We present a comprehensive mid-infrared spectroscopic survey of 124 Herbig Ae/Be stars using newly processed Spitzer/IRS spectra from the newly released CASSISjuice database.Based on prominent dust and molecular signa...We present a comprehensive mid-infrared spectroscopic survey of 124 Herbig Ae/Be stars using newly processed Spitzer/IRS spectra from the newly released CASSISjuice database.Based on prominent dust and molecular signatures(polycyclic aromatic hydrocarbons(PAHs),silicates,and hydrogenated amorphous carbons),we classify the stars into five groups.Our analysis reveals that 64%of the spectra show PAH emission,with detections peaking in the stellar effective temperature range 7000–11,000 K(B9–A5).Silicate features appear in 50%of the sample and likewise diminish at higher temperatures.Additionally,we find that future PAH studies can focus on Herbig Ae/Be stars with a spectral index n_(2−24)>−1 and flared morphologies to maximize PAH detections.The 6.2μm PAH band is the most frequently observed in our sample,shifting blueward with increasing stellar temperature,and this is the largest sample yet used to test that peak shift.The weaker 6.0μm feature does not shift with 6.2μm,implying a distinct origin of C=O(carbonyl)or olefinic C=C stretching relative to C–C vibrations.We examined the 11.0/11.2μm PAH ratio using high-resolution Spitzer spectra for the first time in a sample of Herbig Ae/Be stars,finding a range of ionization conditions.This study provides a strong foundation for future JWST observations of intermediate-mass pre-main-sequence stars.展开更多
Adobe After Effects软件中经常用到的特效合成插件有Particular、E3D、Form、Plexus等,这些插件各自配备了一套独立的粒子系统。当它们处于同一个合成中时,粒子之间并不会自然融合在一起,而是根据上下图层的关系进行相互遮挡。本文深...Adobe After Effects软件中经常用到的特效合成插件有Particular、E3D、Form、Plexus等,这些插件各自配备了一套独立的粒子系统。当它们处于同一个合成中时,粒子之间并不会自然融合在一起,而是根据上下图层的关系进行相互遮挡。本文深入探讨了不同粒子插件之间相互穿插与遮挡的实现方式,并结合实例阐述制作过程,模拟出两种粒子在同一合成空间中交互融合的逼真效果。展开更多
异常检测是保障飞机运行安全的重要手段,现有的固定阈值异常检测方法对数据时序特征利用较少,提取特征的能力较差。为提高飞机运行安全,提出了一种基于LSTM_AE神经网络的无监督离线异常检测的模型,对实际飞行数据进行异常检测。首先使用...异常检测是保障飞机运行安全的重要手段,现有的固定阈值异常检测方法对数据时序特征利用较少,提取特征的能力较差。为提高飞机运行安全,提出了一种基于LSTM_AE神经网络的无监督离线异常检测的模型,对实际飞行数据进行异常检测。首先使用LSTM(Long Short Term Memory)网络提取正常飞行数据的深度时序特征,再基于AE(Auto Encoder)对提取到的时序特征进行训练,利用模型收敛后得到重构误差确定自适应阈值,最后根据训练好的模型和自适应阈值进行异常检测。试验利用NASA公开的ALFA数据集。结果表明:基于LSTM_AE方法优于传统的固定阈值检测方法,可以实现对异常的检测,准确率为0.8717,召回率为0.9872,F1分数为0.9258。展开更多
基金financial support from CHRIST(Deemed to be University,Bangalore)through the SEED money projects(No:SMSS-2335,11/2023&SMSS-2220,12/2022)by the SERB project(CRG/2023/005271).
文摘We present a comprehensive mid-infrared spectroscopic survey of 124 Herbig Ae/Be stars using newly processed Spitzer/IRS spectra from the newly released CASSISjuice database.Based on prominent dust and molecular signatures(polycyclic aromatic hydrocarbons(PAHs),silicates,and hydrogenated amorphous carbons),we classify the stars into five groups.Our analysis reveals that 64%of the spectra show PAH emission,with detections peaking in the stellar effective temperature range 7000–11,000 K(B9–A5).Silicate features appear in 50%of the sample and likewise diminish at higher temperatures.Additionally,we find that future PAH studies can focus on Herbig Ae/Be stars with a spectral index n_(2−24)>−1 and flared morphologies to maximize PAH detections.The 6.2μm PAH band is the most frequently observed in our sample,shifting blueward with increasing stellar temperature,and this is the largest sample yet used to test that peak shift.The weaker 6.0μm feature does not shift with 6.2μm,implying a distinct origin of C=O(carbonyl)or olefinic C=C stretching relative to C–C vibrations.We examined the 11.0/11.2μm PAH ratio using high-resolution Spitzer spectra for the first time in a sample of Herbig Ae/Be stars,finding a range of ionization conditions.This study provides a strong foundation for future JWST observations of intermediate-mass pre-main-sequence stars.
文摘Adobe After Effects软件中经常用到的特效合成插件有Particular、E3D、Form、Plexus等,这些插件各自配备了一套独立的粒子系统。当它们处于同一个合成中时,粒子之间并不会自然融合在一起,而是根据上下图层的关系进行相互遮挡。本文深入探讨了不同粒子插件之间相互穿插与遮挡的实现方式,并结合实例阐述制作过程,模拟出两种粒子在同一合成空间中交互融合的逼真效果。
文摘异常检测是保障飞机运行安全的重要手段,现有的固定阈值异常检测方法对数据时序特征利用较少,提取特征的能力较差。为提高飞机运行安全,提出了一种基于LSTM_AE神经网络的无监督离线异常检测的模型,对实际飞行数据进行异常检测。首先使用LSTM(Long Short Term Memory)网络提取正常飞行数据的深度时序特征,再基于AE(Auto Encoder)对提取到的时序特征进行训练,利用模型收敛后得到重构误差确定自适应阈值,最后根据训练好的模型和自适应阈值进行异常检测。试验利用NASA公开的ALFA数据集。结果表明:基于LSTM_AE方法优于传统的固定阈值检测方法,可以实现对异常的检测,准确率为0.8717,召回率为0.9872,F1分数为0.9258。
