Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. How...Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.展开更多
In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di...In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.展开更多
An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. Fr...An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. From the function approximation perspective, the OSA method shares the same separable approximation format to the one-way wave operator as other separable approximation methods but it is the only global function approximation among these methods. This leads to a difference in the phase error curve, impulse response, and migration result from other separable approximation methods. The difference is that the OSA method has higher accuracy, and the sensitivity to the velocity variation declines with increasing order.展开更多
In this paper,we first initialize the S-product of tensors to unify the outer product,contractive product,and the inner product of tensors.Then,we introduce the separable symmetry tensors and separable anti-symmetry t...In this paper,we first initialize the S-product of tensors to unify the outer product,contractive product,and the inner product of tensors.Then,we introduce the separable symmetry tensors and separable anti-symmetry tensors,which are defined,respectively,as the sum and the algebraic sum of rank-one tensors generated by the tensor product of some vectors.We offer a class of tensors to achieve the upper bound for rank(A)≤6 for all tensors of size 3×3×3.We also show that each 3×3×3 anti-symmetric tensor is separable.展开更多
Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale regi...Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.展开更多
A magnetically separable photocatalyst TiO2/SiO2/NiFe2O4 （TSN） with a typical ferromagnetic hysteresis was prepared by a liquid catalytic phase transfer method. When the intensity of applied magnetic field weakened ...A magnetically separable photocatalyst TiO2/SiO2/NiFe2O4 （TSN） with a typical ferromagnetic hysteresis was prepared by a liquid catalytic phase transfer method. When the intensity of applied magnetic field weakened to zero, the remnant magnetism of the prepared photocatalyst faded to zero. The photocatalytst can be separated from water when an external magnetic field is added and redispersed into aqueous solution after the external magnetic field is eliminated, that makes the photocatalysts promising for wastewater treatment. Transmission electron microscope （TEM） and X-ray diffractometer （XRD） were used to characterize the structure of the photocatalyst indicating that the magnetic SiOffNiFe204 （SN） particle was compactly enveloped by P-25 titania and Tit2 shell was formed. The magnetic composite showed high photocatalytic activity for the degradation of methyl orange in water. A thin SiO2 layer between NiFe2O4 and TiO2 shell prevented effectively the leakage of charges from TiO2 particles to NiFe2O4, which gave rise to the increase in photocatalytic activity. Moreover, the experiment on recycled use of TSN demonstrated a good repeatability of the photocatalytic activity.展开更多
Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in dee...Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.展开更多
A novel three-dimension separable and recyclable r GH-PANI/BiOI photocatalyst with the synergism of adsorption-enrichment and photocatalytic-degradation was successfully prepared via a facile three-step hydrothermal m...A novel three-dimension separable and recyclable r GH-PANI/BiOI photocatalyst with the synergism of adsorption-enrichment and photocatalytic-degradation was successfully prepared via a facile three-step hydrothermal method.The three-dimension reduced graphene oxide hydrogel(rGH)in with flower-like BiOI photocatalyst uniformly distributed not only possesses excellent adsorption and electron transport properties,but also is easy to be separated from water for recycling.In addition,polyphenylamine(PANI)provides superior hole transport ability due to its delocalizedл-лconjugate structure.The cooperation of rGH and PANI greatly enhances the separation efficiency of photogenerated carriers,and finally improves the photocatalytic degradation behaviors.The removal rates of Rhodamine B(RhB)by rGH-PANI/BiOI-70%composite under visible light respectively reach 100%and 50.13%in static and dynamic systems,which are 12.85 and 3.58 times of BiOI,respectively.The removal rate does not show decrease after 5 recycles indicating the excellent separable and recyclable property of rGH-PANI/BiOI photocatalyst.The work provides an essential reference for designing and constructing hydrogel-based ternary composite photocatalysts with excellent synergism of adsorption and photocatalysis,which shows great potential in the treatment of water pollution.展开更多
Using the generalized conditional symmetry approach, we obtain a number of new generalized (1+1)-dimensional nonlinear wave equations that admit derivative-dependent functional separable solutions.
We give the generalized definitions of variable separable solutions to nonlinear evolution equations, and characterize the relation between the functional separable solution and the derivative-dependent functional sep...We give the generalized definitions of variable separable solutions to nonlinear evolution equations, and characterize the relation between the functional separable solution and the derivative-dependent functional separable solution. The new definitions can unify various kinds of variable separable solutions appearing in references. As application, we classify the generalized nonlinear diffusion equations that admit special functional separable solutions and obtain some exact solutions to the resulting equations.展开更多
The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation...The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct fusion.Finally,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods.展开更多
The generalized conditional symmetry approach is applied to study the variable separation of the extended wave equations. Complete classification of those equations admitting functional separable solutions is obtained...The generalized conditional symmetry approach is applied to study the variable separation of the extended wave equations. Complete classification of those equations admitting functional separable solutions is obtained and exact separable solutions to some of the resulting equations are constructed.展开更多
Novel visible light magnetically separable graphene-based BiOBr composite photocatalysts were prepared for the first time. The structures, morphologies and optical properties were characterized by field emission scann...Novel visible light magnetically separable graphene-based BiOBr composite photocatalysts were prepared for the first time. The structures, morphologies and optical properties were characterized by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction and ultravioletvisible spectroscopy, respectively. The photocatalytic activity of the resulting samples was evaluated by degradation of tetracycline under visible light irradiation. An appropriate amount of introduced graphene can significantly enhance the photocatalytic activities. The enhanced activities were mainly attributed to the enhanced light absorption and the interfacial transfer of electrons. The corresponding photocatalytic mechanism was proposed based on the results.展开更多
Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Further...Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.展开更多
An efficient route for the synthesis of 5-substituted 1H-tetrazole via[2＋3]cycloaddition of nitriles and sodium azide is reported usingγ-Fe2O3 nanoparticles as a magnetic separable catalyst.Under optimized condition...An efficient route for the synthesis of 5-substituted 1H-tetrazole via[2＋3]cycloaddition of nitriles and sodium azide is reported usingγ-Fe2O3 nanoparticles as a magnetic separable catalyst.Under optimized conditions,the moderate to good yields（71-95%） can be obtained.The catalyst can be easily separated by a magnet and reused for several circles.展开更多
An accurate and wide-angle one-way propagator for wavefield extrapolation is an important topic for research on wave-equation prestack depth migration in the presence of large and rapid velocity variations. Based on t...An accurate and wide-angle one-way propagator for wavefield extrapolation is an important topic for research on wave-equation prestack depth migration in the presence of large and rapid velocity variations. Based on the optimal separable approximation presented in this paper, the mixed domain algorithm with forward and inverse Fourier transforms is used to construct the 3D one-way wavefield extrapolation operator. This operator separates variables in the wavenumber and spatial domains. The phase shift operation is implemented in the wavenumber domain while the time delay for lateral velocity variation is corrected in the spatial domain. The impulse responses of the one-way wave operator show that the numeric computation is consistent with the theoretical value for each velocity, revealing that the operator constructed with the optimal separable approximation can be applied to lateral velocity variations for the case of small steps. Imaging results of the SEG/EAGE model and field data indicate that the new method can be used to image complex structure.展开更多
Quantum correlations in a family of two-qubit separable classical-quantum correlated states are intensively studied with four different approaches, namely, quantum discord [Phys. Rev. Lett. 88 （2002） 017901], measur...Quantum correlations in a family of two-qubit separable classical-quantum correlated states are intensively studied with four different approaches, namely, quantum discord [Phys. Rev. Lett. 88 （2002） 017901], measurement- induced disturbance （MID） [Phys. Rev. A 77 （2008） 022301], ameliorated MID [J. Phys. A： Math. Theor. 44 （2011） 352002] and quantum dissonance [Phys. Rev. Left. 104 （2010） 080501]. Quantum correlations captured with different approaches are compared and discussed so that their three distinct features are exposed.展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported in part by the National Nature Science Foundation of China(Nos.62173091,62073082)in part by the Natural Science Foundation of Fujian Province(No.2023J01268)in part by the Taishan Scholar Program of Shandong Province.
文摘Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.
文摘In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.
基金sponsored by the National Natural Science Foundation of China (Nos. 40774069 and 40974074)the State Key Program of National Natural Science of China (No. 40830424)the National 973program (No. 007209603)
文摘An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. From the function approximation perspective, the OSA method shares the same separable approximation format to the one-way wave operator as other separable approximation methods but it is the only global function approximation among these methods. This leads to a difference in the phase error curve, impulse response, and migration result from other separable approximation methods. The difference is that the OSA method has higher accuracy, and the sensitivity to the velocity variation declines with increasing order.
文摘In this paper,we first initialize the S-product of tensors to unify the outer product,contractive product,and the inner product of tensors.Then,we introduce the separable symmetry tensors and separable anti-symmetry tensors,which are defined,respectively,as the sum and the algebraic sum of rank-one tensors generated by the tensor product of some vectors.We offer a class of tensors to achieve the upper bound for rank(A)≤6 for all tensors of size 3×3×3.We also show that each 3×3×3 anti-symmetric tensor is separable.
基金Supported by the National Natural Science Foundation of China(61903336,61976190)the Natural Science Foundation of Zhejiang Province(LY21F030015)。
文摘Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.
基金Supported by Shanghai Nano Technology Special Program (No.0452nm017).
文摘A magnetically separable photocatalyst TiO2/SiO2/NiFe2O4 （TSN） with a typical ferromagnetic hysteresis was prepared by a liquid catalytic phase transfer method. When the intensity of applied magnetic field weakened to zero, the remnant magnetism of the prepared photocatalyst faded to zero. The photocatalytst can be separated from water when an external magnetic field is added and redispersed into aqueous solution after the external magnetic field is eliminated, that makes the photocatalysts promising for wastewater treatment. Transmission electron microscope （TEM） and X-ray diffractometer （XRD） were used to characterize the structure of the photocatalyst indicating that the magnetic SiOffNiFe204 （SN） particle was compactly enveloped by P-25 titania and Tit2 shell was formed. The magnetic composite showed high photocatalytic activity for the degradation of methyl orange in water. A thin SiO2 layer between NiFe2O4 and TiO2 shell prevented effectively the leakage of charges from TiO2 particles to NiFe2O4, which gave rise to the increase in photocatalytic activity. Moreover, the experiment on recycled use of TSN demonstrated a good repeatability of the photocatalytic activity.
基金This work was supported by Liaoning Provincial Science Public Welfare Research Fund Project(No.2016002006)Liaoning Provincial Department of Education Scientific Research Service Local Project(No.L201708).
文摘Recently,video-based fire detection technology has become an important research topic in the field of machine vision.This paper proposes a method of combining the classification model and target detection model in deep learning for fire detection.Firstly,the depthwise separable convolution is used to classify fire images,which saves a lot of detection time under the premise of ensuring detection accuracy.Secondly,You Only Look Once version 3(YOLOv3)target regression function is used to output the fire position information for the images whose classification result is fire,which avoids the problem that the accuracy of detection cannot be guaranteed by using YOLOv3 for target classification and position regression.At the same time,the detection time of target regression for images without fire is greatly reduced saved.The experiments were tested using a network public database.The detection accuracy reached 98%and the detection rate reached 38fps.This method not only saves the workload of manually extracting flame characteristics,reduces the calculation cost,and reduces the amount of parameters,but also improves the detection accuracy and detection rate.
基金the National Natural Science Foundation of China(No.21706091)Science and Technology Program of Guangzhou(No.201804010400)Fundamental Research Funds for the Central Universities(No.21617426)。
文摘A novel three-dimension separable and recyclable r GH-PANI/BiOI photocatalyst with the synergism of adsorption-enrichment and photocatalytic-degradation was successfully prepared via a facile three-step hydrothermal method.The three-dimension reduced graphene oxide hydrogel(rGH)in with flower-like BiOI photocatalyst uniformly distributed not only possesses excellent adsorption and electron transport properties,but also is easy to be separated from water for recycling.In addition,polyphenylamine(PANI)provides superior hole transport ability due to its delocalizedл-лconjugate structure.The cooperation of rGH and PANI greatly enhances the separation efficiency of photogenerated carriers,and finally improves the photocatalytic degradation behaviors.The removal rates of Rhodamine B(RhB)by rGH-PANI/BiOI-70%composite under visible light respectively reach 100%and 50.13%in static and dynamic systems,which are 12.85 and 3.58 times of BiOI,respectively.The removal rate does not show decrease after 5 recycles indicating the excellent separable and recyclable property of rGH-PANI/BiOI photocatalyst.The work provides an essential reference for designing and constructing hydrogel-based ternary composite photocatalysts with excellent synergism of adsorption and photocatalysis,which shows great potential in the treatment of water pollution.
基金The project supported by the National Outstanding Youth Foundation of China (No.19925522)+2 种基金the Research Fund for the Doctoral Program of Higher Education of China (Grant.No.2000024832)National Natural Science Foundation of China (No.90203001)
文摘Using the generalized conditional symmetry approach, we obtain a number of new generalized (1+1)-dimensional nonlinear wave equations that admit derivative-dependent functional separable solutions.
基金The project supported by National Natural Science Foundation of China under Grant Nos.10447007 and 10671156the Natural Science Foundation of Shaanxi Province of China under Grant No.2005A13
文摘We give the generalized definitions of variable separable solutions to nonlinear evolution equations, and characterize the relation between the functional separable solution and the derivative-dependent functional separable solution. The new definitions can unify various kinds of variable separable solutions appearing in references. As application, we classify the generalized nonlinear diffusion equations that admit special functional separable solutions and obtain some exact solutions to the resulting equations.
基金supported by the Hunan Provincial Natural Science Foundation of China(2021JJ50074)the Scientific Research Fund of Hunan Provincial Education Department(19B082)+6 种基金the Science and Technology Development Center of the Ministry of Education-New Generation Information Technology Innovation Project(2018A02020)the Science Foundation of Hengyang Normal University(19QD12)the Science and Technology Plan Project of Hunan Province(2016TP1020)the Subject Group Construction Project of Hengyang Normal University(18XKQ02)theApplication Oriented SpecialDisciplines,Double First ClassUniversity Project of Hunan Province(Xiangjiaotong[2018]469)the Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development(2018CT5001)the First Class Undergraduate Major in Hunan Province Internet of Things Major(Xiangjiaotong[2020]248,No.288).
文摘The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable effectiveness.Here,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular architectures.In view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional layer.The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for themodel.To ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the encoder.Then,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct fusion.Finally,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is obtained.The superiority of SepFE is further demonstrated through comparisons with other advanced methods.
文摘The generalized conditional symmetry approach is applied to study the variable separation of the extended wave equations. Complete classification of those equations admitting functional separable solutions is obtained and exact separable solutions to some of the resulting equations are constructed.
基金Funded by National Science Funds for Creative Research Groups of China(No.51421006)Program for Changjiang Scholars and Innovative Research Team in University(No.IRT13061)+6 种基金the National Science Fundation of China for Excellent Young Scholars(No.51422902)the Key Program of National Natural Science Foundation of China(No.41430751)National Science Fund for Distinguished Young Scholars(No.51225901)the National Natural Science Foundation of China(No.51579073)Natural Science Foundation of Jiangsu Province(No.BK20141417)Fundamental Research Funds(No.2016B43814)PAPD
文摘Novel visible light magnetically separable graphene-based BiOBr composite photocatalysts were prepared for the first time. The structures, morphologies and optical properties were characterized by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction and ultravioletvisible spectroscopy, respectively. The photocatalytic activity of the resulting samples was evaluated by degradation of tetracycline under visible light irradiation. An appropriate amount of introduced graphene can significantly enhance the photocatalytic activities. The enhanced activities were mainly attributed to the enhanced light absorption and the interfacial transfer of electrons. The corresponding photocatalytic mechanism was proposed based on the results.
基金supported by NSFC(11471260)the Foundation of Shannxi Education Committee(12JK0850)
文摘Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.
基金the Jiangsu Province Foundation of Natural Science(No.BK2009678) for the financial support
文摘An efficient route for the synthesis of 5-substituted 1H-tetrazole via[2＋3]cycloaddition of nitriles and sodium azide is reported usingγ-Fe2O3 nanoparticles as a magnetic separable catalyst.Under optimized conditions,the moderate to good yields（71-95%） can be obtained.The catalyst can be easily separated by a magnet and reused for several circles.
基金This research is sponsored by China National Natural Science Foundation （N0. 40474047）.
文摘An accurate and wide-angle one-way propagator for wavefield extrapolation is an important topic for research on wave-equation prestack depth migration in the presence of large and rapid velocity variations. Based on the optimal separable approximation presented in this paper, the mixed domain algorithm with forward and inverse Fourier transforms is used to construct the 3D one-way wavefield extrapolation operator. This operator separates variables in the wavenumber and spatial domains. The phase shift operation is implemented in the wavenumber domain while the time delay for lateral velocity variation is corrected in the spatial domain. The impulse responses of the one-way wave operator show that the numeric computation is consistent with the theoretical value for each velocity, revealing that the operator constructed with the optimal separable approximation can be applied to lateral velocity variations for the case of small steps. Imaging results of the SEG/EAGE model and field data indicate that the new method can be used to image complex structure.
基金Supported by the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20103401110007the National Natural Science Foundation of China under Grant Nos.10874122,10975001,51072002,51272003+1 种基金the Program for Excellent Talents at the University of Guangdong,(Guangdong Teacher Letter[1010]No.79)the 211 Project of Anhui University
文摘Quantum correlations in a family of two-qubit separable classical-quantum correlated states are intensively studied with four different approaches, namely, quantum discord [Phys. Rev. Lett. 88 （2002） 017901], measurement- induced disturbance （MID） [Phys. Rev. A 77 （2008） 022301], ameliorated MID [J. Phys. A： Math. Theor. 44 （2011） 352002] and quantum dissonance [Phys. Rev. Left. 104 （2010） 080501]. Quantum correlations captured with different approaches are compared and discussed so that their three distinct features are exposed.
