75.30 0.9974 R2 43.70 0.9271 46.50 0.9167 69.60 0.9137 83.30 0.9872 348.00 0.9735 R3 33.00 0.9070 36.00 0.8929 57.50 0.8803 75.30 0.9832 643.00 0.8163
The effects of reduction temperature and reduction time on the removal rate of magnesium were investigated.

As a catalyst, the reduction behaviour and the degree of reduction of the molybdenum species were highly important in such application.
For identification purposes of crystalline phase composition, diffraction patterns obtained were matched with standard diffraction data (JCPDS) file.
The TPR profile of MoO3 represents two reduction stage (denoted I and II) which stage I owing to peak displayed at early reaction time may regard to the reduction of MoO3 to Mo4O11, while stage II is subsequent to reduction steps of Mo4O11 to MoO2.
Fast reduction of Ag2O was promoting MoO3 to reduce together.
The data obtained from XRD evidenced the presence of Ag2Mo2O7 alloy on MoO3, which led to the effect on enhancing the reduction process by lowering the reduction temperature of MoO3 to MoO2 phase that was completed at after non-isothermal reduction until 700 °C and hold for 30 minutes at 700 °C compared to undoped MoO3.

Implementation of Parallel Lanczos Method for Intrusion Detection with Cloud Technologies Bo Chen 1, a, YuleDeng 2, bandTiemingChen3, c 1Zhejiang University of Technology, Hangzhou China 2Zhejiang University of Technology, Hangzhou China 3Zhejiang University of Technology, Hangzhou China acb@zjut.edu.cn, brainerdun@live.cn, ctmchen@zjut.edu.cn Keywords:IntrusionDetection; Dimensionality Reduction; Cloud Computing Abstract.The aim of dimensionality reduction is to construct a low-dimensional representation of high dimensional input data in such a way, that important parts of the structure of the input data are preserved.This paper proposes to apply the dimensionality reduction to intrusion detection data based on the parallel Lanczos-SVD (PLSVD)with the cloud technologies.
The massive input data is stored on distribution files system, like HDFS.
Dimensionality reduction [2] is a preferablemethodto speed up intrusion detectionfor mass data.
For the PCA dimension reduction, usually only needs to take principal component accounted for over 85% of all principal components, and it is enough for get better data dimension reduction result.
Experimental detection rate and false alarm rate is shown in Table 1: Table1．The detectionrateandfalse positiverateunderthetwo conditions Dataset Normal data Intrusion data Detection rate（%） False alarm rate（%） IFCM PLSVD+IFCM FCM PLSVD+IFCM dataset 1 103510 2103 84.5 84.8 0.82 0.78 dataset 2 104120 2458 83.8 84.1 0.84 0.82 dataset 3 110250 2220 82.6 82.7 0.81 0.8 Average 105960 2260 83.6 83.9 0.82 0.8 From Table 1,we can gethigherdetection rateby using PLSVD dimension reduction algorithm data, compared to clustering directly from raw data,and maintaining a lowfalse detection rate at the same time.

This paper, basis on the rough set theory in data mining and preferential information ,we improve the rough set attribute reduction algorithm, and applied to civil aviation accident analysis to indentify the potential law of accident.
Data Mining, also known as Knowledge Discovery from database, it is a complex process that extracts unknown and valuable mode or knowledge from large data.
Rough sets and data mining is closely related, it provides a new tool for data mining.
First, the implementation objects of data mining are more for relational databases, relational tables can be seen as a decision table in rough set theory, knowledge can be substituted by data, Knowledge processing can be achieved by the data manipulation, it brought great convenience to the application of rough set methods.
In order to extract useful information from these complex data better, to find out the internal rules and patterns in the main affect safety of flight and the accident involved of factors in the wealth information and data, the article improve the algorithm which based on Rough Set Attribute Reduction, it can provide a reference for aviation safety.

Mechanical Diagnosis based on Similarity Extraction of Time Series Chunjie Lv Yongyu Yao Department of Mechanical Engineering, Luoyang Institute of Science and Technology Keywords: similarity, dimension reduction, fault diagnosis, historical data Abstract: The intelligent diagnosis emphasizes the processing method of knowledge of the historical data.
This paper discusses the possibility of the application of similarity extraction and pattern discovery of time series in fault diagnosis by using these historical data, presents the method of time series feature extraction and pattern matching, and advances the possibility of data clustering and pattern discovery based on dimension reduction.
So it is necessary to transform time series data.
DFT is a very transformation, which is separated from the common data.
Retention of 64 bit DWT coefficient Fig. 3 The graph after compression with DWT The results of Wavelet transform could also reflect the overall data trend through data reduction.

Some theories on the basis of data mining as follows: Data Reduction: In this theory, the basis of data mining is to simplify the data.
Data reduction in accuracy for speed to quickly get an approximate answer apply large database query.
Data reduction technologies include singular value decomposition, wavelet, regression, log-linear model, histograms, clustering, sampling and construction of the index tree.
For example, the pattern found in data reduction can be seen as a form or a data compression.
Data access layer is mainly related operations processing and data sources, and data source stores data the user wants to mining.

Experimental 2.1 Preparation of Mg-Sr Alloy by silicothermic vacuum reduction.
Data of time and pressure were recorded every 4 minutes during the total reduction process.
The fastest reduction rate was obtained at point C and the reduction was finished at point D.
Preparation of Mg-Sr Alloys Using Vacuum Reduction - a Thermodynamics Approach [J].
The practical thermodynamics data book of inorganic matter.

In order to research the way of dimensionality reduction for data, we also processed the sample of stress fatigue concentration factor to compare with Principal Component Analysis(PCA).
In order to research the way of dimensionality reduction for data, we also processed the sample of fatigue stress concentration factor to compare with PCA.
Collect data and train by linear kernel function The data about fatigue stress concentration factor is come from Metal material design, selection, prediction.
A part data of sample is listed in the Table.1.
Ning: Design, Researches on Feature Selection and Stability Analysis for High Dimensionality Small Sample Size Data (MS., Xiamen University, China 2014)