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However, researchers have been illustrated that Pawlak’s rough set model has some limitations when handling some practical problems, especially when some noise data are included.
So that the rule properties need to be preserved after attribute reduction.
Attribute reduction in variable precision rough set model In this section, Ziarko’s and Mi’s attribute reduction models will be briefly recalled.
Rule acquisition based on rough set theory should correspond to the original data set.
If the rule properties are changed when reducing attributes, such as deterministic rules are changed to probabilistic rules, even some deterministic rules are eliminated, the obtained rule set will not confirm to the original data set. 3.

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.

Based on the data stored in memory mode, this article proposes a new reduction algorithm.
First, the low-high column of data processing attributes: Because the data in memory stored in binary form, TableElement is short data type defined by the macro, which accounts for two-byte, 32-bit systems, a byte is 8, the low may represent 0-255.
Greater than 255 data, referred to as a high.
Step Four: Then, when all the data which is sequentially in the order of LSD (Least significant digital) of attribute reduction are thrown into the bucket, the initialized objIdx[] array is written the corresponding value, namely updated the data line number, it is sorted.
Subsequent authors will further develop applied research how to use the new algorithm for large data mining.

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

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.

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

In order to promote emissions reductions, carbon emissions reductions shall be discounted.
Selected a representative and large power plant as a case, all the latest data are from the enterprise’s official website.
The main data are shown in Table 1.
Table 1 Data Descriptions of Selected Power Plant Variables/Parameters Value Variables/Parameters Value Reduction period (year) 5 Project’s life cycle (year) 20 Predicted emissions in 5 years (104ton) 450.09 Reduction emissions discountrate (%) 8 Permits distribution in 5 years (104ton) 546.38 Annual return ratio on retudtion investment (ton CO2-e/Yuan) 0.0038 Permits price (RMB Yuan) 25.68 Limits of (104Yuan) 5067.89~ (1) Considered that China's economic and social development plan is usually adjusted for every five years, the reduction period is also assumed as 5 years.
According to the above decision model and related data, the optimal range of emission reduction investment is 5067.89 to (unit: 104Yuan, see Table 1).

It was found that higher content of additives gave larger thermal reduction and larger microsphere (RM1) gave a larger thermal reduction than that of the smaller ones (RM2).
Reading data were recorded every 10 seconds until the reading temperature going to constant.
After that the reading data were collected every 5 minutes for 1 hour and 40 minutes.
This is in accordance with the surface temperature data which indicating that 28 %wt or more of TiO2 can provide sufficient solar reflection and enhances thermal reduction.
It is also suggesting that the single reflectance data is not suitable to represent the thermal protection property of the roof paint.

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.

A private cloud is a proprietary network or a data center that supplies hosted services to a limited number of people.
Through the enterprise private cloud, a factory can obtain the device service, the software service, model service, the data service, the information service and knowledge service from the public cloud.
In addtion, the service cloud should supply safe data storage for the users to record, look up, manage, and reuse their manufacture process data.
According to the business requirements from energy-saving and emission reduction and the new related technologies in this area [7-9], these key technologies are: pinch technology for device improvement in energy-efficiency, the coupling modeling technology to describe the nature of the coupling relation between production and energy consumption, computer simulation for calculate the possible energy-consumption value by reproduction in a virtual environment, dispatch optimization technology, evaluation technology for energy-consumption efficiency, data mining technology for energy-saving oriented etc.
Conclusions Both of global climate improvement and manufacture cost-reduction require the people to make endeavor in work of energy-saving and emission-reduction.