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Based on actual data of vehicle loads on Guangzhou-Shenzhen Expressway and relevant statistical results, mid-span bending moments of long-span virtual simple-supported beams are calculated.
Design vehicle load in General Code for Design of Highway Bridges and Culverts (JTG D60-2004) [1] is based on measured data of vehicle loads in 1990.
Those data were obtained from four representative highway survey stations, daily traffic volume of each station was about 3646 ~ 7114 vehicles and the total data were more than 60000 vehicles [2].
Based on the measured data of vehicle loads in GSE and relevant statistical results, a calculation model of load effects of bridges under random fleet is created.
Based on vehicle load data of different lanes of Guangzhou-Shenzhen Expressway, each-lane load effects of a 1600m simple-supported beam are calculated.

The volume fraction of these small equi-axed grains increased from 19.9% after 75%reduction at 800°C to 67.8% after 75% reduction at 1.100°C.
Dynamic recrystallization (DRX), though, has not yet been reported for these alloys although the present data suggest that a DRX mechanism might be responsible for the remarkable grain refinement after relatively low amounts of strain applied at high temperatures.
Texture and orientation contrast map after PSC at 800°C and a reduction of 0.75.
Dynamic recrystallization (DRX), though, has not yet been reported for these alloys although the present data suggest that a type of dynamic recrystallization may be responsible for the remarkable grain refinement after relatively low amounts of strain applied at high temperatures.
The data of Fig. 5 do not reveal an obvious difference in texture between the recrystallised and the deformed fraction of the microstructure, although there seems to be a tendency that γfibre grains preferentially disappear from the deformed matrix.

In the present work, new data for transitional and turbulent flows and for different types of polymers and concentration are presented.
The Insight 3G software (TSI) was used for data acquisition and generation of the velocity vectors while the data analysis was done using a MatLab code developed in-house.
Reduction of the diameter of the polymer injection point.
Edomwonyi-Otu, “Drag Reduction in Oil-Water Flows,” PhD Thesis.
Gyr, “Heterogeneous drag reduction concepts and consequences,” J.

Energy consumption data of industrial field and industrial added value data of Dongying City are mainly from the Statistical Yearbook of Shandong Province [3] and the Statistical Yearbook of Dongying City [4].
Energy consumption data of transportation field and data of vehicle numbers of different types are mainly from Dongying Bureau of Communications.
Energy consumption data of construction field and building area data of buildings of different types are mainly from the Civil Building Census Data Assembly of Dongying City [5] and the Rural Construction Statistical Yearbook of Shandong Province.
Data of baseline emission factors for regional power grids are from the Department of Climate Change of the National Development and Reform Commission.
Only carbon emission in road transportation is measured due to the limitation of data.

Fig. 1 gives the framework of the data mining of CTGs.
The framework of the data mining of CTGs ReliefF.
More features involved may yield worse results, the reduction of features can improve the classification efficacy in some extent, and researchers can mainly focus on the most important features in data mining for study.
The results are anticipated that the power of some ensemble learning methods in the classification of CTGs data.
Conclusions In this paper, four ensemble learning algorithms of LogitBoost, Stacking, Bagging and AdaBoostM1 are carried out in the data mining of fetal CTGs data based on ReliefF algorithm, and LogitBoost is a better approach compared with the other three ensemble learning methods.

CCM is a classical data reduction scheme for calculating GIIc [10] using the Irwin - Kies equation.
Thus, the GII(δ*) discrete data sets were plotted for each data reduction scheme and an analytical function was used to fit the results for each case.
Fig. 9 compares the bridging laws (traction-separation laws) as inferred by CCM, CBT and CBBM data reduction schemes.
Fracture energy was measured from ENF tests with the use of three different data reduction schemes.
The obtained bridging laws exhibit the same softening behaviour, regardless the data reduction method used.

Obtaining the sample data of fault feature, then the data is divided into training data and test sample data.
Equipment running status data is high-dimensional information obtained by lifting wavelet packet transform method.
In low-dimensional manifold failure feature extracting method, the vibration data is picked from the low-dimensional manifold of high-dimensional euclidean space.
Then divided the data in to training data and test sample data. 3) Training neural network learning algorithm. 4) Solving state trend prediction problem using the trained network.
This paper proposed data-based multi-transform domains non-linear fault trend forecasting method.

Introduction Data Mining (Data Mining) generally speaking is digging out from a number of data knowledge.
The main purpose of data mining is to identify trends and patterns in the data.
Broad -based knowledge discovery -oriented approach to reduction properties, for example, the method in the database by collecting the relevant data sets, followed by application of a series of data to promote technology, involving the concept of tree improvement, property removed attribute threshold control, and other aggregate functions dissemination.
Database Data Warehouse Data Mining Mode Knowledge Fig. 1, Data mining process As we can be seen from Figure 1 the data mining techniques basic process.
Conversion data Data conversion purpose is to convert the data type and value of the source data into a unified format, such as converting a Boolean type to integer ; converting continuous data into discrete types.

Research of Noise Reduction Algorithm Based on Real Time Guoxiang Tong1, a, Fangfang Lv1, b* , Jian Tan 1, c 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology,Shanghai 200093,China aemail: guoxiangtong@163.com, bemail:keykeylvjian@163.com,cemail:Taozhe58@163.com Keywords:Noise reduction; Modified LMS; Step size; SNR; MSE; Misalignment; RNS; Abstract.
Structure of de-noising In order to better illustrate the noise reduction effect of each modified LMS algorithm.
Then the outputted bit-stream was processed by the DSP processing system based on RNS [12] which included three parts: (1) the front part of the data conversion system that changed bit-stream to RNS; (2) the FIR filter processing module based on RNS; (3) the last part of the data conversion system that changed RNS to bit-stream.
Then the signal processed by the DSP system was operated by the D/A converter based on Delta - sigma, the data flow was converted to analog signal.
The combined system not only increased the processing speed of data, but also expanded the processing width of data, which may be achieved three times faster than the original system.

Results of error analysis between main and reduced data after applying the reduction approach.
Reduction criterion is employed with these data to represent it by 27 scenarios while maintaining the precision of hourly data.
The reduction criterion is employed with these data to represent it by 27 scenarios while maintaining precision of main data as shown in Fig. 34.
Obtained scenarios of PV and WT powers after applying reduction criterion with three-years data.
Data availability.