In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hoch...In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
This study employed tri-component continuous monitoring data from 10 measurement points on both sides of a base isolation layer in the basement of a large-span high-rise building in Beijing,as well as from a free-fiel...This study employed tri-component continuous monitoring data from 10 measurement points on both sides of a base isolation layer in the basement of a large-span high-rise building in Beijing,as well as from a free-field station and roof frame,during a Mw 5.5 magnitude earthquake in Pingyuan,Shandong,in 2023.The H/V spectral ratio method was used to evaluate the structural dynamic response characteristics of the building and analyze the regulatory effect of the base-isolation layer on seismic waves.The results indicate that during the earthquake,the peak frequency of the free-field and the measurement points below the base-isolation layer was stable at 0.17 Hz,whereas the main frequency of the measurement points above the base-isolation layer increased to 0.75–1.18 Hz,which is 4–6 times greater than that of the points below.The amplitude was suppressed by more than 70%,confirming that the base isolation layer effectively isolated the low-frequency energy from the ground and increased the response frequency of the building.When the building was excited by an earthquake,a three-tier frequency gradient was formed throughout the building:“base-isolation layer(0.17 Hz)-main body(1.18 Hz)-roof frame(3.83 Hz)”,which can effectively avoid resonance of the entire building.In addition,the composite base-isolation device changed the dynamic characteristics of the structure.The resonance period was extended from 0.74 s(theoretical value without base isolation)to 5.9 s(calculated value),and the resonance frequency was reduced from 1.35 to 0.17 Hz.This finding indicates that the base-isolation layer can enhance seismic performance by increasing flexibility and damping.展开更多
Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance...Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
Tunable mid-infrared lasers are essential for optical sensing and imaging.Existing technologies,however,face challenges in simultaneously achieving broadband spectral tunability and ultra-rapid scan rates,limiting the...Tunable mid-infrared lasers are essential for optical sensing and imaging.Existing technologies,however,face challenges in simultaneously achieving broadband spectral tunability and ultra-rapid scan rates,limiting their utility in dynamic scenarios such as real-time characterization of multiple molecular absorption bands.We present a high-speed approach for broadband wavelength sweeping in the mid-infrared region,leveraging spectral focusing via difference-frequency generation between a chirped fiber laser and an asynchronous,frequency-modulated electro-optic comb.This method enables pulse-to-pulse spectral tuning at a speed of 5.6 THz∕μs with 380 elements.Applied to spectroscopic sensing,our technique achieves broad spectral coverage(2600 to 3780 cm−1)with moderate spectral resolution(8 cm−1)and rapid acquisition time(-6.3μs).Notably,the controllable electro-optic comb facilitates high scan rates of up to 2 Mscans∕s across the full spectral range(corresponding to a speed of 60 THz∕μs),with trade-offs in number of elements(-30)and spectral point spacing or resolution(33 cm−1).Nevertheless,these capabilities make our platform highly promising for applications such as flow cytometry,chemical reaction monitoring,and mid-infrared ranging and imaging.展开更多
To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed,...To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vi...Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.展开更多
As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red ti...As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red tide outbreak can provide guidance to the coastal management,and is of great value to the aquaculture industry and marine environment protection.An approach for the risk assessment of red tide occurrence using spectral indices was made.The optimal spectral indices were explored from three candidates,namely two-band ratio(TBR)method,three-band spectral index(TBSI)method,and fluorescence baseline(FLB)method.The correlations between the spectral indices and the red tide occurrence were quantitatively evaluated through analysis of variance(ANOVA).The risk maps for the Beibu Gulf and the Bohai Bay in China were produced with the normalized spectral indices based on the multi-spectral observation from Sentinel-3 satellite.Results show that both TBR and TBSI values have significant correlations with the occurrences of red tide as the ANOVA results.TBSI illustrated correctly the risk of red tide occurrence in the risk maps and was the optimal spectral index offshore risk assessment of red tide.FLB method failed to recognize the high-risk regions and may not be the appropriate spectral index.The risk assessment method proposed in this study can provide early alarms on red tide occurrence and help timely the countermeasure against potential harms.展开更多
Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the...Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.展开更多
Let G be a graph andαÎ[0,1),Nikiforov merged the adjacency matrix and the signless Laplacian matrix to A_(α)(G)=αD(G)+(1-α)A(G),where D(G)A(G)are the degree diagonal matrix and the adjacency matrix of G,respe...Let G be a graph andαÎ[0,1),Nikiforov merged the adjacency matrix and the signless Laplacian matrix to A_(α)(G)=αD(G)+(1-α)A(G),where D(G)A(G)are the degree diagonal matrix and the adjacency matrix of G,respectively.The spectral radius of A_(α)(G)is called byα-spectral radius of the graph G.In this paper,we study the perturbation of the complete multipartite graphsα-spectral radius when move a vertex from a part to other part of the complete multipartite graphs.Moreover,we give some conditions when theα-spectral Turán of graphs implies the Turán theorem of graphs.展开更多
This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh ...This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh principle and the perturbation of the spectral radius under moving the edge operation,and the extremal hypergraphs are characterized for both supertree and unicyclic hypergraphs.The spectral radius of the graph is generalized.展开更多
A graph G is H-free,if it contains no H as a subgraph.A graph G is said to be H-minor free,if it does not contain H as a minor.In 2010,Nikiforov asked that what the maximum spectral radius of an H-free graph of order ...A graph G is H-free,if it contains no H as a subgraph.A graph G is said to be H-minor free,if it does not contain H as a minor.In 2010,Nikiforov asked that what the maximum spectral radius of an H-free graph of order n is.In this paper,we consider some Brualdi-Solheid-Turan type problems on bipartite graphs.In 2015,Zhai,Lin and Gong in[Linear Algebra Appl.,2015,471:21-27]proved that if G is a bipartite graph with order n≥2k+2 and ρ(G)≥ρ(K_(k,n-k)),then G contains a C_(2k+2) unless G≌K_(k,n-k).First,we give a new and more simple proof for the above theorem.Second,we prove that if G is a bipartite graph with order n≥2k+2 and ρ(G)≥ρ(K_(k,n-k)),then G contains all T_(2k+3) unless G≌K_(k,n-k).Finally,we prove that among all outerplanar bipartite graphs on n≥308026 vertices,K_(1,n-1) attains the maximum spectral radius.展开更多
For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchma...For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.展开更多
For a graph G,a vertex is said to be pendant if its neighborhood contains exactly one vertex.In this paper,we determine the extremal graphs among all n-vertex graphs with the minimum spectral radius andβpendant verti...For a graph G,a vertex is said to be pendant if its neighborhood contains exactly one vertex.In this paper,we determine the extremal graphs among all n-vertex graphs with the minimum spectral radius andβpendant vertices,whereβe{1,2,3,4,n-3,n-2,n-1}.展开更多
In this paper,we define the spectral Einstein functional associated with the Dirac operator for manifolds with boundary.And we give the proof of Kastler-Kalau-Walze type theorem for the spectral Einstein functional as...In this paper,we define the spectral Einstein functional associated with the Dirac operator for manifolds with boundary.And we give the proof of Kastler-Kalau-Walze type theorem for the spectral Einstein functional associated with the Dirac operator on 4-dimensional manifolds with boundary.展开更多
To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is ba...To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.11971418).
文摘In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2025ZD005100)by Beijing Geolight Technology Co.,Ltd.under the project“The Impact of Strong Ground Motion on Buildings”(YF-202520).
文摘This study employed tri-component continuous monitoring data from 10 measurement points on both sides of a base isolation layer in the basement of a large-span high-rise building in Beijing,as well as from a free-field station and roof frame,during a Mw 5.5 magnitude earthquake in Pingyuan,Shandong,in 2023.The H/V spectral ratio method was used to evaluate the structural dynamic response characteristics of the building and analyze the regulatory effect of the base-isolation layer on seismic waves.The results indicate that during the earthquake,the peak frequency of the free-field and the measurement points below the base-isolation layer was stable at 0.17 Hz,whereas the main frequency of the measurement points above the base-isolation layer increased to 0.75–1.18 Hz,which is 4–6 times greater than that of the points below.The amplitude was suppressed by more than 70%,confirming that the base isolation layer effectively isolated the low-frequency energy from the ground and increased the response frequency of the building.When the building was excited by an earthquake,a three-tier frequency gradient was formed throughout the building:“base-isolation layer(0.17 Hz)-main body(1.18 Hz)-roof frame(3.83 Hz)”,which can effectively avoid resonance of the entire building.In addition,the composite base-isolation device changed the dynamic characteristics of the structure.The resonance period was extended from 0.74 s(theoretical value without base isolation)to 5.9 s(calculated value),and the resonance frequency was reduced from 1.35 to 0.17 Hz.This finding indicates that the base-isolation layer can enhance seismic performance by increasing flexibility and damping.
基金supported by the Fundamental Research Funds for the Central Universities(N2001020)the National Natural Science Foundation of China(41201359).
文摘Sand dust belts span approximately one-fifth of the global land surface.In these regions,dust tends to settle on vegetation surfaces,altering the observed reflectance and affecting remote sensing detections.To enhance the accuracy of maize growth monitoring in dust-affected regions,this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture,Xinjiang Uygur Autonomous Region,China,by analyzing changes in canopy reflectance and vegetation indices.First,field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize,and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention.The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework(LESS)model for simulating realistic maize canopy spectra across different dust levels,with validation against Sentinel-2 imagery.Second,on the basis of the simulated and satellite-derived spectra,the dust resistance of 36 common vegetation indices was systematically evaluated,and new robust dust-resistant indices were developed.The results showed that compared with dust-free maize,the canopy reflectance of dust-retained maize followed an increase–decrease–increase pattern,with critical turning points at 735 and 1325 nm.The maximum reflectance difference of–0.11755(change rate:29.002%)occurred within the 735–1325 nm range at 24 g/m^(2)dust retention,and the minimum reflectance difference of 0.04285(change rate:148.950%)was observed in the 350–735 nm range under the same dust retention level.Among the 36 vegetation indices,only the global environment monitoring index(GEMI)and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index(TCARI/OSAVI)exhibited dust resistance,with GEMI being effective below 6 g/m^(2)and TCARI/OSAVI remaining stable across all levels(average ratio:0.970).The newly developed indices in this study,(RE3–RE2)/(NIR–RE2),(RE3–RE2)/(RE4–RE2),and(NIR–RE2)/(RE4–RE2),retained values within the predefined dust-resistant range over the full dust retention levels of 0–24 g/m^(2),thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices.Specially,(RE3–RE2)/(RE4–RE2)performed the most robustly in Sentinel-2 imagery,that is,58.020%of pixels were within the dust-resistant range,and an average ratio of 0.937 was obtained for the original-spectra index.This study provides a scientific basis for crop monitoring and management in dust-affected regions.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0301000)the Chongqing Technology Innovation and Application Develop-ment Project(Grant No.CSTB2022TIAD-DEX0031).
文摘Tunable mid-infrared lasers are essential for optical sensing and imaging.Existing technologies,however,face challenges in simultaneously achieving broadband spectral tunability and ultra-rapid scan rates,limiting their utility in dynamic scenarios such as real-time characterization of multiple molecular absorption bands.We present a high-speed approach for broadband wavelength sweeping in the mid-infrared region,leveraging spectral focusing via difference-frequency generation between a chirped fiber laser and an asynchronous,frequency-modulated electro-optic comb.This method enables pulse-to-pulse spectral tuning at a speed of 5.6 THz∕μs with 380 elements.Applied to spectroscopic sensing,our technique achieves broad spectral coverage(2600 to 3780 cm−1)with moderate spectral resolution(8 cm−1)and rapid acquisition time(-6.3μs).Notably,the controllable electro-optic comb facilitates high scan rates of up to 2 Mscans∕s across the full spectral range(corresponding to a speed of 60 THz∕μs),with trade-offs in number of elements(-30)and spectral point spacing or resolution(33 cm−1).Nevertheless,these capabilities make our platform highly promising for applications such as flow cytometry,chemical reaction monitoring,and mid-infrared ranging and imaging.
基金supported by the Jilin Province Science and Technology Development Plan Item (No.20240402068GH)。
文摘To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
基金supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (No.GK249909299001-036)National Key Research and Development Program of China (No. 2023YFB4502803)Zhejiang Provincial Natural Science Foundation of China (No.LDT23F01014F01)。
文摘Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.
基金Supported by the National Key R&D Program of China(No.2023YFC3108300)the Dalian High-level Talent Innovation Program(No.2022RG02)the Fundamental Research Funds for the Central Universities(No.3132023507)。
文摘As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red tide outbreak can provide guidance to the coastal management,and is of great value to the aquaculture industry and marine environment protection.An approach for the risk assessment of red tide occurrence using spectral indices was made.The optimal spectral indices were explored from three candidates,namely two-band ratio(TBR)method,three-band spectral index(TBSI)method,and fluorescence baseline(FLB)method.The correlations between the spectral indices and the red tide occurrence were quantitatively evaluated through analysis of variance(ANOVA).The risk maps for the Beibu Gulf and the Bohai Bay in China were produced with the normalized spectral indices based on the multi-spectral observation from Sentinel-3 satellite.Results show that both TBR and TBSI values have significant correlations with the occurrences of red tide as the ANOVA results.TBSI illustrated correctly the risk of red tide occurrence in the risk maps and was the optimal spectral index offshore risk assessment of red tide.FLB method failed to recognize the high-risk regions and may not be the appropriate spectral index.The risk assessment method proposed in this study can provide early alarms on red tide occurrence and help timely the countermeasure against potential harms.
基金supported in part by the National Natural Science Foundations of China(No.61801214)the Postgraduate Research Practice Innovation Program of NUAA(No.xcxjh20231504)。
文摘Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.
基金The National Science Foundation of China(12371349,12471331)。
文摘Let G be a graph andαÎ[0,1),Nikiforov merged the adjacency matrix and the signless Laplacian matrix to A_(α)(G)=αD(G)+(1-α)A(G),where D(G)A(G)are the degree diagonal matrix and the adjacency matrix of G,respectively.The spectral radius of A_(α)(G)is called byα-spectral radius of the graph G.In this paper,we study the perturbation of the complete multipartite graphsα-spectral radius when move a vertex from a part to other part of the complete multipartite graphs.Moreover,we give some conditions when theα-spectral Turán of graphs implies the Turán theorem of graphs.
基金Supported by Natural Science Foundation of HuBei Province(2022CFB299).
文摘This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh principle and the perturbation of the spectral radius under moving the edge operation,and the extremal hypergraphs are characterized for both supertree and unicyclic hypergraphs.The spectral radius of the graph is generalized.
基金Supported by NSFC(No.12271162)Natural Science Foundation of Shanghai(No.22ZR1416300).
文摘A graph G is H-free,if it contains no H as a subgraph.A graph G is said to be H-minor free,if it does not contain H as a minor.In 2010,Nikiforov asked that what the maximum spectral radius of an H-free graph of order n is.In this paper,we consider some Brualdi-Solheid-Turan type problems on bipartite graphs.In 2015,Zhai,Lin and Gong in[Linear Algebra Appl.,2015,471:21-27]proved that if G is a bipartite graph with order n≥2k+2 and ρ(G)≥ρ(K_(k,n-k)),then G contains a C_(2k+2) unless G≌K_(k,n-k).First,we give a new and more simple proof for the above theorem.Second,we prove that if G is a bipartite graph with order n≥2k+2 and ρ(G)≥ρ(K_(k,n-k)),then G contains all T_(2k+3) unless G≌K_(k,n-k).Finally,we prove that among all outerplanar bipartite graphs on n≥308026 vertices,K_(1,n-1) attains the maximum spectral radius.
文摘For segmented detectors,surface flatness is critical as it directly influences both energy resolution and image clarity.Additionally,the limited adjustment range of the segmented detectors necessitates precise benchmark construction.This paper proposes an architecture for detecting detector flatness based on channel spectral dispersion.By measuring the dispersion fringes for coplanar adjustment,the final adjustment residual is improved to better than 300 nm.This result validates the feasibility of the proposed technology and provides significant technical support for the development of next-generation large-aperture sky survey equipment.
文摘For a graph G,a vertex is said to be pendant if its neighborhood contains exactly one vertex.In this paper,we determine the extremal graphs among all n-vertex graphs with the minimum spectral radius andβpendant vertices,whereβe{1,2,3,4,n-3,n-2,n-1}.
文摘In this paper,we define the spectral Einstein functional associated with the Dirac operator for manifolds with boundary.And we give the proof of Kastler-Kalau-Walze type theorem for the spectral Einstein functional associated with the Dirac operator on 4-dimensional manifolds with boundary.
基金supported by the National Natural Science Foundation of China(No.61275010)the Ph.D.Programs Foundation of Ministry of Education of China(No.20132304110007)+1 种基金the Heilongjiang Natural Science Foundation(No.F201409)the Fundamental Research Funds for the Central Universities(No.HEUCFD1410)
文摘To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.
