Octacosanol is purified by agitated short-path distillation (SPD). Effects of evaporation temperature,number of SPD steps in series and other distillation method on the octacosanol recovery and decomposition are studi...Octacosanol is purified by agitated short-path distillation (SPD). Effects of evaporation temperature,number of SPD steps in series and other distillation method on the octacosanol recovery and decomposition are studied. Although the experimental results indicate some decomposition when the mixture of higher primary aliphatic alcohols is distillated by SPD, SPD is still an effective method to purify octacosanol. It is concluded that evaporation temperature affects greatly on the purity and recovery of octacosanol.展开更多
针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目...针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。展开更多
基金Supported by the National Natural Science Foundation of China(No. 20176037), and the Development Foundation from the Science & Technology Commission of Tianjin, China (No. 01310864).
文摘Octacosanol is purified by agitated short-path distillation (SPD). Effects of evaporation temperature,number of SPD steps in series and other distillation method on the octacosanol recovery and decomposition are studied. Although the experimental results indicate some decomposition when the mixture of higher primary aliphatic alcohols is distillated by SPD, SPD is still an effective method to purify octacosanol. It is concluded that evaporation temperature affects greatly on the purity and recovery of octacosanol.
文摘针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。
