具有多谱带完美吸收效应的超构材料在光学滤波和折射率传感等多种应用中是理想的材料。提出了一种由银金属上的氮化硅介电纳米空腔阵列组成的多谱带窄带完美吸收超构材料。有限元仿真给出了四个最高可达99.9%的吸收峰,以及最小达到0.74...具有多谱带完美吸收效应的超构材料在光学滤波和折射率传感等多种应用中是理想的材料。提出了一种由银金属上的氮化硅介电纳米空腔阵列组成的多谱带窄带完美吸收超构材料。有限元仿真给出了四个最高可达99.9%的吸收峰,以及最小达到0.74 nm的吸收峰宽。这些吸收谱带来自于表面晶格模式和三个表面等离激元极化子模式。此外,这些模式的谱峰对超构材料几何外形和环境介质光学参数的变化敏感,从而在可见光-近红外范围内可以被调控。用于折射率传感时,其具有347 nm每折射率单位的灵敏度,Figure of Merit达到469。这些特性令这一材料适用于光学滤波器和折射率传感器等用途。展开更多
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarci...Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.展开更多
文摘具有多谱带完美吸收效应的超构材料在光学滤波和折射率传感等多种应用中是理想的材料。提出了一种由银金属上的氮化硅介电纳米空腔阵列组成的多谱带窄带完美吸收超构材料。有限元仿真给出了四个最高可达99.9%的吸收峰,以及最小达到0.74 nm的吸收峰宽。这些吸收谱带来自于表面晶格模式和三个表面等离激元极化子模式。此外,这些模式的谱峰对超构材料几何外形和环境介质光学参数的变化敏感,从而在可见光-近红外范围内可以被调控。用于折射率传感时,其具有347 nm每折射率单位的灵敏度,Figure of Merit达到469。这些特性令这一材料适用于光学滤波器和折射率传感器等用途。
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
基金Project(2301DH09002)supported by the Bureau of Planning and Natural Resources,Chongqing,ChinaProject(2022T3051)supported by the Science and Technology Service Network Initiative,ChinaProject(2018-ZL-01)supported by the Sichuan Transportation Science and Technology,China。
文摘Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment.However,the considerable time and financial burdens of landslide inventories often result in persistent data scarcity,which frequently impedes the generation of accurate and informative landslide susceptibility maps.Addressing this challenge,this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically.Notably,the proposed model,calibrated with the warmup-cosine annealing(WCA)learning rate strategy,demonstrated remarkable predictive capabilities,particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region.This is evidenced by the area under the receiver operating characteristic curve(AUC)values,which exhibited significant improvements of 51.00%,24.40%and 2.15%,respectively,compared to a deep learning model,in contexts where only 1%,5%and 10%of data from the target region were used for retraining.Simultaneously,there were reductions in loss of 16.12%,27.61%and 15.44%,respectively,in these instances.
