For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the p...For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the parameter estimation is ignored, then the system input and outputcan be reduced to zero with an exponential rate.展开更多
Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class o...Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.展开更多
In this paper,we investigate state estimations of a dynamical system in which not only process and measurement noise,but also parameter uncertainties and deterministic input signals are involved.The sensitivity penali...In this paper,we investigate state estimations of a dynamical system in which not only process and measurement noise,but also parameter uncertainties and deterministic input signals are involved.The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model.The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity.Under a few weak assumptions,it is proved that though the derived state estimator is biased,the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded.Numerical simulations show that the obtained robust filter has relatively nice estimation performances.展开更多
Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditi...Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditions of the feedforward and feedback composite control （FFCC） laws for both infinite-time and finite-time are proposed, and the design approaches are given. A disturbance observer is introduced to make the FFCC laws realizable physically. Simulation results indicate that the FFCC laws are robust with respect to external disturbances.展开更多
This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the ...This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.展开更多
In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With t...In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With the advancement of big data and artificial intelligence,the future of oil and gas field development is trending towards intelligent,unmanned,and automated operations.Currently,the optimization of plunger lift working systems is primarily based on expert experience and manual control,focusing mainly on the success of the plunger lift without adequately considering the impact of different working systems on gas production.Additionally,liquid loading in gas wells is a dynamic process,and the intermittent nature of plunger lift requires accurate modeling;using constant inflow dynamics to describe reservoir flow introduces significant errors.To address these challenges,this study establishes a coupled wellbore-reservoir model for plunger lift wells and validates the computational wellhead pressure results against field measurements.Building on this model,a novel optimization control algorithm based on the deep deterministic policy gradient(DDPG)framework is proposed.The algorithm aims to optimize plunger lift working systems to balance overall reservoir pressure,stabilize gas-water ratios,and maximize gas production.Through simulation experiments in three different production optimization scenarios,the effectiveness of reinforcement learning algorithms(including RL,PPO,DQN,and the proposed DDPG)and traditional optimization algorithms(including GA,PSO,and Bayesian optimization)in enhancing production efficiency is compared.The results demonstrate that the coupled model provides highly accurate calculations and can precisely describe the transient production of wellbore and gas reservoir systems.The proposed DDPG algorithm achieves the highest reward value during training with minimal error,leading to a potential increase in cumulative gas production by up to 5%and cumulative liquid production by 252%.The DDPG algorithm exhibits robustness across different optimization scenarios,showcasing excellent adaptability and generalization capabilities.展开更多
Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using c...Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using complex network theory,this study proposes a hierarchy for TSN and introduces the concept of bounded time error.A coupling model between traffic scheduling and time synchronization is established,deriving functional relationships among end-to-end delay,delay jitter,gate window,and time error.These relationships illustrate that time errors can trigger jumps in delay and delay jitter.To evaluate different time errors impact on traffic scheduling performance,an end-to-end transmission experiment scheme is designed,along with the construction of a TSN test platform implementing two representative cases.Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network.Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches.Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors,up to one gating cycle.End-to-end delay jitter tends to increase with the number of hops.When the ratio of time error bound to window exceeds 10%,the number of schedulable traffic flows decreases rapidly.Case B reveals that when time error is below 1μs,the number of schedulable traffic flows begins to increase significantly,approaching full schedulability at errors below 0.6μs.展开更多
This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion...This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a lar...The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.展开更多
In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be describ...In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
Hepatitis B Virus(HBV)infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis.In this paper,we proposed a deterministic mathematical model and a logistic equation to investigat...Hepatitis B Virus(HBV)infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis.In this paper,we proposed a deterministic mathematical model and a logistic equation to investigate the dynamics of liver cirrhosis progression as well as to explain the implications of variations in alcohol consumption on chronic hepatitis B patients,respectively.The intricate interactions between liver cirrhosis,recovery,and treatment dynamics are captured by the model.This study aims to show that alcohol consumption by Hepatitis B-infected individuals accelerates liver cirrhosis progression while treatment of acutely infected individuals reduces it.We proved that a unique solution of the proposed model exists,which is positive and bounded.Using the next-generation matrix approach,two basic reproductive numbers R_(A_(0))and R_(A_(max))are calculated to identify future recurrence.The equilibrium points are calculated,and both equilibria are proved locally and globally asymptotically stable when R_(0)is below and above one,respectively.It is shown that bifurcation exists at R_(0)=1 and a detailed proof for forward bifurcation is given.Furthermore,we performed the sensitivity analysis of the model parameters on R_(0).For the confirmation of analytical work,we performed numerical simulations,and the results indicate that the treatment and the inhibitory effects reduce the risk of developing liver cirrhosis in individuals,while heavy alcohol consumption accelerates markedly the liver cirrhosis progression in patients with chronic hepatitis B.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise...The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise due to the ability of continuous separation of particles by size,shape,deformability,and electrical properties with high resolution.DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes.DLD techniques have been previously reviewed in 2014.Since then,the field has matured as several physics of DLD have been updated,new phenomena have been discovered,and various designs have been presented to achieve a higher separation performance and throughput.Furthermore,some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection.This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection.Furthermore,current challenges and potential solutions of DLD are also discussed.We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications.In particular,the rapid,low-cost,and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics.展开更多
A novel efficient deterministic secure quantum communication scheme based on four-qubit cluster states and single-photon identity authentication is proposed. In this scheme, the two authenticated users can transmit tw...A novel efficient deterministic secure quantum communication scheme based on four-qubit cluster states and single-photon identity authentication is proposed. In this scheme, the two authenticated users can transmit two bits of classical information per cluster state, and its efficiency of the quantum communication is 1/3, which is approximately 1.67 times that of the previous protocol presented by Wang et al [Chin. Phys. Lett. 23 （2006） 2658]. Security analysis shows the present scheme is secure against intercept-resend attack and the impersonator＇s attack. Furthermore, it is more economic with present-day techniques and easily processed by a one-way quantum computer.展开更多
The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multistage compressors in steady state environment by introducing...The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multistage compressors in steady state environment by introducing de- terministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study was to provide insight into the DC characteristics and the in- fluence of DC on the time-averaged flow field of the APES. In Part 2 of this two-part paper, the influence of DC on the time-averaged flow field was systematically studied; Several time-averaging computations boundary conditions and DC were conducted with various for the downstream stator in a transonic compressor stage, by employing the CFD solver developed in Part 1 of this two-part paper. These results were compared with the time-averaged unsteady flow field and the steady one. The study indicat;d that the circumferential- averaged DC can take into account major part of the unsteady effects on spanwise redistribution of flow fields in compres- sors. Furthermore, it demonstrated that both deterministic stresses and deterministic enthalpy fluxes are necessary to reproduce the time-averaged flow field.展开更多
A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a n...A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.展开更多
The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multi- stage compressors in steady state environment by introduc-...The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multi- stage compressors in steady state environment by introduc- ing deterministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study is to provide insight into the DC characteristics and the influence of DC on the time-averaged flow field of the APES. In Part 1 of this two-part paper, firstly a 3D viscous unsteady and time-averaging flow CFD solver is developed to investi- gate the APES technique. Then steady and unsteady simu- lations are conducted in a transonic compressor stage. The results from both simulations are compared to highlight the significance of the unsteady interactions. Furthermore, the distribution characteristics of DC are studied and the DC at the rotor/stator interface are compared with their spatial cor- relations （SC）. Lastly, steady and time-averaging （employing APES with DC） simulations for the downstream stator alone are conducted employing DC derived from the unsteady re- suits. The results from steady and time-averaging simula- tions are compared with the time-averaged unsteady results. The comparisons demonstrate that the simulation employing APES with DC can reproduce the time-averaged field and the 3D viscous time-averaging flow solver is validated.展开更多
基金This project is supported by the National Natural Science Foundation of China
文摘For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the parameter estimation is ignored, then the system input and outputcan be reduced to zero with an exponential rate.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(No.61225014)the National Major Scientific Instruments Development Project(No.61527811)+2 种基金the National Natural Science Foundation of China(Nos.61304084,61374119)the Guangdong Natural Science Foundation(No.2014A030312005)the Space Intelligent Control Key Laboratory of Science and Technology for National Defense.
文摘Recently,an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems.In this paper,a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning.By using a discrete-time extension of deterministic learning algorithm,the general fault functions(i.e.,the internal dynamics)underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function(RBF)networks.Then,a bank of estimators with the obtained knowledge of system dynamics embedded is constructed,and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems.A fault detection decision scheme is presented according to the smallest residual principle,i.e.,the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals.The fault detectability analysis is carried out and the upper bound of detection time is derived.A simulation example is given to illustrate the effectiveness of the proposed scheme.
基金supported in part by the 973 Program(Nos.2009CB320602,2012CB316504)in part by the National Natural Science Foundation of China(Nos.61174122,61021063,60721003,60625305)in part by the Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20110002110045)
文摘In this paper,we investigate state estimations of a dynamical system in which not only process and measurement noise,but also parameter uncertainties and deterministic input signals are involved.The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model.The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity.Under a few weak assumptions,it is proved that though the derived state estimator is biased,the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded.Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
基金This project was supported by the National Natural Science Foundation of China (60574023), the Natural Science Foundation of Shandong Province (Z2005G01), and the Natural Science Foundation of Qingdao City (05-1-JC-94).
文摘Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditions of the feedforward and feedback composite control （FFCC） laws for both infinite-time and finite-time are proposed, and the design approaches are given. A disturbance observer is introduced to make the FFCC laws realizable physically. Simulation results indicate that the FFCC laws are robust with respect to external disturbances.
基金partially supported by National Key Research and Development Program of China(2019YFC1510902)National Natural Science Foundation of China(62073104)+1 种基金Natural Science Foundation of Heilongjiang Province(LH2022F024)China Postdoctoral Science Foundation(2022M710965)。
文摘This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.
基金support from Science Foundation of China University of Petroleum,Beijing(No.2462023YJRC019)National Natural Science Foundation of China(No.52204059)Key Core Technology Research Project Foundation of PetroChina Group(No.2023ZG18).
文摘In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With the advancement of big data and artificial intelligence,the future of oil and gas field development is trending towards intelligent,unmanned,and automated operations.Currently,the optimization of plunger lift working systems is primarily based on expert experience and manual control,focusing mainly on the success of the plunger lift without adequately considering the impact of different working systems on gas production.Additionally,liquid loading in gas wells is a dynamic process,and the intermittent nature of plunger lift requires accurate modeling;using constant inflow dynamics to describe reservoir flow introduces significant errors.To address these challenges,this study establishes a coupled wellbore-reservoir model for plunger lift wells and validates the computational wellhead pressure results against field measurements.Building on this model,a novel optimization control algorithm based on the deep deterministic policy gradient(DDPG)framework is proposed.The algorithm aims to optimize plunger lift working systems to balance overall reservoir pressure,stabilize gas-water ratios,and maximize gas production.Through simulation experiments in three different production optimization scenarios,the effectiveness of reinforcement learning algorithms(including RL,PPO,DQN,and the proposed DDPG)and traditional optimization algorithms(including GA,PSO,and Bayesian optimization)in enhancing production efficiency is compared.The results demonstrate that the coupled model provides highly accurate calculations and can precisely describe the transient production of wellbore and gas reservoir systems.The proposed DDPG algorithm achieves the highest reward value during training with minimal error,leading to a potential increase in cumulative gas production by up to 5%and cumulative liquid production by 252%.The DDPG algorithm exhibits robustness across different optimization scenarios,showcasing excellent adaptability and generalization capabilities.
基金supported in part by the Science and Technology Research and Development Foundation of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ364)in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using complex network theory,this study proposes a hierarchy for TSN and introduces the concept of bounded time error.A coupling model between traffic scheduling and time synchronization is established,deriving functional relationships among end-to-end delay,delay jitter,gate window,and time error.These relationships illustrate that time errors can trigger jumps in delay and delay jitter.To evaluate different time errors impact on traffic scheduling performance,an end-to-end transmission experiment scheme is designed,along with the construction of a TSN test platform implementing two representative cases.Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network.Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches.Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors,up to one gating cycle.End-to-end delay jitter tends to increase with the number of hops.When the ratio of time error bound to window exceeds 10%,the number of schedulable traffic flows decreases rapidly.Case B reveals that when time error is below 1μs,the number of schedulable traffic flows begins to increase significantly,approaching full schedulability at errors below 0.6μs.
基金supported by the National Science and Technology Council,Taiwan[Grant NSTC 111-2628-E-006-005-MY3]supported by the Ocean Affairs Council,Taiwansponsored in part by Higher Education Sprout Project,Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University(NCKU).
文摘This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172318 and 42377186)the National Key R&D Program of China(Grant No.2023YFC3007201).
文摘The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
文摘Hepatitis B Virus(HBV)infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis.In this paper,we proposed a deterministic mathematical model and a logistic equation to investigate the dynamics of liver cirrhosis progression as well as to explain the implications of variations in alcohol consumption on chronic hepatitis B patients,respectively.The intricate interactions between liver cirrhosis,recovery,and treatment dynamics are captured by the model.This study aims to show that alcohol consumption by Hepatitis B-infected individuals accelerates liver cirrhosis progression while treatment of acutely infected individuals reduces it.We proved that a unique solution of the proposed model exists,which is positive and bounded.Using the next-generation matrix approach,two basic reproductive numbers R_(A_(0))and R_(A_(max))are calculated to identify future recurrence.The equilibrium points are calculated,and both equilibria are proved locally and globally asymptotically stable when R_(0)is below and above one,respectively.It is shown that bifurcation exists at R_(0)=1 and a detailed proof for forward bifurcation is given.Furthermore,we performed the sensitivity analysis of the model parameters on R_(0).For the confirmation of analytical work,we performed numerical simulations,and the results indicate that the treatment and the inhibitory effects reduce the risk of developing liver cirrhosis in individuals,while heavy alcohol consumption accelerates markedly the liver cirrhosis progression in patients with chronic hepatitis B.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
基金the scholarship from NUS Graduate School for integrative science and engineering and funding support from Ministry of Education Academic Research Fund,Singapore(AcRF:R-397-000-270-114,R-397-000-183-112).
文摘The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics.In this field,microfluidic deterministic lateral displacement(DLD)holds a promise due to the ability of continuous separation of particles by size,shape,deformability,and electrical properties with high resolution.DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes.DLD techniques have been previously reviewed in 2014.Since then,the field has matured as several physics of DLD have been updated,new phenomena have been discovered,and various designs have been presented to achieve a higher separation performance and throughput.Furthermore,some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection.This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection.Furthermore,current challenges and potential solutions of DLD are also discussed.We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications.In particular,the rapid,low-cost,and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60572071 and 60873101)Natural Science Foundation of Jiangsu Province (Grant Nos BM2006504, BK2007104 and BK2008209)College Natural Science Foundation of Jiangsu Province (Grant No 06KJB520137)
文摘A novel efficient deterministic secure quantum communication scheme based on four-qubit cluster states and single-photon identity authentication is proposed. In this scheme, the two authenticated users can transmit two bits of classical information per cluster state, and its efficiency of the quantum communication is 1/3, which is approximately 1.67 times that of the previous protocol presented by Wang et al [Chin. Phys. Lett. 23 （2006） 2658]. Security analysis shows the present scheme is secure against intercept-resend attack and the impersonator＇s attack. Furthermore, it is more economic with present-day techniques and easily processed by a one-way quantum computer.
基金supported by the National Natural Science Foundation of China (51006006,51136003,50976010,50976009)the National Basic Research Program of China (2012CB72 0205)+2 种基金the Aeronautical Science Foundation of China (2010ZB51)the 111 Project (B08009)the National Science Special Foundation for Post-doctoral Scientists of China (201104049)
文摘The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multistage compressors in steady state environment by introducing de- terministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study was to provide insight into the DC characteristics and the in- fluence of DC on the time-averaged flow field of the APES. In Part 2 of this two-part paper, the influence of DC on the time-averaged flow field was systematically studied; Several time-averaging computations boundary conditions and DC were conducted with various for the downstream stator in a transonic compressor stage, by employing the CFD solver developed in Part 1 of this two-part paper. These results were compared with the time-averaged unsteady flow field and the steady one. The study indicat;d that the circumferential- averaged DC can take into account major part of the unsteady effects on spanwise redistribution of flow fields in compres- sors. Furthermore, it demonstrated that both deterministic stresses and deterministic enthalpy fluxes are necessary to reproduce the time-averaged flow field.
基金supported by the National Science Fund for Distinguished Young Scholars of China(No.61225014)
文摘A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.
基金supported by the National Natural Science Foundation of China (51006006,51136003,50976010,50976009)the National Basic Research Program of China (2012CB720205)+2 种基金the Aeronautical Science Foundation of China (2010ZB51)the 111 Project (B08009)the National Science Special Foundation for Post-doctoral Scientists of China (201104049)
文摘The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multi- stage compressors in steady state environment by introduc- ing deterministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study is to provide insight into the DC characteristics and the influence of DC on the time-averaged flow field of the APES. In Part 1 of this two-part paper, firstly a 3D viscous unsteady and time-averaging flow CFD solver is developed to investi- gate the APES technique. Then steady and unsteady simu- lations are conducted in a transonic compressor stage. The results from both simulations are compared to highlight the significance of the unsteady interactions. Furthermore, the distribution characteristics of DC are studied and the DC at the rotor/stator interface are compared with their spatial cor- relations （SC）. Lastly, steady and time-averaging （employing APES with DC） simulations for the downstream stator alone are conducted employing DC derived from the unsteady re- suits. The results from steady and time-averaging simula- tions are compared with the time-averaged unsteady results. The comparisons demonstrate that the simulation employing APES with DC can reproduce the time-averaged field and the 3D viscous time-averaging flow solver is validated.
