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Analysis on Efficiency Evaluation of Regional Energy-saving and Emission-reduction Based on DEA Model Shuang Wang Economics and Management College, Dalian University, Dalian, 116600, China wshuang1021@sina.com Keywords: Energy-saving and emission-reduction; DEA; Evaluation index system Abstract.
Introduction The concept of "energy-saving and emission reduction" was first proposed in "Eleven-Five" program and made energy-saving and emission reduction work closely together.
CICA in Canada has listed energy-saving and emission-reduction and audit evaluation in different industry, in which multiple aspects of energy-saving and emission-reduction and audit evaluation indicators was included in utilities, manufacturing, and traffic industry etc.
The Brief Review of Evaluation Method  Data envelopment analysis, DEA has developed into a kind of non-parametric frontier efficiency analysis method on the basis of the relative efficiency evaluation by A.Charnes and W.W.Copper scholars etc. this method is often used in input and output system of the production and living for management, decision-making and efficiency and benefit evaluation, etc, at present which has become a widely used and effective analysis tool in the field of management science and system engineering.
It mainly uses the linear programming method, on the basis on the original sample data are divided into input index and output index, gets to evaluate effectively to decision making units(DMU), for the purpose is to reflect whether the DMU can  achieve the decision-making results of “spending as little as possible, to get maximum benefit”.

To eliminate the redundancies, noises, incompletion, and inconsistencies in the data set of sluice monitoring, a method of data preprocessing to implement data mining is proposed by integrating the data preparation process in data mining and data warehouse [4].
Data preprocessing Before starting data mining, data preprocessing should be performed to eliminate or reduce the the redundant, noise, incomplete or inconsistent data.
Processing Data mart Monitoring DB Hydrological, meteorological DB Other data Cleaning Integration Transformation Reduction Data Warehouse Data sets Extraction Loading Input Output Fig. 1 The process of data preprocessing Beginning from selecting the data sources, the operation of data preprocessing searchs the existing rows (records) and columns (attributes) related to the data mining tasks, then performs data cleaning, integration, transformation, and reduction based on the result data sets after extraction, and in the last the process is finished when the data processed are stored in the specified data mining sets.
The data extraction and loading are the operations that must be implemented whatever the data are from any kinds of sources, but it is not necessary for data cleaning, integration, transformation, and reduction in the processing stages.
Only when the data preprocessing is finished can we get clear data to perform data mining operations.

Reduction of GO by TGA.
In order to ensure the reduction of GO by TGA, hydrochloride reduction of GO was carried out to compare the quality of TGA to that of the hydrochloride reduced GO.
This is attributable to the partial reduction of GO to graphene.
These XPS data suggest that the oxygen-containing functional groups have been partially removed restoring majority of the conjugated graphene network after reduction.
Cheng, The reduction of graphene oxide, Carbon 50(2012)3210-3228

Introduction The original features extracted from test data in mechanical fault diagnosis can characterize device state[1].
is the number of testing data points.
represents the kernel matrix for the testing data points.
Acceleration signals were measured using the Dewetron 16 channels data acquisition system and the IMI 603C01 accelerometers.
The data was stored in .mat format for further Matlab operation.

The reported mechanism does not provide information for the reduction kinetics; however, it gives certain suggestions how reduction might occur in complex ore minerals.
After reduction the crucible was cooled down with a furnace to room temperature.
The captured micrographs were analysed using ImageJ 1.8.0_60 software enabling to obtain the data about quantity, size and distribution of the metal particles, Fig. 4.
The data was sorted and entered in RStudio 1.0.143 program, where the normal distribution and homogeneity of variance of the metal particles were estimated.
Vinters, Gaseous Reduction of Iron Oxides: Part III.

Introduction Air pollutant emission reduction effect is an important indicator for the evaluation of port enterprise, national and regional energy saving and emission reduction effectiveness.
Although Eq.1 is relatively simpler, there isstill a considerable amount of work needed not only having to investigate fuel or energy consumption of all national container port,but also to statistic and analyze the data.
Hi,j,k=l=0l(TEFi,l×Tj,k×10-4) (2) In Eq.2, Hi,j,k: same as formula-1; TEFi,l: the ithair pollutant’s emission factor per unit throughput of lthcontainer terminal handling facility(t/ TEU), estimated by fuel consumption method (Eq.1) using the actual investigation data of throughput, fuel or energy consumption in representative container terminal; Tj,k: the throughputamount of jth port’s container terminal in kth year (TEU/a).
Fig. 2 Container port throughput in 2012 and highway distributing minimum mileage Fig.3 Air pollutant emissions in 2013 from port handling and highway distributing In the calculation above mentioned, the state published data of coastal and inland river port throughput and unit energy consumption, container throughput, heavy truck and ordinary truck unit mileage energy consumption in 2013 [24], and container handling facilities energy consumption of unit throughput (coastal and inland river port in the same) estimated by this research investigation are used, respectively, into Eq.4 and Eq.5.
Evaluation of air pollutant emission reduction.According to the published data of coastal and inland river port cargo throughput and container throughput from 2001 to 2013, this research has obtained the non containerized cargo throughput of port, and the classification of statistics formula predicting throughput (Fig.5 to Fig.6(left)), in which the correlation coefficients range from 0.971 to 0.998.

The paper analyses two categories, five terms characters of mountain disaster, which supported by statistics data.
Disaster prevention and reduction to the human settlement construction is basic function, which needs theoretical support by human settlement.
Therefore, the important issues on the mountain town disaster reduction and prevention should be the mountain properties.
Theory Support for Disaster Prevention and Reduction in Southwest Mountainous Cities The Necessary of Theoretical Guidance.
Disaster Reduction in China, 1996,6 (4) , p.39 ~ 41

The dimentional data then will be used to model a steel wheel rim model by using CATIA V5.
The shape optimization process will be applied when the data has be transferred to FEM data.
Each optimization is done beginning at the datum and not continuous from previous reduction to allow comparable results.
Static structural analysis had been done to the datum and optimized design to obtain the results of maximum stress, total deformation and mass reduction.
Eventhough the maximum stress of optimal design selected is not as lower as datum, but if compared to the higher reduction target, 15% of reduction target is the better option.

For NIR data has the character of high dimension, nonlinear, and high noise, we often confront the problem of dimensionality reduction when building the classification model on Near-Infrared spectra data.
Recently, nonlinear dimensionality reduction methods are applied in the spectra data like Isomap [5] and LLE [3].
Firstly, preprocess the training data and do nonlinear dimensional reduction by using S-Isomap.
Table 1 NIR spectra data of five brands’ cut tobacco Dataset No. classes No. variables No.samples NIR data 5 1609 80 Dimensions Reduction.
After KLLE dimension reduction process, the information of embedded data has a little loss.

The main phase of the reduction product was Fe2B, FeB and SiC.
Though the best combination has in the nine experiments, but experiment were needed to increase some data to analyzing if samples has were reacted completely.
The main phase of the reduction product was Fe2B, FeB and SiC.
The main phase of the reduction product was Fe2B, FeB and SiC.
High-temperature reduction of ore comprising ludwigite[J].