Search results

Different paths contain different semantic information such as control dependence, data dependence and so on.
(3) Table 2 shows the mean percentage of test-suite reduction for our reduction strategy.
Program number of unreduced test-suite mean number of reduced test-suite mean percentage of test-suite reduction print_tokens 4129 2605 36.909% print_tokens2 4115 2156 47.606% replace 5542 3251 41.339% schedule 2650 2175 17.925% schedule2 2710 2278 15.941% tacas 1608 11 99.316% tot_info 1052 347 67.015% In order to further investigate the test-suite size reduction of each program, boxplot A boxplot is a standard statistical device for representing data sets.
The box’s height spans the central 50% of the data and its upper and lower ends mark the upper and lower quartiles.
Fig. 3 shows the percentage reduction for our reduction strategy.

So, the indium need to pass to smelting process is complicated to extract[4],Based on this, this paper put forward thermodynamic analysis of reaction conditions of indium in thermal vacuum carbon reduction method under different conditions, The paper studied on the thermodynamics of the carbon reduction of indium ore preparation of indium in vacuum, which provides the basic data for research on post step by vacuum carbothermal reduction of indium.
In vacuum, the oxide reduction with increasing the reduction temperature, can be more completely reduction reaction,as the reaction temperature decreases, the reaction temperature decrease[5].
According to the Handbook of the thermodynamic data of data and calculation method, the formula is as follows[6]: ； ； ， Thermodynamic data According to relevant data check related thermodynamic data are shown in Tab1[7].
Thermodynamic data of the main related substances(/J·mol-1) In2O3 C InO In2O In CO(g) CO2 -925919 0 -271960 -167360 0 -110541 -393505 The basic process of indium ore carbothermal reduction The main reaction of indium mineral carbon thermal reduction process occurs as follows[8]: In2O3+3C=2In(g)+3CO(g) (1) In2O3+C=2InO(g)+CO(g) (2) In2O3+2C=In2O(g)+2CO(g) (3) Indium mine carbothermal reduction reaction process, the intermediate reactions may occur: InO+CO(g)= In(g) +CO2(g) (4) In2O3=In2O(g)+O2(g) (5) 2In2O (g)= 4In (g)+ O2(g) (6) Analysis of the results Indium mine carbothermal reduction process analysis of the main reaction According to the relevant thermodynamic data with reaction formula (1) complete reaction in the vacuum
Practical Handbook of inorganic thermodynamic data[M].Beijing: Metallurgical Industry Press, 2002:6-754

Isothermal Carbothermic Reduction Effect of Temperature on the Reduction of Chromite.
The ores had different reduction rates and reached different extent of reduction.
No trace of Cr reduction was observed for SCO reduction in this temperature (Fig. 2a).
It was also noted that the following equation can be used to fit the data of the early stage of reduction up to a reduction time of 20 minutes: -ln (1 – X) = k . t (3) where equation (3) is the kinetic model for nucleation control (Tanaka, et al 1987).
It was also found that the extent of reduction increased with increasing temperature and reduction time.

Naive Scaler algorithm is used to discrete the risk index data, then an algorithm of attribute reduction based on mutual information entropy is used in decision table to reduce index and build risk index system optimal model.
Naïve Scaler algorithm array the data of decision table in ascending or descending order according to conditional attributes.
The risk index data table is shown in Table 2.
From the result of reduction, “backward purchase method and operate error”, “backward storage technology and operate error”, “backward goods of management technology and operate error”, “a deviation of information in data transfer” are the main factors on the risk index system of distribution obviously.
Conclusions For the distribution risk index of distribution of supply chain in retail enterprises is redundant and it is difficult to determine the decision rules through the complex index, this article provide a risk index reduction model from risk analysis to risk index data discretization ,then to risk index reduction.

The time was calculated by equation (7) with the data of mass loss percentage, and its relationship between -ln(1-fc) is shown in Fig.3.
The relationship of time and 1-(1-f)1/3 was obtained by substitution of the data of mass loss fraction, shown in Fig5.
The relationship of time and 1-2/3f-(1-f)2/3 was obtained by substitution of the data of mass loss fraction, shown in Fig7.
The activation energy was calculated by the data from Fig.4, Fig 6 and Fig 8, while the controlling step was different, shown in Table 2.
If it was, the was 50-75kJ/mol, which was different from the experimental data.

Spectrum Analysis of Cell Resistance Data Sources.
The data sources, which this paper studies, are based on the electrolytic cell control system in 306KA.
And the sampling items adopted in electric-hydraulic control system simulation are data without abnormal step disturbance, removing artificial factors.
Data Pre-processing.
Here the original data takes away the average number of all the analyzing cell resistance to reduce the zero frequency influence.

Over the six data mining methods can be divided into two categories, namely direct and indirect data mining data mining.
Direct Data Mining.
Indirect Data Mining.
Rough set reduction is an important concept for data analysis.
Use reduction results, you can get a preliminary classification of fault data, the rules are diagnosed.

When the data is evenly distributed, regular interval and regular frequency are commonly used.
When data distribution is very uneven, discretization easily leads to information loss, and affects the final classification accuracy.
Therefore we process the simplified data using SVM training and learning techniques and implement affective semantic mapping.
Discretize the data in decision table by K-means clustering method to four categories.
However, in the case of big data, a more reasonable affective semantic classification method needs further study in the future.

Make North Schisandra criterion and sample data as Normal-Weibull distribution to calculate similar.
Knowledge reduction method description Knowledge reduction method is based on rough set[3, 4].
Rough set methods can be applied as a component of hybrid solutions in machine learning and data mining.
Sample data y are normal distribution and standard data x are weibull distribution, the probability density function are , Where is mean of Sample data; is standard deviation of Sample data; is scale parameters; is shape parameters; .is location parameters.
After calculated, the fuzzy centre data of recommended samples are(0.06178±0.044, 0.04656±0.0102, 0.04456±0.0084); the fuzzy centre data of no-recommended samples are(0.093962±0.0757, 0.036608±0.0087, 0.024415±0.0068).

(7) Table1 Thermodynamic data of reactants and resultants substance 3MgO·2SiO2·2H2O(s) -4 364 079 222.170 317.231 132.241 -73.555 MgO(s) -601 241 26.945 48.953 3.138 -11.422 SiO2(s) -910 857 41.463 43.890 38.786 -9.665 H2O(g) -241 814 188.724 29.999 10.711 0.335 MgO·SiO2(s) -1 548 917 67.781 92.257 32.886 -17.866 2MgO·SiO2(s) -2 176 935 95.186 153.929 23.640 -38.493 In order to determine the reaction formula, the Gibbs free energy change in Eqs. 5-7 can be calculated by the thermodynamic data of 3MgO·2SiO2·2H2O(s), MgO(s), SiO2(s), H2O(g), MgO·SiO2(s) and 2MgO·SiO2(s)(see Table1)[11-12], whereandare the standard molar formation enthalpy and the standard molar entropy at the temperature of 298K.
Determination of Reduction Process.
Therefore, the reduction condition is improved effectively.
[11] Dalun Ye, Jianhua Hu, Practical Thermochemical Data of Inorganic, Metallurgy Press, Beijing, 2002
Barin, Thermochemical Data of Pure Substance, Science Press, Beijing, 2003