Notice of Retraction: Coal Mine Enterprises Merger & Acquisition Efficiency Prediction Based on Support Vector Machine

2010 ◽  
Author(s):  
Chen Xiang ◽  
Cai Weihua
Keyword(s):  
Support Vector Machine ◽  
Coal Mine ◽  
Support Vector

Application of RS-SVM in Coal Mine Fire Warning System

2016 ◽  
Vol 693 ◽  
pp. 1331-1338
Author(s):  
Hua Ping Zhou ◽  
Bo Jie Xiong ◽  
Cheng Jun Wang
Keyword(s):  
Support Vector Machine ◽  
Rough Set ◽  
Coal Mine ◽  
Warning System ◽  
Support Vector ◽  
Representation System ◽  
Mine Fire ◽  
Rule Set ◽  
Knowledge Representation System ◽  
Coal Mine Fire

In order to reduce the false alarm rate in coal mine fire warning system, we apply information fusion technology to the system and propose a fire forecast algorithm based on Rough Set Support Vector Machine ( RS-SVM ). Firstly, we map the feature description of coal mine fires to the knowledge representation system described by rough set; Secondly, we discrete the continuous attributes and eliminate the redundant information for attribute reduction to form a rule set of this knowledge representation system; At last, we use the above rule set as the training sample to optimize the parameters for the fire warning support vector machine. The experimental results show that the accuracy of the algorithm is very high. It can make timely and accurate prediction of coal mine fire.

scholarly journals A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877744 ◽  
Author(s):  
Peng Chen ◽  
Yonghong Xie ◽  
Pei Jin ◽  
Dezheng Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Swarm Intelligence ◽  
Coal Mine ◽  
Safety Data ◽  
Mine Safety ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Gas Poisoning

As the integral part of the new generation of information technology, the Internet of things significantly accelerates the intelligent sensing and data fusion in different industrial processes including mining, assisting people to make appropriate decision. These days, an increasing number of coal mine disasters pose a serious threat to people’s lives and property especially in several developing countries. In order to assess the risks arisen from gas explosion or gas poisoning, wireless sensor data should be processed and classified efficiently. Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. Using the popular deep neural networks, convolutional neural network and long short-term memory model, as comparisons, a number of experiments are carried out on several UCI machine learning datasets with different features. Experimental results show that least squares support vector machine optimized by swarm intelligence techniques can effectively handle classification task on different datasets especially on those datasets with limited samples and mixed attributes. The application of least squares support vector machine optimized by swarm intelligence techniques on real coal mine data demonstrates that this algorithm can process the data accurately and timely, therefore can warn of the accidents early in mining workplace.

Fault Diagnosis of Conveyance Machine Based on Fuzzy Support Vector

2011 ◽  
Vol 135-136 ◽  
pp. 547-552
Author(s):  
Yuan Bin Hou ◽  
Ning Li ◽  
Fan Guo ◽  
Jing Chen
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Kernel Function ◽  
Coal Mine ◽  
Fuzzy Theory ◽  
Kernel Functions ◽  
Mine Safety ◽  
Support Vector ◽  
Coal Mine Safety ◽  
Rubber Belt

Aiming random and nonlinearity for conveyance machine of rubber belt in mine, a method of fault diagnosis is presented which fusion of fuzzy theory and support vector machine (FSVM). According to the coal mine safety rules of the regulation, the conveyance machine servicing are deduced eleven faults after analyzing practice statistic data, here, we consider some are fuzzy that the statistic data are divided to the normal kind or fault kind, but some are definite that the statistic data possibility are belong to same kind fault, accordingly, the fuzzy support vectors is established. Farther, two kernel functions of FSVM is made for seeking the problem of random and nonlinearity, which are RBF and TANH. According to the random statistic data and the study sample, analyzing the effect of expense and kernel function in selecting different parameters, the unitary constant is ascertained, next, the FSVM kernel function of fault diagnosis multi-class rules are established, then, this method availability is proved using test data and simulation.

scholarly journals Landslide Susceptibility Zoning Using C5.0 Decision Tree, Random Forest, Support Vector Machine and Comparison of Their Performance in a Coal Mine Area

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiaomei Su ◽  
Weiheng Tao ◽  
Shiguang Mei ◽  
Xiaoyuan Zhang ◽  
Kaixin Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Coal Mine ◽  
Landslide Susceptibility ◽  
Shanxi Province ◽  
Support Vector ◽  
Mine Area ◽  
Ground Collapse ◽  
C5.0 Decision Tree

The main purpose of this study is to establish an effective landslide susceptibility zoning model and test whether underground mined areas and ground collapse in coal mine areas seriously affect the occurrence of landslides. Taking the Fenxi Coal Mine Area of Shanxi Province in China as the research area, landslide data has been investigated by the Shanxi Geological Environment Monitoring Center; adopting the 5-fold cross-validation method, and through Geostatistics analysis means the datasets of all non-landslides and landslides were divided into 80:20 proportions randomly for training and validating models. A set of 15 condition factors including terrain, geological, hydrological, land cover, and human engineering activity factors (distance to road, distance to mined area, ground collapse density) were selected as the evaluation indices to construct the susceptibility assessment model. Three machine learning algorithms for landslide susceptibility prediction (LSP) including C5.0 Decision Tree (C5.0), Random Forest (RF), and Support Vector Machine (SVM) have been selected and compared through the Areas under the Receiver Operating Characteristics (ROC) Curves (AUC), and several statistical estimates. The study revealed that for these three models the value range of prediction accuracies vary from 83.49 to 99.29% (in the training stage), and 62.26–73.58% (in the validation stage). In the two stages, AUCs are between 0.92 to 0.99 and 0.71 to 0.80 respectively. Using Jenks Natural Breaks algorithm, three LSPs levels are established as very low, low, medium, high, and very high probability of landslide by dividing the indices of the LSP. Compared with RF and SVM, C5.0 is considered better in five categories according to quantities and distribution of the landslides and their area percentage for different LSP zones. Four factors such as distance to road, lithology, profile curvature, and ground collapse density are the most suitable condition factors for LSP. The distance to mine area factor has a medium contribution and plays an obvious role in the occurrence of landslides in all the models. The result reveals that C5.0 possesses better prediction efficiency than RF and SVM, and underground mined area and ground collapse sifnigicantly affect significantly the occurrence of landslides in the Fenxi Coal Mine Area.

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