Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design...Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.展开更多
Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually re...Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.展开更多
Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion s...Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.展开更多
In gravity gradient inversion,to choose an appropriate component combination is very important,that needs to understand the function of each component of gravity gradient in the inversion.In this paper,based on the pr...In gravity gradient inversion,to choose an appropriate component combination is very important,that needs to understand the function of each component of gravity gradient in the inversion.In this paper,based on the previous research on the characteristics of gravity gradient components,we propose a reweighted inversion method to evaluate the influence of single gravity gradient component on the inversion resolution The proposed method only adopts the misfit function of the regularized equation and introduce a depth weighting function to overcome skin effect produced in gravity gradient inversion.A comparison between different inversion results was undertaken to verify the influence of the depth weighting function on the inversion result resolution.To avoid the premise of introducing prior information,we select the depth weighting function based on the sensitivity matrix.The inversion results using the single-prism model and the complex model show that the influence of different components on the resolution of inversion results is different in different directions,however,the inversion results based on two kind of models with adding different levels of random noise are basically consistent with the results of inversion without noises.Finally,the method was applied to real data from the Vinton salt dome,Louisiana,USA.展开更多
In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse...In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.展开更多
In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some condition...In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.展开更多
A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes wer...A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.展开更多
This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sa...This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sampling(CARS)algorithm.HIF-1αinhibition during early storage attenuated pH decline and significantly increased total colour change(ΔE)(P<0.05)while reducing myofibril fragmentation compared with controls.Proteomic profiling identified 257 differentially expressed proteins enriched in adenosine 5’-monophosphate(AMP)-activated protein kinase(AMPK),glycolysis,and HIF-1 signalling pathways.CARS analysis highlighted lactate dehydrogenase A(LDHA),phosphoglycerate kinase 1(PGK1;glycolytic enzyme),heat shock protein beta-6(HSPB6),and heat shock protein 90 kDa beta 1(HSP90B1)as key energy metabolism biomarkers.The results suggested that HIF-1 stabilised ATP production under hypoxia conditions by suppressing glycogen synthesis,enhancing glycolysis,modulating HSP activity to preserve cellular homeostasis,and influencing cytoskeletal proteins,thereby affecting meat quality.These results provide novel insights into post-mortem muscle energy metabolism regulation and potential targets for meat quality optimisation.展开更多
Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determ...Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.展开更多
In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)o...In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.展开更多
In this paper,we propose a new method to estimate the diffusion function in the jump-diffusion model.First,a threshold reweighted Nadaraya-Watson-type estimator is introduced.Then,we establish asymptotic normality for...In this paper,we propose a new method to estimate the diffusion function in the jump-diffusion model.First,a threshold reweighted Nadaraya-Watson-type estimator is introduced.Then,we establish asymptotic normality for the estimator and conduct Monte Carlo simulations through two examples to verify the better finite-sampling properties.Finally,our estimator is demonstrated through the actual data of the Shanghai Interbank Offered Rate in China.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensin...An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.展开更多
With the increase of exploration depth,it is more and more difficult to find Au deposits.Due to the limitation of time and cost,traditional geological exploration methods are becoming increasingly difficult to be effe...With the increase of exploration depth,it is more and more difficult to find Au deposits.Due to the limitation of time and cost,traditional geological exploration methods are becoming increasingly difficult to be effectively applied.Thus,new methods and ideas are urgently needed.This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits.For this purpose,48 plant(Seriphidium terrae-albae)and soil(aeolian gravel desert soil)samples were first collected along a sampling line that traverses an Au mineralization alteration zone(Aketasi mining region in an arid region of China)and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device(ASD)FieldSpec3 spectrometer.Then,the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated.Additionally,the characteristic bands were selected from plant spectra using four different methods,namely,genetic algorithm(GA),stepwise regression analysis(STE),competitive adaptive reweighted sampling(CARS),and correlation coefficient method(CC),and were then input into the partial least squares(PLS)method to construct a model for estimating the soil Au content.Finally,the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method.The results were compared with those of a model based on the full spectrum.The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information,and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil.The cross-validated coefficient of determination(R2)and the ratio of the performance to deviation(RPD)between the predicted value and the measured value reached the maximum of 0.8218 and 2.37,respectively,with a minimum value of 6.56μg/kg for the root-mean-squared error(RMSE)in the full spectrum model.However,in the process of modeling,it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method.Compared with the GA,STE,and CC methods,CARS was the superior characteristic band screening method based on the accuracy and complexity of the model.When modeling with characteristic bands,the highest accuracy,R2 of 0.8016,RMSE of 7.07μg/kg,and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra(1/lnR)'(where it represents the first derivative of the reciprocal of the logarithmic spectrum)of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil.Thus,characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content.Finally,this study proposes a method of using plant spectra to find concealed Au deposits,which may have promising application prospects because of its simplicity and rapidity.展开更多
To ensure the quality and safety of pure milk,detection method of typical preservative-potassium sorbate in milk was researched in this paper.Hyperspectral imaging technology was applied to realize rapid detection.Inf...To ensure the quality and safety of pure milk,detection method of typical preservative-potassium sorbate in milk was researched in this paper.Hyperspectral imaging technology was applied to realize rapid detection.Influence factors for hyperspectral data collection for milk samples were firstly researched,including height of sample,bottom color and sample filled up container or not.Pretreatment methods and variable selection algorithms were applied into original spectral data.Rapid detection models were built based on support vector machine method(SVM).Finally,standard normalized variable(SNV)-competitive adaptive reweighted sampling(CARS)and SVM model was chosen in this paper.The accuracies of calibration set and testing set were 0.97 and 0.97,respectively.Kappa coefficient of the model was 0.93.It could be seen that hyperspectral imaging technology could be used to detect for potassium sorbate in milk.Meanwhile,it also provided methodological supports for the rapid detection of other preservatives in milk.展开更多
In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: t...In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: that of the lost decade, that of 2008 and that of the Covid-19 pandemic. To this end, they use a particular type of mathematical-statistical model that is widely applied today in the economic field, namely a simultaneous equation model (SEM). This simultaneous equation model is estimated through an Iteratively reweighted least squares (IRLS) using quarterly historical series in the sample period Q1 1994 - Q2 2020. All data are in real terms. The results, appropriately compared with those of other authors, suggest that the monetary policy has a (limited) impact only on the interbank market. The fiscal policy, instead, has a greater ability to influence the money supply, the private consumption and the inflation expectations.展开更多
The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results ...The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.展开更多
BACKGROUND Persistent postural-perceptual dizziness(PPPD)is a functional disorder,typically preceded by acute vestibular disorders.It is characterized by a shift in processing spatial orientation information,to favor ...BACKGROUND Persistent postural-perceptual dizziness(PPPD)is a functional disorder,typically preceded by acute vestibular disorders.It is characterized by a shift in processing spatial orientation information,to favor visual over vestibular and somatosensory inputs,and a failure of higher cortical mechanisms.To date,no therapies for PPPD have been approved.Kampo medicine hangebyakujutsutemmato(HBT)has been reported to alleviate disturbances of equilibrium.We hypothesized that HBT would be a beneficial treatment for PPPD.AIM To examine the efficacy of HBT for the treatment of PPPD.METHODS Patients with PPPD were enrolled and divided into two groups:The HBT group(n=24)and the non-HBT group(n=14).The participants completed questionnaire surveys[Niigata PPPD questionnaire(NPQ),dizziness handicap inventory,hospital anxiety and depression scale(HADS),orthostatic dysregulation questionnaire,pittsburg sleep quality index(PSQI),and motion sickness scores]before and after HBT treatment.Additionally,to identify HBT responders,multivariate regression analysis was performed using the results of the ques-tionnaire surveys and equilibrium tests;including stabilometry,and caloric,vestibular evoked myogenic response,and head-up tilt tests.RESULTS Thirty-eight outpatients were included in this study,of which 14 patients(3 men,11 women;mean age,63.5±15.9 years)received treatment without HBT,and 24(1 man,23 women;mean age,58.2±18.7 years)received combination treatment with HBT.Following HBT treatment,NPQ scores decreased significantly(baseline 40.1±10.0 vs 2 mo 24.6±17.7,P<0.001).No statistically significant changes were observed in the NPQ scores in the non-HBT group(baseline 38.6±12.2 vs 2 mo 39.4±14.4,P=0.92).Multivariable regression analysis revealed that the results of stabilometry(P=0.02)and the caloric(P=0.03),and head-up tilt tests(P<0.001),HADS(P=0.003),and PSQI(P=0.01)were associated with HBT responsiveness in PPPD patients.CONCLUSION HBT may be an effective adjunct therapy for PPPD.Patients with autonomic dysfunction,unstable balance,semicircular canal paresis,anxiety,and poor sleep quality may be high responders to HBT.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61603322)the Research Foundation of Education Bureau of Hunan Province of China(No.16C1542)
文摘Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.
文摘Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.
基金supported by the Henan Provincial Science and Technology Research Project under Grants 232102211006,232102210044,232102211017,232102210055 and 222102210214the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205+1 种基金the Undergraduate Universities Smart Teaching Special Research Project of Henan Province under Grant Jiao Gao[2021]No.489-29the Doctor Natural Science Foundation of Zhengzhou University of Light Industry under Grants 2021BSJJ025 and 2022BSJJZK13.
文摘Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.
基金supported by the National Key R&D Program of China(Nos.2016YFC0303002 and 2017YFC0601701)China Geological Survey Program(No.DD20191007)
文摘In gravity gradient inversion,to choose an appropriate component combination is very important,that needs to understand the function of each component of gravity gradient in the inversion.In this paper,based on the previous research on the characteristics of gravity gradient components,we propose a reweighted inversion method to evaluate the influence of single gravity gradient component on the inversion resolution The proposed method only adopts the misfit function of the regularized equation and introduce a depth weighting function to overcome skin effect produced in gravity gradient inversion.A comparison between different inversion results was undertaken to verify the influence of the depth weighting function on the inversion result resolution.To avoid the premise of introducing prior information,we select the depth weighting function based on the sensitivity matrix.The inversion results using the single-prism model and the complex model show that the influence of different components on the resolution of inversion results is different in different directions,however,the inversion results based on two kind of models with adding different levels of random noise are basically consistent with the results of inversion without noises.Finally,the method was applied to real data from the Vinton salt dome,Louisiana,USA.
文摘In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.
文摘In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.
文摘A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.
基金supported by the Innovation Research Group Project of the National Natural Science Foundation of China(No.32260555).
文摘This study investigated hypoxia-inducible factor(HIF)-1α-mediated proteomic changes in post-slaughter Tan sheep skeletal muscle and identified energy metabolism biomarkers using the competitive adaptive reweighted sampling(CARS)algorithm.HIF-1αinhibition during early storage attenuated pH decline and significantly increased total colour change(ΔE)(P<0.05)while reducing myofibril fragmentation compared with controls.Proteomic profiling identified 257 differentially expressed proteins enriched in adenosine 5’-monophosphate(AMP)-activated protein kinase(AMPK),glycolysis,and HIF-1 signalling pathways.CARS analysis highlighted lactate dehydrogenase A(LDHA),phosphoglycerate kinase 1(PGK1;glycolytic enzyme),heat shock protein beta-6(HSPB6),and heat shock protein 90 kDa beta 1(HSP90B1)as key energy metabolism biomarkers.The results suggested that HIF-1 stabilised ATP production under hypoxia conditions by suppressing glycogen synthesis,enhancing glycolysis,modulating HSP activity to preserve cellular homeostasis,and influencing cytoskeletal proteins,thereby affecting meat quality.These results provide novel insights into post-mortem muscle energy metabolism regulation and potential targets for meat quality optimisation.
基金funded by the Key Science and Technology Program of Henan Province under Grant No.212102110262Science and Technology Plan Project of Henan Provincial Market Supervision and Administration Bureau under Grant No.2021sj40+1 种基金the Key Research Program of Zhejiang Province under Grant No.2020C02018Scientific Research Projects for College Students under Grant No.2020KX0006,China.The authors acknowledge the support.
文摘Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.
文摘In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.
基金supported by the National Natural Science Foundation of China(Grant Nos.12071257 and 11971267)National Key R&D Program of China(Grant No.2018YFA0703900)+7 种基金Shandong Provincial Natural Science Foundation(Grant No.ZR2019ZD41)the Young Scholars Program of Shandong University.Yuping Song’s research is supported by the National Natural Science Foundation of China(Grant No.11901397)Ministry of Education,Humanities and Social Sciences project(Grant No.18YJCZH153)National Statistical Science Research Project(Grant No.2018LZ05)Youth Academic Backbone Cultivation Project of Shanghai Normal University(Grant No.310-AC7031-19-003021)General Research Fund of Shanghai Normal University(Grant No.SK201720)Key Subject of Quantitative Economics(Grant No.310-AC7031-19-004221)Academic Innovation Team of Shanghai Normal University(Grant No.310-AC7031-19-004228).
文摘In this paper,we propose a new method to estimate the diffusion function in the jump-diffusion model.First,a threshold reweighted Nadaraya-Watson-type estimator is introduced.Then,we establish asymptotic normality for the estimator and conduct Monte Carlo simulations through two examples to verify the better finite-sampling properties.Finally,our estimator is demonstrated through the actual data of the Shanghai Interbank Offered Rate in China.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
基金supported by the National Natural Science Foundation of China(Project Nos.41901268 and 42371385)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002).
文摘An accurate and robust estimation of leaf chlorophyll content(LCC)is very important to better know the process of material and energy exchange between plants and the environment.Compared with traditional remote sensing methods,abundant research has made progress in agronomic parameter retrieval using different CNN frameworks.Nevertheless,limited reports have paid attention to the problems,i.e.,limited measured data,hyperspectral redundancy,and model convergence issues,when concerning CNN models for parameter estimation.Therefore,the present study tried to analyze the effects of synthetic data size expansion employing aGaussian process regression(GPR)model for simulation,input feature optimization using different spectral indices with a competitive adaptive reweighted sampling(CARS)algorithm,model convergence issue combining transfer learning(TL)method for accurate and robust estimation of plant LCC with a deep learning framework(i.e.,ResNet-18)using the ANGERS data(a public dataset containing foliar biochemical parameters spectral data for various plant types).Results showed that ResNet-18 training using 800 simulated reflectances(400–1000 nm)and partial ANGERS data exhibited better results,with an R^(2)value of 0.89,an RMSE value of 6.98μg/cm^(2),an RPD value of 3.70,for LCC retrieval using remanent ANGERS data,thanmodels that using simulations with different amounts of data.The estimation accuracies obviously increased when nine spectral indexes,selected from the CARS algorithm,were used as model input for running the ResNet-18 model(R^(2)=0.96,RMSE=4.65μg/cm^(2),RPD=4.81).In addition,coupling transfer learning with ResNet-18 improved the model convergence rate,and TL-ResNet-18 exhibited accurate results for LCC estimation(R^(2)=0.94,RMSE=5.14μg/cm^(2),RPD=4.65).These results suggest that adding appropriate synthetic data,input features optimization,and transfer learning techniques could be effectively used for improved LCC retrieval with a ResNet-18 model.
基金This research was funded by the National Natural Science Foundation of China(U1803117)the Young Scholars in Western China,Chinese Academy of Sciences(2020-XBQNXZ-014)+3 种基金the Tianchi Doctoral Plan(Y970000317)the Key Project of Natural Science Foundation of China-Xinjiang Joint Fund(U1803241)the Xinjiang Uygur Autonomous Region Talent Special Plan-Tianshan Outstanding Youth(2019Q033)the Geological Exploration Project of Xinjiang Bureau of Geo-exploration and Minera development(XGMB202143).
文摘With the increase of exploration depth,it is more and more difficult to find Au deposits.Due to the limitation of time and cost,traditional geological exploration methods are becoming increasingly difficult to be effectively applied.Thus,new methods and ideas are urgently needed.This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits.For this purpose,48 plant(Seriphidium terrae-albae)and soil(aeolian gravel desert soil)samples were first collected along a sampling line that traverses an Au mineralization alteration zone(Aketasi mining region in an arid region of China)and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device(ASD)FieldSpec3 spectrometer.Then,the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated.Additionally,the characteristic bands were selected from plant spectra using four different methods,namely,genetic algorithm(GA),stepwise regression analysis(STE),competitive adaptive reweighted sampling(CARS),and correlation coefficient method(CC),and were then input into the partial least squares(PLS)method to construct a model for estimating the soil Au content.Finally,the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method.The results were compared with those of a model based on the full spectrum.The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information,and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil.The cross-validated coefficient of determination(R2)and the ratio of the performance to deviation(RPD)between the predicted value and the measured value reached the maximum of 0.8218 and 2.37,respectively,with a minimum value of 6.56μg/kg for the root-mean-squared error(RMSE)in the full spectrum model.However,in the process of modeling,it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method.Compared with the GA,STE,and CC methods,CARS was the superior characteristic band screening method based on the accuracy and complexity of the model.When modeling with characteristic bands,the highest accuracy,R2 of 0.8016,RMSE of 7.07μg/kg,and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra(1/lnR)'(where it represents the first derivative of the reciprocal of the logarithmic spectrum)of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil.Thus,characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content.Finally,this study proposes a method of using plant spectra to find concealed Au deposits,which may have promising application prospects because of its simplicity and rapidity.
基金Supported by the National Key Research and Development Program of China(2016YFD0700204-02)China Agriculture Research System(CARS-36)Heilongjiang Post-doctoral Subsidy Project of China(LBH-Z17020)。
文摘To ensure the quality and safety of pure milk,detection method of typical preservative-potassium sorbate in milk was researched in this paper.Hyperspectral imaging technology was applied to realize rapid detection.Influence factors for hyperspectral data collection for milk samples were firstly researched,including height of sample,bottom color and sample filled up container or not.Pretreatment methods and variable selection algorithms were applied into original spectral data.Rapid detection models were built based on support vector machine method(SVM).Finally,standard normalized variable(SNV)-competitive adaptive reweighted sampling(CARS)and SVM model was chosen in this paper.The accuracies of calibration set and testing set were 0.97 and 0.97,respectively.Kappa coefficient of the model was 0.93.It could be seen that hyperspectral imaging technology could be used to detect for potassium sorbate in milk.Meanwhile,it also provided methodological supports for the rapid detection of other preservatives in milk.
文摘In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: that of the lost decade, that of 2008 and that of the Covid-19 pandemic. To this end, they use a particular type of mathematical-statistical model that is widely applied today in the economic field, namely a simultaneous equation model (SEM). This simultaneous equation model is estimated through an Iteratively reweighted least squares (IRLS) using quarterly historical series in the sample period Q1 1994 - Q2 2020. All data are in real terms. The results, appropriately compared with those of other authors, suggest that the monetary policy has a (limited) impact only on the interbank market. The fiscal policy, instead, has a greater ability to influence the money supply, the private consumption and the inflation expectations.
文摘The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.
文摘BACKGROUND Persistent postural-perceptual dizziness(PPPD)is a functional disorder,typically preceded by acute vestibular disorders.It is characterized by a shift in processing spatial orientation information,to favor visual over vestibular and somatosensory inputs,and a failure of higher cortical mechanisms.To date,no therapies for PPPD have been approved.Kampo medicine hangebyakujutsutemmato(HBT)has been reported to alleviate disturbances of equilibrium.We hypothesized that HBT would be a beneficial treatment for PPPD.AIM To examine the efficacy of HBT for the treatment of PPPD.METHODS Patients with PPPD were enrolled and divided into two groups:The HBT group(n=24)and the non-HBT group(n=14).The participants completed questionnaire surveys[Niigata PPPD questionnaire(NPQ),dizziness handicap inventory,hospital anxiety and depression scale(HADS),orthostatic dysregulation questionnaire,pittsburg sleep quality index(PSQI),and motion sickness scores]before and after HBT treatment.Additionally,to identify HBT responders,multivariate regression analysis was performed using the results of the ques-tionnaire surveys and equilibrium tests;including stabilometry,and caloric,vestibular evoked myogenic response,and head-up tilt tests.RESULTS Thirty-eight outpatients were included in this study,of which 14 patients(3 men,11 women;mean age,63.5±15.9 years)received treatment without HBT,and 24(1 man,23 women;mean age,58.2±18.7 years)received combination treatment with HBT.Following HBT treatment,NPQ scores decreased significantly(baseline 40.1±10.0 vs 2 mo 24.6±17.7,P<0.001).No statistically significant changes were observed in the NPQ scores in the non-HBT group(baseline 38.6±12.2 vs 2 mo 39.4±14.4,P=0.92).Multivariable regression analysis revealed that the results of stabilometry(P=0.02)and the caloric(P=0.03),and head-up tilt tests(P<0.001),HADS(P=0.003),and PSQI(P=0.01)were associated with HBT responsiveness in PPPD patients.CONCLUSION HBT may be an effective adjunct therapy for PPPD.Patients with autonomic dysfunction,unstable balance,semicircular canal paresis,anxiety,and poor sleep quality may be high responders to HBT.
