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Model Order Reduction in Computational Multiscale Fracture Mechanics M.
amcaicedo@cimne.upc.edu, boliver@cimne.upc.edu, cahuespe@intec.unl.edu.ar, dolloberas@cimne.upc.edu Keywords: Model Order Reduction, Reduced-Order Cubature, Fracture, Computational Multi-scale modeling, Continuum Strong Discontinuituy Approach.
This technique uses a directional derivative of a scalar field, based on a location variable (in our case, the average of mesoscale dissipated energy) whose zero level set defines the crack path. 2.2 Model Order Reduction techniques The reduction process is divided into two sequential stages.
These data, together with the material and geometrical parameters, define the set of input parameters for the first and second stage (online part).
High-performance model reduction techniques in computational multiscale homogenization.

But the capacity of dealing with massive data and the efficiency of the mining fault rules are still not ideal.
Information entropy, a measure of the overall uncertainty, can be represented as a data statistical characteristic.
Fault Rules of Power Grid Mining Process Firstly, attribute selection and data cleansing on fault data of power grid, and then setting up fault samples for mining.
The original decision table is established by using fault data.
Conclusions This article uses data mining method which combines rough set attribute reduction theory with association rules in the power grid fault data.

Data Mining Technology in Transformer Condition Assessment Principle of Data Mining.
Data preprocessing.
Data preprocessing is a crucial step before data mining, due to the obtained transformer parameters often contain a lot of noisy, incomplete or inaccurate data.
Test data should be dispersed firstly.
The ant colony algorithm is applied for reduction of transformer diagnostic data [6].

Table 1 Initial sample collection of 15 data No.
According to the rule set after reduction, we get 8 data.
In order to validate the need of attribute reduction, we let the reduction set R = {a2, a6, a7}, and pick up 150 data as training samples, 70 data as test data.
It toke fully advantage of data reduction features of rough set and excellent sample classification capabilities and high computing precision characteristics of SVM.
A classification data mining algorithm based on rough set [J].

Transcription of the X-ray patterns was made using the PDF data base [8].
According to the differential thermal and mass spectrometric analysis data (Fig. 3) when heating the ore at a rate of 10 °C /min in argon flow a weight loss of the sample (TG data) had already appeared at the initial stage of heating and came up to 12.2 % at 1100 °C.
Thermogram (argon, 100 C/min,) ore and mass spectrometric analysis of waste gas data.
The experimental data obtained for the ore sample under the study correspond to those presented in the literature [23].
Diffusion and Defect Data.

The vibration amplitude data for the two conditions were compared to identify the influence of magnet on chatter reduction.
In the present paper a novel chatter control method has been tried for reduction of chatter in turning.
The software FFT module was used to obtain the frequency data from the time domain input.
For the remaining runs there were different degrees of reduction due to magnet application, however, the average of all the reduction is 49.44% or approximately 50%.
Experiment with magnet provided a maximum and average reduction of 87% and 50% respectively. 2.

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

Principle component of raw data is extracted by using improved KPCA.
Through Transmission Control Protocol, these data is transferred from Microprocessor to PC.PC process these data and determine the category of the motion.
When an unknown motion sequence is input, processing raw data with PC.
Dimension Reduction of Motion Data Vpnik etc present the learning method of SVM according to statistical learning theory and introduce the concept of kernel space.
It can map the linearly non-separable data residing in low dimension to high dimension and make data linearly separable.

Preface: Data is the source of knowledge.
While data mining refers to automatically extracted from the data model.
Including many steps: data mining from large database (or data from other sources); Select the appropriate attributes; Select the appropriate sample strategy; Eliminate data in abnormal data and make up the insufficient part; With proper dimension reduction, transform the data mining process and data model fit or match; Identify whether the resulting knowledge you have will be the result of the information or visualization, and then combined with the existing knowledge.
Statistical analysis of test data, clustering is useful on parameter design and experimental data.
Most of the pattern recognition algorithm and the method of dimension reduction, transform and set has a direct reference significance.

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)