Based on the analysis of the deformation styles in different tectonic belts of the MiddleUpper Yangtze region,as well as the dissection of typical hydrocarbon reservoirs,this study determined the controlling effects o...Based on the analysis of the deformation styles in different tectonic belts of the MiddleUpper Yangtze region,as well as the dissection of typical hydrocarbon reservoirs,this study determined the controlling effects of deformations on the hydrocarbon accumulations,obtaining the following results.The Middle-Upper Yangtze region experienced significant deformations during the Late Indosinian(T_(2)–T_(3)),the Middle Yanshanian(J_(3)–K_(1)),and the Himalayan,and five styles of tectonic deformations mainly occurred,namely superimposed deep burial,uplift,compressional thrusting,multi-layer decollement,and secondary deep burial.The distribution of hydrocarbon reservoirs in the piedmont thrust belts is controlled by the concealed structures on the footwall of the deep nappe.The gentle deformation area in central Sichuan experienced differential uplift,structural-lithologic hydrocarbon reservoirs were formed over a wide area.The eastern Sichuan-western Hunan and Hubei deformation area experienced Jura Mountains-type multi-layer detachment,compressional thrusting,and uplift.In relatively weakly folded and uplifted areas,conventional structural-lithologic hydrocarbon reservoirs have undergone adjustment and re-accumulation,and the shale gas resources are well preserved.In the strongly deformed areas,conventional hydrocarbon reservoirs were destroyed,while unconventional hydrocarbon reservoirs have been partially preserved.The marine strata in the Jianghan Basin experienced compression,thrusting,and denudation in the early stage and secondary deep burial in the late stage.Consequently,the unconventional gas resources have been partially preserved in these strata.Secondary hydrocarbon generation become favorable for conventional hydrocarbon accumulations in the marine strata.展开更多
Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every...Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every field where data need to be uploaded to the cloud.Federated learning(FL)is an emerging trend for distributed training of data.The primary goal of FL is to train an efficient communication model without compromising data privacy.The traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic data.This paper presents FL-based traffic flow prediction with spatio-temporal correlation.This work uses a differential privacy(DP)scheme for privacy preservation of participant's data.To the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation.The proposed framework trains the data locally at the client-side with DP.It then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained models.The results of the proposed work show that the FedGCN model accurately predicts the traffic.DP scheme at client-side helps clients to set a budget for privacy loss.展开更多
The perfectly matched layer（PML） was first introduced by Berenger as an absorbing boundary condition for electromagnetic wave propagation.In this article,a method is developed to ex-tend the PML to simulating seismi...The perfectly matched layer（PML） was first introduced by Berenger as an absorbing boundary condition for electromagnetic wave propagation.In this article,a method is developed to ex-tend the PML to simulating seismic wave propagation in fluid-saturated porous medium.This non-physical boundary is used at the computational edge of a Forsyte polynomial convolutional differenti-ator（FPCD） algorithm as an absorbing boundary condition to truncate unbounded media.The incor-poration of PML in Biot＇s equations is given.Numerical results show that the PML absorbing bound-ary condition attenuates the outgoing waves effectively and eliminates the reflections adequately.展开更多
This paper applies the convolutional differentiator method, based on generalized Forsyte orthogonal polynomial （CFPD）, to simulate the seismic wave propagation in two-phase media. From the numerical results we can s...This paper applies the convolutional differentiator method, based on generalized Forsyte orthogonal polynomial （CFPD）, to simulate the seismic wave propagation in two-phase media. From the numerical results we can see that three types of waves, fast P-waves, S-waves and slow P-waves, can be observed in the seismic wave field. The experiments on anisotropic models demonstrate that the wavefront is elliptic instead of circular and S-wave splitting occurs in anisotropic two-phase media. The research has confirmed that the rules of elastic wave propagation in fluid-saturated porous media are controlled by Biot＇s theory. Experiment on a layered fault model shows the wavefield generated by the interface and the fault very well, indicating the effectiveness of CFPD method on the wavefield modeling for real layered media in the Earth. This research has potential applications to the investigation of Earth＇s deep structure and oil/gas exploration.展开更多
A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a ...A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.展开更多
The convolution-type Gurtin variational principle is known as the only variational principle that is, from the mathematics point of view, totally equivalent to the initial value problem system. In this paper, the equa...The convolution-type Gurtin variational principle is known as the only variational principle that is, from the mathematics point of view, totally equivalent to the initial value problem system. In this paper, the equation of motion of rectangular thin plates is first transformed to a new governing equation containing initial conditions by using a convolution method. A convolution-type semi-analytical DQ approach, which involves differential quadrature （DQ） approximation in the space domain and an analytical series expansion in the time domain, is proposed to obtain the transient response solution. This approach offers the same advantages as the Gurtin variational principle and, at the same time, is much simpler in calculation. Numerical results show that it is very accurate yet computationally efficient for the dynamic response of plates.展开更多
Based on the techniques of forward and inverse Fourier transformation, the authors discussed the design scheme of ordinary differentiator used and applied in the simulation of acoustic and elastic wavefields in isotro...Based on the techniques of forward and inverse Fourier transformation, the authors discussed the design scheme of ordinary differentiator used and applied in the simulation of acoustic and elastic wavefields in isotropic media respectively. To compress Gibbs effects by truncation effectively, Hanning window is introduced in. The model computation shows that, the convolutional differentiator method has the advantages of rapidity, low requirements of computer′s inner storage and high precision, which is a potential method of numerical simulation.展开更多
In this paper, we proposed new results in quadruple Laplace transform and proved some properties concerned with quadruple Laplace transform. We also developed some applications based on these results and solved homoge...In this paper, we proposed new results in quadruple Laplace transform and proved some properties concerned with quadruple Laplace transform. We also developed some applications based on these results and solved homogeneous as well as non-homogeneous partial differential equations involving four variables. The performance of quadruple Laplace transform is shown to be very encouraging by concrete examples. An elementary table of quadruple Laplace transform is also provided.展开更多
Four numerical schemes are introduced for the analysis of photocurrent transients in organic photovoltaic devices.Themathematicalmodel for organic polymer solar cells contains a nonlinear diffusion-reaction partial di...Four numerical schemes are introduced for the analysis of photocurrent transients in organic photovoltaic devices.Themathematicalmodel for organic polymer solar cells contains a nonlinear diffusion-reaction partial differential equation system with electrostatic convection attached to a kinetic ordinary differential equation.To solve the problem,Polynomial-based differential quadrature,Sinc,and Discrete singular convolution are combined with block marching techniques.These schemes are employed to reduce the problem to a nonlinear algebraic system.The iterative quadrature technique is used to solve the reduced problem.The obtained results agreed with the previous exact one and the finite element method.Further,the effects of different times,different mobilities,different densities,different geminate pair distances,different geminate recombination rate constants,different generation efficiencies,and supporting conditions on photocurrent have been analyzed.The novelty of this paper is that these schemes for photocurrent transients in organic polymer solar cells have never been presented before,so the results may be useful for improving the performance of solar cells.展开更多
This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the ...This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.展开更多
基金jointly funded by the National Natural Science Foundation(Nos.U19B6003,U20B6001,9175520021,42002137)Chinese Academy of Sciences(CAS)Strategic Leading Science&Technology Program(No.XDA14000000)。
文摘Based on the analysis of the deformation styles in different tectonic belts of the MiddleUpper Yangtze region,as well as the dissection of typical hydrocarbon reservoirs,this study determined the controlling effects of deformations on the hydrocarbon accumulations,obtaining the following results.The Middle-Upper Yangtze region experienced significant deformations during the Late Indosinian(T_(2)–T_(3)),the Middle Yanshanian(J_(3)–K_(1)),and the Himalayan,and five styles of tectonic deformations mainly occurred,namely superimposed deep burial,uplift,compressional thrusting,multi-layer decollement,and secondary deep burial.The distribution of hydrocarbon reservoirs in the piedmont thrust belts is controlled by the concealed structures on the footwall of the deep nappe.The gentle deformation area in central Sichuan experienced differential uplift,structural-lithologic hydrocarbon reservoirs were formed over a wide area.The eastern Sichuan-western Hunan and Hubei deformation area experienced Jura Mountains-type multi-layer detachment,compressional thrusting,and uplift.In relatively weakly folded and uplifted areas,conventional structural-lithologic hydrocarbon reservoirs have undergone adjustment and re-accumulation,and the shale gas resources are well preserved.In the strongly deformed areas,conventional hydrocarbon reservoirs were destroyed,while unconventional hydrocarbon reservoirs have been partially preserved.The marine strata in the Jianghan Basin experienced compression,thrusting,and denudation in the early stage and secondary deep burial in the late stage.Consequently,the unconventional gas resources have been partially preserved in these strata.Secondary hydrocarbon generation become favorable for conventional hydrocarbon accumulations in the marine strata.
文摘Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models.However,data privacy and security are always a challenge in every field where data need to be uploaded to the cloud.Federated learning(FL)is an emerging trend for distributed training of data.The primary goal of FL is to train an efficient communication model without compromising data privacy.The traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic data.This paper presents FL-based traffic flow prediction with spatio-temporal correlation.This work uses a differential privacy(DP)scheme for privacy preservation of participant's data.To the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation.The proposed framework trains the data locally at the client-side with DP.It then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained models.The results of the proposed work show that the FedGCN model accurately predicts the traffic.DP scheme at client-side helps clients to set a budget for privacy loss.
基金supported by the National Natural ScienceFoundation of China (No. 40804008)
文摘The perfectly matched layer（PML） was first introduced by Berenger as an absorbing boundary condition for electromagnetic wave propagation.In this article,a method is developed to ex-tend the PML to simulating seismic wave propagation in fluid-saturated porous medium.This non-physical boundary is used at the computational edge of a Forsyte polynomial convolutional differenti-ator（FPCD） algorithm as an absorbing boundary condition to truncate unbounded media.The incor-poration of PML in Biot＇s equations is given.Numerical results show that the PML absorbing bound-ary condition attenuates the outgoing waves effectively and eliminates the reflections adequately.
基金supported by the National Natural Science Foundation of China(Grant No.40874045)Special Funds for Sciences and Technology Research of Public Welfare Trades(Grant Nos. 200811021 and 201011042)
文摘This paper applies the convolutional differentiator method, based on generalized Forsyte orthogonal polynomial （CFPD）, to simulate the seismic wave propagation in two-phase media. From the numerical results we can see that three types of waves, fast P-waves, S-waves and slow P-waves, can be observed in the seismic wave field. The experiments on anisotropic models demonstrate that the wavefront is elliptic instead of circular and S-wave splitting occurs in anisotropic two-phase media. The research has confirmed that the rules of elastic wave propagation in fluid-saturated porous media are controlled by Biot＇s theory. Experiment on a layered fault model shows the wavefield generated by the interface and the fault very well, indicating the effectiveness of CFPD method on the wavefield modeling for real layered media in the Earth. This research has potential applications to the investigation of Earth＇s deep structure and oil/gas exploration.
基金supported by the the National Natural Science Foundation of China(51877167)。
文摘A reliable transformer protection method is crucial for power systems. Aiming at improving the generalization performance and response speed of multi-feature fusion based transformer protection, this paper presents a dynamic differential current by fusing pre-disturbance and post-disturbance differential currents in real time then developing a dynamic differential current based transformer protection focusing on the feature changes of differential current. Generally, the image of differential current can comprehensively embody the feature changes resulting from any disturbance. In addition, a short window is sometimes sufficient to clearly reflect the internal fault because the differential current will instantly change when an internal fault occurs. Therefore, in order to identify the running states reliably in the shortest possible time, multiple images, including the differential current from a pre-disturbance one cycle to a post-disturbance different time, are combined by time order to define a dynamic differential current. After the protection method is started, this dynamic differential current serves as input for the deep learning algorithm to identify the running states in real time. Once the transformer is identified as a faulty one, a tripping signal is issued and the protection method stops. The dynamic model experiments show that the proposed protection method has a strong generalization ability and rapid response speed.
文摘The convolution-type Gurtin variational principle is known as the only variational principle that is, from the mathematics point of view, totally equivalent to the initial value problem system. In this paper, the equation of motion of rectangular thin plates is first transformed to a new governing equation containing initial conditions by using a convolution method. A convolution-type semi-analytical DQ approach, which involves differential quadrature （DQ） approximation in the space domain and an analytical series expansion in the time domain, is proposed to obtain the transient response solution. This approach offers the same advantages as the Gurtin variational principle and, at the same time, is much simpler in calculation. Numerical results show that it is very accurate yet computationally efficient for the dynamic response of plates.
文摘Based on the techniques of forward and inverse Fourier transformation, the authors discussed the design scheme of ordinary differentiator used and applied in the simulation of acoustic and elastic wavefields in isotropic media respectively. To compress Gibbs effects by truncation effectively, Hanning window is introduced in. The model computation shows that, the convolutional differentiator method has the advantages of rapidity, low requirements of computer′s inner storage and high precision, which is a potential method of numerical simulation.
文摘In this paper, we proposed new results in quadruple Laplace transform and proved some properties concerned with quadruple Laplace transform. We also developed some applications based on these results and solved homogeneous as well as non-homogeneous partial differential equations involving four variables. The performance of quadruple Laplace transform is shown to be very encouraging by concrete examples. An elementary table of quadruple Laplace transform is also provided.
文摘Four numerical schemes are introduced for the analysis of photocurrent transients in organic photovoltaic devices.Themathematicalmodel for organic polymer solar cells contains a nonlinear diffusion-reaction partial differential equation system with electrostatic convection attached to a kinetic ordinary differential equation.To solve the problem,Polynomial-based differential quadrature,Sinc,and Discrete singular convolution are combined with block marching techniques.These schemes are employed to reduce the problem to a nonlinear algebraic system.The iterative quadrature technique is used to solve the reduced problem.The obtained results agreed with the previous exact one and the finite element method.Further,the effects of different times,different mobilities,different densities,different geminate pair distances,different geminate recombination rate constants,different generation efficiencies,and supporting conditions on photocurrent have been analyzed.The novelty of this paper is that these schemes for photocurrent transients in organic polymer solar cells have never been presented before,so the results may be useful for improving the performance of solar cells.
基金supported in part by a grant,PHA1110214,from MOE,Taiwan.
文摘This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.
