A Novel Cross-Media Layered Semantic Mining Model

This paper presents a cross-media semantic mining model （CSMM） based on object semantic. This model obtains object-level semantic information in terms of maximum probability principle. Then semantic templates are trained and constructed with STTS （Semantic Template Training System）, which are taken as the bridge to realize the transition from various low-level media feature to object semantic. Furthermore, we put forward a kind of double layers metadata structure to efficaciously store and manage mined low-level feature and high-level semantic. This model has broad application in lots of domains such as intelligent retrieval engine, medical diagnoses, multimedia design and so on.

Advances in Educational Data Mining Models and the Application of Its Algorithms

In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.

Development of a time-dependent energy model to calculate the mining-induced stress over gates and pillars

Generally, longwall mining-induced stress results from the stress relaxation due to destressed zone that occurs above the mined panel. Knowledge of induced stress is very important for accurate design of adjacent gateroads and intervening pillars which helps to raise the safety and productivity of longwall mining operations. This study presents a novel time-dependent analytical model for determination of the longwall mining-induced stress and investigates the coefficient of stress concentration over adjacent gates and pillars. The model is developed based on the strain energy balance in longwall mining incorporated to a rheological constitutive model of caved materials with time-varying parameters. The study site is the Tabas coal mine of Iran. In the proposed model, height of destressed zone above the mined panel, total longwall mining-induced stress, abutment angle, induced vertical stress, and coefficient of stress concentration over neighboring gates and intervening pillars are calculated. To evaluate the effect of proposed model parameters on the coefficient of stress concentration due to longwall mining, sensitivity analysis is performed based on the field data and experimental constants. Also, the results of the proposed model are compared with those of existing models. The comparative results confirm a good agreement between the proposed model and the in situ measurements. According to the obtained results, it is concluded that the proposed model can be successfully used to calculate the longwall mining-induced stress. Therefore, the optimum design of gate supports and pillar dimensions would be attainable which helps to increase the mining efficiency.

Technological framework of coordinated coal mining and coalbed methane

According to the mining mechanisms of CBM combined with coal structure andthe key technologies of coordinated mining coal and coalbed methane under differentmining coal models, the theoretical foundation of coordinated mining of coal and coalbedmethane was expounded, and the mining models of coordinated mining of coal andcoal-bed methane were classified.This research can provide a reference to safety productionof coal mines and development of CBM resources.

Thrust and Torque Characteristics Based on a New cutter-head Load Model

Full face rock tunnel boring machine（TBM） has been widely used in hard rock tunnels, however, there are few published theory about cutter-head design, and the design criteria of cutter-head under complex geological is not clear yet. To deal with the complex relationship among geological parameters, cutter parameters, and operating parameters during tunneling processes, a cutter-head load model is established by using CSM（Colorado school of mines） prediction model. Force distribution on cutter-head under a certain geology is calculated with the new established load model, and result shows that inner cutters bear more force than outer cutters, combining with disc cutters abrasion; a general principle of disc cutters＇ layout design is proposed. Within the model, the relationship among rock uniaxial compressive strength（UCS）, penetration and thrust on cutter-head are analyzed, and the results shows that with increasing penetration, cutter thrust increases, but the growth rate slows and higher penetration makes lower special energy（SE）. Finally, a fitting mathematical model of ZT（ratio of cutter-head torque and thrust） and penetration is established, and verified by TB880 E, which can be used to direct how to set thrust and torque on cutter-head. When penetration is small, the cutter-head thrust is the main limiting factor in tunneling; when the penetration is large, cutter-head torque is the major limiting factor in tunneling. Based on the new cutter-head load model, thrust and torque characteristics of TBM further are researched and a new way for cutter-head layout design and TBM tunneling operations is proposed.

Geomechanics of subsidence above single and multi-seam coal mining

Accurate prediction of surface subsidence due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This task is further compounded by the growing trend for coal to be extracted from seams either above or below previously extracted coal seams, a practice known as multiseam mining. In order to accurately predict the subsidence above single and multi-seam longwall panels using numerical methods, constitutive laws need to appropriately represent the mechanical behaviour of coal measure strata. The choice of the most appropriate model is not always straightforward. This paper compares predictions of surface subsidence obtained using the finite element method, considering a range of well-known constitutive models. The results show that more sophisticated and numerically taxing constitutive laws do not necessarily lead to more accurate predictions of subsidence when compared to field measurements. The advantages and limitations of using each particular constitutive law are discussed. A comparison of the numerical predictions and field measurements of surface subsidence is also provided.

Impact of longwall mining on groundwater above the longwall panel in shallow coal seams

Since longwall mining causes subsidence through the overlying strata to the ground surface, the surface water and groundwater above the longwall panels may be affected and drained into the lower levels.Therefore, loss or interruption of streams and overburden aquifers is a common concern in coal industry.This paper analyzed the potential effects of longwall mining on subsurface water system in shallow coal seam. In order to monitor different water level fluctuations throughout the mining period, three water wells were drilled down to the proposed deformation zone above the longwall panel. A GGU-SS-FLOW3 D model was used to predict water table contours for the periods of pre- and post-mining conditions. The field data from the three water wells were utilized to calibrate the model. The field test and numerical model can help to better understand the dewatering of shallow aquifers and surface waters related to ground subsidence from longwall mining in shallow coal seam.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.

Gateside packwall design in solid backfill mining-A case study

Based upon characteristic movement features of the overlying strata in solid backfill mining and in-situ observations,an associated model representing a roadway support system has been developed.Based on the Winkler foundation and beam model,the current study presents a static analysis of the model,thus permitting acquisition of a theoretical formula pertaining to roof convergence.Through use of working face 6304-1(Jisan Colliery) as the research setting,the association between roof convergence magnitude and both packwall strength and width have been elucidated.Based upon observed conditions at the working face,realistic packwall parameters have been formulated,with numerical simulation results and field application results indicating that design parameters garnered from the developed formula successfully adapted to local geological movement and deformation.Accordingly,roadway deformation was shown to be within the permissible range,thus satisfying mine production requirements.The proposed method in the current study may give a design basis for pack design in the context of SBM under similar conditions.

Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels.In this study,at first,a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels.The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions.A set of,data mining and machine learning algorithms including Random Forest(RF),M5P,Random Committee,KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel.Results indicated among these five models;RF method indicated the most precise results with the highest R2 value of 0.9.Finally,the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method(SKM)and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

Coal and gas outburst dynamic system

Coal and gas outburst is an extremely complex dynamic disaster in coal mine production process which will damage casualties and equipment facilities, and disorder the ventilation system by suddenly ejecting a great amount of coal and gas into roadway or working face. This paper analyzed the interaction among the three essential elements of coal and gas outburst dynamic system. A stress-seepage-damage coupling model was established which can be used to simulate the evolution of the dynamical system, and then the size scale of coal and gas outburst dynamical system was investigated. Results show that the dynam- ical system is consisted of three essential elements, coal-gas medium （material basis）, geology dynamic environment （internal motivation） and mining disturbance （external motivation）. On the case of CI 3 coal seam in Panyi Mine, the dynamical system exists in the range of 8-12 m in front of advancing face. The size scale will be larger where there are large geologic structures. This research plays an important guid- ing role for developing measures of coal and gas outburst prediction and prevention.

Optimization of Cooling Process of Iron Ore Pellets Based on Mathematical Model and Data Mining

Cooling process of iron ore pellets in a circular cooler has great impacts on the pellet quality and systematic energy exploitation. However, multi-variables and non-visualization of this gray system is unfavorable to efficient production. Thus, the cooling process of iron ore pellets was optimized using mathematical model and data mining techniques. A mathematical model was established and validated by steady-state production data, and the results show that the calculated values coincide very well with the measured values. Based on the proposed model, effects of important process parameters on gas-pellet temperature profiles within the circular cooler were analyzed to better understand the entire cooling process. Two data mining techniques—Association Rules Induction and Clustering were also applied on the steady-state production data to obtain expertise operating rules and optimized targets. Finally, an optimized control strategy for the circular cooler was proposed and an operation guidance system was developed. The system could realize the visualization of thermal process at steady state and provide operation guidance to optimize the circular cooler.

Computational implementation of a GIS developed tool for prediction of dynamic ground movement and deformation due to underground extraction sequence

In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional （4D）. A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional （4D） ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system （GIS） technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.

An improved method to assess the required strength of cemented backfill in underground stopes with an open face

Backfill is increasingly used in underground mines to reduce the surface impact from the wastes produced by the mining operations. But the main objectives of backfilling are to improve ground stability and reduce ore dilution. To this end, the backfill in a stope must possess a minimum strength to remain self-standing during mining of an adjacent stope. This required strength is often estimated using a solution proposed by Mitchell and co-workers, which was based on a limit equilibrium analysis of a wedge exposed by the open face. In this paper, three dimensional numerical simulations have been performed to assess the behavior of the wedge model. A new limit equilibrium solution is proposed, based on the backfill displacements obtained from the simulations. Comparisons are made between the proposed solution and experimental and numerical modeling results. Compared with the previous solution, a better agreement is obtained between the new solution and experimental results for the required cohesion and factor of safety. For large scale(field) conditions, the results also show that the required strength obtained from the proposed solution corresponds quite well to the simulated backfill response.

Dynamic subsidence basins in coal mines based on rock mass rheological theory

In order to investigate the surface deformation caused by coal mining and to reduce environmental damage, more accurate information of dynamic subsidence basins, caused by coal mining, is needed. Based on theological theory, we discuss surface deformation mechanism of dynamic subsidence on the assumption that both the roof and the coal seam are visco-elastic media, put forward the idea that the principle of surface deformation is similar to that of roofs, except for their parameters. Therefore, a surface deforma- tion equation can be obtained, given the equation of the roof deformation derived.from using a HIM rhe- ological model. In the end, we apply the equation of surface deformation as a practical subsidence prediction in a coal mine. Given the theologic properties of a rock mass, the results of our research of a dynamic subsidence basin can predict the development of surface deformation as a function of time, which is more important than the ultimate subsidence itself. The results indicate that using rheological theory to calculate the deformation of a dynamic subsidence basin is suitable and provides some reference for surface deformation of dynamic subsidence basins.

Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

User-Level Sentiment Evolution Analysis in Microblog

People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.