Search results

Strength reduction method.
Strength reduction method Zienkiewicz in 1975 put forward the strength reduction method [7], the safety factor of slope stability was defined as the slope just reached the critical failure, the reduction degree of slope soil shear strength parameters.
(1) Strength reduction method assumes that the reduction coefficient of the whole slope soil has the same value, and the reduction coefficient is the slope stability safety coefficient when the slope reached critical.
Because the slope morphology cannot be described as specific data, its sensitivity cannot be calculated through the derivation.
Non-linear shear strength reduction technique in slope stability calculation.

The paper established Social Accounting Matrix from Inner Mongolia’s statistic data and Input-Output table of 2007.
The purpose of this division is consistent with the Inner Mongolia Autonomous Region input-output tables, saving time for data collection and collation, and operable.
CGE model parameters calibration relies mainly on the basis of a balanced data set, namely social accounting matrix (SAM).
IMSAM table data mainly from the “Inner Mongolia Autonomous Region in 2007 input-output table”[12], “Inner Mongolia Statistical Yearbook 2008”[13], “China Statistical Yearbook”, “China Financial Yearbook”, “China Tax Yearbook”, the customs import and export data, “China Environment Statistical Yearbook 2008 “and so on.
And the higher rate, the more pollution reduction.

Dunteman (as cited in [41]) stated that PCA decomposes the original data into a set of linear variates.
Labib and Vemuri [45] studied the dimensionality reduction of vectors to enable better visualization and analysis of the data.
In sports, PCA is used as a multivariate technique to manage big data [50].
Experimental Setup and Data Collection Three sets of experimental ponds were constructed with varying locations of the points of initiation of hydraulic jump.
[10] Saqqar MM, Pescod MB (1992) Modelling coliform reduction in wastewater stabilization pond.

Cone data indicate that the incorporation of 1% GNP leads to a 12% reduction in the peak heat release rate compared to pure PMMA.
Measurements The X-ray diffraction (XRD) data were obtained at room temperature using a Rigaku Miniflex II Desktop X-ray diffractometer equipped with a Cu-Ka source (λ=1.5404Å).
The incorporation of 1% GNP leads to a 12% reduction in the peak heat release rate (PHRR) compared to pure PMMA.
Increasing the GNP loading brings about a gradual reduction in PHRR; the reductions are 16% and 19% at 2% and 3% loading, respectively.
The reason for the reduction in PHRR may be due to physical barrier effect caused graphene sheets.

Introduction Although technical papers dealing with the properties of concrete subjected to high temperatures are abundant, there are much difference among the data in the literature, on the residual strengths of concrete after sustained elevated temperature exposure, depending on concrete materials, mixture proportion, age and term of exposure, water evaporation, besides the exposure temperatures.
Some data indicated significant reduction in compressive strength after exposure at temperatures of 50℃ to 80℃, although most data indicated no reduction up to 100℃.
The test data shown in Figure.2 in indicated 15 to 25 % reduction in compressive strength when heated at 50℃, with smaller reductions when heated at higher temperatures up to 110℃, and 40 to 45％ reduction in compressive strengths upon exposure.
The tensile strength after exposure expressed by the percentages of those of unheated concrete at the age of 91-days, shown in Figure.2 (b), indicated 10 to 45 % reductions after exposure at 50℃, 15 to 40% reduction at 80℃, with smaller reduction at 110℃ and 25 to 50% reduction at 300 ℃.
Although the data shown in Figure.3 are limited, it is clear that the minimal compressive strength after exposure at 50℃, were associated with the intermediate weight losses of 2.5 to 3.5 % in this experiment due to evaporation of free water.

Fabrication of hexagonal Ni micro/nano plates by a facile hydrothermal reduction route ZHANG Juna*, YAO Hairuib, LI Huaboc, LI Jingjingd, BAI Xiaokange School of Chemical Engineering & Pharmaceutics, Henan University of Science & Technology, Luoyang 471023, P.R.China aj-zhang@126.com, byaohr@163.com, chuaboli2013@126.com, dlijingjing1107@126.com, eBaixk2010@126.com Keywords: fabrication, hexagonal, Ni plate, hydrothermal reduction Abstract.
Herein, hexagonal Ni micro/nanoplates were synthesized by a facile hydrothermal reduction route in aqueous solution with the assistance of polyvinylpyrrolidone.
The N2H4·H2O has the strong reducing ability, but its reduction reaction with Ni2+ has been confirmed to follow such a route: Ni2+ + 3N2H4 → [Ni(N2H4)3]2+; [Ni(N2H4)3]2+ + N2H4 → Ni↓ + 4NH3↑ + 2N2↑ + H2↑ + 2H+.
By means of the N2H4·H2O reductant and PVP additive, the hydrothermal reduction route has been verified to be effective way for the controlled synthesis of hexagonal Ni plates. (2).
In comparison with the magnetism data of other Ni materials the coercivity of hexagonal plate-like Ni powders has greatly increased.

The (0002) peak was found to be located at 38.46 degrees 2theta, using data from PDF4 for the material.  
The texture scanning program used was X'Pert Data Collector.  
The data is summarized in Figs 6 and 7 (a).
Percent elongation and Charpy impact data show a reverse trend
· Charpy impact and percent reduction in area data show large variations in the results

Not only engine efficiency control but also energy saving and emission reduction could be realized.
According to the research methods of system optimization and multi-sensor data fusion technology, this paper introduces the research on multi-sensor data fusion technology based on multi-value logics.
Basic Principle of Multi-value Data Fusion 2.1 Modeling of Multi-value and Multi-sensor Data Fusion Data fusion technology is the basic method, technology and means to study the acquisition, transmission and processing of multiple information in the field of information science, a technology of information expression and also the significant basis of a new generation of intelligent information technology[4-5].
In tree structure, T-operator applies classified control over data samples starting from leaf node and divides data samples into different data control levels according to different results; The condition of decision tree structure not only affects classification efficiency, but also affects classification accuracy, the simplicity and accuracy of the rule extracted from the decision tree.
Conclusion In conclusion, the application of multi-value T-operator data fusion technology can effectively realize rapid and efficient data processing and contribute to system fusion and optimization, thus to improve automobile engine efficiency, realize energy conservation and emission reduction, provide safe control functions, increase new control functions of electronic automobile system, bring new applications to intelligent automobiles and realize new development in multi-sensor fusion technology and optimization control.

A data wrangling methodology is proposed to clean and organize the data for suitable analysis through Exploratory Data Analysis.
In the final stage of the analysis, an Exploratory Data Analysis (EDA) is conducted to summarize and visualize the dataset to identify patterns, trends, and relationships that may exist within the data.
Exploratory Data Analysis.
Data from the production system were processed, combined, and examined using visualization tools to help understand the data.
Drilling process monitoring: A framework for data gathering and feature extraction techniques.

It realizes screening and dimensionality reduction for primitive data through factor analysis(FA), decreases data redundancy regardless of related data, we make use of the nonlinear reflection ability and association learning ability of wavelet neural network, and proposes a model of pressure sensor temperature compensation.
(2)Divide the data into training data and test data
(5)Apply the trained neural network to inspect test data[4]. 5 Temperature Compensation of the Pressure Sensor 5.1 Calibration Data In experiment the PT14 piezoresistive pressure sensor and AD590 temperature transducer are used, their output are UP and Ut, as shown in table 1.
Random function generates the initial data center, extension constant and weights of output.
Table 4 Comparison with WNN Average iteration times Average training time [s] WNN 7 3.4218 WNN Based on FA 3 1.2597 In short, WNN based on FA improves the stability of pressure sensorand accuracy. 6 Summary For the sake of eliminating the temperature influence on the output of sensor, WNN based on FA is proposed, and the validity of the method is tested. it realizes screening and dimensionality reduction for primitive data through factor, decreases data redundancy regardless of related data, we make use of the nonlinear reflection ability and association learning ability of WNN, and propose a model of pressure sensor temperature compensation.