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It is proved that the application of fuzzy control in vehicle emission reduction is feasible through the processing and analysis of test data.
It does not need to the data processing of blur and eliminate blur in this study, because there are only 13 conditions measured in tests.
Put the original test data into formula (1) ~ formula (7), the urea solution needed theoretically to completely eliminate NOx under ESC conditions are obtained as shown in table 3.
Through look-up table, called the corresponding output values while in different conditions, measured the NOx values processed, the data are as shown in table 6.
The application of fuzzy control in vehicle emission reduction is feasible 2.

Lastly, after reaction rate and time of reaction has been determined, the reduction process can be calculated based on following equation: Roρc=[1-(1-R')1/3]=Kt (15) Where R’ = fractional reaction (instantaneous weight of pellet/ initial weight of pellet)sphere Ro = initial radius of the reacting ρc = molar density t = time K = constant Mo = initial weight of pellet (Fe2O3) R’ = (Mo-Mt)/ (Mo-Mf) Mt = weight of pellet at time t (Fe2O3 + Fe) Mf = final weight of pellet (Fe) Results and Discussions Four sets of independent experimental data were generated with different experimental conditions which were employed for different furnace temperatures and porosity based on previous work of Tan, 2012[6].
The set of data combinations with temperature of 700°C and the porosity of 20%, temperature of 700°C and porosity of 40%, temperature of 800°C and porosity of 20%, and temperature of 800°C and porosity of 40% had been tested.
Reaction rate increases rapidly as reduction proceed.
However, the model used does not fit ideally with our experimental data.
Thus, it is obvious that non-isothermal is inclined better to experimental data compared to isothermal predicted transport limited reaction rate for hydrogen reduction of ferric oxide kinetic modeling and obeys the theoretical framework of ore reduction.

The paper proposed a new evaluation indicator for reduction effect and introduced the formula of reduction rate.
The new reduction rate formula solved the problem.
Our experiment data come all from the real-time data of a large steel company.
[6] Boley D, Cao D W,Training support vector machine using adaptive clustering, Proceedings of International Conference on Data Mining, Florida, 2004,pp. 235-242
[7] Yu H, Yang J, Han J W, Making SVMs scalable to large data sets using hierarchical cluster indexing, Data Mining and Knowledge Discovery. 11(2005) 295-321

Formal concept lattices and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing.
FCA and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing.
In addition to being a technique for classifying and defining concepts from data, FCA may be exploited to discover implications among the objects and the properties.
References [1] Yao Y Y.A comparative study of formal concept analysis and rough set theory in data analysis, Rough Sets and Current Trends in Computing.
[3] Petko Valtchev, Rokia Missaoui, Robert Godin: Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges[C].

Substituting the above control experiment data into the above formula, the value of the specific reaction rate at any temperature can be obtained.
Results and Discussion Preliminary Results After many experiments and adjustments, it was found that in the case of 200 pellets, the most consistent with the original experimental data.
The results obtained are not much different from the experimental data in the literature, which proves that this method is feasible.
The degree of reduction and the reduction time of the pellets are obtained in continuous time.
Melt reduction [M].

The new algorithm has been tested based on the Chang’E Data in the Matlab environment.
Their mission is to collect many different types of data at different times and even from the different viewpoints.
Results have been put together and compared with the original data (Figure 4).
Results of the new algorithm for the No.2 area And after these results have been put together and compared with the original data (Figure 6).
[3] LIU han, JIANG HongKun, TIAN XiaoLin, Xu AoAo, A New Fast Auto-Extraction Algorithm of Lunar Craters Based on the Chang’E Data, DEStech Publications, E113

Figure 3.Data after noise reduction.
Data process and fit evaluation of clothing Data process.
Figure 10.Data of torso.
Figure 11.Data of left arm.
Figure 12.Data of right arm.

Calculations were done for the same data as calculations for the active system presented in the previous section.
The FPGA module enabled safe and quick implementation of the control algorithms, while the RT processor managed data exchange with external devices such as memory modules and the operator’s console.
Fig. 7 The electric part of the laboratory workstation The measurement system was adjusted to convert data, sample by sample, with the maximal sampling frequency allowed for the measurement card and transducer.
To avoid problems related to synchronization of measurement data and limitation of maximal sampling frequency, an additional measurement subsystem was implemented.
The applied solution enabled hardware synchronization of data recorded in packets.

In the context of industrial noise reduction, data-driven models are able to analyze large amounts of data, which include gear modification parameters or even manufacturing parameters and their associated noise levels.
The first step in the workflow was to collect data.
The raw data set was divided into training and test sets.
Using synthetic data, the models predicted noise levels with similar accuracy as they did on real data. 4.
Future research directions: • More data: The accuracy could be further improved by incorporating larger data sets

H2: Waste reduction subjective norm has a positive influence on RBI.
In general the response rate was 100%, but since ten questionnaires later were discarded because of missing data, the effective response rate was approximately 98%.
Findings and Discussion SEM using AMOS version 20 [11] was applied to the data in order to test the hypothesised saturated model.
Overall fit indices showed that the hypothesis model fit the data well: x2 (df = 184, N = 470) = 411.056 p < .05, CFI = .924, PGFI = .737, RMSEA = .051 (Low = .045, high = .058).
H3: Predicts that, waste reduction perceive behaviour control has a positive influence on reduce behavioural intention, to increase the waste reduction practice.