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 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
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 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 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
Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to ...Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.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
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
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
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
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
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
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
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.
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 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This project is supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX22_0124)the National Natural Science Foundation of China(NO.61374153).
文摘Considering the fractional-order and nonlinear characteristics of proton exchange membrane fuel cells(PEMFC),a fractional-order subspace identification method based on the ADE-BH optimization algorithm is proposed to establish a fractional-order Hammerstein state-space model of PEMFCs.Herein,a Hammerstein model is constructed by connecting a linear module and a nonlinear module in series to precisely depict the nonlinear property of the PEMFC.During the modeling process,fractional-order theory is combined with subspace identification,and a Poisson filter is adopted to enable multi-order derivability of the data.A variable memory method is introduced to reduce computation time without losing precision.Additionally,to improve the optimization accuracy and avoid obtaining locally optimum solutions,a novel ADEBH algorithm is employed to optimize the unknown parameters in the identification method.In this algorithm,the Euclidean distance serves as the theoretical basis for updating the target vector in the absorption-generation operation of the black hole(BH)algorithm.Finally,simulations demonstrate that the proposed model has small output error and high accuracy,indicating that the model can accurately describe the electrical characteristics of the PEMFC process.
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 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.
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.
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Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
