To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen s...To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.展开更多
Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating a...Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.展开更多
In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity condi...In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.展开更多
An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency...An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.展开更多
In the future connected vehicle environment,the information of multiple vehicles ahead can be readily collected in real-time,such as the velocity or headway,which provides more opportunities for information exchange a...In the future connected vehicle environment,the information of multiple vehicles ahead can be readily collected in real-time,such as the velocity or headway,which provides more opportunities for information exchange and cooperative control.Meanwhile,gyroidal roads are one of the fundamental road patterns prevalent in mountainous areas.To effectively control the system,it is therefore significant to explore the evolution mechanism of traffic flow on gyroidal roads under a connected vehicle environment.In this paper,we present a new continuum model with the average velocity of multiple vehicles ahead on gyroidal roads.The stability criterion and KdV-Burger equation are deduced via linear and nonlinear stability analysis,respectively.Solving the above KdV-Burger equation yields the density wave solution,which explores the formation and propagation property of traffic jams near the neutral stability curve.Simulation examples verify that the model can reproduce complex phenomena,such as shock waves and rarefaction waves.The analysis of the local cluster effect shows that the number of vehicles ahead and the radius information,and the slope information of gyroidal roads can exert a great influence on traffic jams.The effect of the first and second terms are positive,while the last term is negative.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
Outbreaks of hand-foot-mouth disease(HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease(HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average(ARIMA) model for time series analysis was designed in this study. Eighty-four-month(from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination(R^2), normalized Bayesian Information Criterion(BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as(1,0,1)(0,1,1)12, with the largest coefficient of determination(R^2=0.743) and lowest normalized BIC(BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations(P_(Box-Ljung(Q))=0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.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
With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the m...With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.展开更多
The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system sta...The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.展开更多
Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effect...Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.展开更多
A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derive...A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derived from the momentum equation and the flow continuity equation under the condition of quasi-uniform flow. A series of experiments are conducted in a large-scale meandering compound channel. Based on the experimental data, a magnitude analysis is carried out for the governing equation, and two lower-order shear stress terms are ignored. Four groups of experimental data from different sources are used to verify the predictive capability of this model, and good predictions are obtained. Finally, the determination of the velocity parameter and the limitation of this model are discussed.展开更多
Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simu...Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.展开更多
Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and a...Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.展开更多
The distribution of droplet surface pressure is uneven?under the action of high velocity gas streams in gas wells, and there exists a pressure difference which leads to droplet deformation before and after the droplet...The distribution of droplet surface pressure is uneven?under the action of high velocity gas streams in gas wells, and there exists a pressure difference which leads to droplet deformation before and after the droplet. Moreover, it affects the critical liquid carrying rate. The pressure difference prediction model must be determined, because of the existing one lacking theoretical basis. Based on the droplet surface pressure distribution in high velocity gas streams, a new model is established to predict the average differential pressure of droplets. Compared with the new differential pressure prediction results, the existing pressure difference prediction results were overvalued by 46.0%. This article also improves four gas-well critical liquid carrying models using the proposed pressure difference prediction model, and compares with the original one. The result indicates that the critical velocity of the original models is undervalued by 10% or so, due to the overestimate to the pressuredifference. In addition, comparisons of the improved model with original models show that it is necessary to consider the adaptability, because the models have significant differences in results, and different suitability for different well conditions.展开更多
In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochast...In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochastic system by computing the Lyapunov exponent of the linearized system. Further, the global stability of the stochastic model is analyzed based on the singular boundary theory. Moreover, we prove that the model undergoes a Hopf bifurcation and a pitchfork bifurcation. Finally, several numerical examples are provided to illustrate the theoretical results. .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
At present, there are significant regional differences in average life expectancy among countries in the world. Not only is there a great disparity in average life expectancy, but also the gender difference is positiv...At present, there are significant regional differences in average life expectancy among countries in the world. Not only is there a great disparity in average life expectancy, but also the gender difference is positive and negative, and is distributed in a bipolar distribution of “long life in rich countries and short life in poor countries”. This paper analyzes the factors affecting the life grade by using the ordered multivariate discrete selection model and combined with the average life expectancy data of countries all over the world in 2017. The test results show that: 1) The growth of per capita GDP, elderly dependency ratio and the proportion of people using at least basic drinking water services can effectively improve the level of life expectancy;2) The birth rate has an inhibitory effect on the average life expectancy;3) Through model comparison, probit model is more suitable for the analysis of this kind of problems than logit model, and the properties of the obtained model are better.展开更多
基金funding from the Paul ScherrerInstitute,Switzerland through the NES/GFA-ABE Cross Project。
文摘To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.
基金supported by The Technology Innovation Team(Tianshan Innovation Team),Innovative Team for Efficient Utilization of Water Resources in Arid Regions(2022TSYCTD0001)the National Natural Science Foundation of China(42171269)the Xinjiang Academician Workstation Cooperative Research Project(2020.B-001).
文摘Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.
文摘In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFF0607504)。
文摘An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.
基金supported by Guangdong Basic and Applied Research Foundation(Project No.2022A1515010948,2019A1515111200,2019A1515110837,2023A1515011696)the National Science Foundation of China(Project No.72071079,52272310).
文摘In the future connected vehicle environment,the information of multiple vehicles ahead can be readily collected in real-time,such as the velocity or headway,which provides more opportunities for information exchange and cooperative control.Meanwhile,gyroidal roads are one of the fundamental road patterns prevalent in mountainous areas.To effectively control the system,it is therefore significant to explore the evolution mechanism of traffic flow on gyroidal roads under a connected vehicle environment.In this paper,we present a new continuum model with the average velocity of multiple vehicles ahead on gyroidal roads.The stability criterion and KdV-Burger equation are deduced via linear and nonlinear stability analysis,respectively.Solving the above KdV-Burger equation yields the density wave solution,which explores the formation and propagation property of traffic jams near the neutral stability curve.Simulation examples verify that the model can reproduce complex phenomena,such as shock waves and rarefaction waves.The analysis of the local cluster effect shows that the number of vehicles ahead and the radius information,and the slope information of gyroidal roads can exert a great influence on traffic jams.The effect of the first and second terms are positive,while the last term is negative.
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 Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease(HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average(ARIMA) model for time series analysis was designed in this study. Eighty-four-month(from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination(R^2), normalized Bayesian Information Criterion(BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as(1,0,1)(0,1,1)12, with the largest coefficient of determination(R^2=0.743) and lowest normalized BIC(BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations(P_(Box-Ljung(Q))=0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
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With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.
文摘The high potentiality of integrating renewable energies,such as photovoltaic,into a modern electrical microgrid system,using DC-to-DC converters,raises some issues associated with controller loop design and system stability.The generalized state space average model(GSSAM)concept was consequently introduced to design a DC-to-DC converter controller in order to evaluate DC-to-DC converter performance and to conduct stability studies.This paper presents a GSSAM for parallel DC-to-DC converters,namely:buck,boost,and buck-boost converters.The rationale of this study is that modern electrical systems,such as DC networks,hybrid microgrids,and electric ships,are formed by parallel DC-to-DC converters with separate DC input sources.Therefore,this paper proposes a GSSAM for any number of parallel DC-to-DC converters.The proposed GSSAM is validated and investigated in a time-domain simulation environment,namely a MATLAB/SIMULINK.The study compares the steady-state,transient,and oscillatory performance of the state-space average model with a fully detailed switching model.
基金the National Natural Science Foundation of China(U1901601)the National Key Research and Development Program of China(2022YFB3903503)。
文摘Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.
基金Project supported by the National Natural Science Foundation of China(Nos.11171238,51279117,and 11072161)the Program for New Century Excellent Talents in University of China(No.NCET-13-0393)the National Science and Technology Ministry of China(No.2012BAB05B02)
文摘A simple but applicable analytical model is presented to predict the lat- eral distribution of the depth-averaged velocity in meandering compound channels. The governing equation with curvilinear coordinates is derived from the momentum equation and the flow continuity equation under the condition of quasi-uniform flow. A series of experiments are conducted in a large-scale meandering compound channel. Based on the experimental data, a magnitude analysis is carried out for the governing equation, and two lower-order shear stress terms are ignored. Four groups of experimental data from different sources are used to verify the predictive capability of this model, and good predictions are obtained. Finally, the determination of the velocity parameter and the limitation of this model are discussed.
基金The project is partly supported by the National Science Council, Contract Nos. NSC-89-261 l-E-019-024 (JZY), and NSC-89-2611-E-019-027 (CRC).
文摘Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.
基金The National Key Research and Development Program of China under contract No.2017YFC1404000the Basic Scientific Fund for National Public Research Institutes of China under contract No.2018S03the National Natural Science Foundation of China under contract Nos 41776038 and 41821004
文摘Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.
文摘The distribution of droplet surface pressure is uneven?under the action of high velocity gas streams in gas wells, and there exists a pressure difference which leads to droplet deformation before and after the droplet. Moreover, it affects the critical liquid carrying rate. The pressure difference prediction model must be determined, because of the existing one lacking theoretical basis. Based on the droplet surface pressure distribution in high velocity gas streams, a new model is established to predict the average differential pressure of droplets. Compared with the new differential pressure prediction results, the existing pressure difference prediction results were overvalued by 46.0%. This article also improves four gas-well critical liquid carrying models using the proposed pressure difference prediction model, and compares with the original one. The result indicates that the critical velocity of the original models is undervalued by 10% or so, due to the overestimate to the pressuredifference. In addition, comparisons of the improved model with original models show that it is necessary to consider the adaptability, because the models have significant differences in results, and different suitability for different well conditions.
文摘In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochastic system by computing the Lyapunov exponent of the linearized system. Further, the global stability of the stochastic model is analyzed based on the singular boundary theory. Moreover, we prove that the model undergoes a Hopf bifurcation and a pitchfork bifurcation. Finally, several numerical examples are provided to illustrate the theoretical results. .
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.
文摘At present, there are significant regional differences in average life expectancy among countries in the world. Not only is there a great disparity in average life expectancy, but also the gender difference is positive and negative, and is distributed in a bipolar distribution of “long life in rich countries and short life in poor countries”. This paper analyzes the factors affecting the life grade by using the ordered multivariate discrete selection model and combined with the average life expectancy data of countries all over the world in 2017. The test results show that: 1) The growth of per capita GDP, elderly dependency ratio and the proportion of people using at least basic drinking water services can effectively improve the level of life expectancy;2) The birth rate has an inhibitory effect on the average life expectancy;3) Through model comparison, probit model is more suitable for the analysis of this kind of problems than logit model, and the properties of the obtained model are better.