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
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
Eu^(2+)doped fluorosilicate glass-ceramics containing BaF_(2) nanocrystals have high potential as spectral conversion materials for organic solar cells.However,it is difficult to realize the efficient design of BaF_(2...Eu^(2+)doped fluorosilicate glass-ceramics containing BaF_(2) nanocrystals have high potential as spectral conversion materials for organic solar cells.However,it is difficult to realize the efficient design of BaF_(2):Eu^(2+)doped fluorosilicate glass and to vividly observe the glass microstructure in experiment through traditional trial-and-error glass preparation method.BaF_(2):Eu^(2+)doped fluorosilicate glassceramics with high transparency,and high photoluminescence(PL)performance were predicted,designed and prepared via molecular dynamics(MD)simulation method.By MD simulation prediction,self-organized nanocrystallization was realized to inhibit the abnormal growth of nanocrystals due to[AlO_(4)]tetrahedra formed in the fluoride-oxide interface.The introduction of NaF reduces the effective phonon energy of the glass because Na+will prompt Al^(3+)to migrate from the fluoride phase to the silicate phase and interface.The local environment of Eu^(2+)is optimized by predicting the doping concentration of EuF_(3) and 2 mol%EuF3 is the best concentration in this work.Glass-ceramics sample GC2Eu as spectral conversion layer was successfully applied on organic solar cells to obtain more available visible phonons with a high photoelectric conversion efficiency(PCE).This work confirms the guidance of molecular dynamics simulation methods for fluorosilicate glasses design.展开更多
Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration para...Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.展开更多
The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;howev...The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.展开更多
As seismic signals propagate underground,the subsurface media absorb high-frequency components,decreasing seismic resolution and limiting the identification and characterization of thin reservoirs.To address the limit...As seismic signals propagate underground,the subsurface media absorb high-frequency components,decreasing seismic resolution and limiting the identification and characterization of thin reservoirs.To address the limitation,this paper proposes a multichannel spectral fitting(MSF)method.The MSF method aims to enhance seismic resolution by considering the spectral characteristics and the correlations between adjacent seismic traces.The key to the MSF method involves utilizing the amplitude spectrum of the attenuated Ricker wavelet to construct an objective function for spectral fitting,leading to improved seismic resolution.Furthermore,the MSF method establishes the correlation between adjacent seismic traces as a constraint to stably solve the target parameters based on the entire seismic spectrum,which helps obtain horizontally consistent and more realistic seismic signals.Synthetic and field seismic examples demonstrate that the proposed method not only provides higher-resolution seismic signals but also reveals more fine details of thin reservoirs compared to the time-variant spectral whitening method.It is concluded that the MSF method is a promising tool for seismic signal processing.展开更多
Mixed-phase clouds(MPCs)involve complex microphysical and dynamical processes of cloud formation and dissipation,which are crucial for numerical weather prediction and cloud-climate feedback.However,satellite remote s...Mixed-phase clouds(MPCs)involve complex microphysical and dynamical processes of cloud formation and dissipation,which are crucial for numerical weather prediction and cloud-climate feedback.However,satellite remote sensing of MPC properties is still challenging,and there is seldom MPC result inferred from passive spectral observations.This study examines the spectral characteristics of MPCs in the shortwave-infrared(SWIR)channels over the wavelength of 0.4–2.5μm,and evaluates the potential of current operational satellite spectroradiometer channels for MPC retrievals.With optical properties of MPCs based on the assumption of uniform mixing of both ice and liquid water particles,the effects of MPC ice optical thickness fraction(IOTF)and effective radius on associated optical properties are analyzed.As expected,results indicate that the MPC optical properties show features for ice and liquid water clouds,and their spectral variations show noticeable differences from those for homogeneous cases.A radiative transfer method is employed to examine the sensitivity of SWIR channels to given MPC cloud water path(CWP)and IOTF.MPCs have unique signal characteristics in the SWIR spectrum.The 0.87-μm channel is most sensitive to CWP.Meanwhile,the 1.61-and 2.13-μm channels are more sensitive to water-dominated MPCs(IOTF approaching 0),and the 2.25-μm channel is sensitive to both water-dominated and ice-dominated MPCs(IOTF approaching 1).Such spectral differences are potentially possible to be used to infer MPC properties based on radiometer observations,which will be investigated in future studies.展开更多
The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the pr...The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.展开更多
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Project supported by the National Natural Science Foundation of China(52172008,51872255)the Key Research and Development Project of Zhejiang Province(2021C01174)。
文摘Eu^(2+)doped fluorosilicate glass-ceramics containing BaF_(2) nanocrystals have high potential as spectral conversion materials for organic solar cells.However,it is difficult to realize the efficient design of BaF_(2):Eu^(2+)doped fluorosilicate glass and to vividly observe the glass microstructure in experiment through traditional trial-and-error glass preparation method.BaF_(2):Eu^(2+)doped fluorosilicate glassceramics with high transparency,and high photoluminescence(PL)performance were predicted,designed and prepared via molecular dynamics(MD)simulation method.By MD simulation prediction,self-organized nanocrystallization was realized to inhibit the abnormal growth of nanocrystals due to[AlO_(4)]tetrahedra formed in the fluoride-oxide interface.The introduction of NaF reduces the effective phonon energy of the glass because Na+will prompt Al^(3+)to migrate from the fluoride phase to the silicate phase and interface.The local environment of Eu^(2+)is optimized by predicting the doping concentration of EuF_(3) and 2 mol%EuF3 is the best concentration in this work.Glass-ceramics sample GC2Eu as spectral conversion layer was successfully applied on organic solar cells to obtain more available visible phonons with a high photoelectric conversion efficiency(PCE).This work confirms the guidance of molecular dynamics simulation methods for fluorosilicate glasses design.
基金supported by the National Natural Science Foundation of China(No.41804141)。
文摘Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.
基金the National Natural Science Foundation(NNSF)of China under Grant 41927801.
文摘The evaluation of adsorption states and shale gas content in shale fractures and pores relies on the analysis of these fractures and pores.Scanning electron microscopy images are commonly used for shale analysis;however,their low resolution,particularly the loss of high-frequency information at pore edges,presents challenges in analyzing fractures and pores in shale gas reservoirs.This study introduced a novel neural network called the spatial-spectral domain attention network(SSDAN),which employed spatial and spectral domain attention mechanisms to extract features and restore information in parallel.The network generated super-resolution images through a fusion module that included CNN-based spatial blocks for pixel-level image information recovery,spectral blocks to process Fourier transform information of images and enhance high-frequency recovery,and an adaptive vision transformer to process Fourier transform block information,eliminating the need for a preset image size.The SSDAN model demonstrated exceptional performance in comparative experiments on marine shale and marine continental shale datasets,achieving optimal performance on key indicators such as peak signal-to-noise ratio,structural similarity,learned perceptual image patch similarity,and Frechet inception distance while also exhibiting superior visual performance in pore recovery.Ablation experiments further confirmed the effectiveness of the spatial blocks,channel attention,spectral blocks,and frequency loss function in the model.The SSDAN model showed remarkable capability in enhancing the resolution of shale gas reservoir images and restoring high-frequency information at pore edges,thereby validating its effectiveness in unconventional natural gas reservoir analyses.
基金supported in part by the National Natural Science Foundation of China under Grant 42174164 and Grant 41704132in part by the Key Program of the Joint Fund of the Science,Technology,and Education of Sichuan Province,China under Grant 2024NSFSC1955+4 种基金in part by the Natural Science Foundation of Sichuan Province,China under Grant 2024NSFSC0080in part by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project under Grant SKLGP2022Z011in part by the Chengdu University of Technology Postgraduate Innovative Cultivation Program:Spatiotemporal Characterization of Seismic Signals for Tight Channel Sandstone Gas Reservoirs in the Synchro-Squeezing/Extracting Transform Domain under Grant CDUT2023BJCx002in part by the Creative Research Groups of the Natural Science Foundation of Sichuan under Grant 2023NSFSC198414th Five Year Plan Major Science and Technology Project of CNOOC under Grant KJGG2022-0903.
文摘As seismic signals propagate underground,the subsurface media absorb high-frequency components,decreasing seismic resolution and limiting the identification and characterization of thin reservoirs.To address the limitation,this paper proposes a multichannel spectral fitting(MSF)method.The MSF method aims to enhance seismic resolution by considering the spectral characteristics and the correlations between adjacent seismic traces.The key to the MSF method involves utilizing the amplitude spectrum of the attenuated Ricker wavelet to construct an objective function for spectral fitting,leading to improved seismic resolution.Furthermore,the MSF method establishes the correlation between adjacent seismic traces as a constraint to stably solve the target parameters based on the entire seismic spectrum,which helps obtain horizontally consistent and more realistic seismic signals.Synthetic and field seismic examples demonstrate that the proposed method not only provides higher-resolution seismic signals but also reveals more fine details of thin reservoirs compared to the time-variant spectral whitening method.It is concluded that the MSF method is a promising tool for seismic signal processing.
基金supported by the National Natural Science Foundation of China[Grant Nos.42205086 and 42122038]。
文摘Mixed-phase clouds(MPCs)involve complex microphysical and dynamical processes of cloud formation and dissipation,which are crucial for numerical weather prediction and cloud-climate feedback.However,satellite remote sensing of MPC properties is still challenging,and there is seldom MPC result inferred from passive spectral observations.This study examines the spectral characteristics of MPCs in the shortwave-infrared(SWIR)channels over the wavelength of 0.4–2.5μm,and evaluates the potential of current operational satellite spectroradiometer channels for MPC retrievals.With optical properties of MPCs based on the assumption of uniform mixing of both ice and liquid water particles,the effects of MPC ice optical thickness fraction(IOTF)and effective radius on associated optical properties are analyzed.As expected,results indicate that the MPC optical properties show features for ice and liquid water clouds,and their spectral variations show noticeable differences from those for homogeneous cases.A radiative transfer method is employed to examine the sensitivity of SWIR channels to given MPC cloud water path(CWP)and IOTF.MPCs have unique signal characteristics in the SWIR spectrum.The 0.87-μm channel is most sensitive to CWP.Meanwhile,the 1.61-and 2.13-μm channels are more sensitive to water-dominated MPCs(IOTF approaching 0),and the 2.25-μm channel is sensitive to both water-dominated and ice-dominated MPCs(IOTF approaching 1).Such spectral differences are potentially possible to be used to infer MPC properties based on radiometer observations,which will be investigated in future studies.
基金supported by the National Science Foundation of China(Grant Nos.42374205 and 41974179)the Specialized Research Fund of the National Space Science Center,Chinese Academy of Sciences(Grant No.E4PD3010)supported by the Specialized Research Fund for State Key Laboratories.
文摘The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.
