Search results

The higher the temperature of reduction is, the more obvious the yield falls.
Fig.1 Microwave carbon thermal reduction diagrammatic sketch Raw material.
(8) Reorganize and analyze the data Result analyst and discussion Ore phase analysis.
Considering the productivity rate and the grade, the ore selection index is better in the process of 750℃ reduction, magnetic separation in 1.2A current, and the data for both of that are 91.34% and 54.66%.
The higher temperature of reduction is, the more significant grade falls

Magnesium reduction Jar can be critical, consumable parts in magnesium reduction process, this paper Introduced the working environment and the common failure modes of magnesium reduction can, based on the failure can cause magnesium reduction, presents a The new structure, developed a new material, and magnesium can restore the original structure was modified, the use of new casting technology to produce a good magnesium reduction tank.
This paper analyzes the failure of Magnesium reduction Jar cause reduction, developed a new material, and magnesium reduction tank’s original structure was modified, the use of new casting technology to produce a good reduction Jar. 1 A typical structure of reduction Jar and its work environment The size of reduction Jar is320mm×(2650~2700) mm or the standard 339×(2650～2700) mm, wall thickness is 30～35mm, which lie put to gas and coal as main fuel reduction furnace, its openings exposed on one end of a length outside the furnace wall to form a multi-pot reduction furnace structure, the specific structure shown in Figure 1 [1,2].
Field test data show that: shell cracks appear on the site of intensive work exactly corresponds to σ phase precipitation temperature range parts.
Study on the Reduction Jar Used for Mg Making[J].
Research on the Parameters Optimization in the Reduction Process Step of Smelt Magnesium by Silicon－thermo－reduction[D].

The comparisons to different thermal physical properties such as thermal resistance R0, thermal inertia index data D, reduction coefficient of thermal wave transferring V0 and thermal transferring delaying time ξ0 have been performed between Tuzhang dwelling and normal brick house.
The temperature data of Yuanjiang is shown in Fig.1.
Parameters used in present analysis include: values of thermal resistance R0, heat storing coefficient S, thermal inertia index data D, reduction coefficient of thermal wave transferring V0 and thermal transferring delaying time ξ0.
Comparison to brick wall, soil wall has larger thermal inertia index data D.
The comparisons to different thermal physical properties such as thermal resistance R0, thermal inertia index data D, reduction coefficient of thermal wave transferring V0 and thermal transferring delaying time ξ0 have been performed between Tuzhang dwelling and normal brick house.

Rough set data analysis is a symbol method of analyzing relativity and dependence of data.
Continuous data need to be dispersed.
The method of rough set data analysis system actually is the course of reduction of decision table.
Data Discretisation.
The function is logic(xl,x,y) ，xl is original data, x is reduction result data, y is the minimal rule.

Business data External data Business data ETL database ETL data Data warehouse Multi-dimensional data sets online analysis(OLAP) Data mining Front-end tool Query tool Reporting tools Analysis tools Figures 1.
Data dimension Data keyword(PK) Data property ...
Raw data Data mart Data warehouse Data mining training library Clean-up phase Process of building a data earehouse Integration stage Data pre-processing process Reduction phase Figures 3 Data pre-processing process model When the traditional data warehouse architecture building data warehouse, external data sources will be directly loaded into the data warehouse trough ETL tools, which has some deficiencies. 1) Since the complexity of the data pre-processing, direct integration will inevitably lead to that the process not only occupies a number of eternal operational database resources and time, but also affects the efficiency of data warehouse loading data. 2) After the source data extraction, cleaning and conversion, when transmitting to the data warehouse if there are system failures or network failures, the whole data pre-processing process will be only redone, which is a great waste of resources
Operational database Operational database Unstrauctured data Data extraction Data preparation area Data conversion Data load Data warehouse server OLAP analysis tools Data mining tools Figure 4.
The attribute X and Y has no relation ad they are independent. 6) Dimension reduction based on information gain.

The data source of an energy flow diagram is from the statistical data in the energy balance sheet for a region.
Investigation of the city’s energy flows and the major industries’ energy consumption, structure and carbon emission can provide data and decision supports to draft CO2 controlling measures for the dominant industries.
However, concerns and practices on CO2 control just starts, research on air pollutants reduction which collaborates with CO2 emission reduction is very necessary.
This model can be used to evaluate the regional air pollutants, CO2 emission and economic investment, and support data collection and decision makings.
In this paper, only current data and conditions were analyzed.

Remove the noise source data set and independent data, processing the missing data and clean dirty data, considering the time sequence and data changes.
In this paper, data cleaning work including missing values, noise data and inconsistent data processing.
The paper use FAP (Fill in with Average Poll result) [2] attribute structure on employment information, so as to evaluate analysis. 5) Data reduction.
Data reduction techniques can be used to obtain a reduced representation of data sets, although it is much smaller, but still close to maintain the integrity of the source data.
Data reduction method is adopted in this paper is: the data cube aggregation and dimensionality reduction. 6) Boolean transformation.

Lower cold rolling reductions before reversion annealing for the grain size refinement are desired in industrial practice.
However, a high cold rolling reduction to create 100% deformation-induced martensite (DIM), as generally presented, is impractical in industrial processing, and the reductions of 30–40% would be more desirable.
No data are, however, for the behavior of slightly cold-rolled reversed structures under cyclic loading.
The latter data have been published earlier in Refs. 4–6.
After 32% cold rolling reduction, there was 30% retained DA left, but only 4% after 63% reduction.

Rule acquisition is a hot topic in the field of data mining.
Introduction Rough set theory proposed by Pawlak [1] is an effective mathematical tool, which can deal with imprecise, uncertain, inconsistent data.
Essentially, there are only distribution reduction and assignment reduction.
Conclusions In the paper, under the model framework of the granularity of the rough set theory, we use rule extraction algorithm to mining the credibility of the implicit rules from the inconsistent decision-making system, identify data with maximum distribution reduction,which decision-making most likely to occur.
Acknowledgment This work has been supported by the National Natural Science Foundation of China (Grant No. 11205029） References [1] Pawlak Z,“Rough Sets theoretical aspects of reasoning about Data,”Dordrecht Kluwer Academic Publishers, New York 1991,pp.9-30

Data Envelopment Analysis Method The Theory of Data Envelopment Analysis Method.
The basic idea of this model is: Form the analysis of the sample input and output data, the model is to determine the effective decision-making unit , and to determine the production frontier.
is the copper loss of transformer Data model of line loss potential analysis of Gansu Power Grid Suppose that Line loss is x1, transformer loss is x2 and others loss is x3.
An output indicators is the whole network line loss rate, the input and output data of each decision-making unit is given by the following matrix.
(5) (6) In this model, X1i, X2i, X3i and yi are the known data.