In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucia...In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.展开更多
The Bayesian inversion method is a stochastic approach based on the Bayesian theory.With the development of sampling algorithms and computer technologies,the Bayesian inversion method has been widely used in geophysic...The Bayesian inversion method is a stochastic approach based on the Bayesian theory.With the development of sampling algorithms and computer technologies,the Bayesian inversion method has been widely used in geophysical inversion problems.In this study,we conduct inversion experiments using crosshole seismic travel-time data to examine the characteristics and performance of the stochastic Bayesian inversion based on the Markov chain Monte Carlo sampling scheme and the traditional deterministic inversion with Tikhonov regularization.Velocity structures with two different spatial variations are considered,one with a chessboard pattern and the other with an interface mimicking the Mohorovicicdiscontinuity(Moho).Inversions are carried out with different scenarios of model discretization and source–receiver configurations.Results show that the Bayesian method yields more robust single-model estimations than the deterministic method,with smaller model errors.In addition,the Bayesian method provides the posterior probabilistic distribution function of the model space,which can help us evaluate the quality of the inversion result.展开更多
The rational fraction model for the regularized inverse dynamics of a flexible manipulator is presented, the effect of this model on alleviating the ill posed behavior of the calculated torque is demonstrated, and the...The rational fraction model for the regularized inverse dynamics of a flexible manipulator is presented, the effect of this model on alleviating the ill posed behavior of the calculated torque is demonstrated, and the implication of the regularization parameter selection on the trade off between the tracking accuracy and the frequency bandwidth of the calculated torque is addressed as well. The validity of the proposed method is illustrated by means of a computer numeric simulation.展开更多
Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term.A regularized convex term can usually preserve the image edges well in the restored image...Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term.A regularized convex term can usually preserve the image edges well in the restored image.In this paper,we consider a class of convex and edge-preserving regularization functions,i.e.,multiplicative half-quadratic regularizations,and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations.At each Newton iterate,the preconditioned conjugate gradient method,incorporated with a constraint preconditioner,is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix. The eigenvalue bounds of the preconditioned matrix are deliberately derived,which can be used to estimate the convergence speed of the preconditioned conjugate gradient method.We use experimental results to demonstrate that this new approach is efficient, and the effect of image restoration is reasonably well.展开更多
In this paper,we study the state estimation of compressible single phase flow in compressible porous media.The initial pressure distribution is estimated according to discrete adjoint approach based on the collected w...In this paper,we study the state estimation of compressible single phase flow in compressible porous media.The initial pressure distribution is estimated according to discrete adjoint approach based on the collected well pressure data.The first-order Tykhonov regularization method is used to obtain reasonable estimation.By analyzing the optimality condition of estimation problem,the discrete adjoint state equation and discrete adjoint gradient are derived based on the numerical scheme of the continuous equations.A quasi-Newton numerical optimization method related to adjoint gradient is proposed to solve the estimation problem.The estimation results with different regularization coefficients are compared and analyzed by numerical experiments.The deviation between the estimated pressure obtained without regularization and the real pressure is large.Estimation result with smaller deviation and higher smoothness can be obtained through appropriate regularization coefficient.When the observation error is large,the observed values generated by the estimated pressure fit well with the real pressure.展开更多
In order to reveal the epidemic regularity of Huanglongbing （HLB） in different management approaches, different citrus production areas were selected between 2002 and 2012 to compare epidemic regularity of different...In order to reveal the epidemic regularity of Huanglongbing （HLB） in different management approaches, different citrus production areas were selected between 2002 and 2012 to compare epidemic regularity of different types and control effects of different management approaches with plant incidence rate. All survey data in 11 years were used to build a mathematical model, and epidemic evolution and control effects were quantitatively analyzed. The results indicated that diffusion and prevalence of HLB generally increased linearly. In naturally growing citrus orchards without artificial control, the annual diseased plant rate was 11.11%, and the epidemic diffusion model was y1 = 12. 24x - 1.382 8 （ n =9, r =0. 976 9 ＊ ＊ ）. Under general prevention and control conditions, the annual diseased plant rate was 4.69%, the epidemic diffusion model was Y2 = 5. 449 8x - 1.603 5 （ n = 11, r =0. 974 9 ＊ ＊ ）, and the control effect was 43.93% （22.93% - 55.04% ）. In citrus orchards with integrated prevention and control, the epidemic diffusion model was Y3 = 0. 366 3x - 0. 342 2 （ n = 11, r = 0. 989 8 ＊ ＊ ）, the control effect was 96.15% （94.95% -97.40% ）, and the annual diseased plant rate was 0.31%. Thus, HLB is preventable and controllable as long as integrated prevention and control work is implemented well.展开更多
We develop a class of conservative integrators for the regularized logarithmic Schrodinger equation(RLogSE)using the quadratization technique and symplectic Runge-Kutta schemes.To preserve the highly nonlinear energy ...We develop a class of conservative integrators for the regularized logarithmic Schrodinger equation(RLogSE)using the quadratization technique and symplectic Runge-Kutta schemes.To preserve the highly nonlinear energy functional,the regularized equation is first transformed into an equivalent system that admits two quadratic invariants by adopting the invariant energy quadratization approach.The reformulation is then discretized using the Fourier pseudo-spectral method in the space direction,and integrated in the time direction by a class of diagonally implicit Runge-Kutta schemes that conserve both quadratic invariants to round-off errors.For comparison purposes,a class of multi-symplectic integrators are developed for RLogSE to conserve the multi-symplectic conservation law and global mass conservation law in the discrete level.Numerical experiments illustrate the convergence,efficiency,and conservative properties of the proposed methods.展开更多
The 2030 Agenda for Sustainable Development explicitly and specifically includes migration in the global development agenda for the first time, and establishes relevant targets, including promoting regular migration a...The 2030 Agenda for Sustainable Development explicitly and specifically includes migration in the global development agenda for the first time, and establishes relevant targets, including promoting regular migration and protecting the rights of migrant workers. These targets are significant achievements of the United Nations in promoting the topic “Migration and Development” and strengthening the human rights-based global migration governance.However, there are many difficulties in achieving these targets, especially when the rights of regular and irregular migration are undermining national sovereignty and security, or are not in line with national development needs. With the continued spread of the CoVID-19pandemic, rising tensions among major powers, and the prevalence of populism and anti-foreigner sentiment in the West, the process of migration and development has been seriously influenced, and the divergent positions and conflicting interests of countries have pushed the migration targets further out of reach. However, in the long run,exploration in this area will help promote global economic recovery in the post-pandemic era and benefit all parties from the positive interactions of migration, development, and human rights.展开更多
文摘In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.
基金supported by the National Natural Science Foundation of China (grant nos. 41930103 and 41674052)
文摘The Bayesian inversion method is a stochastic approach based on the Bayesian theory.With the development of sampling algorithms and computer technologies,the Bayesian inversion method has been widely used in geophysical inversion problems.In this study,we conduct inversion experiments using crosshole seismic travel-time data to examine the characteristics and performance of the stochastic Bayesian inversion based on the Markov chain Monte Carlo sampling scheme and the traditional deterministic inversion with Tikhonov regularization.Velocity structures with two different spatial variations are considered,one with a chessboard pattern and the other with an interface mimicking the Mohorovicicdiscontinuity(Moho).Inversions are carried out with different scenarios of model discretization and source–receiver configurations.Results show that the Bayesian method yields more robust single-model estimations than the deterministic method,with smaller model errors.In addition,the Bayesian method provides the posterior probabilistic distribution function of the model space,which can help us evaluate the quality of the inversion result.
文摘The rational fraction model for the regularized inverse dynamics of a flexible manipulator is presented, the effect of this model on alleviating the ill posed behavior of the calculated torque is demonstrated, and the implication of the regularization parameter selection on the trade off between the tracking accuracy and the frequency bandwidth of the calculated torque is addressed as well. The validity of the proposed method is illustrated by means of a computer numeric simulation.
基金supported by the National Basic Research Program (No.2005CB321702)the National Outstanding Young Scientist Foundation(No. 10525102)the Specialized Research Grant for High Educational Doctoral Program(Nos. 20090211120011 and LZULL200909),Hong Kong RGC grants and HKBU FRGs
文摘Image restoration is often solved by minimizing an energy function consisting of a data-fidelity term and a regularization term.A regularized convex term can usually preserve the image edges well in the restored image.In this paper,we consider a class of convex and edge-preserving regularization functions,i.e.,multiplicative half-quadratic regularizations,and we use the Newton method to solve the correspondingly reduced systems of nonlinear equations.At each Newton iterate,the preconditioned conjugate gradient method,incorporated with a constraint preconditioner,is employed to solve the structured Newton equation that has a symmetric positive definite coefficient matrix. The eigenvalue bounds of the preconditioned matrix are deliberately derived,which can be used to estimate the convergence speed of the preconditioned conjugate gradient method.We use experimental results to demonstrate that this new approach is efficient, and the effect of image restoration is reasonably well.
文摘In this paper,we study the state estimation of compressible single phase flow in compressible porous media.The initial pressure distribution is estimated according to discrete adjoint approach based on the collected well pressure data.The first-order Tykhonov regularization method is used to obtain reasonable estimation.By analyzing the optimality condition of estimation problem,the discrete adjoint state equation and discrete adjoint gradient are derived based on the numerical scheme of the continuous equations.A quasi-Newton numerical optimization method related to adjoint gradient is proposed to solve the estimation problem.The estimation results with different regularization coefficients are compared and analyzed by numerical experiments.The deviation between the estimated pressure obtained without regularization and the real pressure is large.Estimation result with smaller deviation and higher smoothness can be obtained through appropriate regularization coefficient.When the observation error is large,the observed values generated by the estimated pressure fit well with the real pressure.
基金Supported by Special Fund for Agro-scientific Research in the Public Interest "Research and Demonstration of Comprehensive Prevention and Control Technology against Huanglongbing and Canker"(201003067)
文摘In order to reveal the epidemic regularity of Huanglongbing （HLB） in different management approaches, different citrus production areas were selected between 2002 and 2012 to compare epidemic regularity of different types and control effects of different management approaches with plant incidence rate. All survey data in 11 years were used to build a mathematical model, and epidemic evolution and control effects were quantitatively analyzed. The results indicated that diffusion and prevalence of HLB generally increased linearly. In naturally growing citrus orchards without artificial control, the annual diseased plant rate was 11.11%, and the epidemic diffusion model was y1 = 12. 24x - 1.382 8 （ n =9, r =0. 976 9 ＊ ＊ ）. Under general prevention and control conditions, the annual diseased plant rate was 4.69%, the epidemic diffusion model was Y2 = 5. 449 8x - 1.603 5 （ n = 11, r =0. 974 9 ＊ ＊ ）, and the control effect was 43.93% （22.93% - 55.04% ）. In citrus orchards with integrated prevention and control, the epidemic diffusion model was Y3 = 0. 366 3x - 0. 342 2 （ n = 11, r = 0. 989 8 ＊ ＊ ）, the control effect was 96.15% （94.95% -97.40% ）, and the annual diseased plant rate was 0.31%. Thus, HLB is preventable and controllable as long as integrated prevention and control work is implemented well.
基金supported by the National Natural Science Foundation of China(12271523,11901577,11971481,12071481)the National Key R&D Program of China(SQ2020YFA0709803)+5 种基金the Defense Science Foundation of China(2021-JCJQ-JJ-0538)the National Key Project(GJXM92579)the Natural Science Foundation of Hunan(2020JJ5652,2021JJ20053)the Research Fund of National University of Defense Technology(ZK19-37,ZZKY-JJ-21-01)the Science and Technology Innovation Program of Hunan Province(2021RC3082)the Research Fund of College of Science,National University of Defense Technology(2023-lxy-fhjj-002).
文摘We develop a class of conservative integrators for the regularized logarithmic Schrodinger equation(RLogSE)using the quadratization technique and symplectic Runge-Kutta schemes.To preserve the highly nonlinear energy functional,the regularized equation is first transformed into an equivalent system that admits two quadratic invariants by adopting the invariant energy quadratization approach.The reformulation is then discretized using the Fourier pseudo-spectral method in the space direction,and integrated in the time direction by a class of diagonally implicit Runge-Kutta schemes that conserve both quadratic invariants to round-off errors.For comparison purposes,a class of multi-symplectic integrators are developed for RLogSE to conserve the multi-symplectic conservation law and global mass conservation law in the discrete level.Numerical experiments illustrate the convergence,efficiency,and conservative properties of the proposed methods.
文摘The 2030 Agenda for Sustainable Development explicitly and specifically includes migration in the global development agenda for the first time, and establishes relevant targets, including promoting regular migration and protecting the rights of migrant workers. These targets are significant achievements of the United Nations in promoting the topic “Migration and Development” and strengthening the human rights-based global migration governance.However, there are many difficulties in achieving these targets, especially when the rights of regular and irregular migration are undermining national sovereignty and security, or are not in line with national development needs. With the continued spread of the CoVID-19pandemic, rising tensions among major powers, and the prevalence of populism and anti-foreigner sentiment in the West, the process of migration and development has been seriously influenced, and the divergent positions and conflicting interests of countries have pushed the migration targets further out of reach. However, in the long run,exploration in this area will help promote global economic recovery in the post-pandemic era and benefit all parties from the positive interactions of migration, development, and human rights.
