Search results

Mining test in data of tangshan iron&steel shows that the method is effective in practical application.
The study also distills the eigenvalue of parameter changes, which come to be the data source for the knowledge and information database of blast furnace expert systems, from field investigation and statistical analysis of historical data of blast furnace operation[1].
The Hierarchical Cluster Analysis procedure is limited to smaller data files (hundreds of objects to be clustered) .
The K-Means Cluster Analysis procedure is limited to continuous data and requires you to specify the number of clusters in advance, it has the ability to save distances from cluster centers for each object.and it has the ability to read initial cluster centers from and save final cluster centers to an external SPSS file.Additionally, the K-Means Cluster Analysis procedure can analyze large data files.This paper uses K-Means Cluster Analysis[2]. 1 Select number of clusters Cluster analysis for small samples of data .When the analysis results and the actual data is consistent Statistics All data.The paper selects 1102 samples.
Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables.

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data.
This paper presents a simple method for obtaining interpretable fuzzy models from data.
The methodology used in this article to get fuzzy model from experimental data is structured as follows.
This data set contains 699 instances (patterns) with 683 complete data and 16 samples with missing attributes.
Zurada, Data-Driven Linguistic Modeling Using Relational Fuzzy Rules, IEEE Trans.

In addition, large amount of 15R are formed through carbothermic reduction.
Graphite presents better quality of thermal shock resistance and alkali resistance than SiAlON-bonded silicon carbide in reduction environment.
Graphite Si Al Al2O3 SD 0 56.6 9.1 34.3 S 0 56.6 9.1 34.3 C 26.3 41.7 6.7 25.3 Notes: To make the data of SD, S and C more comparable in the following experiments, the data of sample C in the following Fig. 1 and Table 3 are derived by the calculation of (actual value×100/73.7). 73.7 represents the total percentage of Si, Al and Al2O3 in the ingredients of Sample C.
The AlN polymorph 15R is more pronounced in sample C than SD and S due to carbothermic reduction.
(8) The data in Table 4 show that, for sample S, Z values for axis a and c are very large but equivalent.

If the carrier frequencies are correctly chosen, the transmitted data can be entirely retrieved from the asymmetrically clipped signals with no in-band clipping noise.
The input data sequences X are multiplied by the M different random phase, and we must constraint on the factors in random phase vectors to ensure the multiplied vectors still satisfy the Hermitian symmetry.
Besides , it also needs to know the chosen random phase vector for correctly demodulating the received data symbols.
Therefore we improve the spectrum efficiency without declining the data transmission rate, but this will increase the mean transmission power.
PAR reduction in OFDM via active constellation extension.

One of a challenging problem is how to deal with the data which shown the characteristics of high-dimensional, non-linear and multi-faceted nature.
How to effectively determine the appropriate neighborhood parameters are of great significance to obtain the correct low-dimensional structures for data dimensionality reduction and classification.
Application experiment analysis In order to verify the effectiveness of the proposed approach, we use standard data set of UCI Iris data [4] (sample points N = 150, dimension D = 4), making experimental analysis of mapping error.
The experiment shows that the category labels in the data handling to obtain the optimal adaptive neighborhood parameters, to benefit the purpose of data classification.
Laplacian eigenmaps for dimensionality reduction and data representation.

Introduction This paper uses the finite element strength reduction with the large finite element analysis software named ANSYS.
Through observating the data in Table 2, we find no positive appeared in the table.
So we will construct by means of taking out of the absolute value of the data in the table, and get the tendency chart that the reduction of safety factor with slope change as shown in the Figure 2.
The data in Table 1 is littler than corresponding use traditional ways out of the data. besides, Traditional limit equilibrium method’s data is all smallest in the stability coefficient of lower slope.
Strength reduction finite element method for excavation of slope stability[J].

Distributions of the grain boundary misorientation angles of the initial and cold rolled samples are determined from the EBSD data as shown in Fig. 3.
As rolling reduction increases, the frequencies of both and twinning decease.
Specifically, the fraction of low angle boundaries is 25% after 10% reduction and increases significantly to 54% after 31% reduction.
However, the fraction of (component B) decreases with increasing rolling reduction.
Small rolling reduction (≤ 10%) activates the basal slip primarily.

To improve the performance of SVM using the RBF kernel for medical data, we propose using Bagging of SVM for medical data.
The next step is to transform the processed data.
Additionally, depending on the data set, the bootstrap process of BA-SVMs has decreased the number with samples of reductions ranging from 10 to 30%.
However, there are challenges, including data mining techniques, unbalanced data, noisy data, performance, and scalability.
Data Sci., vol. 2, pp. 13–28, 2016

The work productivity and perceived fatigue data were recorded.
Work productivity data were recorded at 30-minute intervals.
Statistical analysis was carried out to analyze data derived from the experimental task.
The data was tested for normality prior to analysis using Shapiro-Wilk test.
The work productivity data were then analyzed to investigate the effect of production standard times on work productivity.

Training data and test data of BP neural network had been reduced by rough set.
Training data and test data of BP neural network had been reduced by rough set.
Discretization data sample are shown in table 2, and Testing Data sample are shown in table 3 [9].
Training model was built with BP neural network, and data sample in table 1 was used as training data, and data sample in table 3 was used as testing data.
Rough set can simply data sample and it can save storage space, and it can avoid of data explosion.