A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily s...A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of a...Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.展开更多
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)fram...Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach.展开更多
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel a...This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel approach that incorporates an L1-regularizer term in BF weight state estimation. We start by explaining the CMBB formation mechanism under conditions where there is a mismatch in the far-field signal model. Subsequently, we reformulate the BF weight state estimation challenge using a method known as variable-splitting, turning it into a noise minimization problem. This problem combines both linear and nonlinear quadratic terms with an L1-regularizer that promotes the sparsity. The optimization strategy is based on a variable-splitting method, implemented using the Alternating Direction Method of Multipliers(ADMM). Furthermore, a variable-splitting framework is developed to enhance BF weight state estimation, employing a Kalman Smoother(KS) optimization algorithm. The approach integrates the Rauch-TungStriebel smoother to perform posterior-smoothing state estimation by leveraging prior data. We provide proof of convergence for both linear and nonlinear CMBB state estimation technology using the variable-splitting KS and the iterated extended Kalman smoother. Simulations corroborate our theoretical analysis, showing that the proposed method achieves robust stability and effective convergence, even when faced with signal model mismatches.展开更多
Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve ef...Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve efficient,secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world.As a key component of the CPSs,the wireless network is vulnerable to various malicious attacks due to its openness[1].DoS attack is one of the most common attacks,characterized of simple execution and significant destructiveness[2].To mitigate the economic losses and environmental damage caused by DoS attacks,it is crucial to model and investigate data transmissions in CPSs.展开更多
The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with ...The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation....For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.展开更多
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.展开更多
Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe stat...Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe states for phase estimation with a fixed mean particle number.By searching for the maximum quantum Fisher information,we optimize the probe states,which are superior to the path-entangled Fock states.Comparing the mean particle number(n)with the dimension of the probe states in Fock space(N+1),when n≤N,our optimal probe states can provide a better performance than the n00n states.When n>N,our optimal probe states can also remain optimal if the dimension of the probe states is large enough.展开更多
Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee...Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.展开更多
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref...The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.展开更多
The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three...Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three important states(the Greenberger-Horne-Zeilinger(GHZ)state,the maximal spin squeezed state,and the spin coherent state)of a spin-S under both individual dephasing and collective dephasing by general Gaussian noise,ranging from the Markovian limit to the extreme non-Markovian limit.Whether or not the noise is Markovian,the spin coherent state is always worse than the classical scheme under collective dephasing although it is equivalent to the classical scheme under individual dephasing.Moreover,the maximal spin squeezed state always give the best sensing precision(and outperforms the widely studied GHZ state)in all cases.This establishes the general advantage of the spin squeezed state for noisy frequency estimation in many quantum sensing platforms.展开更多
Quantum phase estimation based on Gaussian states plays a crucial role in many application fields.In this paper,we study the precision bound for the scheme using two-mode squeezed Gaussian states.The quantum Fisher in...Quantum phase estimation based on Gaussian states plays a crucial role in many application fields.In this paper,we study the precision bound for the scheme using two-mode squeezed Gaussian states.The quantum Fisher information is calculated and its maximization is used to determine the optimal parameters.We find that two single-mode squeezed vacuum states are the optimal Gaussian inputs for a fixed two-mode squeezing process.The corresponding precision bound is sub-Heisenberg-limited and scales as N^(-1)/2.For practical purposes,we consider the effects originating from photon loss.The precision bound can still outperform the shot-noise limit when the lossy rate is below 0.4.Our work may demonstrate a significant and promising step towards practical quantum metrology.展开更多
This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation ...This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12104190,12104189,12204312)the Natural Science Foundation of Jiangsu Province(Grant No.BK20210874)+2 种基金General project of Natural Science Research in Colleges And Universities of Jiangsu Province(Grant No.20KJB140008)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20224BAB211014 and 20232BAB201042)Key Laboratory of Tian Qin Project(Sun Yat-sen University)。
文摘A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
基金supported in part by the National Natural Science Foundation of China(61873106,62303109)Start-Up Research Fund of Southeast University(RF1028623002)Shenzhen Science and Technology Program(JCYJ20230807114609019)
文摘Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金supported in part by the Frontier Technology R&D Plan of Jiangsu Province(BF2024065)the Shenzhen Science and Technology Program(JCYJ20230807114609019)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX22_0236).
文摘Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
基金supported in Natural Science Foundation of Shandong Province,China(ZR2013FM018)。
文摘This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel approach that incorporates an L1-regularizer term in BF weight state estimation. We start by explaining the CMBB formation mechanism under conditions where there is a mismatch in the far-field signal model. Subsequently, we reformulate the BF weight state estimation challenge using a method known as variable-splitting, turning it into a noise minimization problem. This problem combines both linear and nonlinear quadratic terms with an L1-regularizer that promotes the sparsity. The optimization strategy is based on a variable-splitting method, implemented using the Alternating Direction Method of Multipliers(ADMM). Furthermore, a variable-splitting framework is developed to enhance BF weight state estimation, employing a Kalman Smoother(KS) optimization algorithm. The approach integrates the Rauch-TungStriebel smoother to perform posterior-smoothing state estimation by leveraging prior data. We provide proof of convergence for both linear and nonlinear CMBB state estimation technology using the variable-splitting KS and the iterated extended Kalman smoother. Simulations corroborate our theoretical analysis, showing that the proposed method achieves robust stability and effective convergence, even when faced with signal model mismatches.
文摘Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve efficient,secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world.As a key component of the CPSs,the wireless network is vulnerable to various malicious attacks due to its openness[1].DoS attack is one of the most common attacks,characterized of simple execution and significant destructiveness[2].To mitigate the economic losses and environmental damage caused by DoS attacks,it is crucial to model and investigate data transmissions in CPSs.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272123 and 12302047)the Natural Science Foundation of Jiangsu Province(Grant No.BK20231185)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0192).
文摘The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金supported by the National Natural Science Foundation of China(62176214).
文摘For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
基金supported by the National Natural Science Foundation of China(Grant No.12405026)the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBA050001)。
文摘Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe states for phase estimation with a fixed mean particle number.By searching for the maximum quantum Fisher information,we optimize the probe states,which are superior to the path-entangled Fock states.Comparing the mean particle number(n)with the dimension of the probe states in Fock space(N+1),when n≤N,our optimal probe states can provide a better performance than the n00n states.When n>N,our optimal probe states can also remain optimal if the dimension of the probe states is large enough.
文摘Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
基金The National Natural Science Foundation of China under contract No. 42274159the Project supported by Key Laboratory of Space Ocean Remote Sensing and Application,MNR under contract No.2023CFO016。
文摘The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
基金supported by the National Natural Science Foundation of China(NSFC)Grant No.12274019the NSAF grant in NSFC with Grant No.U2230402。
文摘Exploring the quantum advantages of various non-classical quantum states in noisy environments is a central subject in quantum sensing.Here we provide a complete picture for the frequency estimation precision of three important states(the Greenberger-Horne-Zeilinger(GHZ)state,the maximal spin squeezed state,and the spin coherent state)of a spin-S under both individual dephasing and collective dephasing by general Gaussian noise,ranging from the Markovian limit to the extreme non-Markovian limit.Whether or not the noise is Markovian,the spin coherent state is always worse than the classical scheme under collective dephasing although it is equivalent to the classical scheme under individual dephasing.Moreover,the maximal spin squeezed state always give the best sensing precision(and outperforms the widely studied GHZ state)in all cases.This establishes the general advantage of the spin squeezed state for noisy frequency estimation in many quantum sensing platforms.
基金Project supported by the National Natural Science Foundation of China(Grant No.12104193)the Program of Zhongwu Young Innovative Talents of Jiangsu University of Technology(Grant No.20230013)。
文摘Quantum phase estimation based on Gaussian states plays a crucial role in many application fields.In this paper,we study the precision bound for the scheme using two-mode squeezed Gaussian states.The quantum Fisher information is calculated and its maximization is used to determine the optimal parameters.We find that two single-mode squeezed vacuum states are the optimal Gaussian inputs for a fixed two-mode squeezing process.The corresponding precision bound is sub-Heisenberg-limited and scales as N^(-1)/2.For practical purposes,we consider the effects originating from photon loss.The precision bound can still outperform the shot-noise limit when the lossy rate is below 0.4.Our work may demonstrate a significant and promising step towards practical quantum metrology.
基金National Natural Science Foundation of China(Grant No.62266045)National Science and Technology Major Project of China(No.2022YFE0138600)。
文摘This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.
