The Kunlunshan Mountain Ms8.1 earthquake, occurred in Nov.14, 2001, is the first event with magnitude more than 8 in the China earthquake monitoring history, specifically at the beginning of digital techniques in prec...The Kunlunshan Mountain Ms8.1 earthquake, occurred in Nov.14, 2001, is the first event with magnitude more than 8 in the China earthquake monitoring history, specifically at the beginning of digital techniques in precursor monitoring networks. Any investigation of recorded data on this earthquake is very important for testing the operation of the digital monitoring networks and understanding the preparation, occurrence, and adjustment of stress/strain of strong continental earthquakes. In this paper we investigated the coseismic response changes of well water level of groundwater and volume strain meter of bore hole in digital earthquake monitoring network of Capital area and its vicinity, due to the Nov.14, 2001 Ms8.1 Kunlun Mountain earthquake. The responding time, shapes or manners, amplitudes, and lasting time of well water level and strain-meters to seismic wave are studied in comparison. Then we discussed the possibility that the response changes of groundwater to strong distant earthquakes can be understood as one kind of observing evidence of stress/strain changes induced by distant earthquake.展开更多
Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem ...Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.展开更多
基金supported by Natural Science Foundation of China(41274061 and 40374019)
文摘The Kunlunshan Mountain Ms8.1 earthquake, occurred in Nov.14, 2001, is the first event with magnitude more than 8 in the China earthquake monitoring history, specifically at the beginning of digital techniques in precursor monitoring networks. Any investigation of recorded data on this earthquake is very important for testing the operation of the digital monitoring networks and understanding the preparation, occurrence, and adjustment of stress/strain of strong continental earthquakes. In this paper we investigated the coseismic response changes of well water level of groundwater and volume strain meter of bore hole in digital earthquake monitoring network of Capital area and its vicinity, due to the Nov.14, 2001 Ms8.1 Kunlun Mountain earthquake. The responding time, shapes or manners, amplitudes, and lasting time of well water level and strain-meters to seismic wave are studied in comparison. Then we discussed the possibility that the response changes of groundwater to strong distant earthquakes can be understood as one kind of observing evidence of stress/strain changes induced by distant earthquake.
基金supported by the National Key R&D Program of China(2022YFB4701502)the“Leading Goose”R&D Program of Zhejiang(2023C01177)+1 种基金the Key Research Project of Zhejiang Lab(2021NB0AL03)the Key R&D Project on Agriculture and Social Development in Hangzhou City(Asian Games)(20230701 A05).
文摘Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.
