Similarity model tests of movement and deformation of coal-rock mass below stopes

For a study of the movement and deformation of coal-rock mass and low protected seams below a stope,as well as for fracture developments and rules of evolution of permeability,we designed a plane strain model test stand to carry out model tests of similar materials in order to improve the effect of gas drainage from low protected seams and to measure the movement and deformation of coal-rock mass using a method of non-contact close-range photogrammetry.Our results show that 1) using paraffin melting to take the place of coal seam mining can satisfy the mining conditions of a protective seam;2) coal-rock mass under goafs has an upward movement after the protective seam has been mined,causing floor heaving;3) low protected seams become swollen and deformed,providing a good pressure-relief effect and causing the coal-rock mass under both sides of coal pillars to become deformed by compression and 4) the evolution of permeability of low protected seams follows the way of initial values→a slight decrease→a great increase→stability→final decrease.Simultaneously,the coefficient of air permeability increased at a decreasing rate with an increase in interlayer spacing.

Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

Experimental study on instability mechanism and critical intensity of rainfall of high-steep rock slopes under unsaturated conditions

Two critical factors,namely intense precipitation and intricate excavation,can trigger rock mass disasters in mining operations.In this study,an indoor rainfall system was developed to precisely regulate the flow and intensity of precipitation.A large-scale model experiment was conducted on a self-designed physical simulation experiment platform to investigate the failure and instability of high-steep rock slopes under unsaturated conditions.The real-time reproduction of the progressive failure process in high-steep rock slopes enabled the determination of the critical rainfall intensity and revealed the mechanism underlying slope instability.Experiment results indicated that rainfall may be the primary factor contributing to rock mass instability,while continuous pillar mining exacerbates the extent of rock mass failure.The critical failure stage of high-steep rock slopes occurs at a rainfall intensity of 40 mm/h,whereas a rainfall exceeding 50 mm can induce critical instability and precipitation reaching up to 60 mm will result in slope failure.The improved region growing segmentation method(IRGSM)was subsequently employed for image recognition of rock mass deformation in underground mines.Herein an error comparison with the simple linear iterative cluster(SLIC)superpixel method and the original region growing segmentation method(ORGSM)showed that the average identification error in the X and Y directions by the method was reduced significantly(1.82%and 1.80%in IRGSM;4.70%and 6.26%in SLIC;9.45%and 12.40%in ORGSM).Ultimately,the relationship between rainfall intensity and failure probability was analyzed using the Monte Carlo method.Moreover,the stability assessment criteria of rock slope under unsaturated condition were quantitatively and accurately evaluated.

Experimental study on the movement law of overlying rock non-pillar coal overhead mining

Pillarless coal mining technology is a new practical technology.Based on the compensating mechanical behavior of the Negative Poisson’s Ratio(NPR)anchor cable on the roof,the roadway was successfully retained by the top cutting and pressure relief technology.This study utilizes the Digital Speckle Monitoring(DIC monitoring),stress-strain monitoring,and infrared thermal imaging systems to conduct physical model experiment of similar materials from the displacement,stress-strain,and temperature fields to investigate in depth the fracture change law of the overlying rock.In addition,it uses FLAC3D numerical simulation to invert the surface displacement settlement.The results show that the non-pillar overhead mining under the 110 mining method has little influence on the rock crack in the middle of the coal seam,and the crack development area is mainly concentrated in the overlying rock mass of the upward coal seam.The compensatory mechanical behavior of NPR anchor cable and the dilatation characteristics of rock mass have a good effect of retaining roadway along goaf,and can also reduce surface settlement.The 110 mining method provides a scientific basis for ecological environment protection and the development of other kilometer deep soft rock high ground stress underground projects.

Experimental Study on Anchoring Physical Properties of Different Anchoring Lengths Coupled with Temperature and Pressure

Aiming at deep roadway anchorage solids, laboratory similar model tests were used to reveal the mechanical properties of anchorage solids with different anchorage lengths under the coupling effect of temperature and pressure, and SPSS statistical analysis software was used to conduct linear regression analysis of the ultimate anchorage force obtained from the tests. The results show that: through multiple linear regression analysis, the influence degree of temperature and pressure coupling on the ultimate anchorage force is arranged in order of anchoring length > surrounding rock strength > temperature > side pressure coefficient, and the linear regression equation of the model is obtained. Compared with the linear regression equation of simulation results, the model has a high explanatory ability.

Influence of dynamic pressure on deep underground soft rock roadway support and its application

Due to high ground stress and mining disturbance, the deformation and failure of deep soft rock roadway is serious, and invalidation of the anchor net-anchor cable supporting structure occurs. The failure characteristics of roadways revealed with the help of the ground pressure monitoring. Theoretical analysis was adopted to analyze the influence of mining disturbance on stress distribution in surrounding rock,and the change of stress was also calculated. Considering the change of stress in surrounding rock of bottom extraction roadway, the displacement, plastic zone and distribution law of principal stress difference under different support schemes were studied by means of FLAC3D. The supporting scheme of U-shaped steel was proposed for bottom extraction roadway that underwent mining disturbance. We carried out a similarity model test to verify the effect of support in dynamic pressure. Monitoring results demonstrated the change rules of deformation and stress of surrounding rock in different supporting schemes. The supporting scheme of U-shaped steel had an effective control on deformation of surrounding rock. The scheme was successfully applied in underground engineering practice, and achieved good technical and economic benefits.

Experimental study on the deformation and failure mechanism of overburden rock during coal mining using a comprehensive intelligent sensing method

Understanding the spatiotemporal evolution of overburden deformation during coal mining is still a challenge in engineering practice due to the limitation of monitoring techniques. Taking the Yangliu Coal Mine as an example, a similarity model test was designed and conducted to investigate the deformation and failure mechanism of overlying rocks in this study. Distributed fiber optic sensing(DFOS), highdensity electrical resistivity tomography(HD-ERT) and close-range photogrammetry(CRP) technologies were used in the test for comprehensive analyses. The combined use of the three methods facilitates the investigation of the spatiotemporal evolution characteristics of overburden deformation, showing that the mining-induced deformation of overburden strata was a dynamic evolution process. This process was accompanied by the formation, propagation, closure and redevelopment of separation cracks.Moreover, the key rock stratum with high strength and high-quality lithology played a crucial role in the whole process of overburden deformation. There were generally three failure modes of overburden rock layers, including bending and tension, overall shearing, and shearing and sliding. Shear failure often leads to overburden falling off in blocks, which poses a serious threat to mining safety. Therefore, realtime and accurate monitoring of overburden deformation is of great significance for the safe mining of underground coal seams.

An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

SysFinder：A customized platform for search,comparison and assisted design of appropriate animal models based on systematic similarity

Animal models are increasingly gaining values by cross-comparisons of response or resistance to clinical agents used for patients.However,many disease mechanisms and drug effects generated from animal models are not transferable to human.To address these issues,we developed SysFinder（http：//lifecenter.sgst.cn/SysFinder）,a platform for scientists to find appropriate animal models for translational research.SysFinder offers a ＂topic-centered＂ approach for systematic comparisons of human genes,whose functions are involved in a specific scientific topic,to the corresponding homologous genes of animal models.Scientific topic can be a certain disease,drug,gene function or biological pathway.SysFinder calculates multi-level similarity indexes to evaluate the similarities between human and animal models in specified scientific topics.Meanwhile,SysFinder offers species-specific information to investigate the differences in molecular mechanisms between humans and animal models.Furthermore,SysFinder provides a userfriendly platform for determination of short guide RNAs（sgRNAs） and homology arms to design a new animal model.Case studies illustrate the ability of SysFinder in helping experimental scientists.SysFinder is a useful platform for experimental scientists to carry out their research in the human molecular mechanisms.

An Uncertainty Enhanced Trust Evolution Strategy for e-Science

Resources shared in e-Science have critical requirements on security.Thus subjective trust management is essential to guarantee users＇ collaborations and communications on such a promising infrastructure.As an important nature of subjective trust,uncertainty should be preserved and exhibited in trust definition,representation and evolution.Consider the drawbacks of existing mechanisms based on random mathematics and fuzzy theory,this paper designs an uncertainty enhanced trust evolution strategy based on cloud model theory.We define subjective trust as trust cloud.Then we propose new algorithms to propagate,aggregate and update trust.Furthermore,based on the concept of similar cloud,a method to assess trust level is put forward.The simulation results show the effiectiveness,rationality and efficiency of our proposed strategy.

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.