Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core functi...Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.展开更多
High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it...High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.展开更多
Accurately reconstructing rock structures using numerical methods is vital in rock mechanics research community,especially when obtaining rock samples is difficult and expensive.The reconstructed models must reflect t...Accurately reconstructing rock structures using numerical methods is vital in rock mechanics research community,especially when obtaining rock samples is difficult and expensive.The reconstructed models must reflect the comprehensive characteristics of natural rock,including mineral content and spatial distributions.This study employs the bubbling method to reconstruct granite containing multiple minerals in both two-(2D)and three-dimensions(3D),proposing a general procedure for granite structure reconstruction.The bubbling method utilizes numerous bubbles(hemispheres or spheres)of varying sizes and gradually changing properties,which are randomly overlapped to create a heterogeneous plane(2D)or space(3D).The properties of these overlapped areas are adjusted based on the sum of neighboring bubbles'properties,allowing specific regions with extreme properties to be selected and intercepted to form the desired mineral shapes.The results demonstrate that the reproduced granite samples can accurately exhibit the mineral distributions and sizes of real granite,quantified by fractal dimension(D)and the hourglass parameter(V_(Sum)=V_(Total)).The proposed method is also suitable for reconstructing anisotropic granite models,with anisotropy described by a fitted elliptic curve derived from ratios between directional mineral sizes and cross-sectional dimensions.Based on these findings,a series of numerical granite models with similar structures were reconstructed and tested.Results indicate that different mineral distributions significantly impact the macroscopic mechanical behaviors,but variability in numerical simulation results decreases with increasing specimen size.The compressive and tensile strength values of the reconstructed numerical models show less variation than those of natural granite specimens.This suggests that,beyond mineral distribution,other factors such as internal defects within natural granite contribute to the observed discrepancies.Additionally,the bubbling method shows great potential for modeling porous structures and offers high computational efficiency.展开更多
In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine ...In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.展开更多
Different from the previous qualitative analysis of linear systems in time and frequency domains, the method for describing nonlinear systems quantitatively is proposed based on correlated dimensions. Nonlinear dynami...Different from the previous qualitative analysis of linear systems in time and frequency domains, the method for describing nonlinear systems quantitatively is proposed based on correlated dimensions. Nonlinear dynamics theory is used to analyze the pressure data of a contrarotating axial flow fan. The delay time is 18 and the embedded dimension varies from 1 to 25 through phase-space reconstruction. In addition, the correlated dimensions are calculated before and after stalling. The results show that the correlated dimensions drop from 1. 428 before stalling to 1. 198 after stalling, so they are sensitive to the stalling signal of the fan and can be used as a characteristic quantity for the judging of the fan stalling.展开更多
Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combin...Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.展开更多
The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstructio...The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.展开更多
Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reco...Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly.展开更多
The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the...The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the existing climate models suffer from constraints imposed by computational resources and model complexity,leading to outputs with coarse spatio-temporal resolution.Current meteorological super-resolution techniques predominantly focus on singledimensional(spatial or temporal)enhancements,failing to effectively reconstruct dynamic spatio-temporal coupled features.To address these limitations,this study proposes a Spatio-Temporal Multi-Scale Residual Network(ST-MSRN),which integrates a Multi-Scale Residual Feature Block(MSRFB)with a Channel Stacking Mechanism.The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns,while the integrated Efficient Multiscale Attention(EMA)module adaptively weights features based on spatio-temporal heterogeneity.Experimental results demonstrate:(1)Successful upscaling from 1.5°spatial/3-day temporal to 0.25°/daily resolution;(2)Superior performance over traditional methods(spline/nearest-neighbor interpolation)and mainstream deep learning methods,with marked improvements in key indicators such as structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)for temperature and precipitation data,while the mean absolute error(MAE)and mean squared error(MSE)have been significantly reduced.This work establishes a new paradigm for Earth system data enhancement,particularly advancing extreme weather early warning systems through physics-aware deep learning architectures.展开更多
To study the airflow distribution in human nasal cavity during respiration and the characteristic parameters of nasal structure, three-dimensional, anatomically accurate representations of 30 adult nasal cavity models...To study the airflow distribution in human nasal cavity during respiration and the characteristic parameters of nasal structure, three-dimensional, anatomically accurate representations of 30 adult nasal cavity models were recons- tructed based on processed tomography images collected from normal people. The airflow fields in nasal cavities were simulated by fluid dynamics with finite element software ANSYS. The results showed that the difference of human nasal cavity structure led to different airflow distribution in the nasal cavities and variation of the main airstream passing through the common nasal meatus. The nasal resistance in the regions of nasal valve and nasal vestibule accounted for more than half of the overall resistance. The characteristic model of nasal cavity was extracted on the basis of characteristic points and dimensions deduced from the original models. It showed that either the geometric structure or the airflow field of the two kinds of models was similar. The characteristic dimensions were the characteristic parameters of nasal cavity that could properly represent the original model in model studies on nasal cavity.展开更多
Probability density function (PDF) method is proposed for analysing the structure of the reconstructed attractor in computing the correlation dimensions of RR intervals of ten normal old men. PDF contains important in...Probability density function (PDF) method is proposed for analysing the structure of the reconstructed attractor in computing the correlation dimensions of RR intervals of ten normal old men. PDF contains important information about the spatial distribution of the phase points in the reconstructed attractor. To the best of our knowledge, it is the first time that the PDF method is put forward for the analysis of the reconstructed attractor structure. Numerical simulations demonstrate that the cardiac systems of healthy old men are about 6-6.5 dimensional complex dynamical systems. It is found that PDF is not symmetrically distributed when time delay is small, while PDF satisfies Gaussian distribution when time delay is big enough. A cluster effect mechanism is presented to explain this phenomenon. By studying the shape of PDFs, that the roles played by time delay are more important than embedding dimension in the reconstruction is clearly indicated. Results have demonstrated that the PDF method represents a promising numerical approach for the observation of the reconstructed attractor structure and may provide more information and new diagnostic potential of the analyzed cardiac system.展开更多
Methane in-situ explosion fracturing(MISEF)enhances permeability in shale reservoirs by detonating desorbed methane to generate detonation waves in perforations.Fracture propagation in bedding shale under varying expl...Methane in-situ explosion fracturing(MISEF)enhances permeability in shale reservoirs by detonating desorbed methane to generate detonation waves in perforations.Fracture propagation in bedding shale under varying explosion loads remains unclear.In this study,prefabricated perforated shale samples with parallel and vertical bedding are fractured under five distinct explosion loads using a MISEF experimental setup.High-frequency explosion pressure-time curves were monitored within an equivalent perforation,and computed tomography scanning along with three-dimensional reconstruction techniques were used to investigate fracture propagation patterns.Additionally,the formation mechanism and influencing factors of explosion crack-generated fines(CGF)were clarified by analyzing the morphology and statistics of explosion debris particles.The results indicate that methane explosion generated oscillating-pulse loads within perforations.Explosion characteristic parameters increase with increasing initial pressure.Explosion load and bedding orientation significantly influence fracture propagation patterns.As initial pressure increases,the fracture mode transitions from bi-wing to 4–5 radial fractures.In parallel bedding shale,radial fractures noticeably deflect along the bedding surface.Vertical bedding facilitates the development of transverse fractures oriented parallel to the cross-section.Bifurcation-merging of explosioninduced fractures generated CGF.CGF mass and fractal dimension increase,while average particle size decreases with increasing explosion load.This study provides valuable insights into MISEF technology.展开更多
A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environ...A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.展开更多
Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of ...Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.展开更多
Animal fossils in archaeological sites are closely related to human activities. The environment and human activities, such as hunting-selection, cook process, traditional culture and habits can be partly inferred from...Animal fossils in archaeological sites are closely related to human activities. The environment and human activities, such as hunting-selection, cook process, traditional culture and habits can be partly inferred from the variety of fauna, fragmentation of the bones, and the human marks on bones' sur-faces. So far, researches about marks on fossils are few in China, and are mainly observed directly by eyes. Light Microscopes and Scanning Electron Microscopes are also applied to the observation abroad. These methods could provide us a lot of information, but are mainly confined to 2 dimensions. In this paper, we analyze human marks on the surface of animal fossils through three dimensions re-construction and isoline analysis, which enable us observe and measure in 3 dimensions. This method gives us a lot of information as follows: the formation of the marks, the tools that produced the marks, the cutting edge, movement and micro-abrasion of the tools. Through study of human marks on the surface of animal fossils unearthed from Bailongdong Cave in Yunxi, Hubei Province, we have got the characteristics of the marks, and further deepen cognition of the cutting edge, cutting orientation, cut-ting sequence, as well as micro-abrasion of tools during the formation of these marks. This is the first to use virtual three dimensions reconstruction in studying the human marks on the surface of animal fossils in China.展开更多
基金supported by the Natural Science Foundation of China(Grant No.52574047 and Grant No.52374045)Key Project of Sichuan Provincial Joint Fund for Science Technology and Education,China(Grant No.2025NSFSC2008).
文摘Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.
基金Key Basic Research Project of Strengthening the Foundations Plan of China (Grant No.2019-JCJQ-ZD-360-12)National Defense Basic Scientific Research Program of China (Grant No.JCKY2021208B011)to provide fund for conducting experiments。
文摘High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.
基金funded by the National Key Research and Development Program of China(Grant No.2022YFC2903904)National Natural Science Foundation of China(Grant Nos.U1906208 and U21A20106).
文摘Accurately reconstructing rock structures using numerical methods is vital in rock mechanics research community,especially when obtaining rock samples is difficult and expensive.The reconstructed models must reflect the comprehensive characteristics of natural rock,including mineral content and spatial distributions.This study employs the bubbling method to reconstruct granite containing multiple minerals in both two-(2D)and three-dimensions(3D),proposing a general procedure for granite structure reconstruction.The bubbling method utilizes numerous bubbles(hemispheres or spheres)of varying sizes and gradually changing properties,which are randomly overlapped to create a heterogeneous plane(2D)or space(3D).The properties of these overlapped areas are adjusted based on the sum of neighboring bubbles'properties,allowing specific regions with extreme properties to be selected and intercepted to form the desired mineral shapes.The results demonstrate that the reproduced granite samples can accurately exhibit the mineral distributions and sizes of real granite,quantified by fractal dimension(D)and the hourglass parameter(V_(Sum)=V_(Total)).The proposed method is also suitable for reconstructing anisotropic granite models,with anisotropy described by a fitted elliptic curve derived from ratios between directional mineral sizes and cross-sectional dimensions.Based on these findings,a series of numerical granite models with similar structures were reconstructed and tested.Results indicate that different mineral distributions significantly impact the macroscopic mechanical behaviors,but variability in numerical simulation results decreases with increasing specimen size.The compressive and tensile strength values of the reconstructed numerical models show less variation than those of natural granite specimens.This suggests that,beyond mineral distribution,other factors such as internal defects within natural granite contribute to the observed discrepancies.Additionally,the bubbling method shows great potential for modeling porous structures and offers high computational efficiency.
基金Supported by the Naltural Science Foundation of Hunan Province(97JJY1006)Open Foundation of Stalte Key Lab. of Theory and Chief Technology on ISN of Xidian University(991894102)
文摘In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.
基金Supported by the Natural Science Foundation of Jiangsu Province(BK2005018)the Graduate Research and Innovation Plan of Jiangsu Province(CX07B-061Z)~~
文摘Different from the previous qualitative analysis of linear systems in time and frequency domains, the method for describing nonlinear systems quantitatively is proposed based on correlated dimensions. Nonlinear dynamics theory is used to analyze the pressure data of a contrarotating axial flow fan. The delay time is 18 and the embedded dimension varies from 1 to 25 through phase-space reconstruction. In addition, the correlated dimensions are calculated before and after stalling. The results show that the correlated dimensions drop from 1. 428 before stalling to 1. 198 after stalling, so they are sensitive to the stalling signal of the fan and can be used as a characteristic quantity for the judging of the fan stalling.
基金supported by National Natural Science Foundation of China(No.61103123)Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
文摘Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.
基金Supported by Major State Basic Research Program of China ("973" Program,No. 2011CB610505)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120032110029)
文摘The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.
基金Supported by the Key Program of National Natural Science Foundation of China(Nos.61077071,51075349)Program of National Natural Science Foundation of Hebei Province(Nos.F2011203207,F2010001312)
文摘Phase space reconstruction is the first step of recognizing the chaotic time series.On the basis of differential entropy ratio method,the embedding dimension opt m and time delay t are optimal for the state space reconstruction could be determined.But they are not the optimal parameters accepted for prediction.This study proposes an improved method based on the differential entropy ratio and Radial Basis Function(RBF)neural network to estimate the embedding dimension m and the time delay t,which have both optimal characteristics of the state space reconstruction and the prediction.Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively,and both the prediction accuracy and prediction length are improved greatly.
基金supported by the National Key Research and Development Program of China,(No.2024YFC3013100)the Joint Research Project for Meteorological Capacity Improvement(No.24NLTSZ007)China Meteorological Administration(CMA)Youth Innovation Team(CMA2024QN06).
文摘The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the existing climate models suffer from constraints imposed by computational resources and model complexity,leading to outputs with coarse spatio-temporal resolution.Current meteorological super-resolution techniques predominantly focus on singledimensional(spatial or temporal)enhancements,failing to effectively reconstruct dynamic spatio-temporal coupled features.To address these limitations,this study proposes a Spatio-Temporal Multi-Scale Residual Network(ST-MSRN),which integrates a Multi-Scale Residual Feature Block(MSRFB)with a Channel Stacking Mechanism.The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns,while the integrated Efficient Multiscale Attention(EMA)module adaptively weights features based on spatio-temporal heterogeneity.Experimental results demonstrate:(1)Successful upscaling from 1.5°spatial/3-day temporal to 0.25°/daily resolution;(2)Superior performance over traditional methods(spline/nearest-neighbor interpolation)and mainstream deep learning methods,with marked improvements in key indicators such as structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)for temperature and precipitation data,while the mean absolute error(MAE)and mean squared error(MSE)have been significantly reduced.This work establishes a new paradigm for Earth system data enhancement,particularly advancing extreme weather early warning systems through physics-aware deep learning architectures.
基金the National Natural Science Foundation of China (1047202510672036)the Natural Science Foundation of Liaoning Province,China (20032109)
文摘To study the airflow distribution in human nasal cavity during respiration and the characteristic parameters of nasal structure, three-dimensional, anatomically accurate representations of 30 adult nasal cavity models were recons- tructed based on processed tomography images collected from normal people. The airflow fields in nasal cavities were simulated by fluid dynamics with finite element software ANSYS. The results showed that the difference of human nasal cavity structure led to different airflow distribution in the nasal cavities and variation of the main airstream passing through the common nasal meatus. The nasal resistance in the regions of nasal valve and nasal vestibule accounted for more than half of the overall resistance. The characteristic model of nasal cavity was extracted on the basis of characteristic points and dimensions deduced from the original models. It showed that either the geometric structure or the airflow field of the two kinds of models was similar. The characteristic dimensions were the characteristic parameters of nasal cavity that could properly represent the original model in model studies on nasal cavity.
文摘Probability density function (PDF) method is proposed for analysing the structure of the reconstructed attractor in computing the correlation dimensions of RR intervals of ten normal old men. PDF contains important information about the spatial distribution of the phase points in the reconstructed attractor. To the best of our knowledge, it is the first time that the PDF method is put forward for the analysis of the reconstructed attractor structure. Numerical simulations demonstrate that the cardiac systems of healthy old men are about 6-6.5 dimensional complex dynamical systems. It is found that PDF is not symmetrically distributed when time delay is small, while PDF satisfies Gaussian distribution when time delay is big enough. A cluster effect mechanism is presented to explain this phenomenon. By studying the shape of PDFs, that the roles played by time delay are more important than embedding dimension in the reconstruction is clearly indicated. Results have demonstrated that the PDF method represents a promising numerical approach for the observation of the reconstructed attractor structure and may provide more information and new diagnostic potential of the analyzed cardiac system.
基金funded by the National Key Research and Development Program of China(No.2020YFA0711800)the National Science Fund for Distinguished Young Scholars(No.51925404)+2 种基金the National Natural Science Foundation of China(No.12372373)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_2909)the Graduate Innovation Program of China University of Mining and Technology(No.2024WLKXJ134)。
文摘Methane in-situ explosion fracturing(MISEF)enhances permeability in shale reservoirs by detonating desorbed methane to generate detonation waves in perforations.Fracture propagation in bedding shale under varying explosion loads remains unclear.In this study,prefabricated perforated shale samples with parallel and vertical bedding are fractured under five distinct explosion loads using a MISEF experimental setup.High-frequency explosion pressure-time curves were monitored within an equivalent perforation,and computed tomography scanning along with three-dimensional reconstruction techniques were used to investigate fracture propagation patterns.Additionally,the formation mechanism and influencing factors of explosion crack-generated fines(CGF)were clarified by analyzing the morphology and statistics of explosion debris particles.The results indicate that methane explosion generated oscillating-pulse loads within perforations.Explosion characteristic parameters increase with increasing initial pressure.Explosion load and bedding orientation significantly influence fracture propagation patterns.As initial pressure increases,the fracture mode transitions from bi-wing to 4–5 radial fractures.In parallel bedding shale,radial fractures noticeably deflect along the bedding surface.Vertical bedding facilitates the development of transverse fractures oriented parallel to the cross-section.Bifurcation-merging of explosioninduced fractures generated CGF.CGF mass and fractal dimension increase,while average particle size decreases with increasing explosion load.This study provides valuable insights into MISEF technology.
基金co-supported by the National Natural Science Foundation of China(Nos.61671463,61471379,61790551 and 61102166)。
文摘A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.
基金supported by the National Natural Science Foundation of China (61320106006, 61532006, 61502042)
文摘Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.
基金Supported by Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-106)National Natural Science Foundation of China (Grant No. 40772016)the Project of Chongqing City Government (Grant No. 08JWSK039)
文摘Animal fossils in archaeological sites are closely related to human activities. The environment and human activities, such as hunting-selection, cook process, traditional culture and habits can be partly inferred from the variety of fauna, fragmentation of the bones, and the human marks on bones' sur-faces. So far, researches about marks on fossils are few in China, and are mainly observed directly by eyes. Light Microscopes and Scanning Electron Microscopes are also applied to the observation abroad. These methods could provide us a lot of information, but are mainly confined to 2 dimensions. In this paper, we analyze human marks on the surface of animal fossils through three dimensions re-construction and isoline analysis, which enable us observe and measure in 3 dimensions. This method gives us a lot of information as follows: the formation of the marks, the tools that produced the marks, the cutting edge, movement and micro-abrasion of the tools. Through study of human marks on the surface of animal fossils unearthed from Bailongdong Cave in Yunxi, Hubei Province, we have got the characteristics of the marks, and further deepen cognition of the cutting edge, cutting orientation, cut-ting sequence, as well as micro-abrasion of tools during the formation of these marks. This is the first to use virtual three dimensions reconstruction in studying the human marks on the surface of animal fossils in China.
