As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantiz...As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ)was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.We also studied the performance of linear discriminant analysis,and support vector machine on the same data set.Among these three classifiers,LVQ had the highest correct classification rate of 99.17%.The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.展开更多
In this paper,we explore whether a feature selection method can improve model performance by using some classical machine learning models,artificial neural network,k-nearest neighbor,partial least squares-discriminati...In this paper,we explore whether a feature selection method can improve model performance by using some classical machine learning models,artificial neural network,k-nearest neighbor,partial least squares-discrimination analysis,random forest,and support vector machine(SVM),combined with the feature selection methods,distance correlation coefficient(DCC),important weight of linear discriminant analysis(IW-LDA),and Relief-F algorithms,to discriminate eight species of wood(African rosewood,Brazilian bubinga,elm,larch,Myanmar padauk,Pterocarpus erinaceus,poplar,and sycamore)based on the laser-induced breakdown spectroscopy(LIBS)technique.The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis.The feature spectral lines are selected out based on the important weight assessed by DCC,IW-LDA,and Relief-F.All models are built by using the different number of feature lines(sorted by their important weight)as input.The relationship between the number of feature lines and the correct classification rate(CCR)of the model is analyzed.The CCRs of all models are improved by using a suitable feature selection.The highest CCR achieves(98.55...0.39)%when the SVM model is established from 86 feature lines selected by the IW-LDA method.The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.展开更多
Thermal-actuation and piezoresistive-detection effects have been employed to pump the effective quality factor of MEMS resonators,targeting simple self-oscillation and better sensing performance in the air.However,the...Thermal-actuation and piezoresistive-detection effects have been employed to pump the effective quality factor of MEMS resonators,targeting simple self-oscillation and better sensing performance in the air.However,the ratio of the pumped effective quality factor to the inherent mechanical quality factor,crucial for characterizing the amplification,is hard to obtain.The main difficulty stems from hidden Lorentz peaks caused by feedthrough effects and the pump effect once the current is applied.In this paper,we demonstrated the presence of high-order harmonic components in the output of thermal-piezoresistive resonators when the oscillation amplitude is sufficiently large.By utilizing second-order harmonics,we achieved the improvement in signal-to-bias ratio of,20.85 dB compared to that without feedthrough cancellation and 9.67 dB compared to that using a de-embedded method when the bias current is 6.20 mA.Furthermore,the inherent mechanical quality factor is extracted at a low current of 1.8 mA with a value of 5800 using the second-order harmonics,and a nearly two orders of magnitude enhancement in Q factor can be obtained before entering the self-oscillation regime.An amplitude bias instability as good as 55 ppm and a frequency bias instability as good as 10 ppb are realized in the nonlinear operation regime with a pumped effective quality factor of 576k.The paper contributes to the fundamental understanding of the Q pump effect together with harmonic analysis of the thermal-piezoresistive resonators and also pushes forward the development of low-power consumption self-oscillation resonant sensors.展开更多
Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is u...Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.展开更多
基金supported by National Natural Science Foundation of China(No.62075011)Graduate Technological Innovation Project of Beijing Institute of Technology(No.2019CX20026)。
文摘As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ)was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.We also studied the performance of linear discriminant analysis,and support vector machine on the same data set.Among these three classifiers,LVQ had the highest correct classification rate of 99.17%.The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.
基金support from National Natural Science Foundation of China(No.62075011)Graduate Technological Innovation Project of Beijing Institute of Technology(No.2019CX20026)。
文摘In this paper,we explore whether a feature selection method can improve model performance by using some classical machine learning models,artificial neural network,k-nearest neighbor,partial least squares-discrimination analysis,random forest,and support vector machine(SVM),combined with the feature selection methods,distance correlation coefficient(DCC),important weight of linear discriminant analysis(IW-LDA),and Relief-F algorithms,to discriminate eight species of wood(African rosewood,Brazilian bubinga,elm,larch,Myanmar padauk,Pterocarpus erinaceus,poplar,and sycamore)based on the laser-induced breakdown spectroscopy(LIBS)technique.The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis.The feature spectral lines are selected out based on the important weight assessed by DCC,IW-LDA,and Relief-F.All models are built by using the different number of feature lines(sorted by their important weight)as input.The relationship between the number of feature lines and the correct classification rate(CCR)of the model is analyzed.The CCRs of all models are improved by using a suitable feature selection.The highest CCR achieves(98.55...0.39)%when the SVM model is established from 86 feature lines selected by the IW-LDA method.The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.
基金supported by the National Key R&D Program of China No.2023YFB3208800the National Science Foundation of China No.52475606 and 52435012+1 种基金the Fundamental Research Funds for the Central Universities,Innovation Capability Support Program of Shaanxi(Program No.2024RS-CXTD-17)Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2023JC-XJ-07).
文摘Thermal-actuation and piezoresistive-detection effects have been employed to pump the effective quality factor of MEMS resonators,targeting simple self-oscillation and better sensing performance in the air.However,the ratio of the pumped effective quality factor to the inherent mechanical quality factor,crucial for characterizing the amplification,is hard to obtain.The main difficulty stems from hidden Lorentz peaks caused by feedthrough effects and the pump effect once the current is applied.In this paper,we demonstrated the presence of high-order harmonic components in the output of thermal-piezoresistive resonators when the oscillation amplitude is sufficiently large.By utilizing second-order harmonics,we achieved the improvement in signal-to-bias ratio of,20.85 dB compared to that without feedthrough cancellation and 9.67 dB compared to that using a de-embedded method when the bias current is 6.20 mA.Furthermore,the inherent mechanical quality factor is extracted at a low current of 1.8 mA with a value of 5800 using the second-order harmonics,and a nearly two orders of magnitude enhancement in Q factor can be obtained before entering the self-oscillation regime.An amplitude bias instability as good as 55 ppm and a frequency bias instability as good as 10 ppb are realized in the nonlinear operation regime with a pumped effective quality factor of 576k.The paper contributes to the fundamental understanding of the Q pump effect together with harmonic analysis of the thermal-piezoresistive resonators and also pushes forward the development of low-power consumption self-oscillation resonant sensors.
基金supported by the National Key Scientific Instrument and Equipment Development Project of China(No.61527802)。
文摘Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.
