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Rule data discovery and miming of providing some valuable approach to study the meteorological data quality control, meteorological data Study of Data Mining Model to Multi-Dimensional Time Series Recently, data mining multidimensional time sequence has applied gradually in meteorological fields and made great achievements.
Preprocess the meteorological data Because the data in data mining have multi-dimension, high redundancy, complexity in size and mass data, algorithm and tools of data mining is not directly applied in data mining process must data pretreatment firstly.
There are two data preprocessing step, building meteorological data mining database, data dimension of treatment.
In addition, 2004-2007 data is used as the model training data ,while others are acting as the model test data .
Conclusion This paper presents a new multidimensional time series data mining model, this model meteorological data redundant dimension reduction algorithm is used to decrease redundant size and complexity of the data mining slope extremum piecewise linear fitting method is used to realize the multidimensional meteorological time series segmentation, data compression and eigen value extraction, reduce the difficulty of the data mining method, then use k­means cluster to make the symbols of sequence, The results of experiment show that this model has a great practicability.

Therefore, a demand reduction effort even for DR needs to maintain, if not to improve, the established PUE rating of a data center.
Power Models for Data Center Components.
Efficiently Cooled Data Centers.
Ordinary Data Centers.
For data centers, there are few attempts, if any, that addresses the relationship between power reduction efforts and energy efficiency issues.

First, the data are refined by the data reduction technique, which makes it keep the data structure of the original data and have higher efficiency.
This procedure is called data reduction algorithm (DRA).
Let denote the original data set, denote the data set after reduction and denote the distance from data to data set of .
The fast speed of ERKPCM on three data sets owes to the data reduction technique, which makes it keep the data structure of the original data and have higher efficiency.
In the meanwhile ERKPCM has a high computational efficiency on most of the test data sets because the data size is decreased by the data reduction technique.

Relevant literatures proposed electricity energy-saving key technical support system architecture, such as energy consumption, pollutant emissions supervision and inspection and data analysis support system, energy saving and emission reduction targets, evaluation and assessment technical support system[5].
Since the specific condition for each area is different, emission capacity can be calculated by substituting corresponding data into the simplified model.
Our data is mainly from Jilin Grid Inc.
To calculate the capacity for 2011, the data we need is as following: coal power for 2011’s Jilin is 5.684×1010kWh, or 1.7×107t for coal consumption, wind power is 3.968×109kWh, or 1.19×106t for coal consumption.
Limited by data, we can only calculate operational emission reduction capability for Jilin grid taking one year as the unit time.

Analyze and conclude the relationship data between existing-pipeline-controlled rainfall return period and runoff coefficient, by using source control.
Calculate and conclude the design scale of ecological measures and storage pools, draw the corresponding generalized model, by using peak flow reduction, volume reduction and flow reduction measures.
That includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc.
Fig5 The melts generally model of flood volume reduction Determine the scale of flowing reduction mode Flowing reduction measures include stormwater detention zone, vegetation swales, storage pool etc.
Fig.7 The schematic picture of flood rate reduction Conclusion This paper presents several measures of runoff reduction, which includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc. and analyzes the handling facilities scale of these various measures in Zhangjiakou city, which is selected as a study area.

The data from the field blast test was compared to transducers with vibration reduction and transducers without vibration reduction.
Test equipment was several Kistler211B series sensors, Kistler5148 M06 signal conditioning module and PXI data acquisition instrument.
The Analysis of Vibration Test Data.
Applied the installation structure in explosion shock wave pressure test, can effectively restrain the additional impact caused by vibration, thereby improving the accuracy of the measured data.
The research of shock wave pressure testing in blast field and the data processing method.

The Analysis of Dynamics Incenting incentive on Enterprise to Energy Saving and Emission Reduction The so-called dynamics incenting enterprise to energy saving and emission reduction are the driving force which can promote enterprises voluntarily and positively behave on energy to conservation and emissions reduction.
The concrete analysis results are as follows: Table.1 lists related data from 2005 to 2010 of industrial enterprise energy consumption and pollutant emission in Jilin province.
Taking data in Table.2 on behalf of the situation about energy consumption and pollutant emission of enterprise in Jilin Province, we take the above indicators as driving forces to do the corresponding scatter diagram as shown in Figure.1 to Figure.5.
That is to say, each index is higher; the energy consumption and pollutant emission is lower, and the better energy conservation and emission Table.1 Related data from 2005 to 2010 of industrial enterprise energy consumption and pollutant emission in Jilin province Item Year Energy consumption per unit industrial added value (tons of standard coal/ten thousand Yuan) Carbon dioxide emissions per unit value added (tons/ten thousand Yuan) Sulfur dioxide emissions per unit value added (tons/ten thousand Yuan) Effluent volume per unit value added (tons/ten thousand Yuan) Exhaust gas emissions per unit value added (million cubic meters/ten thousand Yuan) 2005 3.25 7.34 0.02 30.20 3.62 2006 2.80 6.77 0.02 23.70 3.23 2007 2.37 5.74 0.02 18.27 2.64 2008 1.98 5.10 0.01 14.27 2.29 2009 1.62 4.27 0.01 12.30 3.15 2010 1.62 3.48 0.01 9.84 2.10 Data source: statistical yearbook of Jilin province or calculated on the basis of statistical yearbook Table.2 Energy Consumption and Pollution
The marketization of energy conversation and emission reduction will give way to enterprises that actively promote energy conservation and emission reduction, which are much more able to benefit from it.

Key Words: Mg-Sr alloys; Thermodynamics; Vacuum reduction 1.
In the melt-leaching-reduction process, the driving force kinetics for Sr reduction are very low at the reaction temperatures normally used[2].
Thermodynamics Analysis of MgO and SrO Co-reduction 3.1 Method of Gibbs Free Energy Evaluation and Establishment of Oxygen Potential Diagram All the thermodynamics data for Sr compounds and reactions involved in this paper were calculated using a Gibbs free energy function method using original data taken from the Ye-Hu thermodynamics data book.[6] The mathematical model for the evaluation of the Standard Gibbs Free Energy is ΔG Θ =A+BT (1) The oxygen potential diagram of Sr, Mg, Si and Al is plotted in Fig. 4.
The data indicates the following: (1) Tc decreases as pv decreases
H.: The practical thermodynamics data book of inorganic matter.

Knowledge reduction is one of the most important contributions of rough set theory to machine learning, pattern recognition and data mining.
So, quicker methodologies for attribute reduction are better for huge data processing.
Experiment Results In the first experiment, the uci KDDCUP99 data set is used. 10%, 20%, 30%, …, and 100% records of the KDDCUP99 data set are selected to create a testing data set each time respectively.
Half (50%) of the entire data set was split as and used as the training data set, while the remaining 50% was used as the testing data set.
This attribute reduction algorithm can process huge data sets efficiently.

The data suggested that copper (II) was reduced and the resulting copper (I) complexed, with both reactions probably mediated by sulfur oxy-anions produced indirectly by the bacteria, in the anoxic sulfur column bioreactors.
Chemical reduction and complexation of copper.
Results and Discussion Performance of the iron reduction system.
Theory of reduction of copper (II) and complexation of copper (I).
Conclusions A two-stage bio-reductive process for recovering copper from PLS, devised and tested at the laboratory scale, generated interesting and unexpected data.