The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a ta...The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.展开更多
This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assim...This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.展开更多
A magnetically stabilized fluidized bed (MSB) is a highly efficient filter that takes the advantage of both fluidized beds and fixed beds. This paper presents the research to collect aerosol in airflow with a MSB. The...A magnetically stabilized fluidized bed (MSB) is a highly efficient filter that takes the advantage of both fluidized beds and fixed beds. This paper presents the research to collect aerosol in airflow with a MSB. The filtering model of MSB is established with its parameters including magnetic Geld intensity, gas superficial velocity, average grain-size, and bed height on the collection efficiency of MSB. The model is verified by experiments.展开更多
A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance ...A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance as the interacting multiple model filter at the price ofless computational cost. Numerically robust implementation of the filter is presented to meetpractical applications. An example on bearings-only guidance demonstrates the effect of the proposedalgorithm.展开更多
Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term...Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.展开更多
In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant ac...In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant acceleration(CA) models to noise effect reduction, the autoregressive(AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi(EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model(IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers.展开更多
With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirement...With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.展开更多
The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient...The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient(MIC),interpretable ML,and automated ML.It is previously shown that the solidvolume fraction has the greatest effect on the drag force.The present study aims to quantitativelyinvestigate the influence of flow properties on mesoscale drag correction(H_(d)).The MIC results showstrong correlations between the features(i.e.,slip velocity(u^(*)_(sy))and particle volume fraction(εs))and thelabel H_(d).The interpretable ML analysis confirms this conclusion,and quantifies the contribution of u^(*)_(sy),εs and gas pressure gradient to the model as 71.9%,27.2%and 0.9%,respectively.Automated ML without theneed to select the model structure and hyperparameters is used for modeling,improving the predictionaccuracy over our previous model(Zhu et al.,2020;Ouyang,Zhu,Su,&Luo,2021).展开更多
Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Bas...Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.展开更多
Mesoscale structures that form in gas-solid flows considerably affect interphase heat transfer.A filtered interphase heat transfer model accounts for the effects of unresolved mesoscale structures is required in coars...Mesoscale structures that form in gas-solid flows considerably affect interphase heat transfer.A filtered interphase heat transfer model accounts for the effects of unresolved mesoscale structures is required in coarse-grid simulations.In the literature,researchers obtain the filtered interphase heat transfer coefficient using a correction(Q)to the microscopic interphase heat transfer coefficient.Available models are based on filtered data in the range 0<Q<1.However,the percentage of filtered data in the range Q<0 and Q>1 is non-negligible.This percentage can reach approximately 20%when the dimensionless filter size is smaller than 1.028(66.7×the particle diameter).We proposed an improved filtered interphase heat transfer model by considering the data in the range Q<0 and Q>1.We evaluated the predictive power of our model in an a priori test.Our model has much better performance than other models when the dimensionless filter size△<8.222.展开更多
This paper addresses a robust H∞filter design problem for nonlinear systems with time-varying delay through TakagiSugeno(T-S) fuzzy model approach. Firstly, by introducing free-weighting matrix method combined with a...This paper addresses a robust H∞filter design problem for nonlinear systems with time-varying delay through TakagiSugeno(T-S) fuzzy model approach. Firstly, by introducing free-weighting matrix method combined with a matrix decoupling approach and adopting an improved integral inequality method without ignoring any integral term, less conservative results are achieved. Next,based on the model, new delay-dependent sufficient conditions are derived, which are less conservative than the existing ones via solving the linear matrix inequalities(LMIs). Lastly, simulations show a significant improvement over the previous results.展开更多
The influence of a vertical jet located at the distributor in a cylindrical fluidized bed on the flow behavior of gas and particles was predicted using a filtered two-fluid model proposed by Sundaresan and coworkers. ...The influence of a vertical jet located at the distributor in a cylindrical fluidized bed on the flow behavior of gas and particles was predicted using a filtered two-fluid model proposed by Sundaresan and coworkers. The distributions of volume fraction and the velocity of particles along the lateral direction were investigated for different jet velocities by analyzing the simulated results. The vertical jet penetration lengths at the different gas jet velocities have been obtained and compared with predictions derived from empirical correlations; the predicted air jet penetration length is discussed. Agreement between the numerical simulations and experimental results has been achieved.展开更多
This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, ge...This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.展开更多
Allan variance(AV)stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme.However,the latter usually employs a traditional hard threshold or soft ...Allan variance(AV)stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme.However,the latter usually employs a traditional hard threshold or soft threshold that presents some mathematical problems.An adaptive dual threshold for discrete wavelet transform(DWT)denoising function overcomes the disadvantages of traditional approaches.Assume that two thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two thresholds assure continuity and overcome previous difficulties.On the basis of AV,an application for strap-down inertial navigation system(SINS)stochastic model extraction assures more efficient tuning of the augmented 21-state improved exact modeling Kalman filter(IEMKF)states.The experimental results show that the proposed algorithm is superior in denoising performance.Furthermore,the improved filter estimation of navigation solution is better than that of conventional Kalman filter(CKF).展开更多
For the performance issues of satellite transceivers suffering passive intermodulation interference,a novel and effective digital suppression algorithm is presented in this paper.In contrast to analog approaches,digit...For the performance issues of satellite transceivers suffering passive intermodulation interference,a novel and effective digital suppression algorithm is presented in this paper.In contrast to analog approaches,digital passive intermodulation(PIM) suppression approaches can be easily reconfigured and therefore are highly attractive for future satellite communication systems.A simplified model of nonlinear distortion from passive microwave devices is established in consideration of the memory effect.The multiple high-order PIM products falling into the receiving band can be described as a bilinear predictor function.A suppression algorithm based on a bilinear polynomial decorrelated adaptive filter is proposed for baseband digital signal processing.In consideration of the time-varying characteristics of passive intermodulation,this algorithm can achieve the rapidness of online interference estimation and low complexity with less consumption of resources.Numerical simulation results show that the algorithm can effectively compensate the passive intermodulation interference,and achieve a high signal-to-interference ratio gain.展开更多
An ensemble-based assimilation system that used the MASINGAR ink-2 (Model of Aerosol Species IN the Global AtmospheRe Mark 2) dust forecasting model and satellite-derived aerosol optical thickness (AOT) data. proc...An ensemble-based assimilation system that used the MASINGAR ink-2 (Model of Aerosol Species IN the Global AtmospheRe Mark 2) dust forecasting model and satellite-derived aerosol optical thickness (AOT) data. processed in the JAXA (Japan Aerospace Exploration Agency) Satellite Monitoring for Environmental Studies (JASMES) system with MODIS (Moderate Resolution Imaging Spectroradiometer) observations. was used to quantify the impact of assimilation on forecasts of a severe Asian dust storm during May 10-13. 2011. The modeled bidirectional reflectance function and observed vegetation index employed in JASMES enable AOT retrievals in areas of high surface reflectance, making JASMES effective for dust forecasting and early warning by enabling assimilations in dust storm source regions. Forecasts both with and without assimilation were validated using PM^0 observations from China, Korea, and Japan in the TEMM WG1 dataset. Only the forecast with assimilation successfully captured the contrast between the core and tail of the dust storm by increasing the AOT around the core by 70-150% and decreasing it around the tail by 20-30% in the 18-h forecast. The forecast with assimilation improved the agreement with observed PMlo concentrations, but the effect was limited at downwind sites in Korea and Japan because of the lack of observational constraints for a mis-forecasted dust storm due to cloud.展开更多
基金supported by the National Natural Science Foundation of China(61671181).
文摘The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.
基金sponsored by the U.S. National Science Foundation (Grant No.ATM0205599)the U.S. Offce of Navy Research under Grant N000140410471Dr. James A. Hansen was partially supported by US Offce of Naval Research (Grant No. N00014-06-1-0500)
文摘This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.
文摘A magnetically stabilized fluidized bed (MSB) is a highly efficient filter that takes the advantage of both fluidized beds and fixed beds. This paper presents the research to collect aerosol in airflow with a MSB. The filtering model of MSB is established with its parameters including magnetic Geld intensity, gas superficial velocity, average grain-size, and bed height on the collection efficiency of MSB. The model is verified by experiments.
文摘A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance as the interacting multiple model filter at the price ofless computational cost. Numerically robust implementation of the filter is presented to meetpractical applications. An example on bearings-only guidance demonstrates the effect of the proposedalgorithm.
基金This work was supported by the National Natural Science Foundation of China(Grant No.2017YFC0403605 and No.11601419).
文摘Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.
文摘In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi(AREV) model is proposed. In contrast to weakness of traditional constant velocity(CV) and constant acceleration(CA) models to noise effect reduction, the autoregressive(AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi(EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model(IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers.
基金supported by the Foundation of Key Laboratory of Near-Surface。
文摘With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.
基金This work was supported by the National Natural ScienceFoundation of China(No.U1862201,91834303 and 22208208)the China Postdoctoral Science Foundation(No.2022M712056)the China National Postdoctoral Program for Innovative Talents(No.BX20220205).
文摘The present study extracts human-understandable insights from machine learning(ML)-based mesoscale closure in fluid-particle flows via several novel data-driven analysis approaches,i.e.,maximal information coefficient(MIC),interpretable ML,and automated ML.It is previously shown that the solidvolume fraction has the greatest effect on the drag force.The present study aims to quantitativelyinvestigate the influence of flow properties on mesoscale drag correction(H_(d)).The MIC results showstrong correlations between the features(i.e.,slip velocity(u^(*)_(sy))and particle volume fraction(εs))and thelabel H_(d).The interpretable ML analysis confirms this conclusion,and quantifies the contribution of u^(*)_(sy),εs and gas pressure gradient to the model as 71.9%,27.2%and 0.9%,respectively.Automated ML without theneed to select the model structure and hyperparameters is used for modeling,improving the predictionaccuracy over our previous model(Zhu et al.,2020;Ouyang,Zhu,Su,&Luo,2021).
文摘Interacting Multiple Model Kalman-Particle Filter (IMMK-PF) has the advantages of particle filter and Kalman filter and good computation efficiency compared with Interacting Multiple Model Particle Filter (IMMPF). Based on IMMK-PF, an adaptive sampling target tracking algorithm for Phased Array Radar (PAR) is proposed. This algorithm first predicts Posterior Cramer-Rao Bound Matrix (PCRBM) of the target state, then updates the sample interval in accordance with change of the target dynamics by comparing the trace of the predicted PCRBM with a certain threshold. Simulation results demonstrate that this algorithm could solve the nonlinear motion and the nonlinear relationship between radar measurement and target motion state and decrease computation load.
基金This work was supported by the HPC Platform at Xi’an Jiao Tong UniversityNational Natural Science Foundation of China(grant numbers 52006172,21978228)+2 种基金Shaanxi Provincial Natural Science Basic Research Program-Youth Fund Project(grant number 2020JQ-050)Shaanxi Creative Talents Promotion Plan-Technological Innovation Team(grant number 2019TD-039)Fundamental Research Funds for the Central Universities(grant number cxtd2017004).
文摘Mesoscale structures that form in gas-solid flows considerably affect interphase heat transfer.A filtered interphase heat transfer model accounts for the effects of unresolved mesoscale structures is required in coarse-grid simulations.In the literature,researchers obtain the filtered interphase heat transfer coefficient using a correction(Q)to the microscopic interphase heat transfer coefficient.Available models are based on filtered data in the range 0<Q<1.However,the percentage of filtered data in the range Q<0 and Q>1 is non-negligible.This percentage can reach approximately 20%when the dimensionless filter size is smaller than 1.028(66.7×the particle diameter).We proposed an improved filtered interphase heat transfer model by considering the data in the range Q<0 and Q>1.We evaluated the predictive power of our model in an a priori test.Our model has much better performance than other models when the dimensionless filter size△<8.222.
基金supported in part by Funds of National Science of China(No.61174215)
文摘This paper addresses a robust H∞filter design problem for nonlinear systems with time-varying delay through TakagiSugeno(T-S) fuzzy model approach. Firstly, by introducing free-weighting matrix method combined with a matrix decoupling approach and adopting an improved integral inequality method without ignoring any integral term, less conservative results are achieved. Next,based on the model, new delay-dependent sufficient conditions are derived, which are less conservative than the existing ones via solving the linear matrix inequalities(LMIs). Lastly, simulations show a significant improvement over the previous results.
基金This work was supported by the Natural Science Foundation of China through Grant No. 21676051, New Century Excellent Talents in University (NCET-12-0703). One of the authors (Shuyan Wang) thanks the China Scholarship Council (CSC) for providing financial support to the Sundaresan's group of Princeton University.
文摘The influence of a vertical jet located at the distributor in a cylindrical fluidized bed on the flow behavior of gas and particles was predicted using a filtered two-fluid model proposed by Sundaresan and coworkers. The distributions of volume fraction and the velocity of particles along the lateral direction were investigated for different jet velocities by analyzing the simulated results. The vertical jet penetration lengths at the different gas jet velocities have been obtained and compared with predictions derived from empirical correlations; the predicted air jet penetration length is discussed. Agreement between the numerical simulations and experimental results has been achieved.
基金supported by the Science Fundamental Research Project of Jiangsu Normal University,China(No.9212812101)
文摘This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.
文摘Allan variance(AV)stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme.However,the latter usually employs a traditional hard threshold or soft threshold that presents some mathematical problems.An adaptive dual threshold for discrete wavelet transform(DWT)denoising function overcomes the disadvantages of traditional approaches.Assume that two thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two thresholds assure continuity and overcome previous difficulties.On the basis of AV,an application for strap-down inertial navigation system(SINS)stochastic model extraction assures more efficient tuning of the augmented 21-state improved exact modeling Kalman filter(IEMKF)states.The experimental results show that the proposed algorithm is superior in denoising performance.Furthermore,the improved filter estimation of navigation solution is better than that of conventional Kalman filter(CKF).
基金supported by the National Natural SciencFoundation of China(Nos.U1636125,61601027)
文摘For the performance issues of satellite transceivers suffering passive intermodulation interference,a novel and effective digital suppression algorithm is presented in this paper.In contrast to analog approaches,digital passive intermodulation(PIM) suppression approaches can be easily reconfigured and therefore are highly attractive for future satellite communication systems.A simplified model of nonlinear distortion from passive microwave devices is established in consideration of the memory effect.The multiple high-order PIM products falling into the receiving band can be described as a bilinear predictor function.A suppression algorithm based on a bilinear polynomial decorrelated adaptive filter is proposed for baseband digital signal processing.In consideration of the time-varying characteristics of passive intermodulation,this algorithm can achieve the rapidness of online interference estimation and low complexity with less consumption of resources.Numerical simulation results show that the algorithm can effectively compensate the passive intermodulation interference,and achieve a high signal-to-interference ratio gain.
文摘An ensemble-based assimilation system that used the MASINGAR ink-2 (Model of Aerosol Species IN the Global AtmospheRe Mark 2) dust forecasting model and satellite-derived aerosol optical thickness (AOT) data. processed in the JAXA (Japan Aerospace Exploration Agency) Satellite Monitoring for Environmental Studies (JASMES) system with MODIS (Moderate Resolution Imaging Spectroradiometer) observations. was used to quantify the impact of assimilation on forecasts of a severe Asian dust storm during May 10-13. 2011. The modeled bidirectional reflectance function and observed vegetation index employed in JASMES enable AOT retrievals in areas of high surface reflectance, making JASMES effective for dust forecasting and early warning by enabling assimilations in dust storm source regions. Forecasts both with and without assimilation were validated using PM^0 observations from China, Korea, and Japan in the TEMM WG1 dataset. Only the forecast with assimilation successfully captured the contrast between the core and tail of the dust storm by increasing the AOT around the core by 70-150% and decreasing it around the tail by 20-30% in the 18-h forecast. The forecast with assimilation improved the agreement with observed PMlo concentrations, but the effect was limited at downwind sites in Korea and Japan because of the lack of observational constraints for a mis-forecasted dust storm due to cloud.