The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagg...The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.展开更多
The research constructed varying parameter state-space model and per- formed estimation on dynamic relationship between urban-rural migration and aggre- gate consumption expenditure on basis of dual economic structure...The research constructed varying parameter state-space model and per- formed estimation on dynamic relationship between urban-rural migration and aggre- gate consumption expenditure on basis of dual economic structure. The results showed that urban consumption growth made the most contribution to aggregate consumption growth, followed by urban-rural migration caused consumption. The role of rural consumption growth kept stable, but consumption caused by population growth was decreasing. Therefore, China consumption growth mainly relies on urban consumption expenditure and urban-rural migration.展开更多
The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model fo...The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.展开更多
In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are co...In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.展开更多
The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the ...The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave.In this research,a novel time domain approach combining dynamic finite element method and state-space model(SSM)is established for the refined analysis of floating bridges.The dynamic coupled effects induced by wave excitation load,radiation load and buffeting load are carefully simulated.High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain.The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons.The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory.The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions.The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.展开更多
This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation....This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.展开更多
The increasingly widening income gap between urban and rural areas is affected by many factors. Using the stepwise regression analysis,we find that urbanization level,socio-economic development,education level,financi...The increasingly widening income gap between urban and rural areas is affected by many factors. Using the stepwise regression analysis,we find that urbanization level,socio-economic development,education level,financial development scale and financial development efficiency have the greatest impact on the income gap between urban and rural areas. By cointegration test,it is found that there is a long-term equilibrium relationship between these five variables and the income gap between urban and rural areas. We build the state-space model to research the dynamic impact of these factors on the income gap between urban and rural areas. The results show that by improving the level of urbanization,we can effectively narrow the income gap between urban and rural areas,while socio-economic development,the improvement of education level,expansion of financial development scale and financial development efficiency all significantly expand the income gap between urban and rural areas.展开更多
This paper presents the development of dynamic simulation of a flexible plate structure with various boundary conditions. A flexible square plate is considered. A finite-difference method is used to discretise the gov...This paper presents the development of dynamic simulation of a flexible plate structure with various boundary conditions. A flexible square plate is considered. A finite-difference method is used to discretise the governing partial differential equation formulation describing its dynamic behaviour. The model thus developed has been validated against characteristic parameters of the plate. The model thus developed is further formulated using discrete state-space representation, to allow easy and fast implementation for simulating the dynamic behaviour of the plate with various boundary conditions. The simulation algorithm thus developed is validated, and accurate results with representation of the first five modes of vibration of the plate have been achieved. The algorithm thus developed is used in subsequent research work as a platform for development and verification of suitable control strategies for vibration suppression of flexible plate structures.展开更多
The fractional frequency transmission system is an emerging technology for long-distance wind power integration,and the modular multilevel matrix converter(M3C)is the keen equipment.Since the M3C directly connects two...The fractional frequency transmission system is an emerging technology for long-distance wind power integration,and the modular multilevel matrix converter(M3C)is the keen equipment.Since the M3C directly connects two ac grids with different frequencies,the external and internal harmonics have complex coupling relationships with a unique dual-fundamental-frequency spectrum,which has not been properly investigated due to a lack of an effective method.To address this issue,a novel harmonic state-space method is proposed to achieve comprehensive modelling of the harmonic dynamics of the M3C.Based on the principle of two-dimensional Fourier transform,the decomposition of the dual-fundamental-frequency harmonics is realized,and the multiplicative coupling between time-domain variables is modelled through double-layer convolution on the frequency domain.Besides,the general expression of the proposed method is provided,which highlights a modularized matrix with easy scalability to meet different truncation requirements.Then,the HSS model of M3C considering the close-loop control is established,based on which a panoramic harmonic coupling relationship between the system-and the low-frequency side is concluded.Finally,the M3C model and harmonic coupling relationship are validated by simulation tests conducted in MATLAB/Simulink environment.展开更多
We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian sta...We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian state-space models to capture periodicity,trends,and random fluctuations in case counts.We explore the relationships between COVID-19 cases,hospitalizations,workdays,and wastewater viral loads through dynamic regression models,offering insights into how these factors influence public health outcomes.Our analysis extends to multivariate covariance estimation,utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data.Results demonstrate the significance of incorporating environmental covariates,such as wastewater data,in improving model robustness and uncovering the complex interplay between epidemiological factors.This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data.By illustrating the application of advanced modelling techniques,this study contributes to a deeper understanding of disease transmission and informs public health interventions.展开更多
We present an exploration of advanced stochastic simulation techniques for state-space models,with a specific focus on their applications in infectious disease modelling.Utilizing COVID-19 surveillance data from the p...We present an exploration of advanced stochastic simulation techniques for state-space models,with a specific focus on their applications in infectious disease modelling.Utilizing COVID-19 surveillance data from the province of Ontario,Canada,we employ Markov Chain Monte Carlo(MCMC)and Sequential Monte Carlo(SMC)methods to detect structural changes and pre-dict future trends in case counts.Our approach begins with the application of a Kalman smoothing technique,integrated with MCMC for state sampling within local level and seasonal models,alongside Bayesian inference for non-linear dynamic regression models.We then assess the effectiveness of various priors,including normal,Student's t,Laplace,and horseshoe distributions,in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter.Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures,offering valuable insights for real-time monitoring and forecasting in public health.This study emphasizes the efficacy of state-space models,particu-larly when enhanced with sophisticated prior distributions,in providing a nuanced understanding of infectious disease transmission.展开更多
The state space average model of switching converters transforms time varying differential equations into time invariant differential equations by the averaging method in math.The model has merits of simple,clear phys...The state space average model of switching converters transforms time varying differential equations into time invariant differential equations by the averaging method in math.The model has merits of simple,clear physical conception and easy to design control system,but it exhibits significant steadystate error and delayed dynamic response in some special parameters or state conditions.Besides,the conventional state space average model(CSSAM)can’t reflect how much the switching period influences system performance.The averaging method based on exact time domain solution approximation for the state variable is established in this paper.Subsequently,a second-order state-space average model(SOSSAM)which extends the constant term in CSSAM to a combination of constant term and linear term of the switching period is proposed.This model inherits the advantages of CSSAM and improves accuracy of steady state performance and dynamic response of switching converters.Influence of switching period to system performance is reflected,which lays a foundation for analyzing system performance and designing a control system of switching converters.展开更多
An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equ...An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.展开更多
State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control ...State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints. However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control.展开更多
Pertaining to dynamic systems in general, a review is given of relations between mathematical descriptions in the frequency domain or time domain and state-space descriptions. For the analysis of hydrodynamic problems...Pertaining to dynamic systems in general, a review is given of relations between mathematical descriptions in the frequency domain or time domain and state-space descriptions. For the analysis of hydrodynamic problems in ocean engineering wave forces may be represented by convolution integrals. The paper presents a method to construct a finite-order state-space model which represents a good approximation to such a convolution integral. The method utilizes a particular algorithm to compute the partial derivative of the exponential function of a (state-space) matrix with respect to the matrix elements. The method is applied to an example of fitting a state space model of order five to the free oscillations corresponding to wave radiation in a transient experiment with an oscillating water column.展开更多
In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.T...In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.展开更多
Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids;they are interconnected by matching the input and output states of the n...Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids;they are interconnected by matching the input and output states of the network and converters.Virtual resistors have been widely used in existing models to generate a voltage for state-space models of the network that require voltage inputs.This paper accurately quantifies the adverse impacts of adding the virtual resistance and proposes an alternative method for network modelling that eliminates the requirement of the virtual resistor when interfacing converters with microgrids.The proposed nonlinear method allows initialization,time-domain simulations of the nonlinear model,and linearization and eigenvalue generation.A numerically linearized small-signal model is used to generate eigenvalues and is compared with the eigenvalues generated using the existing modelling method with virtual resistances.Deficiencies of the existing method and improvements offered by the proposed modelling method are clearly quantified.Electromagnetic transient(EMT)simulations using detailed switching models are used for validation of the proposed modelling method.展开更多
基金The National Natural Science Foundation of China (No.50422283)the Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No.2008-K5-14)
文摘The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.
基金Supported by Programs for Science and Technology Development of Hubei Rural Practical Talents Team Office(2013LK001)~~
文摘The research constructed varying parameter state-space model and per- formed estimation on dynamic relationship between urban-rural migration and aggre- gate consumption expenditure on basis of dual economic structure. The results showed that urban consumption growth made the most contribution to aggregate consumption growth, followed by urban-rural migration caused consumption. The role of rural consumption growth kept stable, but consumption caused by population growth was decreasing. Therefore, China consumption growth mainly relies on urban consumption expenditure and urban-rural migration.
基金funded by the National Natural Science Foundation of China (Nos. 11572069 and 51775541)the China Postdoctoral Science Foundation (No. 2016M601354)
文摘The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.
基金Supported in part by the National Thousand Talents Program of Chinathe National Natural Science Foundation of China(61473054)the Fundamental Research Funds for the Central Universities of China
文摘In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.
基金financially supported by the Program of Science and Technology Innovation Action Plan,Shanghai,China(Grant No.20200741600).
文摘The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave.In this research,a novel time domain approach combining dynamic finite element method and state-space model(SSM)is established for the refined analysis of floating bridges.The dynamic coupled effects induced by wave excitation load,radiation load and buffeting load are carefully simulated.High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain.The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons.The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory.The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions.The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.
文摘This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.
基金Supported by Humanities and Social Sciences Project of the Ministry of Education(10YJC790111)
文摘The increasingly widening income gap between urban and rural areas is affected by many factors. Using the stepwise regression analysis,we find that urbanization level,socio-economic development,education level,financial development scale and financial development efficiency have the greatest impact on the income gap between urban and rural areas. By cointegration test,it is found that there is a long-term equilibrium relationship between these five variables and the income gap between urban and rural areas. We build the state-space model to research the dynamic impact of these factors on the income gap between urban and rural areas. The results show that by improving the level of urbanization,we can effectively narrow the income gap between urban and rural areas,while socio-economic development,the improvement of education level,expansion of financial development scale and financial development efficiency all significantly expand the income gap between urban and rural areas.
文摘This paper presents the development of dynamic simulation of a flexible plate structure with various boundary conditions. A flexible square plate is considered. A finite-difference method is used to discretise the governing partial differential equation formulation describing its dynamic behaviour. The model thus developed has been validated against characteristic parameters of the plate. The model thus developed is further formulated using discrete state-space representation, to allow easy and fast implementation for simulating the dynamic behaviour of the plate with various boundary conditions. The simulation algorithm thus developed is validated, and accurate results with representation of the first five modes of vibration of the plate have been achieved. The algorithm thus developed is used in subsequent research work as a platform for development and verification of suitable control strategies for vibration suppression of flexible plate structures.
基金supported by the National Natural Science Foundation of China(52207103)in part by Basic and Appiled Basic Research Foundational of Guangdong Province(2020A1515111117).
文摘The fractional frequency transmission system is an emerging technology for long-distance wind power integration,and the modular multilevel matrix converter(M3C)is the keen equipment.Since the M3C directly connects two ac grids with different frequencies,the external and internal harmonics have complex coupling relationships with a unique dual-fundamental-frequency spectrum,which has not been properly investigated due to a lack of an effective method.To address this issue,a novel harmonic state-space method is proposed to achieve comprehensive modelling of the harmonic dynamics of the M3C.Based on the principle of two-dimensional Fourier transform,the decomposition of the dual-fundamental-frequency harmonics is realized,and the multiplicative coupling between time-domain variables is modelled through double-layer convolution on the frequency domain.Besides,the general expression of the proposed method is provided,which highlights a modularized matrix with easy scalability to meet different truncation requirements.Then,the HSS model of M3C considering the close-loop control is established,based on which a panoramic harmonic coupling relationship between the system-and the low-frequency side is concluded.Finally,the M3C model and harmonic coupling relationship are validated by simulation tests conducted in MATLAB/Simulink environment.
基金funded by the NSERC/Mitacs/Sanofi Alliance program.
文摘We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian state-space models to capture periodicity,trends,and random fluctuations in case counts.We explore the relationships between COVID-19 cases,hospitalizations,workdays,and wastewater viral loads through dynamic regression models,offering insights into how these factors influence public health outcomes.Our analysis extends to multivariate covariance estimation,utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data.Results demonstrate the significance of incorporating environmental covariates,such as wastewater data,in improving model robustness and uncovering the complex interplay between epidemiological factors.This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data.By illustrating the application of advanced modelling techniques,this study contributes to a deeper understanding of disease transmission and informs public health interventions.
基金partially funded by the NSERC/Mitacs/Sanofi Alliance program.
文摘We present an exploration of advanced stochastic simulation techniques for state-space models,with a specific focus on their applications in infectious disease modelling.Utilizing COVID-19 surveillance data from the province of Ontario,Canada,we employ Markov Chain Monte Carlo(MCMC)and Sequential Monte Carlo(SMC)methods to detect structural changes and pre-dict future trends in case counts.Our approach begins with the application of a Kalman smoothing technique,integrated with MCMC for state sampling within local level and seasonal models,alongside Bayesian inference for non-linear dynamic regression models.We then assess the effectiveness of various priors,including normal,Student's t,Laplace,and horseshoe distributions,in capturing abrupt changes within the data using a Rao-Blackwellized par-ticle filter.Our findings highlight the superior performance of the horseshoe prior in identifying change points and adapting to complex data structures,offering valuable insights for real-time monitoring and forecasting in public health.This study emphasizes the efficacy of state-space models,particu-larly when enhanced with sophisticated prior distributions,in providing a nuanced understanding of infectious disease transmission.
基金supported by National Natural Science Foundation of China(518707211).
文摘The state space average model of switching converters transforms time varying differential equations into time invariant differential equations by the averaging method in math.The model has merits of simple,clear physical conception and easy to design control system,but it exhibits significant steadystate error and delayed dynamic response in some special parameters or state conditions.Besides,the conventional state space average model(CSSAM)can’t reflect how much the switching period influences system performance.The averaging method based on exact time domain solution approximation for the state variable is established in this paper.Subsequently,a second-order state-space average model(SOSSAM)which extends the constant term in CSSAM to a combination of constant term and linear term of the switching period is proposed.This model inherits the advantages of CSSAM and improves accuracy of steady state performance and dynamic response of switching converters.Influence of switching period to system performance is reflected,which lays a foundation for analyzing system performance and designing a control system of switching converters.
基金supported by the National Natural Science Foundation of China(41576103)
文摘An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.
基金Supported by the National Natural Science Foundation of China (No. 60572005)
文摘State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints. However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control.
文摘Pertaining to dynamic systems in general, a review is given of relations between mathematical descriptions in the frequency domain or time domain and state-space descriptions. For the analysis of hydrodynamic problems in ocean engineering wave forces may be represented by convolution integrals. The paper presents a method to construct a finite-order state-space model which represents a good approximation to such a convolution integral. The method utilizes a particular algorithm to compute the partial derivative of the exponential function of a (state-space) matrix with respect to the matrix elements. The method is applied to an example of fitting a state space model of order five to the free oscillations corresponding to wave radiation in a transient experiment with an oscillating water column.
基金The authors disclosed receipt of the following financial support for the research,authorship,and/or publication of this article.This research is supported by the National Key R&D Program of China(2022YFB2601900)the R&D Program of Beijing Municipal Education Commission(KM202310016010)+3 种基金Jiangsu Technology Industrialization and Research Center of Ecological Road Engineering,Suzhou University of Science and Technology(GCZX2203)Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC(MK202202)National Natural Science Foundation of China(No.5197082697)Natural Science Foundation of Beijing(No.Z21013).
文摘In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.
基金supported in part by Natural Sciences and Engineering Research Council(NSERC)of Canada,MITACS,Manitoba HVDC Research Center。
文摘Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids;they are interconnected by matching the input and output states of the network and converters.Virtual resistors have been widely used in existing models to generate a voltage for state-space models of the network that require voltage inputs.This paper accurately quantifies the adverse impacts of adding the virtual resistance and proposes an alternative method for network modelling that eliminates the requirement of the virtual resistor when interfacing converters with microgrids.The proposed nonlinear method allows initialization,time-domain simulations of the nonlinear model,and linearization and eigenvalue generation.A numerically linearized small-signal model is used to generate eigenvalues and is compared with the eigenvalues generated using the existing modelling method with virtual resistances.Deficiencies of the existing method and improvements offered by the proposed modelling method are clearly quantified.Electromagnetic transient(EMT)simulations using detailed switching models are used for validation of the proposed modelling method.
