With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.
As faults in the solar water heaters are structurally complicated and highly correlated, an approach of fault diagnosis on the basis of support vector machine and D-S evidence theory has been proposed in this study, attempting to enhance the system's thermal efficiency and ensure its safety. In the approach presented, information of audio conditions, temperature at the outlet of solar thermal collectors, hourly flow and hourly heat transfer rate are accessible, which facilitate the feature evidence and are diagnosed by using "one-against-one" multi-class support vector machine. Experiments are conducted to diagnose fault information fusion and the results show that the diagnosis approach proposed in this study is of high reliability with fewer uncertainties, indicating that the approach is capable to recognize and diagnose solar water heater faults accurately.
Research on the particle size of blast piles has always been an essential issue in mining engineering. Reasonable blasting parameters can reduce mining costs and reduce the workload of secondary crushing, which can significantly improve mining efficiency. The usual particle size analysis methods include the sieving method, the large particle size statistical method and other manual measurement methods. Nevertheless, these methods have the disadvantages of a high labor intensity, low efficiency and low precision. This paper analyzes UAV image information based on the single-picture photogrammetry method of computer image processing technology. A two-dimensional empirical wavelet transform (EWT) is used for image noise reduction. The nonlocal multiscale fractional differential (NMFD) enhances the texture of dark images and uses superpixel image segmentation technology so that the processed image can meet the granularity statistical study requirements of blast piles. The research results show that the accuracy of the ore particle size distribution by the method proposed in this paper is more than 90%.
The traffic congestion situation is an important reference indicator for the orderly control and management of traffic systems. As intelligent transport systems (ITS) become increasingly popular, the challenge of realizing real-time traffic congestion situation assessments (TCSAs) in the post-traffic era is particularly important. In this study, we propose a TCSA scheme for multi-metric fuzzy integrated evaluation based on three predicted vehicle traffic parameters for the 5G Internet of Vehicles (5G-IoV) environment, which is dedicated to accelerating the development of ITS. Firstly, the scheme uses dynamic multi-model adaptive exponential smoothing (DMMAES), which can calculate the optimal smoothing coefficients and weight of each model based on historical prediction errors to predict the average speed and traffic volume and then calculate the predicted traffic speed, traffic flow density, and road saturation of the three traffic congestion indicators. Secondly, the predicted values of the three traffic congestion indicators are used as fuzzy comprehensive evaluation, taking into account the vagueness of the traffic congestion levels, the uncertainty of the indicators, and the conflict among the indicators, using a trapezoidal affiliation function to determine the degree of affiliation of each indicator through the adaptive CRITIC method to determine the weights. Finally, the predicted traffic congestion situations are classified into five levels. The effectiveness of the scheme was verified by the measured data of Yanta North Road in Xi’an. The results showed that the traffic congestion level predicted by TCSA was basically consistent with the actual situation and had a high prediction accuracy.
Mining industry is a very important industry in China. Its tax and fee policy is related to the whole industry. This paper summarizes the main types of taxes and fees involved in iron ore in China, Australia, Russia, Cameroon, Brazil, India and South Africa, and also compare the comprehensive tax and fee burden rates of iron ore companies in China with those of four major international mining companies, Rio Tinto, BHP Billiton, Valley and FMG. It concludes that the comprehensive tax and fee burden rates of iron ore companies in China are higher about 6% than those of the four major international mining companies. The main reasons for the increase are unreasonable industrial orientation of mining industry, high resource tax and value-added tax rates, few deductible items of value-added tax, and unreasonable collection of local special taxes and fees. Finally the author puts forward five suggestions on how to reduce the tax burden of iron ore companies. First, the government can improve the tax system and form a scientific, reasonable tax system. The second is adjusting the mining industry from the second industry to the primary industry. The third is to formulate a national strategy to ensure the safety of iron ore supply. The fourth is to uniformly determine the collection standard of resource tax rate by the country, cancel the unreasonable tax and fee collected by local government. The fifth is to increase fuel tax credits for mining companies.
In order to solve the inefficient use of multi-source heterogeneous data information cross fusion and the low accuracy of prediction of landslide displacement, the current research proposed a new prediction model combining variable selection, sparrow search algorithm, and deep extreme learning machine. A cement mine in Fengxiang, Shaanxi Province, was studied as a case. The study first identified the variables related to landslide displacement of rock slope, and removed redundant variables by using Pearson correlation and gray correlation analysis. To avoid the impacts of random input weights and random thresholds in the DELM model, the SSA algorithm is used to optimize the model’s parameters, which can generate the optimal parameter combinations. The results showed an enhanced generalization ability of the model by removal of redundant variables by Pearson correlation and gray correlation analysis, and higher accuracy in the prediction of landside displacement of rock slope by SSA-DELM compared to other traditional machine learning algorithms. The current study is significant in the literature on rock slope disaster analysis.
Horizontal pillars are the main load-bearing elements in underground stopes. The stability of pillars directly influences the overall safety of mines, and their thickness directly affects the ore loss and the economy of mining. However, traditional design methods have shortcomings, such as shape simplification and the consideration of too few factors, which are not compatible with horizontal pillars in the filling method. An innovative method to accurately determine the safety thickness of irregular pillars under the filling condition was proposed in this study. First, the formula for calculating overlying backfill load to mine steeply inclined ore bodies was derived by analyzing the condition of irregular horizontal pillars. Support from the lower backfill body was considered further. A 3D numerical model of horizontal pillars that uses contact element was presented to study the effect of the maximum principal stress and the deflection on the pillar thickness. Finally, based on the maximal tension stress theory, the critical safety thickness with different safety factors was calculated, and the FLAC3D numerical simulation method was used to calculate the stability of mined-out areas on large-scale reserving horizontal pillars with safety thickness. Results show that the maximum tensile stress does not exceed the ultimate tensile strength of horizontal pillars under the critical safety thickness whose value obtained by the new method is smaller than the value calculated through the traditional design methods. In addition, deflections are restricted to an acceptable range. The pillars and stopes remain stable and massive destruction is not found. The validity and security of the safety thickness formed in the new design method were confirmed through the simulation experiment, which provide some reference and experience for the design of the safety thickness of horizontal pillars in similar mine sites by using the filling method.
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