Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution g...Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. ...By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.展开更多
The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal mod...The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.展开更多
Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation...Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.展开更多
To study the parameter estimating effects of a free-floating tumbling space target,the extended Kalman filter(EKF)scheme is utilized with different high-nonlinear translational and rotational coupled kinematic&dyn...To study the parameter estimating effects of a free-floating tumbling space target,the extended Kalman filter(EKF)scheme is utilized with different high-nonlinear translational and rotational coupled kinematic&dynamic models on the LIDAR measurements.Applying the aforementioned models and measurements results in the situation where one single state can be estimated differently with varying accuracies since the EKFs based on different models have different observabilities.In the proposed EKFs,the traditional quaternions based kinematics and dynamics and the dual vector quaternions(DVQ)based kinematics and dynamics are used for the modeling of the relative motions between a chaser satellite and an uncooperative target.In the non-contact estimating scenarios,only highly nonlinear relative attitude and range measurements:the grapple fixture on the target measured from the chaser satellite via vision-based sensors,can be used.By evaluating the results of the EKFs,the observability properties of each EKF are studied analytically and numerically with the the Observability Gramian matrices(OG)and the standard deviations for every estimated parameters.The analysis of observability perform intensive studies and reveal the intrinsic factors that affect the accuracy and stability of the parameters estimation of an uncooperative space target.Finally,the analytical and numerical results show the optimal composition of the kinematic&dynamic models and measurements.展开更多
This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniqu...This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.展开更多
The resource parameter estimation using stochastic finite element, geostatistics etc. is a key point on uncertainty, risk analysis, optimization [1-5] etc. In this view, the paper presents some consideration on: 1) St...The resource parameter estimation using stochastic finite element, geostatistics etc. is a key point on uncertainty, risk analysis, optimization [1-5] etc. In this view, the paper presents some consideration on: 1) Stochastic finite element estimation. The concept of random element is simplified as a stochastic finite element (SFE) taking into account a parallelepiped element with eight nodes in which are given the probability density functions (pdf) on its point supports. In this context it is shown: a—the stochastic finite element is a linear interpolator, related to the distributions given at each nodes;b—the distribution pdf in whatever point x ∈ V;c—the estimation of the mean value of Z(x);2) Volume integrals calculus;3) SFE in geostatistics approaches;4) SFE in PDE solution. Finally, some conclusions are presented underlying the importance of SFE展开更多
The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistan...The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistant and optimized)to a quantum probe coupled to a bosonic environment. To demonstrate the precision of cutoff frequency estimation, we theoretically derive the quantum Fisher information(QFI) and quantum signal-to-noise ratio(QSNR) across sub-Ohmic,Ohmic, and super-Ohmic environments, and investigate their behaviors through numerical examples. The results indicate that, compared to the equidistant π-pulse sequence, the optimized π-pulse sequence significantly shortens the time to reach maximum QFI while enhancing the precision of cutoff frequency estimation, particularly in deep sub-Ohmic and deep super-Ohmic environments.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of ...The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.展开更多
The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critica...The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.展开更多
The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes fu...In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.展开更多
Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter esti...Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation.Firstly,a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics.Then,an estimator based on parameter estimation is designed,and its stability is verified.This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient.Finally,the approach is validated through joint simulations on the MATLAB/Simulink and AMESim platforms,as well as a hardware-in-the-loop semi-physical simulation experimental platform that accounts for system delay and noise conditions.The results indicate that the proposed algorithm effectively tracks changes in the adhesion coefficient during train braking,including the decrease in adhesion when the train brakes and slides,and the overall increase as the train speed decreases.The effectiveness of the algorithm was verified by setting different test conditions.The results show that the estimation algorithm can accurately estimate the adhesion coefficient,and through error analysis,it is found that the error between the estimated value of the adhesion coefficient and the theoretical value of the adhesion coefficient is within 5%.The adhesion coefficient obtained through the online estimation method based on the parameter estimation proposed in this paper demonstrates strong followability in both simulation and practical applications.展开更多
Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we f...Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we focus on the previously neglected aspect of the impact of the squeezing phase on the precision of quantum phase and amplitude estimation based on a simple model of a two-level system(TLS) interacting with a squeezed reservoir. We derive the optimal squeezed phase-matching conditions for phase φ and amplitude θ parameters, which are crucial for enhancing the precision of quantum parameter estimation. The robustness of the squeezing-enhanced quantum Fisher information against departures from these conditions is examined, demonstrating that minor deviations from phase-matching can still result in remarkable precision of estimation. Additionally, we provide a geometric interpretation of the squeezed phase-matching conditions from the classical motion of a TLS on the Bloch sphere. Our research contributes to a deeper understanding of the operational requirements for employing squeezed reservoir engineering to advance quantum parameter estimation.展开更多
In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource man...In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.展开更多
Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced...Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.展开更多
Based on the study of passive articulated rover,a complete suspension kinematics model from wheel to inertial reference frame is presented,which uses D-H method of manipulator and presentation with Euler angle of pitc...Based on the study of passive articulated rover,a complete suspension kinematics model from wheel to inertial reference frame is presented,which uses D-H method of manipulator and presentation with Euler angle of pitch,roll and yaw.An improved contact model is adopted aimed at the loose and rough lunar terrain.Using this kinematics model and numerical continuous and discrete Newton's method with iterative factor,the numerical method for estimation of kinematical parameters of articulated rovers on loose and rough terrain is constructed.To demonstrate this numerical method,an example of two torsion bar rocker-bogie lunar rover with eight wheels is presented.Simulation results show that the numerical method for estimation of kinematical parameters of articulated rovers based on improved contact model can improve the precision of kinematical estimation on loose and rough terrain and decrease errors caused by contact models established based on general hypothesis.展开更多
Underwater target localization and parameters(azimuth and range) estimation by the method of utilizing explosions as underwater sound sources are described in this paper.The narrow beam reverberation model of the targ...Underwater target localization and parameters(azimuth and range) estimation by the method of utilizing explosions as underwater sound sources are described in this paper.The narrow beam reverberation model of the target echo signal is researched to estimate the target azimuth in reverberation background.Estimation errors of target azimuth and range are studied and proved to approximately meet Gauss distribution.Then the variance formula of target range error is deduced.Simulation experiments are applied to research the target range error and its standard deviation,and a series of measures to improve the estimation accuracy of target range are proposed.It is confirmed by the data processing results of simulations and lake experiments that the proposed method can accurately locate underwater target at a long distance on the condition of a certain underwater explosion range error.展开更多
基金supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids.This study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on operation.The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately.The PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis behavior.The suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its superiority.The results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the models.The statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing optimizers.The convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive optimizers.Moreover,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
基金supported by the National Natural Science Foundation of China(6120113461201135)+2 种基金the 111 Project(B08038)the Fundamental Research Funds for the Central Universities(72124669)the Open Research Fund of the Academy of Application(2014CXJJ-TX06)
文摘By using the sparsity of frequency hopping（FH） signals,an underdetermined blind source separation（UBSS） algorithm is presented. Firstly, the short time Fourier transform（STFT） is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival（DOA） of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio（SNR） is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.
基金supported by the National Natural Science Foundation of China (60875019)
文摘The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-20-11A)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(Grant No.BK19AF005).
文摘Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.
文摘To study the parameter estimating effects of a free-floating tumbling space target,the extended Kalman filter(EKF)scheme is utilized with different high-nonlinear translational and rotational coupled kinematic&dynamic models on the LIDAR measurements.Applying the aforementioned models and measurements results in the situation where one single state can be estimated differently with varying accuracies since the EKFs based on different models have different observabilities.In the proposed EKFs,the traditional quaternions based kinematics and dynamics and the dual vector quaternions(DVQ)based kinematics and dynamics are used for the modeling of the relative motions between a chaser satellite and an uncooperative target.In the non-contact estimating scenarios,only highly nonlinear relative attitude and range measurements:the grapple fixture on the target measured from the chaser satellite via vision-based sensors,can be used.By evaluating the results of the EKFs,the observability properties of each EKF are studied analytically and numerically with the the Observability Gramian matrices(OG)and the standard deviations for every estimated parameters.The analysis of observability perform intensive studies and reveal the intrinsic factors that affect the accuracy and stability of the parameters estimation of an uncooperative space target.Finally,the analytical and numerical results show the optimal composition of the kinematic&dynamic models and measurements.
基金Project supported by the National Natural Science Foundation of China (Grant No 10647141)
文摘This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.
文摘The resource parameter estimation using stochastic finite element, geostatistics etc. is a key point on uncertainty, risk analysis, optimization [1-5] etc. In this view, the paper presents some consideration on: 1) Stochastic finite element estimation. The concept of random element is simplified as a stochastic finite element (SFE) taking into account a parallelepiped element with eight nodes in which are given the probability density functions (pdf) on its point supports. In this context it is shown: a—the stochastic finite element is a linear interpolator, related to the distributions given at each nodes;b—the distribution pdf in whatever point x ∈ V;c—the estimation of the mean value of Z(x);2) Volume integrals calculus;3) SFE in geostatistics approaches;4) SFE in PDE solution. Finally, some conclusions are presented underlying the importance of SFE
基金Project supported by the National Natural Science Foundation of China (Grant No. 62403150)the Innovation Project of Guangxi Graduate Education (Grant No. YCSW2024129)the Guangxi Science and Technology Base and Talent Project (Grant No. Guike AD23026208)。
文摘The cutoff frequency is one of the crucial parameters that characterize the environment. In this paper, we estimate the cutoff frequency of the Ohmic spectral density by applying the π-pulse sequences(both equidistant and optimized)to a quantum probe coupled to a bosonic environment. To demonstrate the precision of cutoff frequency estimation, we theoretically derive the quantum Fisher information(QFI) and quantum signal-to-noise ratio(QSNR) across sub-Ohmic,Ohmic, and super-Ohmic environments, and investigate their behaviors through numerical examples. The results indicate that, compared to the equidistant π-pulse sequence, the optimized π-pulse sequence significantly shortens the time to reach maximum QFI while enhancing the precision of cutoff frequency estimation, particularly in deep sub-Ohmic and deep super-Ohmic environments.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
基金supported by the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2024ZR03)the National Natural Science Foundation of China(No.42407248)+2 种基金the Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC-[2023]-YB066)the Key Laboratory of Smart Earth(No.KF2023YB04-02)the Fundamental Research Funds for the Central Universities。
文摘The constitutive model is essential for predicting the deformation and stability of rocksoil mass.The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors.However,constitutive model parameters cannot be evaluated accurately with a limited amount of test data,resulting in uncertainty in the prediction of stress-strain curves.This paper proposes a Bayesian analysis framework to address this issue.It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models.Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide,a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework.Moreover,the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated.Results show that reasonable assessments of the constitutive model parameter can be well realized.The variability of stress-strain curves is strongly related to the model prediction performance.The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation.Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.
基金National Key R&D Program of China(No.2017YFB1304000)Fundamental Research Funds for the Central Universities,China(No.2232023G-05-1)。
文摘The high-speed winding spindle employs a flexible support system incorporating rubber O-rings.By precisely configuring the structural parameters and the number of the O-rings,the spindle can stably surpass its critical speed points and maintain operational stability across the entire working speed range.However,the support stiffness and damping of rubber O-rings exhibit significant nonlinear frequency dependence.Conventional experimental methods for deriving equivalent stiffness and damping,based on the principle of the forced non-resonance method,require fabricating custom setups for each O-ring specification and conducting vibration tests at varying frequencies,resulting in low efficiency and high costs.This study proposes a hybrid simulation-experimental method for dynamic parameter identification.Firstly,the frequency-dependent dynamic parameters of a specific O-ring support system are experimentally obtained.Subsequently,a corresponding parametric finite element model is established to simulate and solve the equivalent elastic modulus and equivalent stiffness-damping coefficient of this O-ring support system.Ultimately,after iterative simulation,the simulated and experimental results achieve a 99.7%agreement.The parametric finite element model developed herein can directly simulate and inversely estimate frequency-dependent dynamic parameters for O-rings of different specifications but identical elastic modulus.
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
文摘In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
基金supported by the National Natural Science Foundation of China(grant/award number 52072266).
文摘Aiming to address the challenge of directly measuring the real-time adhesion coefficient between wheels and rails,this paper proposes an online estimation algorithm for the adhesion coefficient based on parameter estimation.Firstly,a force analysis of the single-wheel pair model of the train is conducted to derive the calculation relationship for the wheel-rail adhesion coefficient in train dynamics.Then,an estimator based on parameter estimation is designed,and its stability is verified.This estimator is combined with the wheelset force analysis to estimate the wheel-rail adhesion coefficient.Finally,the approach is validated through joint simulations on the MATLAB/Simulink and AMESim platforms,as well as a hardware-in-the-loop semi-physical simulation experimental platform that accounts for system delay and noise conditions.The results indicate that the proposed algorithm effectively tracks changes in the adhesion coefficient during train braking,including the decrease in adhesion when the train brakes and slides,and the overall increase as the train speed decreases.The effectiveness of the algorithm was verified by setting different test conditions.The results show that the estimation algorithm can accurately estimate the adhesion coefficient,and through error analysis,it is found that the error between the estimated value of the adhesion coefficient and the theoretical value of the adhesion coefficient is within 5%.The adhesion coefficient obtained through the online estimation method based on the parameter estimation proposed in this paper demonstrates strong followability in both simulation and practical applications.
基金Project supported by the National Natural Science Foundation of China (Grant No. 12265004)Jiangxi Provincial Natural Science Foundation (Grant No. 20242BAB26010)+1 种基金the National Natural Science Foundation of China (Grant No. 12365003)Jiangxi Provincial Natural Science Foundation (Grant Nos. 20212ACB211004 and 20212BAB201014)。
文摘Squeezed reservoir engineering is a powerful technique in quantum information that combines the features of squeezing and reservoir engineering to create and stabilize non-classical quantum states. In this paper, we focus on the previously neglected aspect of the impact of the squeezing phase on the precision of quantum phase and amplitude estimation based on a simple model of a two-level system(TLS) interacting with a squeezed reservoir. We derive the optimal squeezed phase-matching conditions for phase φ and amplitude θ parameters, which are crucial for enhancing the precision of quantum parameter estimation. The robustness of the squeezing-enhanced quantum Fisher information against departures from these conditions is examined, demonstrating that minor deviations from phase-matching can still result in remarkable precision of estimation. Additionally, we provide a geometric interpretation of the squeezed phase-matching conditions from the classical motion of a TLS on the Bloch sphere. Our research contributes to a deeper understanding of the operational requirements for employing squeezed reservoir engineering to advance quantum parameter estimation.
基金supported in part by the National Natural Science Foundation of China(No.62271248)the Natural Science Foundation of Jiangsu Province(No.BK20230090)the Key Laboratory of Land Satellite Remote Sensing Application through the Ministry of Natural Resources of China(No.KLSMNR-K202303).
文摘In recent years,the development of domestic commercial synthetic aperture radar(SAR)is in full swing,with multiple commercial SAR satellites in orbit,showing great potential in disaster monitoring,natural resource management and deformation observation.Fucheng-1 is the first C-band commercial SAR satellite for interferometric SAR(InSAR)service developed by Spacety China,which marks the gradual maturity of China’s remote sensing data service.Based on the raw data collected by Fucheng-1,this paper firstly introduces the range-Doppler algorithm(RDA),then illustrates the parameter estimation method on the basis of fractional Fourier transform(FrFT)to realize the accurate estimation of azimuth chirp rate,which effectively improves imaging quality.Finally,the L1-norm regularization based sparse imaging method is utilized to reconstruct images from down-sampled data.Experimental results show that the sparse imaging algorithm can accurately reconstruct the down-sampled Fucheng-1 data and suppress sidelobes and clutter.
基金supported by National Natural Science Foundation of China(Grant Nos.52279137,52009090).
文摘Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.
基金Sponsored by the National High Technology Research and Development Program of China(863 Program)(Grant No.2006AA04Z231)the National Science Foundation of Heilongjiang Province(Grant No.ZJG0709)"The 111 Project"(Grant No.B07018)
文摘Based on the study of passive articulated rover,a complete suspension kinematics model from wheel to inertial reference frame is presented,which uses D-H method of manipulator and presentation with Euler angle of pitch,roll and yaw.An improved contact model is adopted aimed at the loose and rough lunar terrain.Using this kinematics model and numerical continuous and discrete Newton's method with iterative factor,the numerical method for estimation of kinematical parameters of articulated rovers on loose and rough terrain is constructed.To demonstrate this numerical method,an example of two torsion bar rocker-bogie lunar rover with eight wheels is presented.Simulation results show that the numerical method for estimation of kinematical parameters of articulated rovers based on improved contact model can improve the precision of kinematical estimation on loose and rough terrain and decrease errors caused by contact models established based on general hypothesis.
基金supported by the National Natural Science Foundation of China(61431020,61571434)
文摘Underwater target localization and parameters(azimuth and range) estimation by the method of utilizing explosions as underwater sound sources are described in this paper.The narrow beam reverberation model of the target echo signal is researched to estimate the target azimuth in reverberation background.Estimation errors of target azimuth and range are studied and proved to approximately meet Gauss distribution.Then the variance formula of target range error is deduced.Simulation experiments are applied to research the target range error and its standard deviation,and a series of measures to improve the estimation accuracy of target range are proposed.It is confirmed by the data processing results of simulations and lake experiments that the proposed method can accurately locate underwater target at a long distance on the condition of a certain underwater explosion range error.
