Experimental study discovered that: (i) coal rocks and sandstone in the course of loading have three types of infrared thermal image features and three types of infrared radiation temperature features; (ii) infrared d...Experimental study discovered that: (i) coal rocks and sandstone in the course of loading have three types of infrared thermal image features and three types of infrared radiation temperature features; (ii) infrared detection is comparable with acoustic emission detection and electrical resistance detection. Generally, the infrared forewarning of coal rocks’ failure comes later than that of acoustic and electrical resistance, but the infrared radiation temperature forewarning of coal burst comes earlier. On the basis of comprehensive study and analysis, it was suggested that stress nearby 0.79 σ c should be taken as the stress-caution-zone for rock mass failure, ground pressure and its disasters monitoring.展开更多
Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the gro...Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.展开更多
It is possible to understand the lightning activities in a specific region and compare test results of different apparatus only when a reliable evaluation of detection efficiency distribution pertaining to a particula...It is possible to understand the lightning activities in a specific region and compare test results of different apparatus only when a reliable evaluation of detection efficiency distribution pertaining to a particular lightning location system(LLS)is available. Based on the data in 1992.an approximate evaluation of detection efficiency spatial distribu- tion for single-station lightning location system(M-LDARS)and LLP three-station lightning loca- tion system in Beijing-Tianjin-Hebei area is presented in the paper,showing that the average detec- tion efficiencies are smaller than 48% and 46% respectively.In addition,the article offers an eval- uation of spatial distribution of ground stroke density(D_g)and positive stroke percentage,indicat- ing that six high stroke density zones exist along the southeastern sides of the Taihang and Yan- shan Mountains.The stroke density of mountainous region is higher than that of the plain region, in contrast to the case of positive stroke percentage.Also,it is shown that within a radius of 250 km,the average of D_g is 1.2(km^(-2) a^(-1))while the average positive stroke percentage is 10.9%. Finally,the paper proposes a possible test method of accurately evaluating the spatial distribution of detection efficiency(A).展开更多
Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recu...Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.展开更多
文摘Experimental study discovered that: (i) coal rocks and sandstone in the course of loading have three types of infrared thermal image features and three types of infrared radiation temperature features; (ii) infrared detection is comparable with acoustic emission detection and electrical resistance detection. Generally, the infrared forewarning of coal rocks’ failure comes later than that of acoustic and electrical resistance, but the infrared radiation temperature forewarning of coal burst comes earlier. On the basis of comprehensive study and analysis, it was suggested that stress nearby 0.79 σ c should be taken as the stress-caution-zone for rock mass failure, ground pressure and its disasters monitoring.
基金supported by the National Defense Pre-Research Foundation of China(0102015012600A2203)。
文摘Continuous and stable tracking of the ground maneuvering target is a challenging problem due to the complex terrain and high clutter. A collaborative tracking method of the multisensor network is presented for the ground maneuvering target in the presence of the detection blind zone(DBZ). First, the sensor scheduling process is modeled within the partially observable Markov decision process(POMDP) framework. To evaluate the target tracking accuracy of the sensor, the Fisher information is applied to constructing the reward function. The key of the proposed scheduling method is forecasting and early decisionmaking. Thus, an approximate method based on unscented sampling is presented to estimate the target state and the multi-step scheduling reward over the prediction time horizon. Moreover, the problem is converted into a nonlinear optimization problem, and a fast search algorithm is given to solve the sensor scheduling scheme quickly. Simulation results demonstrate the proposed nonmyopic scheduling method(Non-MSM) has a better target tracking accuracy compared with traditional methods.
文摘It is possible to understand the lightning activities in a specific region and compare test results of different apparatus only when a reliable evaluation of detection efficiency distribution pertaining to a particular lightning location system(LLS)is available. Based on the data in 1992.an approximate evaluation of detection efficiency spatial distribu- tion for single-station lightning location system(M-LDARS)and LLP three-station lightning loca- tion system in Beijing-Tianjin-Hebei area is presented in the paper,showing that the average detec- tion efficiencies are smaller than 48% and 46% respectively.In addition,the article offers an eval- uation of spatial distribution of ground stroke density(D_g)and positive stroke percentage,indicat- ing that six high stroke density zones exist along the southeastern sides of the Taihang and Yan- shan Mountains.The stroke density of mountainous region is higher than that of the plain region, in contrast to the case of positive stroke percentage.Also,it is shown that within a radius of 250 km,the average of D_g is 1.2(km^(-2) a^(-1))while the average positive stroke percentage is 10.9%. Finally,the paper proposes a possible test method of accurately evaluating the spatial distribution of detection efficiency(A).
基金supported by the National Natural Science Foundation of China(Grant No.52090082)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2020ME243)the Shanghai Committee of Science and Technology(Grant No.19511100802)。
文摘Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.
