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It can be effective for large-scale incomplete ocean data reduction and it also provides a strong basis for decision making for the marine environment processing and follow-up processing.
The prevalence of incomplete data in marine monitoring and other areas of the internet of things bring tremendous difficulties to data fusion, data mining.
In order to mining knowledge from incomplete data, Literature [6] constructed a new similar relationship.
These studies are for static data, but in reality in many databases are dynamic.
Conclusions The traditional approach to deal with incomplete data is make it completed.

In point cloud data pre-processing, salient outliers are manually removed; Gaussian filter is used for noise suppression; curvature-based sampling is adopted for data reduction.
Data reduction is needed for high efficiency.
In the encircle box reduction, an encircle box which is composed of a quadrangle is used to filtrate data inside or outside the box.
The reduction results of the point cloud data shows in Fig.4.
Then, Gaussian filter, curvature-based sampling data reduction, etc. are used in data pre-processing.

The reduction degrees of FeO and Cr2O3 are 68.06% and 52.24% respectively, and comprehensive reduction degree is 58.52%.
However, the data of july to november showed, using the relative lower proportion of fine ore, the greater the number of using pellets, the more technical indicators were attained than fine ore were brought into the furnace directly.
Table 8 The chemical composition of finished pellets Name TCr TFe MCr MFe TC Percent(%) 33.50 22.04 17.50 15.00 2.90 Chrome concentrate pellets reduction used to express the reduction degree of its formula: (10) (11) (12) Formula: MFe——Metallic iron content of reduction kind, %; TFe——Total iron content of reduction kind, %; MCr——Metallic chromium content of reduction kind, %; TCr——Total chromium content of reduction kind, %; ——The reduction degree of iron, %; ——The reduction degree of chromium, %; ——The comprehensive reduction degree , %; Brought the table 5 data, concluded: =68.06%； =52.24%； =58.52%。
Table 9 The mass balance of grate-rotary kiln system Entry Output Item Unit(kg/min) Item Unit(kg/min) Green ball Dehydration loss Ash and bulk materials loss Iron reduction loss Chromium reduction loss Oxidation of coke powder loss Finished pellets 631.31 According to the law of conservation of mass, the material entry should be equal to the output item, then: (16) Brought the data and calculated kg/min, amounted green ball 59.23t/h.
New technology of chrome ore pre-reduction[J].

To use data mining technology, firstly, we must ensure that the historical data adapt to data mining, which needs data preparation, that is data preprocessing.
Data preprocessing includes three parts: 1) Data selection: Data selection is to collect the internal data and external data related to the information of load,and to choose the data applyed to data mining.Data selection, including attribute selection and data sampling is to select data fields and tuples in the data source.
Data integration is to put datas into unique data store[3].
Raw data through data selection, cleaning, integration and conversion generate the data mining library,to prepare for data mining.
It is denoted by IND (P) Reduction and Relative Reduction.

Application of a Wavelet Extension De-noising Method In Seismic Data Processing Ye Wua, Bo Zhangb and Jia Weic 1 Institute of Disaster-Prevention Science & Technology, Yanjiao, Sanhe city, 065201 China asun_wuye@163.com, bzhangbo199011@163.com, cwj@163.com Keywords: Wavelet Transform; extension; seismic data; signal de-noising Abstract.
We have removed the high frequency noise in seismic data based on the suppressing detail components method, Fourier transform filtering method, WED method and reconstructing the 5th layer approximate coefficient method respectively, and the results show that the WED method can more effectively restrain noise than the other methods.
[6] Shu Li, Yucheng Shi, Yuankun Huo, Yan Tang, Based on the MATLAB seismic signal wavelet noise reduction, Gansu science and technology , 26(15), (2010), pp:54-55
[9] Junhua Zhang, Youxi Le, The wavelet transform and the fractal properties in the improvement of seismic data of the application of the resolution.
[12] Jingguang Ceng, Yaqin Shu, Yong Zhong, Fractal and chaos characteristics of seismic data, Petroleum geophysical exploration, 30(6), (1995), 743-748.

The reduction jar, as a core device of the reduction furnace, has many disadvantages, such as easy to wear, short service life, and high cost accounting for about 25% of the total cost[4].
The thermodynamic data of the reactants and the resultants in Eq. 1 can be referred in relative data manual[21].
Reduction Reaction by Adding SiO2.
[16] Hongxiang Liu, The experimental research and the equipment test of reduction magnesium by vacuum-aided carbothermal reduction, Dissertation of Kunming University of Science and Technology, Kunming, 2008
[21] Ihsan Barin, Thermochemical Data of Pure Substance, Science Press, Beijing, 2003

This paper describes a dimension reduction method of input vector to improve classification efficiency of LVQ neural network, where GA is used to decrease the redundancy of input data.
Dimension reduction by GA is present in section Ⅲ.
denotes the number of samples in test data set.
· The Origin of Data In this paper, we choose the UCI data sets [7] as our test data, which are considered the standard data sets to compare the capability of various algorithms in data mining domain.
TABLE Ⅰ Experimental Data Sets Data Set Number of Attributes Number of Instances Number of Classes Ionosphere 34 351 2 Vehicle 18 946 4 Sonar 60 208 2 Waveform 40 5000 3 Breast Cancer 32 569 2 Vote 16 436 2 · Evaluative Method For each data set we chose in the table 1, 80 percent data instances are selected at random as training data, and the 20 percent remainder data instances are considered as test data.

Methodology The methodology is divided into several stages includes preliminary stage of study, data collection and analysis, discussions and conclusion.
The review from the PLUS Expressway record on energy consumption for the energy usage had been carried out.The data collection is in the form of questionnaire survey.
All the data from questionnaire survey is analysed using SPSS (Statistical Package for the Social Sciences) software.
From the data gathered, the potential retrofits to reduce energy consumption at the RSAs have been identified.
USA: Reed Construction Data (2006)

Reduction in Majority-Carrier Concentration in Lightly-Doped 4H-SiC Epilayers by Electron Irradiation Hideharu Matsuura1,a, Hideki Yanagisawa1, Kozo Nishino1, Takunori Nojiri1, Shinobu Onoda2,b, and Takeshi Ohshima2,c 1Osaka Electro-Communication University, 18-8 Hatsu-cho, Neyagawa, Osaka 572-8530, Japan 2Japan Atomic Energy Agency, 1233 Watanuki-machi, Takasaki, Gunma 370-1292, Japan a matsuura@isc.osakac.ac.jp, bonoda.shinobu@jaea.go.jp, cohshima.takeshi20@jaea.go.jp Keywords: Electron irradiation, Al-doped 4H-SiC, N-doped 4H-SiC, Reduction of acceptor densities, Reduction of donor densities, Radiation damage.
The mechanisms for the reduction in the hole concentration in lightly Al-doped p-type 4H-SiC epilayers by electron irradiation as well as in the electron concentration in lightly N-doped n-type 4H-SiC epilayers by electron irradiation are investigated.
Introduction By comparing electron-radiation damage in p-type 4H-SiC with that in p-type Si [1,2], it was found that the reduction in the temperature-dependent hole concentration, , in Al-doped p-type 4H-SiC by electron irradiation was much larger than in Al-doped p-type Si.
By fitting the curve to the experimental data, and were determined to be and [2].
By fitting the curve to the experimental data, the values of and were determined to be and , respectively.

Our data structure is a practical linear space data structure that supports range selection queries in time with preprocessing time.
Our data structure is a complete binary tree.
This reduction is applied recursively by the algorithm.
This reduction is applied recursively by the algorithm.
The new data structure has similar or better speed than existing data structures but uses less space in the worst case.