菏泽联通156局MGW1方案

本文是为配合菏泽节能减排工程,156局机房整合,MGW1从5楼搬到2楼,需要将MGW1所带IBSC、RNC逐步割接至MGW2下。在MGW1完全空载的情况下,将MGW1下电搬迁。然后再调测上电,并进行全面测试,再将IBSC、RNC逐步割回MGW1的过程。

Recovery of high purity silicon from SoG crystalline silicon cutting slurry waste

The composition and size distribution of cutting waste were characterized. The Si-rich powders were obtained from the cutting waste using a physical sedimentation process, and then further purified by removing impurity using acid leaching. The effects of process parameters such as acid leaching time, temperature and the ratio of solid to liquid on the purification efficiency were investigated, and the parameters were optimized. Afterwards, the high-purity Si ingot was obtained by melting the Si-rich powders in vacuum furnace. Finally, the high purity Si with 99.96%Si, 1.1×10^-6 boron （B）, and 4.0×10^-6 phosphorus （P） were obtained. The results indicate that it is feasible to extract high-purity Si, and further produce SoG-Si from the cutting slurry waste.

Context Information and Fragments Based Cross-Domain Word Segmentation

A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.

Performance of recycling abrasives in rock cutting by abrasive water jet

Rock cutting performance of recycling abrasives was investigated in terms of cutting depth, kerf width, kerf taper angle and surface roughness. Gravity separation technique was employed to separate the abrasives and the rock particles. The recycling abrasive particles were then dried and sieved for determination of their disintegration behaviors. Before each cutting with recycling abrasives, the abrasive particles less than 106 ?m were screened out. It is revealed that a considerable amount of used abrasives can be effectively reused in the rock cutting. The reusabilities of abrasives are determined as 81.77%, 57.50%, 34.37% and 17.72% after the first, second, third and fourth cuttings, respectively. Additionally, it is determined that recycling must be restricted three times due to the excessive disintegration of abrasives with further recycling. Moreover, it is concluded that cutting depth, kerf width and surface roughness decreases with recycling. No clear trend is found between the kerf taper angle and recycling. Particle size distribution is determined as an important parameter for improving the cutting performance of recycling abrasives.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

Influence of brittleness and confining stress on rock cuttability based on rock indentation tests

In order to understand the influence of brittleness and confining stress on rock cuttability,the indentation tests were carried out by a conical pick on the four types of rocks.Then,the experimental results were utilized to take regression analysis.The eight sets of normalized regression models were established for reflecting the relationships of peak indentation force(PIF)and specific energy(SE)with brittleness index and uniaxial confining stress.The regression analyses present that these regression models have good prediction performance.The regressive results indicate that brittleness indices and uniaxial confining stress conditions have non-linear effects on the rock cuttability that is determined by PIF and SE.Finally,the multilayer perceptual neural network was used to measure the importance weights of brittleness index and uniaxial confining stress upon the influence for rock cuttability.The results indicate that the uniaxial confining stress is more significant than brittleness index for influencing the rock cuttability.

Interactive image segmentation with a regression based ensemble learning paradigm

To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.

Multiple walking human recognition based on radar micro-Doppler signatures

The recognition of human movements based on radar m-D(micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple frequency components overlapping seriously in the radar echoes from walking humans, it is a very difficult work to recognize walking humans based on radar echoes. In this paper, a recognition method of walking humans based on radar m-D signatures is proposed. In this method, the m-D spectrum is generated by generalized S transform first,and then the entropy segmentation is used to segment the interesting region from the original spectrum. Next,the m-D features are extracted from the m-D region. Lastly, the support vector machine is used to recognize different walking human targets. The simulation experiments considering two factors of height and velocity are also conducted to test the performance of this proposed method.