Search results

Feature Extraction from Engine's Data Based on Rough Sets Theory WANG Feng, JIA Lixin Beijing Military Representative Bureau, Beijing, China, 100042 Email:shangmachangcun@126.com Key words: rough set; engine; feature extraction; reduction Abstract.
This paper introduced rough set theory for analyzing the reduction process data that under load in situ of different engine hours from a special vehicle, which aims to extract the characteristic parameters from the complex data that can characterize the engine technical condition.
As the data reduction of rough set method is based on the basis of discrete data tables, and the practical application of most of the data is in a continuous value.
Therefore, the data in the original decision-making system must be processed discretely.
Data in the table for decision-making can be identified using the matrix method to write programs for simple properties, and then get, for the nuclear properties, for the attribute reduction result.

At present, domestic and international usually use drag reduction rate as indicator. 1.1 Drag Reduction Rate Calculate the drag reduction rate by the friction pressure drop after DRA added to the liquid in the indoor test section of loop.
Then evaluate the effect of DRA by the drag reduction rate.
The reduction of pipe flow resistance, in fact, is the reduction of friction coefficient.
Then, according to equation (1-3), we can calculate flowrate-enhancing rate as equation (1-5) under the conditions of friction pressure drop after DRA added is and flow rate of basic data test condition is
Including a fluid pressure buffer tank, pump, valves, fixed-length test pipeline loop system． 1-Pressure level impulse 2-Return tank 3-Dilution mixing tank 4-Nitrogen tank 5-gear pump 6-bottom valve of buffer tank 7-vent pipe 8-flowmeter D,C,B-pressure transducer Figure 2-1 Drag reduction rate loop test device Experimental Results and Analysis 3.1 The drag reduction rate and flow increase rate at difference concentration of DRA Table 3-1 DRA evaluation Experimental data concentration（ppM） 10 15 20 25 30 Drag reduction rate（%） First 45.02573 50.65915 59.35945 67.77344 66.83987 Second 43.48938 51.58114 56.90592 67.77344 66.42953 Average 44.25756 51.12015 58.13268 67.77344 66.6347 flow increase rate（%） First 39.46696 48.10752 64.97512 87.6865 84.72997 Second 37.3459 49.66902 59.68489 87.6865 83.47109 Average 38.40643 48.88827 62.33 87.6865 84.10053 Figure 3-1 Drag reduction rate of domestic 3.2 The Relationship of concentration and the drag reduction rate of DRA Figure

Definition 4: Data sheet, C is condition attributes set, a set of all the necessary attributes in C is called the core of C, written as Core(C), the core of data sheet is public part of all reduction.
The attributes reduction of continuous fault data The diesel engine vibration data about large maintenance machinery of the decision Table shown in Table 2 shown to illustrate.
Rough Sets: Theoretical aspects of reasoning about data[M].
Rough Sets and intelligent data analysis[J].
Principle and Algorithm of Data Mining[M].

And then it substituted equation (8) with the data at 900-1050 ºC of Fig.2., we obtained Fig.3..
Therefore carbon gasification was controlling step of whole reduction process.
Fig.5 was obtained by substitution of the data of Fig.2 at 900-1050℃.
Fig.7 was obtained by substitution of the data of Fig.2 at 900-1050 ºC.
The reduction process of carbon-bearing pellet includes two stages and the reduction rate of the first stage is faster than the second one. 3.

The influences of reaction temperature and time on the reduction ratio of magnesia were studied.
The reduction ratio of magnesia increases with the increase in the reaction temperature and time.
The wMg is the weight fraction of magnesium in the briquette before thermal reduction.
Data were collected over the 2θ range of 10-90º.
Specially, the reduction ratio of magnesia can be up to 73% after 1 hrs aluminothermic reduction at 1200°C.

The base data of power plants are shown in table 1.
Tab.1 Base data of power plants Power Plant Net coal consumption rate/(g/kWh) SO2 mg/m3 NOx mg/m3 EAF Net loss/% Water consumption t/h Power plant 1 340 390 380 0.90 6.7 800 Power plant 2 330 380 350 0.91 6.9 820 Power plant 3 310 61 310 0.92 7.1 200 Power plant 4 320 55 120 0.91 7.2 80 Power plant 5 303 65 250 0.90 7.3 350 The indexes of energy-saving and emission reduction and the comprehensive evaluation indexes are shown in table 2.
In order to compare the effect of energy-saving and emission reduction, the basic data of power plant 3, 4, 5 should be calculated.
Tab.3 Calculation data of power plants Plant Coal consumption t/h Flue gas/(m3/h) SO2 kg/h NOx kg/h Water consumption t/h Order Power plant 3 186 1116000 68 245.5 300 2 Power plant 4 192 1152000 57.6 138.2 80 1 Power plant 5 181.8 1090800 81.8 272.7 350 3 According to the table 3, although the coal consumption of power plant 5 is low, compared to power plant 4, the SO2 and NOx emission per hour is higher.
According to the comparison of the computational data, the proposed evaluation method of this paper can fully reflect the situation of energy-saving and emission reduction.

On account of severe water pollution condition, this paper combines structure emissions reduction, engineering emissions reduction and management emissions reduction (namely SEM emissions reduction), builds an optimization model for total amount control of regional water pollution, and puts the above model into practice to validate its validity and reliability.
Constraint of Emissions Reduction Goals for Regional Water Pollutants.
Constraints of Industrial Emissions Reduction for Regional Water Pollutants.
The data about water pollutants increments in different industry are shown in Table 1.
Table 1 Increasing amount of each water pollutant of different industries in 2015 (unit: 104 tons) Pollutants category Industry categories To 2015 Industry Urban Agriculture COD 9.27 14.21 11.13 34.6 NH3-N 1.43 1.55 0.28 3.26 Combining the 12th Five-year environmental protection plan for total amount control of regional water pollutants in the case province, as Table 2, using the main control pollution data in 2010 as the base data of environmental emissions, the reduction of COD and NH3-N should be achieved 44.3 (104 tons) and 11.05 (104 tons) by calculation.

The Forecasting Model Based on Data Mining Technology The data mining also called the knowledge discovered in the database is to discover the effective, novel, having the latent value knowledge from the mass data.
T0 (m)), then each historical water pollution density data all all regarded as a vector.
Forecasted Result Analysis The following data come from Shayin River.
The data include sides. one is the water level,water flow,density of COD of upstream of the river.
Rough Set Spatial Data Modeling for Data Mining.

And then, the actual reduction ratios were optimized incorporation to the relative reduction ratios of the sub-regions and the total emission reduction target of the province by means of GA, a new optimizing method [9].
The basic idea of TOPSIS-GA are 1) to obtain the relative reduction ratios of the sub-regions by TOPSIS, 2) to optimize the actual reduction ratios of the sub-regions by GA, and 3) to calculate the reduction responsibilities of the sub-regions based on the actual reduction ratios and base-year emissions.
Specific steps are as follows: 1) Construct data matrix , , (1) where, m is the number of sub-regions, n is the number of indexes, is the original value, is the normalized value, ,, is the weighted value, , is the weight of index j, determined by entropy-weighting method as Eq. 2
The data used in this article was collected by ‘Shandong Statistic Year Book 2011’ (Table 2).
The reduction ratios of Dezhou, Liaocheng, Binzhou, Rizhao and Zaozhuang are higher than the province’s average reduction ratio (12.9%).

Energy saving and emission reduction are becoming strategic issues in China.
Current Research about Energy Saving and Emission Reduction Performance Evaluation Foreign scholars study energy saving and emission reduction early.
Performance Evaluation Index of Energy Saving and Emission Reduction Complete and effective evaluation index of energy saving and emission reduction should be established.
Reduction of pollutant emission can effectively promote energy saving and emission reduction.
We set as all the matters to be studied. is characterized by a set of data.