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Data preprocessing.
For the experimental data having a characteristic of smaller-the-better should be normalized as following: (3) Based on the data sequences after data preprocessing calculated by equation 2 and equation 3, the gray relational coefficient can be defined as the following: (4) where and are reference and comparability sequences respectively; ρ, ranging from 0 to 1, is distinguishing coefficient and 0.5 is generally used [7].
So the data preprocessing was preformed by using equation 2 and equation 3.
Based on the preprocessed data, the gray relational coefficients and grade values for each experiment were calculated by using equation 4.
The preprocessed data and calculated result were presented in Table 2.

China azdx.jn@163.com Keywords: Network Intrusion Detection, Data mining, Fuzzy C-Means, Rough sets.
IDSs are also categorized into network-based and host-based in terms of source of data.
Rough set theory does not require any prior or additional information about the data and provides a systematic method capable of searching and identifying the relationships within the data features of a relational database.
Experiments and Analysis Experimental Design. 1．Preprocessing (Clean data and handle missing and incomplete data). 2．Feature selection methods based on Rough set theory (Feature selection with GFSA and CFSA). 3．Cluster group of data by using Fuzzy C-Means.
Data normalization. 4.

Building energy saving is one of the most important areas in energy saving and emission reduction, but it needs more funds and technology to achieve the target of building energy saving.
In addition, although the total amount of building energy-savings and emission reductions is large, reductions of individual project is small.
Certified Emission Reductions (CERs) is also small.
On the other hand, the emission reductions should be quantifiable, but some difficulties associated with monitoring and verification exists in building energy saving CDM projects.
Determine the data gathering and monitoring methods of building energy consumption. 4) Intensify propaganda to push the building energy efficiency transactions carrying out smoothly.

These models contribute to increase the stability and efficiency of the reduction process [1, 2].
The high generalization capability also provides robustness to the model when data or examples are under the effect of noise [7].
This database was separated three subsets, 70% of data for training, 15% for the validation phase and 15% for testing.
This demonstrates that the model exhibits a good agreement in simulating the phenomenon of silicon incorporation, since the mathematical model in different periods of operation showed a good agreement with the actual data.
The developed mathematical model showed excellent agreement with the actual data of the reduction process in blast furnaces, because the ANN were able to accurately predict silicon content in hot metal.

The study was conducted at discrete intervals ∆t = 8 h using data recorded by the furnace computer measuring system and methods of statistical data processing.
In spite of furnaces are equipped with modern computer systems for recording and visualization of measurement data, metallurgical parameters of the process are not directly measured.
Research was carried out at discrete intervals ∆t = 8 h using data recorded by computer measuring system of the furnace.
Appropriate selection of data and statistical tests showed that the electrodes slipping was one of the most significant factors impact on performance of the examined ferrosilicon furnace.
This range was determined by appropriate data selection and statistical data processing.

Eq. 2 was used to determine the TAN reductions [4].
The data were then fed back into the model to identify the best parameters.
Actual Volume Used (ml) TAN (KOH/g) TAN Reduction % Blank 0.2 - - Run 1 0.3 0.073 97.2 Run 2 0.4 0.146 94.4 Run 3 0.8 0.438 83.3 Run 4 1.0 0.583 77.8 Run 5 0.6 0.292 88.9 Run 6 0.4 0.146 94.4 Run 7 0.9 0.511 80.5 Run 8 0.7 0.365 86.1 Run 9 0.6 0.292 88.9 Run 10 0.7 0.365 86.1 Run 11 0.7 0.365 86.1 Run 12 0.8 0.438 83.3 Run 13 2.2 1.459 44.4 Experimental data based on CCD.
Table 3: TAN reduction (%) from CCD runs.
Fig. 1: Contour plots for TAN reduction %.

The biological selenate reduction generated alkalinity, increasing the wastewater pH from 6.0 to 8.6.
The IFBR was monitored and controlled using data acquisition and control hardware (CompactRio National Instruments, USA) and software (Labview, National Instrument, USA).
Data collection and read trimming/filtering was performed using TorrentSuite 5.0.
The formation of a red precipitate indicated reduction of selenate to elemental selenium.
The biological selenate reduction generated alkalinity, increasing the wastewater pH from 6.0 to 8.6.

Therefore, it can also provide useful data to governments for calculating the environmental benefit if they carry out a green roof policy in mitigating heat island effect in the future.
The analysis of this paper is using the data collecting from these two roof area.
The comparators were also installed on normal roof on the same building top to collate data for making comparative analyses. 3.
Data analysis In this research, the green coverage ratio is 24.42% by calculating the greening area within a radius of 10 meters.
This study not only made comparative analyses by summer and winter data; but it also tried to analyze the main factors of green roof in mitigating heat island effect.

Therefore, this paper presents an approach to interpret in-process measurement data and to derive electric energy savings potentials.
Based on this, the gathered data are used to derive suitable measures to realise energy savings.
Figure 2: Modular methodology allocated to the data levels of a factory The developed methodology consists of the five modules.
To be applied by machine operating companies, the developed module supports a holistic analysis by considering both quantitative and qualitative input data.
During the load curve interpretation, the energy performance indicators ϰ are calculated to elaborate the gathered measurement data.

Accordingly, the wavelength changes can be regarded as the initial data of alarm.
The deviation of the real-time value and this mean value can act as valid data.
Both groups include 12 sets of actual intrusion data and 18 sets of non-intrusion data.
Get waveform data: Process the sets of measured signals and get the waveform data, the dimension of which is 64 and the sum is 1.
Apply the feature of central moment of 4D to the 64D waveform data.