To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focuse...To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.展开更多
This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines （SVMs）. The results showed that the O-H in-plan...This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines （SVMs）. The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.展开更多
The potential of support vector machines （SVMs） for the substructure elucidation of infrared spectra have been investigated. The trained SVMs can identify the 16 substructures with high accuracy.
A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.T...A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.展开更多
BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method t...BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.展开更多
In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characte...In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils.展开更多
We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air a...We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.展开更多
Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameter...Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameters extracted from the facial regions of interest or several hand-crafted features that are commonly used in visible spectrum. Till now there are no image features specially designed for thermal infrared images. In this paper, we propose using the deep Boltzmann machine to learn thermal features for emotion recognition from thermal infrared facial images. First, the face is located and normalized from the thermal infrared im- ages. Then, a deep Boltzmann machine model composed of two layers is trained. The parameters of the deep Boltzmann machine model are further fine-tuned for emotion recognition after pre-tralning of feature learning. Comparative experimental results on the NVIE database demonstrate that our approach outperforms other approaches using temperature statistic features or hand-crafted features borrowed from visible domain. The learned features from the forehead, eye, and mouth are more effective for discriminating valence dimension of emotion than other facial areas. In addition, our study shows that adding unlabeled data from other database during training can also improve feature learning performance.展开更多
基金supported by the State Administration of Forestry and Grass of the 948 Project of China(Grant No.2015-4-52)the support of the Fundamental Research Funds for the Central Universities(Grant No.2572017DB05)the support of the Natural Science Foundation of Heilongjiang Province(Grant No.C2017005)
文摘To improve the accuracy in recognizing defects on wood surfaces,a method fusing near infrared spectroscopy(NIR)and machine vision was examined.Larix gmelinii was selected as the raw material,and the experiments focused on the ability of the model to sort defects into four types:live knots,dead knots,pinholes,and cracks.Sample images were taken using an industrial camera,and a morphological algorithm was applied to locate the position of the defects.A portable near infrared spectrometer(900–1800 nm)collected the spectra of these positions.In addition,principal component analysis was utilized on these variables from spectral information and principal component vectors were extracted as the inputs of the model.The results show that a back propagation neural network model exhibited better discrimination accuracy of 92.7%for the training set and 92.0%for the test set.The research reveals that the NIR fusing machine vision is a feasible tool for detecting defects on board surfaces.
基金This work is partially supported by the National Natural Science Foundation of China （No 29877016）.
文摘This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines （SVMs）. The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.
文摘The potential of support vector machines （SVMs） for the substructure elucidation of infrared spectra have been investigated. The trained SVMs can identify the 16 substructures with high accuracy.
文摘A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.
基金the National Natural Science Foundation of China,No.61975069 and No.62005056Natural Science Foundation of Guangxi Province,No.2021JJB110003+2 种基金Natural Science Foundation of Guangdong Province,No.2018A0303131000Academician Workstation of Guangdong Province,No.2014B090905001Key Project of Scientific and Technological Projects of Guangzhou,No.201604040007 and No.201604020168.
文摘BACKGROUND Crohn’s disease(CD)is often misdiagnosed as intestinal tuberculosis(ITB).However,the treatment and prognosis of these two diseases are dramatically different.Therefore,it is important to develop a method to identify CD and ITB with high accuracy,specificity,and speed.AIM To develop a method to identify CD and ITB with high accuracy,specificity,and speed.METHODS A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB.Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.RESULTS The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm^(-1) and 1234 cm^(-1) bands,and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy,specificity,and sensitivity of 91.84%,92.59%,and 90.90%,respectively,for the differential diagnosis of CD and ITB.CONCLUSION Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level,and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.
基金supported jointly by the National Key Research Program of China (Nos. 2016YFC0502102, 2016YFC0502300)‘‘Western light’’ talent training plan (Class A)+5 种基金Chinese academy of science and technology services network program (No. KFJ-STS-ZDTP-036)international cooperation agency international partnership program (Nos. 132852KYSB20170029, 2014-3)Guizhou high-level innovative talent training program ‘‘ten’’ level talents program (No. 2016-5648)United fund of karst science research center (No. U1612441)International cooperation research projects of the national natural science fund committee (Nos. 41571130074, 41571130042)Science and Technology Plan of Guizhou Province of China (No. 2017–2966)
文摘In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils.
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)Brain Research Center(BRC)(2012K001127),The MKE(10033634-2012-21)National Research Foundation of Korea(NRF)(2012-0005787)
文摘We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.
基金This paper was supported by the National Natural Science Foundation of China （Grant Nos. 61175037, 61228304）, Special Innovation Project on Speech of Anhui Province （11010202192）, Project from Anhui Science and Technology Agency （1106c0805008） and the Fundamental Research Funds for the Central Universities. We also acknowledge partial support from the US National Science Foundation （1205664）.
文摘Facial expression and emotion recognition from thermal infrared images has attracted more and more attentions in recent years. However, the features adopted in current work are either temperature statistical parameters extracted from the facial regions of interest or several hand-crafted features that are commonly used in visible spectrum. Till now there are no image features specially designed for thermal infrared images. In this paper, we propose using the deep Boltzmann machine to learn thermal features for emotion recognition from thermal infrared facial images. First, the face is located and normalized from the thermal infrared im- ages. Then, a deep Boltzmann machine model composed of two layers is trained. The parameters of the deep Boltzmann machine model are further fine-tuned for emotion recognition after pre-tralning of feature learning. Comparative experimental results on the NVIE database demonstrate that our approach outperforms other approaches using temperature statistic features or hand-crafted features borrowed from visible domain. The learned features from the forehead, eye, and mouth are more effective for discriminating valence dimension of emotion than other facial areas. In addition, our study shows that adding unlabeled data from other database during training can also improve feature learning performance.
