In this work,p⁃phenylenediamine and L⁃cysteine were used as raw materials,and water⁃soluble N,S co⁃doped carbon dots(N,S⁃CDs)with excellent performance were prepared through a one⁃step solvothermal method.The morpholo...In this work,p⁃phenylenediamine and L⁃cysteine were used as raw materials,and water⁃soluble N,S co⁃doped carbon dots(N,S⁃CDs)with excellent performance were prepared through a one⁃step solvothermal method.The morphology and structure of N,S⁃CDs were characterized by transmission electron microscope,X⁃ray diffrac⁃tion,Fourier transform infrared spectroscopy,and X⁃ray photoelectron spectroscopy,and the basic photophysical properties were investigated via UV⁃Vis absorption spectra and fluorescence spectra.Meanwhile,the N,S⁃CDs have excellent luminescence stability with pH,ionic strength,radiation time,and storage time.Experimental results illus⁃trated the present sensor platform exhibited high sensitivity and selectivity in response to baicalein with a detection limit of 85 nmol·L-1.The quenching mechanism is proved to be the inner filter effect.In addition,this sensor can also detect baicalein in biofluids(serum and urine)with good accuracy and reproducibility.展开更多
弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于...弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于增量支持向量机和D-S(increment support vector machine-Dempster-Shafer,ISVM-DS)证据理论的融合识别方法。首先,训练多个波段传感器红外特征的支持向量数据描述(support vector data description,SVDD)模型,生成壳向量并训练其ISVM模型;接着,采用ISVM模型的后验概率生成基本概率赋值(basic probability assignment,BPA);最后,利用D-S证据理论对多个证据的BPA进行融合,输出分类结果。实验结果表明,该方法能有效提高目标识别的准确性。展开更多
文摘In this work,p⁃phenylenediamine and L⁃cysteine were used as raw materials,and water⁃soluble N,S co⁃doped carbon dots(N,S⁃CDs)with excellent performance were prepared through a one⁃step solvothermal method.The morphology and structure of N,S⁃CDs were characterized by transmission electron microscope,X⁃ray diffrac⁃tion,Fourier transform infrared spectroscopy,and X⁃ray photoelectron spectroscopy,and the basic photophysical properties were investigated via UV⁃Vis absorption spectra and fluorescence spectra.Meanwhile,the N,S⁃CDs have excellent luminescence stability with pH,ionic strength,radiation time,and storage time.Experimental results illus⁃trated the present sensor platform exhibited high sensitivity and selectivity in response to baicalein with a detection limit of 85 nmol·L-1.The quenching mechanism is proved to be the inner filter effect.In addition,this sensor can also detect baicalein in biofluids(serum and urine)with good accuracy and reproducibility.
文摘弹道中段目标为一个目标群,包括弹头、诱饵、碎片等,并且由于距离传感器较远,红外成像为点目标,可用信息较少,因此单一的红外传感器往往难以满足识别要求,需要融合多个传感器进行识别。针对红外多传感器的融合识别问题,本文提出了基于增量支持向量机和D-S(increment support vector machine-Dempster-Shafer,ISVM-DS)证据理论的融合识别方法。首先,训练多个波段传感器红外特征的支持向量数据描述(support vector data description,SVDD)模型,生成壳向量并训练其ISVM模型;接着,采用ISVM模型的后验概率生成基本概率赋值(basic probability assignment,BPA);最后,利用D-S证据理论对多个证据的BPA进行融合,输出分类结果。实验结果表明,该方法能有效提高目标识别的准确性。
