The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an a...The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an axial pump.The proposed methodology is applied in the following steps.First,VMD is applied for decomposing vibration signals into various frequency bands,called as modes.After computing energy of each VMD,the lower(minimum)and upper(maximum)bounds from these energy readings are extracted for defect conditions,such as outer race,inner race,worn piston,faulty cylinder and valve plate,and blocked hole of the piston.Thereafter,energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets(SVNSs).Then,the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency bands.The mode having maximum neutrosophic entropy value is designated to the“most sensitive”frequency band.Thereafter,envelope demodulation is applied to the most sensitive selected frequency band for finding defective components.The proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.展开更多
Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effecti...Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms.展开更多
The rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy(FTIR)and attenuated total reflection(ATR).By the subtracted spectra between glucose aqueous s...The rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy(FTIR)and attenuated total reflection(ATR).By the subtracted spectra between glucose aqueous solution and de-ionized water,absorption peaks are calculated in fingerprint area.Based on these absorption peaks and multiple linear regression(MLR)model,discrete band selection method of absorption peaks disturbance model(APDM)was developed.5 absorption peaks 1150 cm^-1,1103 cm^-1,1078 cm^-1,1034 cm^-1,991 cm^-1 were found in fingerprint area.Used these absorption peaks to establish absorption peaks disturbance model,the optimal wavelength combinations are 1140 cm^-1,1096 cm^-1,1084 cm^-1,1030 cm^-1,993 cm^-1,the corresponding C-RMSEP and C-RP are 1.164 mmol/L and 0.828 respectively.The results show that the optimal prediction effect of APDM was obviously better than the one of the Partial least squares(PLS)model,and the complexity of the optimal model is reduced greatly also.The results also provide a theoretical basis for design of small and portable human serum glucose spectrometer.展开更多
Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems,...Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.展开更多
Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analy...Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.展开更多
Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving ...Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regressi...The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.展开更多
The need for wide-band clock and data recovery （CDR） circuits is discussed. A 2 Gbps to 12 Gbps continuous-rate CDR circuit employing a multi-mode voltage-control oscillator （VCO）, a frequency detector, and a phas...The need for wide-band clock and data recovery （CDR） circuits is discussed. A 2 Gbps to 12 Gbps continuous-rate CDR circuit employing a multi-mode voltage-control oscillator （VCO）, a frequency detector, and a phase detector （FD＆PD） is described. A new automatic frequency band selection （FBS） without external reference clock is proposed to select the appropriate mode and also solve the instability problem when the circuit is powering on. The multi-mode VCO and FD/PD circuits which can operate at full-rate and half-rate modes facilitate CDR with six operation modes. The proposed CDR structure has been modeled with MATLAB and the simulated results validate its feasibility.展开更多
Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands.Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-...Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands.Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image analysis.In this paper,we propose a new band selection algorithm using sparse nonnegative matrix factorization (sparse NMF).Though acting as a clustering method for band selection,sparse NMF need not consider the distance metric between different spectral bands,which is often the key step for most common clustering-based band selection methods.By imposing sparsity on the coefficient matrix,the bands' clustering assignments can be easily indicated through the largest entry in each column of the matrix.Experimental results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification.展开更多
Chang’E-1(CE-1)Interference Imaging Spectrometer(IIM)dataset suffers from the weak response in the near infrared(NIR)bands,which are the important wavelength for retrieving the minerals and elements of the Moon.In th...Chang’E-1(CE-1)Interference Imaging Spectrometer(IIM)dataset suffers from the weak response in the near infrared(NIR)bands,which are the important wavelength for retrieving the minerals and elements of the Moon.In this paper,the cross-calibration was implemented to the IIM hyperspectral data for improving the weak response in NIR bands.The results show that the cross-calibrated IIM spectra were consistent to the Earth-based telescopic spectra,which suggests that the cross-calibration yields acceptable results.For further validating the influence of the cross-calibration on the FeO inversion and searching the optimal bands to retrieve lunar FeO contents,four band selection schemes were designed to retrieve FeO using the original and cross-calibrated IIM spectra.By comparing the distribution patterns and histograms of the IIM derived FeO contents with the Clementine derived FeO,the IIM 891 nm band after cross-calibration showed a higher accuracy in the FeO inversion,hence most useful for lunar FeO inversion.展开更多
As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of d...As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.展开更多
Hyperspectral imaging,with many narrow bands of spectra,is strongly capable to detect or classify objects.It has been become one research hotspot in the field of near-ground remote sensing.However,the higher demands f...Hyperspectral imaging,with many narrow bands of spectra,is strongly capable to detect or classify objects.It has been become one research hotspot in the field of near-ground remote sensing.However,the higher demands for computing and complex operating of instrument are still the bottleneck for hyperspectral imaging technology applied in field.Band selection is a common way to reduce the dimensionality of hyperspectral imaging cube and simplify the design of spectral imaging instrument.In this research,hyperspectral images of blueberry fruit were collected both in the laboratory and in field.A set of spectral bands were selected by analyzing the differences among blueberry fruits at different growth stages and backgrounds.Furthermore,a normalized spectral index was set up using the bands selected to identify the three growth stages of blueberry fruits,aiming to eliminate the impact of background included leaf,branch,soil,illumination variation and so on.Two classifiers of spectral angle mapping(SAM),multinomial logistic regression(MLR)and classification tree were used to verify the results of identification of blueberry fruit.The detection accuracy was 82.1%for SAM classifier using all spectral bands,88.5%for MLR classifier using selected bands and 89.8%for decision tree using the spectral index.The results indicated that the normalization spectral index can both lower the complexity of computing and reduce the impact of noisy background in field.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.U1909217,U1709208)the Zhejiang Provincial Natural Science Foundation of China(No.LD21E050001)the Zhejiang Special Support Program for High-level Personnel Recruitment of China(No.2018R52034).
文摘The underlying study investigates single valued neutrosophic entropy based adaptive sensitive frequency band selection for variational mode decomposition(VMD)for the purpose of identifying defective components in an axial pump.The proposed methodology is applied in the following steps.First,VMD is applied for decomposing vibration signals into various frequency bands,called as modes.After computing energy of each VMD,the lower(minimum)and upper(maximum)bounds from these energy readings are extracted for defect conditions,such as outer race,inner race,worn piston,faulty cylinder and valve plate,and blocked hole of the piston.Thereafter,energy interval ranges are obtained and further converted into the form of single valued neutrosophic sets(SVNSs).Then,the proposed neutrosophic entropy measure is deployed for quantifying the non-linear connection between each bearing defect conditions and various frequency bands.The mode having maximum neutrosophic entropy value is designated to the“most sensitive”frequency band.Thereafter,envelope demodulation is applied to the most sensitive selected frequency band for finding defective components.The proposed neutrosophic entropy and VMD based methodology is effective in providing a better insight for selecting suitable frequency band for carrying out envelope demodulation in comparison to existing methods.
基金supported by the National Natural Science Foundation of China under Grant No.61806138Key R&D program of Shanxi Province(High Technology)under Grant No.201903D121119Science and Technology Development Foundation of the Central Guiding Local under Grant No.YDZJSX2021A038.
文摘Compressed sensing(CS),as an efficient data transmission method,has achieved great success in the field of data transmission such as image,video and text.It can robustly recover signals from fewer Measurements,effectively alleviating the bandwidth pressure during data transmission.However,CS has many shortcomings in the transmission of hyperspectral image(HSI)data.This work aims to consider the application of CS in the transmission of hyperspectral image(HSI)data,and provides a feasible research scheme for CS of HSI data.HSI has rich spectral information and spatial information in bands,which can reflect the physical properties of the target.Most of the hyperspectral image compressed sensing(HSICS)algorithms cannot effectively use the inter-band information of HSI,resulting in poor reconstruction effects.In this paper,A three-stage hyperspectral image compression sensing algorithm(Three-stages HSICS)is proposed to obtain intra-band and inter-band characteristics of HSI,which can improve the reconstruction accuracy of HSI.Here,we establish a multi-objective band selection(Mop-BS)model,amulti-hypothesis prediction(MHP)model and a residual sparse(ReWSR)model for HSI,and use a staged reconstruction method to restore the compressed HSI.The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS,and it performs best among all comparison algorithms.
基金This work was financially supported by the Natural Science Foundation of Hainan Province(417087)the Key Research and Development Program of Hainan Province(ZDYF2018007)+1 种基金the Science and Research Project of Hainan Province Education Department(No.Hnky2015-1)Research Fund for Advanced Talents of Hainan University(No.kyqd1577).
文摘The rapid quantification method of human serum glucose was established by using the Fourier transform infrared spectroscopy(FTIR)and attenuated total reflection(ATR).By the subtracted spectra between glucose aqueous solution and de-ionized water,absorption peaks are calculated in fingerprint area.Based on these absorption peaks and multiple linear regression(MLR)model,discrete band selection method of absorption peaks disturbance model(APDM)was developed.5 absorption peaks 1150 cm^-1,1103 cm^-1,1078 cm^-1,1034 cm^-1,991 cm^-1 were found in fingerprint area.Used these absorption peaks to establish absorption peaks disturbance model,the optimal wavelength combinations are 1140 cm^-1,1096 cm^-1,1084 cm^-1,1030 cm^-1,993 cm^-1,the corresponding C-RMSEP and C-RP are 1.164 mmol/L and 0.828 respectively.The results show that the optimal prediction effect of APDM was obviously better than the one of the Partial least squares(PLS)model,and the complexity of the optimal model is reduced greatly also.The results also provide a theoretical basis for design of small and portable human serum glucose spectrometer.
基金support by the National Natural Science Foundation of China (Grant No. 62005049)Natural Science Foundation of Fujian Province (Grant Nos. 2020J01451, 2022J05113)Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province (Grant No. JAT210035)。
文摘Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
基金Yulin Science and Technology Bureau production Project“Research on Smart Agricultural Product Traceability System”(No.CXY-2022-64)Light of West China(No.XAB2022YN10)+1 种基金The China Postdoctoral Science Foundation(No.2023M740760)Shaanxi Province Key Research and Development Plan(No.2024SF-YBXM-678).
文摘Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.
文摘Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
基金Funded by the National Natural Science Foundation of China (No. 62073153)The Major Scientific and Technological Innovation Projects in Shandong Province (No.2019JZZY010448)The Key Research and Development Plan of Shandong Province of China (No.2019GSF109018)。
文摘The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.
基金supported by the Hubei Natural Science Foundation of China underGrant No. 2010CDB02706the Fundamental Research Funds for the Central Universities under Grant No. C2009Q060
文摘The need for wide-band clock and data recovery （CDR） circuits is discussed. A 2 Gbps to 12 Gbps continuous-rate CDR circuit employing a multi-mode voltage-control oscillator （VCO）, a frequency detector, and a phase detector （FD＆PD） is described. A new automatic frequency band selection （FBS） without external reference clock is proposed to select the appropriate mode and also solve the instability problem when the circuit is powering on. The multi-mode VCO and FD/PD circuits which can operate at full-rate and half-rate modes facilitate CDR with six operation modes. The proposed CDR structure has been modeled with MATLAB and the simulated results validate its feasibility.
基金Project (No.60872071) supported by the National Natural Science Foundation of China
文摘Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral bands.Band selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image analysis.In this paper,we propose a new band selection algorithm using sparse nonnegative matrix factorization (sparse NMF).Though acting as a clustering method for band selection,sparse NMF need not consider the distance metric between different spectral bands,which is often the key step for most common clustering-based band selection methods.By imposing sparsity on the coefficient matrix,the bands' clustering assignments can be easily indicated through the largest entry in each column of the matrix.Experimental results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification.
基金supported by the National Basic Research Program of China (Grant No. 2010CB951603)Shanghai Science and Technology Support Program Special for EXPO (Grant No. 10DZ0581600)+2 种基金the Open Research Funding Program of KLGIS (Grant No. KLGIS2011A09)the National Natural Science Foundation of China (Grant No. 41172296)the Program for New Century Excellent Talents in University (Grant No. NCET-11-0242)
文摘Chang’E-1(CE-1)Interference Imaging Spectrometer(IIM)dataset suffers from the weak response in the near infrared(NIR)bands,which are the important wavelength for retrieving the minerals and elements of the Moon.In this paper,the cross-calibration was implemented to the IIM hyperspectral data for improving the weak response in NIR bands.The results show that the cross-calibrated IIM spectra were consistent to the Earth-based telescopic spectra,which suggests that the cross-calibration yields acceptable results.For further validating the influence of the cross-calibration on the FeO inversion and searching the optimal bands to retrieve lunar FeO contents,four band selection schemes were designed to retrieve FeO using the original and cross-calibrated IIM spectra.By comparing the distribution patterns and histograms of the IIM derived FeO contents with the Clementine derived FeO,the IIM 891 nm band after cross-calibration showed a higher accuracy in the FeO inversion,hence most useful for lunar FeO inversion.
文摘As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.
基金The work was financially supported by the National Key Research and Development Program of China Sub-project(No.2016YFD0700103)the National Natural Science Foundation of China(No.61805073)+1 种基金Innovation Scientists and Technicians Talent Projects of Henan Provincial Department of Education(No.19HASTIT021)Henan provincial science and technology project(No.182102110201&No.192102110204).
文摘Hyperspectral imaging,with many narrow bands of spectra,is strongly capable to detect or classify objects.It has been become one research hotspot in the field of near-ground remote sensing.However,the higher demands for computing and complex operating of instrument are still the bottleneck for hyperspectral imaging technology applied in field.Band selection is a common way to reduce the dimensionality of hyperspectral imaging cube and simplify the design of spectral imaging instrument.In this research,hyperspectral images of blueberry fruit were collected both in the laboratory and in field.A set of spectral bands were selected by analyzing the differences among blueberry fruits at different growth stages and backgrounds.Furthermore,a normalized spectral index was set up using the bands selected to identify the three growth stages of blueberry fruits,aiming to eliminate the impact of background included leaf,branch,soil,illumination variation and so on.Two classifiers of spectral angle mapping(SAM),multinomial logistic regression(MLR)and classification tree were used to verify the results of identification of blueberry fruit.The detection accuracy was 82.1%for SAM classifier using all spectral bands,88.5%for MLR classifier using selected bands and 89.8%for decision tree using the spectral index.The results indicated that the normalization spectral index can both lower the complexity of computing and reduce the impact of noisy background in field.
