Search results

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.

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.

Reduction in area is affected by second phases and inclusions.
Tensile strength-reduction in area balance Figure 6(a) and (b) shows the variation of percentage elongation and reduction in area as a function of the volume fraction of cementite.
The test data of conventional ferrite-pearlite steel [12], tempered martensitic steel [12] and bainite steel [13] are also plotted in Fig. 7 for the purpose of comparison.
Reduction in area is a measure of formability as well as uniform elongation.
[12] NIMS data base, Materials Information Technology Station, National Institute for Materials Science, Tsukuba, Japan

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].

It indicates that analyzing slope stability with strength reduction method is feasible.
Strength reduction method Calculation Principle.
is the reduction factor.
According to the geological exploration data, after ground leveling, the slope security level is secondary.
In Fig. 4, three curves mutate near the reduction factor of 2.0.

The new formulation, which is calibrated on the basis of simulation buckling data available in literature, corrects a small issue of the previous one, giving at the same time more reliable and consistent results.
As far as imperfection effect on shell buckling is concerned, EN1999-1-5 is based on the traditional, empirical "Lower Bound Design Philosophy", according to which a knock-down factor of buckling loads, usually denoted by α, is introduced in order to fit the lower limit of the scattered experimental and numerical data.
Because of the great scattering observed in numerical buckling data, a further semi-probabilistic analysis has been carried out for the evaluation of the lower bound of buckling loads of imperfect cylinders subjected to axial compression.
To this purpose numerical data have been treated in stochastic way, in order to extrapolate lower values of ultimate load, corresponding to a given fractile value (5%) [4,5,7,8].
Likewise the α formulas given in the first issue of EC9, also the proposed expressions have been fitted on the basis of a wide amount of both numerical and experimental data available in literature but, contrary to the codified ones, they only depend on geometrical parameters, thus eliminating the dependence on the yield stress f0.

The CFD model of the truck is built using the FLUENT software and the simulation results are compared with the wind tunnel test data to verify the accuracy of simulation model.
The energy department and research institutes began the drag reduction study of the heavy-duty vehicles and developed many kinds of drag reduction devices and low aerodynamic drag commercial vehicles[6-8].
There are many kinds of air deflectors, and the drag reduction capability of them has huge distinction.
The air deflector extending length has minimum influence on drag reduction performance of the air deflector.
An Experimental Study of Drag Reduction Devices for a Trailer Underbody and Base.

The storage and management of solar energy data are of good performance.
In the solar energy engineering, it is often to store the acquisition data, query and update the solar energy data.
Fig.3 Data input interface developed by VC++ MFC These measured data can be input into SQL Server database with the help of the user interface developed by VC++ MFC, as Fig.3 shows.
Fig.5 Solar radiation curve in Shanghai throughout a whole year Table 3 Table Structure of Assessment for Energy Saving Structure ID Location Starting_time Ending_time Energy_saving CarbonDioxide_reduction int nvarchar(50) data data bigint bigint data 1 Beijing 2009-05-01 2010-05-01 7994592 681955 2 Shanghai 2009-06-01 2010-05-01 18510300 1578967 3 Zhengzhou 2009-07-01 2010-05-01 5274220 449902 … … Take Shanghai as an example and choose the date 2009-06-10 to simulate and predict.
The data of energy saving is as shown in table 3.

Generally, the technologies of noise control and vibration reduction in railway including isolation, absorption, sound insulation, and sound-absorbing [3].
With a condition of 60km/h speed, analysis results of the data from 2000 Hz to 22000 Hz dispalyed that the equivalent sound level would be reduced for up to 5.4 dBA, and the maximum sound level is diminished about 2.7 to 7.4 dBA.
The function of noise reduction by dampers is due mainly to the significant inhibition of the noise from wheel rail, whose frequency is rather high.
The vibration data of track and platform under the 2000 Hz~22000 Hz band demonstrated a decreasing trend, reflected as more than 2.2~9.6dB reduction in vibration acceleration level, and more than 0.7~8.6dB of the maxmium vibration acceleration level of tracks diminished.
The total noise of the train with a low speed is comprised by a large number of wheel noise contribution, hence, the reduction function by masked damper would be limited to some extent.

Box 4500, 90014 Oulu, Finland asatu.tamminen@ee.oulu.fi, bhenna.tiensuu@ee.oulu.fi, cilmari.juutilainen@ee.oulu.fi, djuha.roning@ee.oulu.fi Keywords: quality control, process modelling, data mining, variability reduction Abstract.
The Data Sets First, we look briefly into the data sets analysed in our recent research projects.
To summarize, the quality of the data affects directly that of the results and the cost-effectiveness of the data mining process.
Often, the problem of data analysis is the quantity of data.
Data An.