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The monitoring system of water environment is composed of data collection, data transmission, data storage and data reasoning components.
Introduction Cluster analysis is an important component of data mining, mainly used for finding the valuable data distribution and data models in the potential data.
The algorithm is not sensitive to dirty data and abnormal data.
Summary The water environment monitoring system composed of data collection, data transmission, data storage and data reasoning components.
Outliers in Statistical Data[M] .

It provides a theoretical basis for the implement of liquid core heavy reduction rolling mill. 2.
Simulation conditions: rolling reduction is 70mm, line speed is 0.9m/s, and materials are 2Cr12NiMoWV and 5Cr4W5Mo2V.
Simulation conditions: rolling reduction is 70mm, line speed is 0.9m/s, material is 5Cr4W5Mo2V.
Simulation conditions: rolling reduction is 90mm, line speed is 1.09m/s, material is 5Cr4W5Mo2V.
[4] Lu Huimin, Shen Yunyuan: Mechanical Engineering Material Properties Data Manual.

Based on a two-week benchmark data log, the result shows a total energy reduction from 210 kWh to 71 kWh, representing a saving of 65%.
Table 1 lists the technical data of all the lamps in the system.
Power is computed from voltage and current data logs.
Figure 1 Typical half hour power consumption plot This part of the research involves capturing live data from the system by the aid of a data logger to get a clear understanding of the usage pattern.
As the demand was not uniform, it was inevitable to go for long term data capturing and monitoring.

Then, the Hadamard transform is performed to reduce the correlation of the input data sequences.
Finally, data signals are successfully received after the P/S conversion.
CCDF denotes the probability that the PAPR of a data block exceeds the threshold value.
PAPR Reduction Technique Hadamard Transform is a precoding technique that reduces the correlation of the input data.
The data sequencesafter the Hadamard transform can be represented as: (5) Partial Transmit Sequence Scheme.In the PTS scheme, an input data block of N symbols is partitioned into several disjoint subblocks.

The kinetic parameters of each stage of LiFePO4 material prepared by carbothermal reduction method were calculated using the Doyle-Ozawa and Kissinger methods.
Carbonthermal reduction technology has been studied by lots of domestic and foreign researchers, however kinetics of carbonthermal reduction process is rarely investigated.
LiFePO4 material is synthesized by carbothermal reduction method using FePO4 as iron sources in this paper.
The optimized synthesis process and the design and optimization of equipment for mass production could be obtained by the data and results. 2.
The apparent activation energy of each step of LiFePO4 material prepared by carbothermal reduction method is calculated using the Doyle-Ozawa and Kissinger methods.

Algorithm based on wavelet was studied for noise reduction of data cloud, and one volume calculation model was raised.
Wavelet and Riemann algorithms were used to reduce noise of measured data and volume calculation.
Comparison Experiment And Data Analysis To verify the measuring method discussed above, comparison experiment system was designed.
Because the data from laser scanning method is arranged by linear array, the B-spline or NURBS was used to reconstruct the surface of horizontal tank shell.
Zhao: 3D Laser Scanner Data Acquisition System Developed (In Chinese).

A newly developed response prediction technique has been successfully used for the identification of more detailed information from limited sets of data.
They are written as follows Simple Vibration Test Upgrade and Optimization Response Prediction Structure Limited Data Full Field Response Data { }{ } { }{ } { }{ } { }{ } { }{ } { }{ } 2 T n n 2 2 T T n n n n U v RU v MAC= U v U v RU v RU v                  
Conclusion With the response prediction technique, the full field response data can be deduced from the limited data obtained by simple vibration test instead of conducting full-size test, which can present reference for the succeeding upgrade and optimization in dynamic design.
References [1] Chipman,C, Expansion of Real Time Operating Data [D],Master's Thesis,University of Massachusetts Lowell,May 2009 [2] P.Pingle, C.Niezrecki, P.Avitabile.
Real Time Operating Data Expansion for Dynamic Stress and Dynamic Strain Fatigue Accumulation [A].

Fig. 1 X-ray diffraction data for the mixture of Fe2O3-Ca MA powders as a function of total milling time.
Fig. 4 X-ray diffraction data for the mixture of Fe2O3-Ca MA powders ball-milled for various time intervals and subsequently heat treated up to 600°C.
Fig. 4 shows the X-ray diffraction data for the mixture of Fe2O3-Ca MA powders ball-milled for various time intervals and subsequently heat treated up to 600°C.
Hence, the magnetic data of Fe2O3-Ca MA powders can supply a better understanding of the evidences for the solid state reduction process of Fe2O3-Ca system, as well as the amount of magnetic phase and microstructures.
The X-ray diffraction and magnetic data have been discussed simultaneously in order to achieve a better understanding of the solid state reduction induced by MA.

Degradation of Azo Dyes by the Reduction and Oxidation with Nano Sized Zero Valented Iron B.H.
Pre-reduction by nZVI could convert azo dye to products whose oxidation is more degradable and therefore enhances the removal efficiency.
The successful use of ZVI has been reported to decolorize azo dye solution by reduction of the -N=N- bond at the iron surface [2,3,4,5].
The laboratory synthesized nZVI used in this study had the specific surface area of 27.9m2/g, which is smaller than the data reported in the literature.
Compared to various H2O2 dosages, pre-reduction of Orange II with nZVI enhanced the TOC removal by 5~15%.

The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ).
Data Extraction In the present study, the performance of the proposed stock selection mechanism was evaluated using electronic stock data extracted from the TEJ database [11] over the period extending from the first quarter of 1998 to 2/27/2009. 4.
Step 1: Data collection and attribute determination In each quarter, the 53 attributes of each specified stock item within the TEJ database are collected automatically, and the user is given the opportunity to modify the choice of financial ratios used for attribute reduction in the initial GRA process, to select a new GRA model for attribute reduction purposes, and to modify the decision-making attributes used to filter the stocks in the lower approximate set.
Step 2: Data preprocessing Having collected the relevant financial data for each quarterly period, a basic pre-processing operation is performed to improve the efficiency of the GRA attribute reduction process.
Data collection and attribute determination Start Data preprocessing Attribute reduction using GRA model Fuzzy C-means clustering Selection and filtering of feasible stocks Fund allocation Renew the GRA Method No Yes Yes No Continue investment?