This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set a...This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties.Assisted with enhanced robust tubes,the chance constraints are then formulated into a deterministic form.To alleviate the online computational burden,a novel event-triggered stochastic model predictive control is developed,where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance.Two triggering parametersσandγare used to adjust the frequency of solving the optimization problem.The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined.Finally,numerical studies on the control of a heating,ventilation,and air conditioning(HVAC)system confirm the efficacy of the proposed control.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epi...A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.展开更多
Several mathematical models have been developed to investigate the dynamics of tuberculosis(TB) and hepatitis B virus(HBV).Numerous current models for TB,HBV,and their co-dynamics fall short in capturing the important...Several mathematical models have been developed to investigate the dynamics of tuberculosis(TB) and hepatitis B virus(HBV).Numerous current models for TB,HBV,and their co-dynamics fall short in capturing the important and practical aspect of unpredictability.It is crucial to take into account a stochastic co-infection HBV-TB epidemic model since different random elements have a substantial impact on the overall dynamics of these diseases.We provide a novel stochastic co-model for TB and HBV in this study,and we establish criteria on the uniqueness and existence of a nonnegative global solution.We also looked at the persistence of the infections as long its dynamics are governable by the proposed model.To verify the theoretical conclusions,numerical simulations are presented keeping in view the associated analytical results.The infections are found to finally die out and go extinct with certainty when Lévy intensities surpass the specified thresholds and the related stochastic thresholds fall below unity.The findings also demonstrate the impact of noise on the decline in the co-circulation of HBV and TB in a given population.Our results provide insights into effective intervention strategies,ultimately aiming to improve the management and control of TB and HBV co-infections.展开更多
A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, an...A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, and the transition probability matrix is directly calculated by the results of a discrete element method （DEM） simulation. The Markov property of the BFB is discussed by the comparison results calculated from both static and dynamic transition probability matrices. The static matrix is calculated based on the Markov chain while the dynamic matrix is calculated based on the memory property of the particle movement. Results show that the difference in the trends of particle movement between the static and dynamic matrix calculation is very small. Besides, the particle mixing curves of the MCM and DEM have the same trend and similar numerical values, and the details show the time averaged characteristic of the MCM and also expose its shortcoming in describing the instantaneous particle dynamics in the BFB.展开更多
To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using ...To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.展开更多
This paper discusses the principles of geologic constraints on reservoir stochastic modeling. By using the system science theory, two kinds of uncertainties, including random uncertainty and fuzzy uncertainty, are rec...This paper discusses the principles of geologic constraints on reservoir stochastic modeling. By using the system science theory, two kinds of uncertainties, including random uncertainty and fuzzy uncertainty, are recognized. In order to improve the precision of stochastic modeling and reduce the uncertainty in realization, the fuzzy uncertainty should be stressed, and the ＂geological genesis-controlled modeling＂ is conducted under the guidance of a quantitative geological pattern. An example of the Pingqiao horizontal-well division of the Ansai Oilfield in the Ordos Basin is taken to expound the method of stochastic modeling.展开更多
When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertain...When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.展开更多
In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driv...In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.展开更多
Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochas...Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian distributions.However,measured surface-layer wind velocities show a strong skewness and kurtosis.This paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis.Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model.Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated velocities.This improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations.Moreover,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations.Thus,the non-Gaussian model is worth applying to wind field simulation in the surface layer.展开更多
A 3D stochastic modeling was carried out to simulate the dendritic grains during solidification of aluminum alloys, including time-dependent calculations for temperature field, solute redistribution in liquid, curvatu...A 3D stochastic modeling was carried out to simulate the dendritic grains during solidification of aluminum alloys, including time-dependent calculations for temperature field, solute redistribution in liquid, curvature of the dendritic tip, and growth anisotropy. The nucleation process was treated by continuous nucleation. A 3D simplified grain shape model was established to represent the equiaxed dendritic grain. Based on the Cellular Automaton method, a grain growth model was proposed to capture the neighbor cells of the nucleated cell. During growing, each grain continues to capture the nearest neighbor cells to form the final shape. When a neighbor cell was captured by other grains, the grain growth along this direction would be stopped. Three-dimensional calculations were performed to simulate the evolution of dendritic grain. In order to verify the modeling results, the predictions were compared with the observation on samples cast in the sand mold and the metal mold.展开更多
Stochastic modelling of hydrological time series with insufficient length and data gaps is a serious challenge since these problems significantly affect the reliability of statistical models predicting and forecasting...Stochastic modelling of hydrological time series with insufficient length and data gaps is a serious challenge since these problems significantly affect the reliability of statistical models predicting and forecasting skills.In this paper,we proposed a method for searching the seasonal autoregressive integrated moving average(SARIMA)model parameters to predict the behavior of groundwater time series affected by the issues mentioned.Based on the analysis of statistical indices,8 stations among 44 available within the Campania region(Italy)have been selected as the highest quality measurements.Different SARIMA models,with different autoregressive,moving average and differentiation orders had been used.By reviewing the criteria used to determine the consistency and goodness-of-fit of the model,it is revealed that the model with specific combination of parameters,SARIMA(0,1,3)(0,1,2)_(12),has a high R^(2) value,larger than 92%,for each of the 8 selected stations.The same model has also good performances for what concern the forecasting skills,with an average NSE of about 96%.Therefore,this study has the potential to provide a new horizon for the simulation and reconstruction of groundwater time series within the investigated area.展开更多
Stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil char...Stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil characteristics. The study is based on about twenty location and soil characteristics, majority of them are observed through laboratory analysis of soil and water samples collected from nearly thee hundred locations of drinking water sources, wells and bore wells selected at random from the district of Kasaragod. The water contamination in wells are found to be relatively more as compared to bore wells. The study reveals that only 7 % of the wells and 40 o~ of the bore wells of the district are within the permissible limit of WHO standard of drinking water quality. The level of contamination is very high in the hospital premises and is very low in the forest area. Two separate multiple ordinal logistic regression models are developed to predict the level of coliform count, one for well and the other for bore well. The significant feature of this study is that in addition to scientifically proving the dependence of the water quality on the distances from waste disposal area and septic tanks etc., it highlights the dependence of two other very significant soil characteristics, the soil organic carbon and soil porosity. The models enable to predict the quality of water in a location based on the set of soil and location characteristics. One of the important uses of the model is in fixing safe locations for waste dump area, septic tank, digging well etc. in town planning, designing residential layouts, industrial layouts, hospital/hostel construction etc. This is the first ever study to describe the ground water quality in terms of the location and soil characteristics.展开更多
The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same ...The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.展开更多
The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavio...The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.展开更多
In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a...In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a wave-group propagation formulation with phase spectrum model built up on the frequency components’ starting-time of phase evolution. The present paper aims at extending the formulation to the simulation of non-stationary random seismic ground motions. The ground motion records associated with N–S component of Northridge Earthquake at the type-II site are investigated. The frequency components’ starting-time of phase evolution of is identified from the ground motion records, and is proved to admit the Gamma distribution through data fitting. Numerical results indicate that the simulated random ground motion features zeromean, non-stationary, and non-Gaussian behaviors, and the phase spectrum model with only a few starting-times of phase evolution could come up with a sound contribution to the simulation.展开更多
Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a n...Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.展开更多
A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed.The scatterers surrounding the UAV and ground station...A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed.The scatterers surrounding the UAV and ground station are assumed to be distributed on the surface of two cylinders in the proposed model.The impact of UAV rotations and accelerated motion is considered to describe channel non-stationarity.The computational methods of the corresponding time-variant parameters,such as UAV antenna array angles,time delays,and maximum Doppler frequencies,are theoretically deduced.The model is then used to derive channel statistical properties such as space-time correlation functions and Doppler power spectral density.Finally,numerical simulations are run to validate the channel s statistical properties.The simulation results show that increasing the UAV and ground station accelerations can reduce the time correlation function and increase channel non-stationarity in the time domain.Furthermore,the UAV s rotation significantly influences the spatial correlation function,with rolling having a greater influence than pitching.Similarly,the different directions of UAV movement significantly impact the Doppler power spectral density.展开更多
The COVID-19 pandemic has become a great challenge to scientific, biological and medical research as well as to economic and social sciences. Hence, the objective of infectious disease modeling-based data analysis is ...The COVID-19 pandemic has become a great challenge to scientific, biological and medical research as well as to economic and social sciences. Hence, the objective of infectious disease modeling-based data analysis is to recover these dynamics of infectious disease spread and to estimate parameters that govern these dynamics. The random aspect of epidemics leads to the development of stochastic epidemiological models. We establish a stochastic combined model using numerical scheme Euler, Markov chain and Susceptible-Exposed-Infected-Recovery (SEIR) model. The combined SEIR model was used to predict how epidemics will develop and then to act accordingly. These COVID-19 data were analyzed from several countries such as Italy, Russia, USA and Iran.展开更多
基金supported by the National Nature Science Foundation of China(62073194)the Natural Science Foundation of Shandong Province of China(ZR2023MF028)the Taishan Scholars Program of Shandong Province(tsqn202312008)
文摘This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties.Assisted with enhanced robust tubes,the chance constraints are then formulated into a deterministic form.To alleviate the online computational burden,a novel event-triggered stochastic model predictive control is developed,where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance.Two triggering parametersσandγare used to adjust the frequency of solving the optimization problem.The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined.Finally,numerical studies on the control of a heating,ventilation,and air conditioning(HVAC)system confirm the efficacy of the proposed control.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
文摘A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.
文摘Several mathematical models have been developed to investigate the dynamics of tuberculosis(TB) and hepatitis B virus(HBV).Numerous current models for TB,HBV,and their co-dynamics fall short in capturing the important and practical aspect of unpredictability.It is crucial to take into account a stochastic co-infection HBV-TB epidemic model since different random elements have a substantial impact on the overall dynamics of these diseases.We provide a novel stochastic co-model for TB and HBV in this study,and we establish criteria on the uniqueness and existence of a nonnegative global solution.We also looked at the persistence of the infections as long its dynamics are governable by the proposed model.To verify the theoretical conclusions,numerical simulations are presented keeping in view the associated analytical results.The infections are found to finally die out and go extinct with certainty when Lévy intensities surpass the specified thresholds and the related stochastic thresholds fall below unity.The findings also demonstrate the impact of noise on the decline in the co-circulation of HBV and TB in a given population.Our results provide insights into effective intervention strategies,ultimately aiming to improve the management and control of TB and HBV co-infections.
基金The National Science Foundation of China(No.51276036,51306035)the Fundamental Research Funds for the Central Universities(No.KYLX_0114)
文摘A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, and the transition probability matrix is directly calculated by the results of a discrete element method （DEM） simulation. The Markov property of the BFB is discussed by the comparison results calculated from both static and dynamic transition probability matrices. The static matrix is calculated based on the Markov chain while the dynamic matrix is calculated based on the memory property of the particle movement. Results show that the difference in the trends of particle movement between the static and dynamic matrix calculation is very small. Besides, the particle mixing curves of the MCM and DEM have the same trend and similar numerical values, and the details show the time averaged characteristic of the MCM and also expose its shortcoming in describing the instantaneous particle dynamics in the BFB.
基金Supported by the National 863 Program of China （No.2006AA12Z325） and the National Natural Science Foundation of China （No.40274005）.
文摘To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.
文摘This paper discusses the principles of geologic constraints on reservoir stochastic modeling. By using the system science theory, two kinds of uncertainties, including random uncertainty and fuzzy uncertainty, are recognized. In order to improve the precision of stochastic modeling and reduce the uncertainty in realization, the fuzzy uncertainty should be stressed, and the ＂geological genesis-controlled modeling＂ is conducted under the guidance of a quantitative geological pattern. An example of the Pingqiao horizontal-well division of the Ansai Oilfield in the Ordos Basin is taken to expound the method of stochastic modeling.
文摘When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.
基金the Science and Technology Commission of Shanghai Municipality(No.19511103503)。
文摘In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.
基金financial support for this research from a USDA-AFRI Foundational Grant (Grant No. 2012-67013-19687)from the Illinois State Water Survey at the University of Illinois at Urbana—Champaign
文摘Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality,gene flow(through pollen drift),and plant disease transmission(spore dispersion).Although Lagrangian stochastic(LS)models describe stochastic wind behaviors,such models assume that wind velocities follow Gaussian distributions.However,measured surface-layer wind velocities show a strong skewness and kurtosis.This paper presents an improved model,a non-Gaussian LS model,which incorporates controllable non-Gaussian random variables to simulate the targeted non-Gaussian velocity distribution with more accurate skewness and kurtosis.Wind velocity statistics generated by the non-Gaussian model are evaluated by using the field data from the Cooperative Atmospheric Surface Exchange Study,October 1999 experimental dataset and comparing the data with statistics from the original Gaussian model.Results show that the non-Gaussian model improves the wind trajectory simulation by stably producing precise skewness and kurtosis in simulated wind velocities without sacrificing other features of the traditional Gaussian LS model,such as the accuracy in the mean and variance of simulated velocities.This improvement also leads to better accuracy in friction velocity(i.e.,a coupling of three-dimensional velocities).The model can also accommodate various non-Gaussian wind fields and a wide range of skewness–kurtosis combinations.Moreover,improved skewness and kurtosis in the simulated velocity will result in a significantly different dispersion for wind/particle simulations.Thus,the non-Gaussian model is worth applying to wind field simulation in the surface layer.
文摘A 3D stochastic modeling was carried out to simulate the dendritic grains during solidification of aluminum alloys, including time-dependent calculations for temperature field, solute redistribution in liquid, curvature of the dendritic tip, and growth anisotropy. The nucleation process was treated by continuous nucleation. A 3D simplified grain shape model was established to represent the equiaxed dendritic grain. Based on the Cellular Automaton method, a grain growth model was proposed to capture the neighbor cells of the nucleated cell. During growing, each grain continues to capture the nearest neighbor cells to form the final shape. When a neighbor cell was captured by other grains, the grain growth along this direction would be stopped. Three-dimensional calculations were performed to simulate the evolution of dendritic grain. In order to verify the modeling results, the predictions were compared with the observation on samples cast in the sand mold and the metal mold.
文摘Stochastic modelling of hydrological time series with insufficient length and data gaps is a serious challenge since these problems significantly affect the reliability of statistical models predicting and forecasting skills.In this paper,we proposed a method for searching the seasonal autoregressive integrated moving average(SARIMA)model parameters to predict the behavior of groundwater time series affected by the issues mentioned.Based on the analysis of statistical indices,8 stations among 44 available within the Campania region(Italy)have been selected as the highest quality measurements.Different SARIMA models,with different autoregressive,moving average and differentiation orders had been used.By reviewing the criteria used to determine the consistency and goodness-of-fit of the model,it is revealed that the model with specific combination of parameters,SARIMA(0,1,3)(0,1,2)_(12),has a high R^(2) value,larger than 92%,for each of the 8 selected stations.The same model has also good performances for what concern the forecasting skills,with an average NSE of about 96%.Therefore,this study has the potential to provide a new horizon for the simulation and reconstruction of groundwater time series within the investigated area.
文摘Stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil characteristics. The study is based on about twenty location and soil characteristics, majority of them are observed through laboratory analysis of soil and water samples collected from nearly thee hundred locations of drinking water sources, wells and bore wells selected at random from the district of Kasaragod. The water contamination in wells are found to be relatively more as compared to bore wells. The study reveals that only 7 % of the wells and 40 o~ of the bore wells of the district are within the permissible limit of WHO standard of drinking water quality. The level of contamination is very high in the hospital premises and is very low in the forest area. Two separate multiple ordinal logistic regression models are developed to predict the level of coliform count, one for well and the other for bore well. The significant feature of this study is that in addition to scientifically proving the dependence of the water quality on the distances from waste disposal area and septic tanks etc., it highlights the dependence of two other very significant soil characteristics, the soil organic carbon and soil porosity. The models enable to predict the quality of water in a location based on the set of soil and location characteristics. One of the important uses of the model is in fixing safe locations for waste dump area, septic tank, digging well etc. in town planning, designing residential layouts, industrial layouts, hospital/hostel construction etc. This is the first ever study to describe the ground water quality in terms of the location and soil characteristics.
文摘The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.
文摘The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.
文摘In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a wave-group propagation formulation with phase spectrum model built up on the frequency components’ starting-time of phase evolution. The present paper aims at extending the formulation to the simulation of non-stationary random seismic ground motions. The ground motion records associated with N–S component of Northridge Earthquake at the type-II site are investigated. The frequency components’ starting-time of phase evolution of is identified from the ground motion records, and is proved to admit the Gamma distribution through data fitting. Numerical results indicate that the simulated random ground motion features zeromean, non-stationary, and non-Gaussian behaviors, and the phase spectrum model with only a few starting-times of phase evolution could come up with a sound contribution to the simulation.
基金supported by Comunidad de Madrid within the framework of the Multiannual Agreement with Universidad Politécnica de Madrid to encourage research by young doctors(PRINCE).
文摘Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.
基金The Pre-Research Fund of Science and Technology on Near-Surface Detection Laboratory(No.6142414190405,6142414200505)the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province(No.BA2019025).
文摘A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed.The scatterers surrounding the UAV and ground station are assumed to be distributed on the surface of two cylinders in the proposed model.The impact of UAV rotations and accelerated motion is considered to describe channel non-stationarity.The computational methods of the corresponding time-variant parameters,such as UAV antenna array angles,time delays,and maximum Doppler frequencies,are theoretically deduced.The model is then used to derive channel statistical properties such as space-time correlation functions and Doppler power spectral density.Finally,numerical simulations are run to validate the channel s statistical properties.The simulation results show that increasing the UAV and ground station accelerations can reduce the time correlation function and increase channel non-stationarity in the time domain.Furthermore,the UAV s rotation significantly influences the spatial correlation function,with rolling having a greater influence than pitching.Similarly,the different directions of UAV movement significantly impact the Doppler power spectral density.
文摘The COVID-19 pandemic has become a great challenge to scientific, biological and medical research as well as to economic and social sciences. Hence, the objective of infectious disease modeling-based data analysis is to recover these dynamics of infectious disease spread and to estimate parameters that govern these dynamics. The random aspect of epidemics leads to the development of stochastic epidemiological models. We establish a stochastic combined model using numerical scheme Euler, Markov chain and Susceptible-Exposed-Infected-Recovery (SEIR) model. The combined SEIR model was used to predict how epidemics will develop and then to act accordingly. These COVID-19 data were analyzed from several countries such as Italy, Russia, USA and Iran.
