Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors

Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines.To address this issue,a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database.Four assessment indexes are used in the model,which are the dynamic failure duration(DT),elastic energy index(WET),impact energy index(KE)and uniaxial compressive strength(RC).Four membership functions,including linear(L),parabolic(P),S and Weibull(W)functions,are proposed to measure the uncertainty level of individual index.The corresponding weights are determined through information entropy(EN),analysis hierarchy process(AHP)and synthetic weights(CW).Simultaneously,the classification criteria,including unascertained cluster(UC)and credible identification principle(CIP),are analyzed.The combination algorithm,consisting of P function,CW and CIP(P-CW-CIP),is selected as the optimal classification model in function of theory analysis and to train the samples.Ultimately,the established ensemble model is further validated through test samples with 100%accuracy.The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines.

SAR image de-noising based on texture strength and weighted nuclear norm minimization

As synthetic aperture radar（SAR） has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization（WNNM） is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis（PCA） and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures（BLS-GSM） method, non-local means（NLM） filtering in terms of both quantitative measure and visual perception quality.

Optimization of BF Slag for High Cr_2O_3 Vanadium-titanium Magnetite

In order to clarify the slag system of high Cr2O3 vanadium-titanium magnetite smelting in BF （blast furnace）, the melting properties of slag samples prepared by analytically pure reagents were measured. By means of orthogonal test synthetic weighted score method, the optimal slag for high Cr2O3 vanadium-titanium magnetite was obtained, which contained 10% MgO, 8% TiO2 and 15% Al2O3, with the binary basicity being 1.15. In addition, the effects of basicity, MgO, TiO2 and A12 03 on slag melting properties were investigated by single factor test, and the results showed that, with increasing the basicity or TiO2 content, melting temperature （Tin） increased, whereas initial vis- cosity （r/0） and high temperature viscosity （r/h） decreased. With increasing the MgO content, Tm decreased firstly and then increased. With increasing the Al2 O3 content, Tm increased, and η0 and r/h decreased firstly and then increased.