Modeling for Safety Evaluation of Coal Mine Roof Based on Information Fusion

2010 ◽  
Vol 143-144 ◽  
pp. 439-443 ◽  
Author(s):  
Rui Sheng Jia ◽  
Hong Mei Sun ◽  
Chong Qing Zhang ◽  
Xue Ting Lv
Keyword(s):  
Information Fusion ◽  
Coal Mine ◽  
Information Quality ◽  
Simulation Analysis ◽  
Evaluation Model ◽  
Evidence Theory ◽  
Safety Evaluation ◽  
Sensor Data ◽  
Source Information ◽  
Mine Roof

Factors that affect the safety of coal mine roof is a multi-faceted, information fusion technology can take full advantage of multi-source information complementary, comprehensive, and improving information quality and credibility of coal mine roof safety. In analyzing the current monitoring means, a coal mine roof safety evaluation model is presented based on information fusion, and given information processing steps of multi-sensor data analysis, processing, distribution and integration based on Dempster-Shafer evidence theory; For the elimination of multi-source data fusion of uncertain factors, proposed coal mine roof safety decision-making rules; The simulation analysis shows that the validity of the model and practicality.

scholarly journals Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenming Sun ◽  
Dong Li
Keyword(s):  
Coal Mine ◽  
Evaluation Model ◽  
Weighted Least Squares ◽  
Evidence Theory ◽  
Safety Evaluation ◽  
Mine Safety ◽  
Sensor Data ◽  
Support Vector ◽  
Coal Mine Safety ◽  
High Conflict

Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

scholarly journals Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 993 ◽  
Author(s):  
Bin Yang ◽  
Dingyi Gan ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Incomplete Information ◽  
Information Fusion ◽  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Open World ◽  
Belief Entropy ◽  
Open World Assumption ◽  
Deng Entropy

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

Evaluation Method of Coal Mine Safety Based on Evidence Theory

2012 ◽  
Vol 524-527 ◽  
pp. 426-430
Author(s):  
Gang Xu ◽  
Yang Ding ◽  
Tian Jun Zhang
Keyword(s):  
Coal Mine ◽  
Evaluation Method ◽  
Production Systems ◽  
Evaluation Model ◽  
Evidence Theory ◽  
Mine Safety ◽  
Coal Mine Safety ◽  
Mine Production ◽  
Set Up ◽  
Decision Making Rule

Coal mine safety assessment is an important ways for identification and elimination of danger in coal mine production systems. This paper introduce D-S evidence theory in evaluation of coal mine safety to solve the uncertainty problem of randomicity and faintness in evaluation of coal mine safety. The evaluation model of coal mine safety is set up based on evidence theory and the detailed arithmetic of evidence theory is brought forward, and according to some decision making rule the Chaohua Coal Mine has been evaluated. The results show that the model can solve the problem of uncertainty preferable and evaluation results with more accuracy and reliability.

Research on Evaluation of Vehicle Dangerous Traveling State Based on Information Fusion Method

2013 ◽  
Vol 791-793 ◽  
pp. 1018-1022
Author(s):  
Peng Wang ◽  
Zhi Qiang Liu
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Evidence Theory ◽  
Recognition System ◽  
Fusion Method ◽  
Source Information ◽  
State Recognition ◽  
Research On Evaluation ◽  
Lane Departure ◽  
Level Information

An evaluation system of vehicle traveling state was proposed,and an unsafe vehicle traveling state recognition system was established using multi-level information fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of vehicle traveling characteristics, combing BP neural network with Dempster-Shafer evidence theory technique, the multi-information decision-level fusion was proposed to estimate the different kind model of the vehicle status. To verify the proposed strategies,the vehicle traveling posture evaluation system was established. The lane departure parameters and the relative distance parameters were studied in order to get the characterization of the vehicle traveling status information. The simulation results indicate that the adaptability and accuracy and the intelligence level of driving characterization estimation are significantly improved by using the pattern classification and decision technology of multi-source information fusion.

scholarly journals Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254
Author(s):  
Bo Li ◽  
Fuwen Pang
Keyword(s):  
Information Fusion ◽  
Time Complexity ◽  
Euclidean Distance ◽  
Evidence Theory ◽  
Classification Model ◽  
Support Vector ◽  
Source Information ◽  
Distance Ratio ◽  
Slack Variable ◽  
Nonlinear Support

To deal with highly time complexity and unstable assessments for conflicting evidences from various navigation factors, we put forward an innovative assessment scheme of navigation risk based on the improved multi-source information fusion techniques. Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. The slack variable is adaptively computed based on the Euclidean distance ratio. Considering the unsatisfactory characteristics of the standard Dempster–Shafer evidence theory, the optimal combination rule is derived step by step. What"s more, the lowly dimensional Kalman filter is applied to forecast the navigation risk. Simultaneously, the time complexity of each technique is analyzed. With respect to the vessel navigation risk, the assessment results are provided to indicate the reliability and efficiency of the proposed scheme.

A Network Security Situational Awareness Model Based on Information Fusion

2013 ◽  
Vol 846-847 ◽  
pp. 1632-1635
Author(s):  
Abasi
Keyword(s):  
Network Security ◽  
Information Fusion ◽  
Situational Awareness ◽  
Evidence Theory ◽  
Arma Model ◽  
Situational Factors ◽  
Source Information ◽  
Network Nodes ◽  
Model Based ◽  
The Future

Security situational awareness has become a hot topic in the area of network securityresearch in recent years. The existing security situational awareness methods are analyzed and compared in details, and thus a newnetwork security situational awareness model based on information fusion is proposed. This modelfuses multi-source information from a mass of logs by introducing the modified D-S evidence theory,gets the values of nodes security situational awareness by situational factors fusion using attacks threat,and vulnerability information which network nodes have and successful attacks depend on, computesthe value of network security situational awareness by nodes situation fusion using service informationof the network nodes, and draws the security-situation-graph of network. Then, it analyzes the timeseries of the computing results by ARMA model to forecast the future threat in network security.Finally an example of actual network datasets is given to validate the network security situationalawareness model and algorithm. The results show that this model and algorithm is more effective andaccurate than the existing security situational awareness methods.

Sign in / Sign up

Export Citation Format

Share Document