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Another is empirical formula class which gets empirical formula according to regression analysis of statistical data.
Based on these models, new methods of output calculation based on equations of rock motion in cone crusher chamber and size reduction based on the population balance model have been developed by Evertsson(2000).
Besides,the interparticle breakage size reduction optimization model of compressing crusher is accomplished, which is used to optimize the PYB900 cone crusher turn out to increase the contents of fine products[18].
Hulthén and Evertsson（2009）introduce measurement devices which can be mass fl{TTP}-1278 ow meters on the process analyze data from the process and convert them to a desired CSS value, an algorithm was developed.
The results of the simulations show the dynamics of the crusher and the interaction of the rock material and the machine as well as the breakage and the size reduction process of the rock particles[33] .

The high specific strength of the magnesium alloy makes it a valuable choice for automotive, aerospace and sporting industries, where the weight reduction is a critical consideration in design.
The tension and compression of a cylindrical specimen are firstly analyzed to verify the computational procedures and compare the numerical results with test data.
It can be seen that the computed stress-strain curves for both tension and compression agree well with test data until the true strain is as large as 0.067; the calculated true stress is slightly higher than the test data for compression and lower than the test data for tension when strain is greater than 0.067.
The uniaxial tension and compression of cylindrical specimens are first computed and compared with test data.
The test data of AZ80 are provided by our project partners Dr.

Treat them as the training data set and test data set for experiment, and construction of tree-level classification model.
Respectively, there are 8 sub-classes on the third layer, including machine learning, data mining, intrusion detection, network security, etc.
Proc. of the Advances in Intelligent Data Analysis, Springer-Verlag, 1999:487-497 [3] Yiming Yang, Thomas Ault, Thomas Pierce, et al.
Blocking reduction strategies in hierarchical text classification.
IEEE Trans. on Knowledge and Data Engineering,2004,16(10):1305-1308 [5] Liu Li, He Zhong-shi, Term selection and weighting approach based on key words in text categorization.

All the researches undertaken bring valuable data needed to predict the collapse of massive masonry buildings in national and world heritage. 1.
a) Representation of experimental data, b) Curve σ - ε with relative values In the Fig 1.
Input data required for building analysis.
A series of mechanical tests were performed on brick pieces; PTFE sheets were placed between the samples and the contact plates of the device (Fig. 4); A MTS compression hydraulic machine (2500kN) was used alongside a data control and recovery unit.
The data obtained from the measurements were analyzed and processed to obtain the data in Table 2.

Without separate pilot inserted into the OFDM data, the data stream is divided into blocks of length N and modulated by IFFT matrix
In the ith OFDM symbol, the ith OFDM data block and the ith OFDM data frame are defined respectively as: (4) where includes the OFDM symbol and the guard interval and in the back and front, which satisfies in the proposed model.
If the CIR is obtained by estimation, one-tap frequency-domain equalizer can be used to get the transmitted data.
The frequency-domain response of OFDM data blockbecomes after P points FFT.
Combined channel estimation and PAPR reduction technique for MIMO-OFDM systems with null subcarriers[J].

Forest coverage prediction based on least squares support vector regression algorithm is presented in the paper.Forest coverage data of Heilongjiang from 1994 to 2005 are used to study the effectiveness of least squares support vector regression algorithm.The prediction results of the proposed least squares support vector regression model by using the training samples with the different dimensional input vector are given in the study.
Introduction Least square support vector regression(LSSVR) is a novel statistical learning method,which has the better generalization ability than artificial neural network[1-3].Forest coverage prediction based on least squares support vector regression algorithm is presented in the paper.Forest coverage data of Heilongjiang from 1994 to 2005 are used to study the effectiveness of least squares support vector regression algorithm.The prediction results of the proposed least squares support vector regression model by using the training samples with the different dimensional input vector are given in the study.
The Lagrange function is introduced to solve the optimization problem, which is as follows: (5) The optimization conditions of the formula are given as: (6) Eliminating and,the regression function of least squares support vector regression can be written as (7) Experimental Analysis for Forest Coverage Prediction Forest coverage data of Heilongjiang from 1994 to 2005 are used to study the effectiveness of least squares support vector regression algorithm.Fig.1~Fig.3 show the prediction results of the proposed least squares support vector regression model by using the training samples with the different dimensional input vector.
Portilla-Figueras,Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions,Neurocomputing Vol.72 (2009), p.3683-3691

This research forms part of an ongoing development of a data base with which to interpret the impacts of leaching conditions in heaps on microbial activity without having to disrupt metal production by invasive sampling campaigns.
There are comparatively few data on the effects of physico-chemical parameters on acidophiles utilising organic compounds.
This took place ahead of the reduction in reduced material present at pH 5.0, but lagged slightly behind at pH 7.0.
These experimental data indicate that mixotrophic and heterotrophic species can tolerate, or adapt to, pH gradients in heaps when suitable organic substrates are present.

The reduction of Ag+ to AgNP was initiated using sunlight with coffee pulp aqueous extract as a reducing agent.
The FTIR spectroscopy data identified the functional group vibrations and revealed any structural changes after the reaction.
FTIR spectra of coffee pulp (CP) and coffee pulp/AgNP (CP-AgNP) The proposed mechanism of the reduction of AgNP is shown in Fig. 4.
Proposed mechanism of the reduction of Ag+ ions to AgNP In Fig. 5 (a and b), the SEM images of the CP-AgNP reveal a spherical morphology of the synthesized AgNP.
In the first proposed mechanism, the half-cell reduction potentials of silver and mercury are shown in equations (2) and (3): Ag+ + e- ⟶ Ag(s) E° = +0.80 V (2) 2Hg2+ + 2e- ⟶ Hg22+ E° = +0.92 V (3) The half-cell reduction potential of mercuric ions (Hg2+) is greater than silver ions (Ag+).

As a result, PCA retains the relationship structure among process variables, in accordance with data acquisition degree of variation, this scheme is optimal.
When significance level is fixed, control up limit of (11) is more conservative than (10), with the data increasing, these two control limit draw near.
The basic idea is to the direct design of fault detection system from test data and without system model.
Subspace method aided data-driven design of fault detection and isolation systems.
Subspace aided data-driven design of robust fault detection and isolation systems.

The data obtained were processed with the program "Fullprof" [6].
Concentration dependences of (1) net magnetic moment based on the magnetic measurements data, (2) magnetic moment of the tetrahedral sublattice based on the neutron diffraction data and (3) high-field magnetic susceptibility at 4.2 K.
Dark symbols designate the data from the paper presented, light ones stand for the data from Ref. [2] A further increase of Sc concentration leads to the reduction of the net magnetic moment and the magnetic moment of the tetrahedral sublattice.
The data in [5] suggest that the local canted spin configurations transform into the collinear spin ordering with increasing temperature.
Summary Thus, the total bulk of the experimental data testifies to the local character of the deviations from the collinearity at x > 0.7 in the ferrogarnets investigated.