The frequent rebellions in Northern Manchuria during the Third Revolutionary War occurred in the special context of the struggle between the Kuomintang(KMT)and the Communist Party of China(CPC)for Northeast China afte...The frequent rebellions in Northern Manchuria during the Third Revolutionary War occurred in the special context of the struggle between the Kuomintang(KMT)and the Communist Party of China(CPC)for Northeast China after the victory of the Anti-Japanese War.The rebellion reached its peak during the KMTs attack on Northeast China,followed by a second wave of rebellion after the defeat in the Defensive Battle of Siping.It tended to disappear after the downfall of the Jiang Pengfei Group.In addition to the blind recruitment of the CPC in traditional narratives,the instigation of the KMT,the traditional mutiny of the old army,the limitations of the early work of the Northeast Anti-Japanese United Army,the early activities of the KMT,and the regional conflicts between the local and foreign forces are also important reasons for the concentration of rebellions.展开更多
Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of i...Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.展开更多
文摘The frequent rebellions in Northern Manchuria during the Third Revolutionary War occurred in the special context of the struggle between the Kuomintang(KMT)and the Communist Party of China(CPC)for Northeast China after the victory of the Anti-Japanese War.The rebellion reached its peak during the KMTs attack on Northeast China,followed by a second wave of rebellion after the defeat in the Defensive Battle of Siping.It tended to disappear after the downfall of the Jiang Pengfei Group.In addition to the blind recruitment of the CPC in traditional narratives,the instigation of the KMT,the traditional mutiny of the old army,the limitations of the early work of the Northeast Anti-Japanese United Army,the early activities of the KMT,and the regional conflicts between the local and foreign forces are also important reasons for the concentration of rebellions.
基金This work is supported by the National Natural Science Foundation of China(Grant No.51991392)Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3-3)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0904).
文摘Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.
