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Many enterprises have strengthened the management and technique innovation of energy saving and emission reduction to improve the performance of energy conservation and emissions reduction.
Index weights were determined by both subjective and objective method, which fully considers the experience of experts and objective information of data.
Meanwhile, the energy saving and emission reduction target and relevant requirement of government should be considered, so that it can reflect progress of energy saving and emission reduction in thermal power enterprise prominently, effectively and comprehensively.
According to the measured data of evaluation index of energy saving and emission reduction in thermal power, the matter-element to be evaluated can be built.
And the performance level of energy saving and emission reduction in this plant project belongs to ‘high’level.

Data and model Data sources and processing.
Historical data include construction value added and energy (fossil fuels listed in Table 1) end-use of the seven regions in East China from 2000 to 2010, sourced from [5][6] and [7] over the years.
Construction value added of each regions in 2020 are predicted with Grey Prediction Model GM(1,1) based on historical data[8].
Especially Shandong would become the focus of emission reduction in East China, since the proportion of its CO2 intensity reduction was close to 60%.
The Preferring potential case stimulates decision makers take differences among regions’ emission reduction space into account, and expect provinces with more reduction space to undertake greater reduction burden.

Only I− has a higher promotion of the reduction of bromate at higher concentration than 15. 64 μmol/L, it is due to the strongly reduction potential E0(•I/ I−).
Results and discussion Fig. 1 The effects of NO3− on the Fig. 2 The effects of SO42− on the photocatalytic reduction of bromate photocatalytic reduction of bromate Effects of NO3−, SO42− and HCO3−/CO32−.
Figs. 4 and 5 show the effects of Cl− and I− on the photocatalytic reduction of bromate.
Only I− has a higher promotion of the reduction of bromate at higher concentration than 15. 64 μmol/L, it is due to the strongly reduction potential E0(•I/ I−).
A: Chem. 108(1997), p.37 [22] Wardman P.: J.Phys.Chem.Ref.Data 18(1989), p.1637

DWT-MCDF results in high accuracies with the least sensitivity to training data abundance.
The major drawback of dimension reduction methods is that these work in specific scenarios and no general technique is available that suits all hyperspectral data.
We propose a new classification algorithm based on sparse representation for the classification of hyperspectral data using few training samples in the original high dimensional space and no dimension reduction is applied.
Sparsity is an effective model that deals with constructing of efficient representations of data as linear combination of a few typical patterns which are learned from the data itself.
Support vector machine classifiers as applied to aviris data.

Four study steps are involved: 1) collection of data for a supply chain model constructed; 2) creation of a value stream map and identification of non value added activities; 3) analysis of root causes of non value added activities, or wastes, identified in the first two steps; and 4) proposal of practical guidelines to reduce or eliminate the wastes.
To lower the cost, reduction or elimination of wastes is a must.
First, data are collected to be employed in a supply chain model construct.
Data are collected from upstream to downstream of the supply chain.
And that cost reduction should focus on these two stakeholders.

Then follow the general steps of data mining to research and analyze the enrollment data.
Introduction Data Mining (Data Mining-DM) is a decision support process.
Combined with a university enrollment data for analysis, information on the candidate’s personal is been attribute reduction.
Some dependencies between data can be summarized through a set of attribute after reduction.
In this paper, the data source is from graduate enrollment data.

Research on I/O Space Reduction of Large-scale Software Systems Gao Zhiyuan1, a, Huang Haifeng1,b, Yao Jianguo1,c, Cao Yang1,d and Yang shengchun1,e 1 China Electric Power Research Institute,Nanjing 210003, Jiangsu Province,China agaozhiyuan@epri.sgcc.com.cn, bhuanghaifeng@epri.sgcc.com.cn, cyaojianguo@epri.sgcc.com.cn, dcaoyang@epri.sgcc.com.cn, eyangshengchun@epri.sgcc.com.cn Keywords: Software Testing, Combinatorial Testing(CT), Test Model, Test Suite, Test Reduction Abstract.
I/O space reduction laws for large-scale software systems Based on the above model, we make a deep analysis on I/O space for large-scale software systems, and the results reveal some important laws.
Toward a Theory of Test Data Selection.
Pairwise Test Data Generation Based on Solution Space Tree.
Black-box Test Reduction Using Input-Output Analysis.

The experimental aerodynamic data proves that streamwise traveling wave airfoil can increase lift and reduce air drag.
The results prove the lift up and drag reduction effect.
The lift and drag experimental data shows in Fig. 6.
The data will be averaged and put into Tecplot software.
Morel, Turbulence reduction in a boundary layer by a local spanwise oscillating surface, Phys.

The data mining technique is employed and the massive spectra are identified quickly and efficiently.
The experimental data The experimental data are the entire SDSS DR9 spectra which is the SDSS's newest data release including the first spectra of the Baryon Oscillation Spectroscopic Survey.
Fig. 1 CVs template spectra from SDSS The training data set consists two parts.
PCA is a popular solution of dimensionality reduction.
The results of our data mining belong to these types.

An investigation shows that least-squares fitting can significantly decrease random error by incorporating data from the intermediate phase values, but it cannot completely eliminate nonlinear error.
Theoretical analyses and experiment results show that this method can greatly save data acquisition time and improve the precision.
Results showed that least-squares fitting can significantly reduce random errors on the condition that the data acquisition time and reliability of original TPU algorithm remain unchanged.
Both theoretical and experimental results showed that this algorithm worked well in significantly reducing the measurement error and saving data acquisition time.
Summary Accuracy, data acquisition time, computation time and reliability are four main evaluation indexes for temporal phase unwrapping algorithms.