A complete set of techniques for the design,installation,maintenance and use of vegetable tunnel houses in tropical island countries was developed,which achieved the goals of rain prevention,wind protection,corrosion ...A complete set of techniques for the design,installation,maintenance and use of vegetable tunnel houses in tropical island countries was developed,which achieved the goals of rain prevention,wind protection,corrosion resistance,insect and bird prevention,water-saving irrigation,and economic efficiency,significantly improving the production capacity and technical level of vegetables in Samoa.The techniques have become a key vegetable production technology promoted nationwide in Samoa,with universal promotion value for tropical island countries.展开更多
After some tragic fire events,Directive 2004/54/EC was issued to ensure a minimum safety level for tunnels belonging to the Trans-European Road Network longer than 500 m.Nowadays,most of the Italian road tunnels are s...After some tragic fire events,Directive 2004/54/EC was issued to ensure a minimum safety level for tunnels belonging to the Trans-European Road Network longer than 500 m.Nowadays,most of the Italian road tunnels are still not in compliance with the minimum safety requirements,thus refurbishment works are often planned.By developing a traffic macro-simulation model,this paper aims at assessing the resilience of an existing twin-tube motorway tunnel when one of its tubes is partially or completely closed due to planned activities.Several scenarios were investigated,also considering the availability or not of an alternative itinerary in the surrounding transportation network.The average vehicles’speed was used as a functionality parameter,while the resilience metrics were the resilience loss,the recovery speed,and the resilience index.The findings showed higher resilience losses for complete closure rather than partial closure of the tube under planned refurbishment works.The implementation of digital technologies,such as variable message signs,might reduce the resilience loss of the tunnel system.This research might represent a reference for tunnel management agencies in the choice of the most appropriate traffic control strategy to improve tunnel resilience in the event of planned activities.展开更多
The western coast of Hainan Island exhibits a savanna landscape. Many types of sand dunes, including transverse dune ridges, longitudinal dune ridges, elliptical dunes, coppice dunes, and climbing dunes, are widely di...The western coast of Hainan Island exhibits a savanna landscape. Many types of sand dunes, including transverse dune ridges, longitudinal dune ridges, elliptical dunes, coppice dunes, and climbing dunes, are widely distributed in the coastal zone. In winter, high-frequency and high-energy NE winds （dominant winds） are prevalent, with a resultant drift direction （RDD） of S35.6°W. In spring, low-frequency and low-energy SW secondary winds prevail, with a RDD of N25.1°E. Wind tunnel simulations revealed that the airflow over the dune surface is the main factor controlling the erosion and deposition patterns of dune surfaces and the morphological development of dunes. In the region＇s bidirectional wind environment, with two seasonally distinct energy levels, the airflow over the surface of elliptical dunes, barchan dunes, and transverse dune ridges will exhibit a transverse pattern, whereas the airflow over longitudinal dunes ridges exhibits a lateral pattern and that over climbing dunes exhibits a climbing-circumfluent pattern. These patterns represent different dynamic processes. The coastal dunes on the western coast of Hainan Island are influenced by factors such as onshore winds, sand sources, coastal slopes, rivers, and forest shelter belts. The source of the sand that supplements these dunes particularly influences the development pattern： when there is more sand, the pattern shows positive equilibrium deposition between dune ridges and dunes; otherwise, it shows negative equilibrium deposition. The presence or absence of forest shelter belts also influences deposition and dune development patterns and transformation of dune forms. Coastal dunes and inland desert dunes experience similar dynamic processes, but the former have more diversified shapes and more complex formation mechanisms.展开更多
As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive.Currently,und...As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive.Currently,under conditions of strong noise and complex working environments,traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy.To address these issues,we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network(RDMA-WDAN)for TBM main bearing fault diagnosis.Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules,achieving better domain adaptation despite significant domain interference.The residual denoising component utilizes a convolutional block to extract noise features,removing them via residual connections.Meanwhile,the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features,concentrating on deep fault features.During training,a weighting mechanism is introduced to prioritize domain samples with clear fault features.This optimizes the deep feature extractor to obtain common features,enhancing domain adaptation.A low-speed and heavy-loaded bearing testbed was built,and fault data sets were established to validate the proposed method.Comparative experiments show that in noise domain adaptation tasks,proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%,23.088%,43.133%,16.344%,5.022%,and 9.233%over dense connection network(DenseNet),squeeze–excitation residual network(SE-ResNet),antinoise multiscale convolutional neural network(ANMSCNN),multiscale attention module-based convolutional neural network(MSAMCNN),domain adaptation network,and hybrid weighted domain adaptation(HWDA).In combined noise and working condition domain adaptation tasks,the RDMA–WDAN improves the accuracy by 45.672%,23.188%,43.266%,16.077%,5.716%,and 9.678%compared with baseline models.展开更多
文摘A complete set of techniques for the design,installation,maintenance and use of vegetable tunnel houses in tropical island countries was developed,which achieved the goals of rain prevention,wind protection,corrosion resistance,insect and bird prevention,water-saving irrigation,and economic efficiency,significantly improving the production capacity and technical level of vegetables in Samoa.The techniques have become a key vegetable production technology promoted nationwide in Samoa,with universal promotion value for tropical island countries.
文摘After some tragic fire events,Directive 2004/54/EC was issued to ensure a minimum safety level for tunnels belonging to the Trans-European Road Network longer than 500 m.Nowadays,most of the Italian road tunnels are still not in compliance with the minimum safety requirements,thus refurbishment works are often planned.By developing a traffic macro-simulation model,this paper aims at assessing the resilience of an existing twin-tube motorway tunnel when one of its tubes is partially or completely closed due to planned activities.Several scenarios were investigated,also considering the availability or not of an alternative itinerary in the surrounding transportation network.The average vehicles’speed was used as a functionality parameter,while the resilience metrics were the resilience loss,the recovery speed,and the resilience index.The findings showed higher resilience losses for complete closure rather than partial closure of the tube under planned refurbishment works.The implementation of digital technologies,such as variable message signs,might reduce the resilience loss of the tunnel system.This research might represent a reference for tunnel management agencies in the choice of the most appropriate traffic control strategy to improve tunnel resilience in the event of planned activities.
基金National Natural Science Foundation of China, No.40671186 No.40271012
文摘The western coast of Hainan Island exhibits a savanna landscape. Many types of sand dunes, including transverse dune ridges, longitudinal dune ridges, elliptical dunes, coppice dunes, and climbing dunes, are widely distributed in the coastal zone. In winter, high-frequency and high-energy NE winds （dominant winds） are prevalent, with a resultant drift direction （RDD） of S35.6°W. In spring, low-frequency and low-energy SW secondary winds prevail, with a RDD of N25.1°E. Wind tunnel simulations revealed that the airflow over the dune surface is the main factor controlling the erosion and deposition patterns of dune surfaces and the morphological development of dunes. In the region＇s bidirectional wind environment, with two seasonally distinct energy levels, the airflow over the surface of elliptical dunes, barchan dunes, and transverse dune ridges will exhibit a transverse pattern, whereas the airflow over longitudinal dunes ridges exhibits a lateral pattern and that over climbing dunes exhibits a climbing-circumfluent pattern. These patterns represent different dynamic processes. The coastal dunes on the western coast of Hainan Island are influenced by factors such as onshore winds, sand sources, coastal slopes, rivers, and forest shelter belts. The source of the sand that supplements these dunes particularly influences the development pattern： when there is more sand, the pattern shows positive equilibrium deposition between dune ridges and dunes; otherwise, it shows negative equilibrium deposition. The presence or absence of forest shelter belts also influences deposition and dune development patterns and transformation of dune forms. Coastal dunes and inland desert dunes experience similar dynamic processes, but the former have more diversified shapes and more complex formation mechanisms.
基金supported by the National Natural Science Foundation of China(Grant No.52375255)Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。
文摘As a critical component of a tunnel boring machine(TBM),the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive.Currently,under conditions of strong noise and complex working environments,traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy.To address these issues,we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network(RDMA-WDAN)for TBM main bearing fault diagnosis.Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules,achieving better domain adaptation despite significant domain interference.The residual denoising component utilizes a convolutional block to extract noise features,removing them via residual connections.Meanwhile,the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features,concentrating on deep fault features.During training,a weighting mechanism is introduced to prioritize domain samples with clear fault features.This optimizes the deep feature extractor to obtain common features,enhancing domain adaptation.A low-speed and heavy-loaded bearing testbed was built,and fault data sets were established to validate the proposed method.Comparative experiments show that in noise domain adaptation tasks,proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%,23.088%,43.133%,16.344%,5.022%,and 9.233%over dense connection network(DenseNet),squeeze–excitation residual network(SE-ResNet),antinoise multiscale convolutional neural network(ANMSCNN),multiscale attention module-based convolutional neural network(MSAMCNN),domain adaptation network,and hybrid weighted domain adaptation(HWDA).In combined noise and working condition domain adaptation tasks,the RDMA–WDAN improves the accuracy by 45.672%,23.188%,43.266%,16.077%,5.716%,and 9.678%compared with baseline models.
