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The comparison highlights the sensitivity of the fatigue crack propagation life to the selected LSP pattern configuration (i.e. the width of the LSP treated strip and the relative position to the crack centre) which have to be accurately setup in order to exploit the full potentiality of the LSP process in increasing the fatigue life and avoid undesired reduction of the component performances.
FCP Specimens configuration (in mm) The experimental crack growth rate data as a function of the Stress Intensity Factor (SIF) are reported in Fig.3 for the three investigated specimens.
A crack growth slowdown due to the laser peening has been highlighted in the crack propagation rate experimental data at the middle of the treated pattern.
The experimental data obtained on simple specimen show no improvement achieved with the selected configuration of the LSP treated pattern.
Using a previously validated FEM approach to predict the residual stress profile along the entire width of the LSP treated specimen, together with an analytical crack propagation model, a comparison of the FCP predicted performances has been done with the experimental data available, showing a good agreement.

Whether Artificial Neural Network (ANN) or genetic algorithm (GA) is used in the further process of telling structural damage status from damage index, this reduction will make remarkable time saving.
Introduction Due to the ability of performing local analysis on a signal, i.e. to zoom in any interval of time or space, wavelet analysis is capable of revealing some hidden aspects of the data that other signal analysis techniques fail to detect.
Data required for training ANN or running GA are usually provided by numerical simulations based on established FE structural model.
Then the concept of entropy is introduced as the criteria to carry out data mining in the decomposition process by WPA algorithm.
Conclusions The new method to build damage index combined with WPA algorithm and data mining concept is present in this paper.

The proposed iron oxidation model in “Ferrovum” spp. was supported by the transcriptome data of “Ferrovum” sp.
Taken together, these processes generate the necessary energy carriers and reduction equivalents for nutrient assimilation, metabolism and growth of iron oxidizing microorganisms.
Genes mapping to rRNA and tRNA genes were eliminated prior to data analysis.
While HiPIPs are known as electron shuttles in various electron transfer processes including iron oxidation in Acithiobacillus ferrivorans [14], the involvement of hypothetical proteins remains speculative without further experimental data.
Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics 30 (2014) 2114–2120

According to the formulas, this paper used Matlab to program, handled the datum and got the relationship images of the molding pressure and the length- radius ratio and that the molding pressure and the density.
Then according to the formulas, this article used the Matlab software to program, handled the datum and got the relationship images.
Model establishment and the mechanical analysis The forces the mould undergone are shown in figure 2.Because in the whole athletic process, the shape of materials changed from the natural state to clearance reduction, then from elastic deformation to plastic deformation, and finally from plasticizing to the shaping stage, the forces are constantly changing and friction and pressure are not fully comply with the formula f=μN.
Get .Do the work of Integral to both sides and get: (6) Discussion Take a group of experimental data of the CXJ1500 ring molding machine as an example to illustrate the forces.

Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data.
On Huey Fang et al. [2] Reviews about the biological data for disease genes.
The collected genetic disease datasets are preprocessed like data cleaning, data integration, data transformation and data reduction.
Result Analysis The Table 2. shows the different genetic disease data results.
Genetic Disease Data Result Set.

Table 2-2 Road surface damage detection equipment Test content Device name Work/principle Test index Highway Pavement Cracks The PCR traffic data collection instrument Manual measuring All kinds of road surface disease Pavement damage camera car Film photography/indoor artificial interpretation All kinds of road surface disease RT2000 system CCD digital camera/artificial interpretation All kinds of road surface disease ARAN system CCD digital camera/image automatic recognition Pavement crack WayLink system CCD digital camera/image automatic recognition Pavement crack The road test data processing Pavement rapid detection data processing technique have certain research at home and abroad, has built up a mature overseas commodity software, the data can be acquired directly from the testing equipment.
With laser flatness gauge, digital video, GIS, GPS, high-speed computer network such as a variety of advanced technology, road data at the same time, the information collected in the formation of highway database in time, through a database query, data access to any sections of highway and its dynamic images.
Japanese development of check-out car damaged roads, adopted the MO and video tapes, analysis of cross-sectional shape and flatness data on a computer.
Test data processing technology has yet to be studied from three aspects, namely: detecting data automatic acquisition technology, data noise reduction technology, data storage technology; And study the compatibility of the detecting data and expert system.
The future road surface detection system should have the following characteristics: 1) Real-time data acquisition and processing surface characteristics; (2) Accurate positioning pavement disease characteristics; Results the versatility and openness; (3) Data processing, management software, the function is all ready; (4) System a high degree of automation, automatic correction, without human intervention.

So the independent components can be estimated: (2) ICA is a linear method that has been used widely to perform data redundancy reduction and feature extraction for linear model.
The main idea of KICA is to map the input data into an implicit feature space F firstly:Φ: x∈RN→∈Φ(x)∈F.
Then ICA is performed in F to produce a set of nonlinear features of input data [5-6].
The input data X is whitened in feature space F.
The vibration data include a total of 512 sampling points are taken as observing signal x1 for analysis.

Compare the coordinate data changes,get observation displacement change data to realize the monitoring of landslide displacement.Figure 1 illustrates the position change from point Pt1 to Pt2 and its displacement L for: (1) In order to measure the landslide displacement on a certain point of GPS receiver position , usually by two ways single point absolute positioning and multipoint relative positioning.
Using those measurments as the foundation, according to the known satellite instantaneous coordinates to determine the position of the user receiver antenn.Multipoint relative positioning is using many sets of GPS receiver simultaneously for observation.And these GPS receiving outputs of the original observation data will be put into the background computer to carry on the finite difference mathematical operation to get the receivers’ relative position coordinates.
(3) the GPS data processing results show that, in the horizontal monitoring precision can reach submillimeter level, in the height direction of the monitoring precision can reach millimeter level, can fully conform to the slope deformation monitoring accuracy.
Disaster Reduction in China, 2002 [2] Zeng Xuping.
GPS landslide monitoring and data analysis,Wuhan mapping science and technology university Master degree’s Thesis, 2000.5 [3] Li Jingfeng.GPS application in monitoring rockfall landslide, The Yangtze river resources and environment,Vol.51996.8 [4] Xian Xunqing, Miao Shengjun,Cai Meifeng,Yu Lin.GPS slope deformation monitoring data processing and precision analysis.

The results show that ensemble learning on hyperspectral data can achieve higher classification accuracy.
The task is to use{Lk}to get a better learner, it's action better than a single data set learner h (x, L) .
WEKA as a public data mining working platform set a lot of machine learning algorithm can take on the task of data mining , including data pretreatment, classification, regression, clustering, association rules and in the new interactive interface of visualization. 2) Experimental data and preprocessing The data is located in the gansu Ji Ling 701 uranium deposits of remote sensing image, this area mainly in the mountainous, the certain typical features types including: river, ridge, sand, slope, cultivated land, forest,and house.
Compare with the classification result of UCI data TABLE IV.
pair of algorithms, each entry w/t/l in bottom of Table Ⅳ{TTP}8547 means that the classifi{TTP}-1279 er at the corresponding row wins in w data sets, ties in t data sets,and loses in data sets, compared to the classifi{TTP}-1279 er at the corresponding column.

To develop such a data-rich approach digital image correlation (DIC) is used to collect the displacement data and infra-red thermography (IRT) is used to collect temperature data.
However, it is essential that the images are synchronised temporally with the load data and any strain data collected from sensors on the specimen or loading rig.
Strain data is obtained from the images using DIC.
The strain gauge data indicates a reduction in the value of Young’s modulus with increasing strain rate, which is not as expected.
The approach also suffers from data mismatch.