In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
An ultra-wideband impulse radar was studied for the detection of buried life in coal mines. An improved Empirical Mode Decomposition (EMD) method based on a cross-correlation filter was proposed for reduction of multi...An ultra-wideband impulse radar was studied for the detection of buried life in coal mines. An improved Empirical Mode Decomposition (EMD) method based on a cross-correlation filter was proposed for reduction of multipath and noise interference. Multipath interference was first removed by cross-corre- lation filtering. Then the delays of each pulse in every echo were summed. An EMD algorithm was used for noise reduction for the total delay of each echo. The corresponding EMD results of every echo were then summed and averaged. Finally, evidence for the existence of buried life and their position were obtained from amplitude-frequency curves of the averaged EMD results. Detailed simulation experi- ments are presented to validate the effectiveness of this proposed method. The experimental results show that this method can efficiently eliminate multipath interference and reduce noise interference in echoes, which makes detection and location of buried life in coal mines more accurate.展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金support forour work by the National Science and Technology Support Project of China (No. 2006BAK03B00)
文摘An ultra-wideband impulse radar was studied for the detection of buried life in coal mines. An improved Empirical Mode Decomposition (EMD) method based on a cross-correlation filter was proposed for reduction of multipath and noise interference. Multipath interference was first removed by cross-corre- lation filtering. Then the delays of each pulse in every echo were summed. An EMD algorithm was used for noise reduction for the total delay of each echo. The corresponding EMD results of every echo were then summed and averaged. Finally, evidence for the existence of buried life and their position were obtained from amplitude-frequency curves of the averaged EMD results. Detailed simulation experi- ments are presented to validate the effectiveness of this proposed method. The experimental results show that this method can efficiently eliminate multipath interference and reduce noise interference in echoes, which makes detection and location of buried life in coal mines more accurate.
