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Estimation of truck mass and center of gravity using data-driven mechanics:a comparative study of TSO-SVM,CS-BP,SSA-ELM,and WOA-XGBoost in automotive dynamics 认领 引用
1
作者 Wei Liu Yuanyou Fu +2 位作者 Yongjun Pan Weicheng Huang Aki Mikkola 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期160-177,共18页
Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This s... Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This study introduces a datadriven mechanics framework integrating four hybrid machine learning(ML)models-tuna search-optimized support vector machine,cuckoo search-optimized BP neural networks,sparrow search algorithm-optimized extreme learning machine,and whalesearch-optimized XGBoost-to enable estimation.A 17-degree-of-freedom multibody dynamics model,incorporating suspension kinematics via a semirecursive formulation,generates simulation datasets linking real-time tuck states(pitch,roll)to massand CG.Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters,enhancing training efficiency.Validation against multibody benchmarks confirms accuracy,while robustness is demonstrated across driving scenariosand noise.By unifying data-driven ML with physics-based mechanics,this approach advances parameter estimation,bridgingtruck dynamics with computational intelligence for automotive design. 展开更多
关键词 Parameter estimation Data-driven mechanics Multibody dynamics Center of gravity Hybrid machine learning Real-time estimation
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A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation 认领 引用
2
作者 Zahid Javid Kush Lohana +1 位作者 Danial Murtaza William Holderbaum 《Energy Engineering》 EI 2026年第7期525-547,共23页
This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weight... This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weighting mechanism.The proposed method addresses key challenges in modern PS-SE,including measurement uncertainties,bad data detection and handling,and convergence reliability.By incorporating an adaptive weighting mechanism,the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements,enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements.The performance of the proposed method is rigorously evaluated against established state estimation techniques,including WLS,EKF,Bayesian method,Huber-Adaptive Method(HAM)and Neural network-based Method.Simulations are performed on IEEE 14-bus,IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy,convergence behavior,computational efficiency,and robustness in the presence of bad data.Results highlight the superior performance of the hybrid method,which achieves higher accuracy and robust convergence properties while requiring 40%fewer iterations than conventional WLS.Despite its enhanced capabilities,the computational burden remains comparable to traditional techniques,making it highly suitable for real-time applications.These findings underscore the proposed hybrid method as a significant advancement in power system state estimation,offering a reliable,efficient,and robust solution for modern power system monitoring and control.It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids. 展开更多
关键词 Adaptive estimation hybrid estimator power system monitoring state estimation
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Adaptive robust control of multi-DOF hydraulic manipulator with precise online load estimation 认领 引用
3
作者 Qixian Wang Manzhi Qi +2 位作者 Yangxiu Xia Shizhao Zhou Zheng Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期155-166,共12页
Hydraulic manipulator shows vast application potential in heavy load working conditions.However,achieving high control precision in these devices is notably more challenging compared to electric manipulators,due to th... Hydraulic manipulator shows vast application potential in heavy load working conditions.However,achieving high control precision in these devices is notably more challenging compared to electric manipulators,due to the complexities introduced by uncertainties and high-order nonlinear dynamics.The presence of heavy unknown payload further reduces control accuracy.In this paper,a new load estimation method based on direct/indirect adaptive robust controller(DIARC) is proposed to facilitate online payload estimation and compensate the impact of the unknown payload.To compensate for the high-order dynamics,backstepping strategy is utilized.A modified recursive least squares adaptive law is also developed to realize,precise,real-time payload estimation under dynamic conditions.By incorporating this accurate load mass estimation into the control strategy,an improvement in overall control performance can be achieved.The effectiveness of this enhanced controller with load mass estimation is initially verified through simulations conducted in MATLAB.The close-loop control performance is further analyzed and validated on a four-degree-of-freedom hydraulic manipulator.The experimental results indicate that,under dynamic scenarios,our proposed control method succeeds in achieving precise online load mass estimation,achieving an average estimation error of 2.8% for a 7.5 kg payload.Furthermore,enhanced control accuracy is achieved compared to traditional controllers,with the maximum tracking error being only 0.69° during the simulated operation scenario.This research provides a viable solution for precision control and load estimation in hydraulic manipulators,eliminating the need for costly forceorque sensors. 展开更多
关键词 Hydraulic manipulator Adaptive control Load estimation
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SRKSE:Off-Grid Sub-Nyquist Channel Parameter Estimation for Signals of Opportunity 认领 引用
4
作者 Bian Zhiang Lu Hu +4 位作者 Wang Zhisen Li Hao He Xin Chen Jinyu Xiao Jin 《China Communications》 SCIE EI CSCD 2026年第2期1-19,共19页
In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant chal... In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication. 展开更多
关键词 channel estimation compressed sensing deep learning DUN off-grid sub-Nyquist TOA
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Fast Anomaly Detection and Joint State Estimation for Perturbed Nonlinear Systems With Prolonged Output Anomalies 认领 引用
5
作者 Guangdeng Chen Xiao-Jie Peng +2 位作者 Yan Lei Chao Huang Hongyi Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1301-1313,共13页
State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast a... State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast anomaly detection and state estimation for perturbed nonlinear systems where actual outputs may be anomalous over a prolonged period.First,a fixedtime observer is constructed.By leveraging integral-type composite Lyapunov functions and homogeneity theory,the error bounds are proven under varying scenarios involving model disturbances,measurement noise,and nonlinearity.Based on these bounds,a fast anomaly detection mechanism is designed.Next,a cascade predictor is developed based on the fixed-time observer,which uses historical outputs from a previous time window to predict the current system state.Simultaneously,an algorithm is proposed to determine the reference historical output based on anomaly detection results,improving long-term prediction accuracy and mitigating the impact of anomaly detection delays.Finally,the secure state estimation is derived by fusing states from the fixed-time observer and the cascade predictor,depending on the anomaly detection results.The effectiveness of the proposed method is demonstrated through simulations on autonomous vehicles. 展开更多
关键词 Anomaly detection cascade predictor fixed-time observer output anomalies secure state estimation
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Unified physics-informed subspace identification and transformer learning for lithium-ion battery state-of-health estimation 认领 引用
6
作者 Yong Li Hao Wang +3 位作者 Chenyang Wang Liye Wang Chenglin Liao Lifang Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期350-369,I0009,共20页
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 LiFePO4and public LiCoO2datasets 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
关键词 Lithium-ion battery Transformer learning Physics-informed modeling Subspace identification State-of-health estimation
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Mixed-state Berry curvature in quantum multiparameter estimations 认领 引用
7
作者 Xiaoguang Wang Xiao-Ming Lu +2 位作者 Yunbo Zhang Libin Fu Shu Chen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第5期80-84,共5页
For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-st... For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations.Through spectral decomposition,we derive the mixed-state Berry curvature for both the full-rank and non-full-rank density matrices.As an example,we obtain the exact expression of the Berry curvature for an arbitrary qubit state. 展开更多
关键词 Berry curvature Uhlmann curvature mixed states quantum parameter estimation
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Robust Multitask Diffusion Bias-Compensated LMS Algorithm for Distributed Estimation with Noisy Link and Input 认领 引用
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作者 Xuzhao Chai Linru Zhang +2 位作者 Pengwei Wen Sheng Zhang Haiquan Zhao 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期137-149,共13页
This paper presents a robust multitask diffusion average bias compensation least mean square(RM-DABC-LMS)algorithm for distributed estimation in noisy input and communication link noise.The algorithm utilizes a robust... This paper presents a robust multitask diffusion average bias compensation least mean square(RM-DABC-LMS)algorithm for distributed estimation in noisy input and communication link noise.The algorithm utilizes a robust cost function based on the maximum Versoria criterion,incorporates bias compensation,and applies adaptive combination coefficients to reduce noise impacts.Theoretical analysis demonstrates the stability of the algorithm,providing closed-form expressions for the steady-state mean square deviation(MSD).A compression diffusion strategy is introduced to reduce communication cost of the RM-DABC-LMS algorithm,ensuring fast convergence and accurate estimation.Simulation results indicate that the proposed algorithm outperforms existing methods in noisy environments,achieving faster convergence and lower steady-state error. 展开更多
关键词 multitask networks robust bias compensation average estimate
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State estimation improvement in strongly coupled data assimilation with a two-scale Lorenz model 认领 引用
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作者 Yanqiu Gao Shaoting Ge +2 位作者 Jicai Zhang Yiting Wang Qun He 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第1期176-189,共14页
Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to ad... Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to address crossscale interactions in CDA is an important issue.In particular,the cross-scale interactions in the strongly coupled data assimilation(SCDA)framework pose substantial challenges.In this study,increasing the state estimation accuracy using an ensemble adjustment Kalman filter based on the two-scale Lorenz’96(tsL96)model is investigated.Using the SCDA framework,we adopt cross-component localization factors and several covariance inflation schemes to address the filter divergence problem.The results show that ensembles of an appropriate size can achieve good assimilation results,the optimal localization parameters are scale-dependent for the model variables,and the adaptive inflation scheme outperforms the static fixed and relaxation-to-prior spread schemes.Although these experiments were carried out using an ideal framework,this study provides a valuable reference for improving estimation accuracy with the SCDA framework in operational simulation and prediction models. 展开更多
关键词 state estimation ensemble adjustment Kalman filter strongly coupled data assimilation multiple-scale
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Towards Real-Time Multi-Person Pose Estimation via Feature Selection and Sharpening Mechanisms 认领 引用
10
作者 Chengang Dong Yongkang Ding Jianwei Hu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期888-908,共21页
Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as ... Real-time multi-person pose estimation(MPE)built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes.However,due to factors such as high model complexity and limited expression of keypoint information,both the efficiency and accuracy of real-time MPE remain to be improved.To mitigate the adverse impacts caused by the aforementioned issues,this work develops FSEM-Pose,a real-time MPE model rooted in the YOLOv10 framework.In detail,first,FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution(FS-Conv),which effectively reduces the backbone size while maximizing the retention of spatial information from the input image.Second,FSEM-Pose incorporates a Feature Saliency Enhancement Module(FSEM)to strengthen the feature encoding of human keypoints,thereby improving the accuracy of pose estimation.Finally,FSEM-Pose further enhances inference efficiency via a lightweight optimization of the head using shared convolutional layers.Our method achieves competitive results across multiple accuracy and efficiency metrics on the MS COCO 2017 and CrowdPose datasets.While being lightweight in design,it improves average precision(AP)by 2.1%and 2.5%,respectively. 展开更多
关键词 Pose estimation feature sharpening lightweight YOLOv10
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Off-Grid Parameter Estimation for OFDM-Based ISAC Systems with Incomplete Data and Demodulation Errors 认领 引用
11
作者 Feng Xinxin Wu Weilin +1 位作者 Ling Muyao Zheng Haifeng 《China Communications》 SCIE EI CSCD 2026年第3期199-216,共18页
In the 6G environment,addressing challenges like missing data,demodulation errors,and offgrid issues during target parameter estimation is a significant hurdle for integrated sensing and communication(ISAC)systems.In ... In the 6G environment,addressing challenges like missing data,demodulation errors,and offgrid issues during target parameter estimation is a significant hurdle for integrated sensing and communication(ISAC)systems.In the ISAC framework,a commonly used method for parameter estimation is compressive sensing.However,it often struggles with off-grid problems in continuous parameter estimation.In contrast,the atomic norm has been proven effective in overcoming these off-grid issues,making it a more suitable approach for continuous parameter estimation.In this paper,we investigate the application of atomic norm in ISAC and propose an ISAC model based on orthogonal frequency division multiplexing(OFDM)for parameter estimation.We utilize the atomic norm under conditions of incomplete data and demodulation errors.To enhance the convergence speed and accuracy of our algorithm,we implement the alternating direction method of multipliers(ADMM)for iterative processing.We refer to this algorithm as ANMI.Building on this foundation,we develop a deep unfolding network algorithm,ANMIADMM-Net,which further mitigates the impact of missing data and demodulation errors on target parameter estimation by training optimal parameters.Experimental results demonstrate that our proposed ANMI and ANMI-ADMM-Net accurately estimate target parameters even in the presence of missing data and demodulation errors,exhibiting superior precision and robustness compared to traditional methods. 展开更多
关键词 ADMM atomic norm deep unfolding networks ISAC off-grid target parameter estimation
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X-ray pulsar-based airborne clock error estimation using Gaussian process 认领 引用
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作者 Yusong WANG Yidi WANG +4 位作者 Kun JIANG Wenhai JIAO Minzhang SONG Liansheng LI Wei ZHENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期541-550,共10页
In order to autonomously calibrate the airborne clocks of deep space explorers,this paper proposes an X-ray pulsar-based airborne clock error estimation method.A pulse phase propagation model incorporating the clock e... In order to autonomously calibrate the airborne clocks of deep space explorers,this paper proposes an X-ray pulsar-based airborne clock error estimation method.A pulse phase propagation model incorporating the clock error is derived.Given that both the clock noise and the pulsar timing noise are of power-law spectral densities,their combination is modeled as a Fractional Brownian Motion(FBM)with a fractional-order power spectral density.The clock error series is modeled as a Gaussian Process(GP)with a mean function in the form of 2-order polynomial and an FBM-based covariance function.Finally,the clock error and the hyperparameters of GP are fast estimated by an iterated estimation method.The proposed method is validated via the real clock error data of the G05 satellite in the Global Positioning System(GPS)and the real data of pulsars from the Neutron star Interior Composition ExploreR(NICER). 展开更多
关键词 Atomic clock Clock error estimation Fractional Brownian motion Gaussian process X-ray pulsar
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Robust Human Pose Estimation and Action Recognition Utilizing Feature Extraction 认领 引用
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作者 Sheng Luo Rashid Abbasi +7 位作者 Hao Wang Jinghua Xu Dongyang Lyu Aaron Zhang Farhan Amin Isabel de la Torre Gerardo Mendez Mezquita Henry Fabian Gongora 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期870-887,共18页
Human pose estimation is crucial across diverse applications,from healthcare to human-computer interaction.Integrating inertial measurement units(IMUs)with monocular vision methods holds great potential for leveraging... Human pose estimation is crucial across diverse applications,from healthcare to human-computer interaction.Integrating inertial measurement units(IMUs)with monocular vision methods holds great potential for leveraging complementary modalities;however,existing approaches are often limited by IMU drift,noise,and underutilization of visual information.To address these limitations,we propose a novel dual-stream feature extraction framework that effectively combines temporal IMU data and single-view image features for improved pose estimation.Short-term dependencies in IMU sequences are captured with convolutional layers,while a Transformerbased architecture models long-range temporal dynamics.To mitigate IMU drift and inter-sensor inconsistencies,a complementary filtering module is introduced alongside a cross-channel interaction mechanism.Features from the IMU and image streams are then fused via a dedicated fusion module and further refined utilizing a high-precision regression head for accurate pose prediction.Experimental results on benchmark datasets demonstrate that our method significantly outperforms existing techniques in terms of estimation,accuracy,and robustness,validating the effectiveness of our dual-stream architecture. 展开更多
关键词 Human pose estimation dual-stream network inertial measurement units(IMU)
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An Attention-Based 6D Pose Estimation Network for Weakly Textured Industrial Parts 认领 引用
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作者 Song Xu Liang Xuan +1 位作者 Yifeng Li Qiang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2148-2166,共19页
The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly fa... The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation. 展开更多
关键词 Industrial robots pose estimation industrial parts attention mechanism weak texture
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Design of Composite Two-Channel Disturbance Estimation Adaptive Controller for Backlash-Like Hysteretic Nonlinear Systems 认领 引用
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作者 LIU Qunpo LI Jiakun +2 位作者 FEI Shumin BU Xuhui NAOHIKO Hanajima 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期106-116,共11页
For nonlinear systems with backlash-like hysteresis characteristics and external disturbance,a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy o... For nonlinear systems with backlash-like hysteresis characteristics and external disturbance,a composite two-channel disturbance estimation adaptive controller is proposed to improve the trajectory tracking accuracy of the system.The unmodeled hysteresis and external disturbances are treated as lumped uncertainties,which are approximated by radial basis neural network and disturbance estimator respectively.These approximations are then linearly fused to form the compensation term for the lumped uncertainty.The second order linear filter is employed to estimate multiple differential terms,which are integrated into the controller design and dynamic system state updates,thereby reducing computational complexity.A weighted fusion mechanism is implemented for the two channels,and the adaptive update rate for each channel is determined based on the deviation between the lumped uncertainty reference value and the output of each channel.To address the challenges posed by the discontinuity of deviation and maintain system stability,the first-order low-pass filter is applied to smooth the deviation,enhancing system robustness.A trajectory tracking simulation of a single-input single-output nonlinear system is conducted to compare the performance of the proposed controller with baseline controllers,demonstrating the effectiveness of the composite two-channel disturbance estimation adaptive controller. 展开更多
关键词 composite adaptive control disturbance estimator external disturbance hysteretic nonlinearity neural network
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State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning 认领 引用
16
作者 Shuang Gao Xiaoli Luan +2 位作者 Biao Huang Shunyi Zhao Fei Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期715-727,共13页
This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliab... This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliable,and the target domain model has significant model parameter uncertainties.To enhance estimation performance in the target domain,the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator.More specifically,this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain.Using numerical examples and a 1-DOF torsion system,we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties.The results highlight its capability to improve estimation accuracy in practical scenarios,showcasing its potential for real-world applications. 展开更多
关键词 Markovian jump linear systems parameter uncertainty state estimation structure variational inference transfer learning
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Near-Surface Q-Factor Estimation Using a Non-Gaussian Energy Spectrum 认领 引用
17
作者 Ze-lin Li Jiang-nan Tong +2 位作者 Ya-nan Jiang Tong Chen Guo-fa Li 《Applied Geophysics》 SCIE CSCD 2026年第2期735-749,871,共15页
Accurate near-surface Q-factor estimation is essential for attenuation compensation,high-resolution imaging,and shallow-structure characterization.However,the reliability of conventional methods deteriorates in strong... Accurate near-surface Q-factor estimation is essential for attenuation compensation,high-resolution imaging,and shallow-structure characterization.However,the reliability of conventional methods deteriorates in strongly attenuating media because they commonly rely on weak-attenuation approximations or Gaussian spectrum assumptions.This paper proposes a near-surface Q-factor estimation method based on non-Gaussian energy spectrum.The proposed method has three main advantages.First,it is formulated on the energy spectrum rather than the amplitude spectrum,which improves spectral concentration and numerical stability.Second,it replaces the Gaussian assumption with an exponential frequency-weighted energy-spectrum model,thereby allowing for non-Gaussian spectrum shapes.Third,it employs an exact absorption coeffi cient derived from the complex wavenumber-Q relation,thus avoiding the weak-attenuation approximation in low-Q media.Synthetic cross-hole experiments show that the proposed method provides more accurate and more stable estimates than the spectral ratio and centroid frequency shift methods,especially under strong attenuation and noisy conditions.Field downhole data further demonstrate that the method can identify attenuation responses and support layered Q characterization in the near surface. 展开更多
关键词 near-surface attenuation Q-factor estimation energy spectrum centroid frequency shift
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PSSE:Private Set-Valued State Estimation of Cyber-Physical Systems 认领 引用
18
作者 Hao Liu Yuzhe Li Ben Niu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期810-821,共12页
This paper investigates set-valued state estimation of cyber-physical systems(CPSs)with unknown-but-bounded(UBB)noises.Note that constrained polynomial zonotopes(CPZs)are utilized to characterize both convex and non-c... This paper investigates set-valued state estimation of cyber-physical systems(CPSs)with unknown-but-bounded(UBB)noises.Note that constrained polynomial zonotopes(CPZs)are utilized to characterize both convex and non-convex sets of noises.However,privacy issues should be taken into account since outsourcing the set-valued operations to the cloud-based node is required when collecting measurements from distributed sensors.In order to address this issue,two different set-valued estimation protocols employing partially homomorphic encryption(PHE)are proposed to guarantee the corresponding privacy.Furthermore,it is proved that the proposed protocols can ensure privacy against sensor coalition,cloud coalition,and user coalition,respectively.Finally,numerical examples are provided to show the advantages and effectiveness of our results. 展开更多
关键词 Constrained polynomial zonotopes (CPZs) partially homomorphic encryption (PHE) privacy-preserving set-valued estimation
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Deep learning-based end-to-end depth estimation from a single-frame fringe pattern with the FDSUNet++network 认领 引用
19
作者 Xinjun ZHU Jialei ZHANG +2 位作者 Limei SONG Hongyi WANG and Ruiqin TIAN 《Optoelectronics Letters》 EI 2026年第6期373-378,共6页
Recently the depth estimation methods based on deep learning(DL)retain challenging to estimate a high-precision depth map in fringe projection structured light three-dimensional(3D)measurement with limited information... Recently the depth estimation methods based on deep learning(DL)retain challenging to estimate a high-precision depth map in fringe projection structured light three-dimensional(3D)measurement with limited information from a single-frame fringe pattern.In this letter,we proposed a FDSUNet++convolutional neural network(CNN),which consists of a UNet++base model,an improved squeeze-andexcitation(ISE)block,a Fourier transform(FT)data preprocessing block,and a discrete wavelet transform(DWT)block.The proposed ISE block can improve the ability of feature extraction and the designed FT data preprocessing block preserves the key features of the fringe pattern by FT.The introduced DWT block reduces the complexity and training cost of the model.By integrating these three blocks into the UNet++,it can better achieve depth estimation.Experimental results from two structured light datasets demonstrate that the proposed FDSUNet++outperforms the state-of-the-art networks,achieving the best performance in both qualitative and quantitative evaluation. 展开更多
关键词 neural network cnn which single frame fringe pattern fdsunet unet depth estimation fringe projection discrete wavelet deep learning
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Time Delay Estimation of Target Echo Signal Based on Multi-bright Spot Echoes 认领 引用
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作者 Ge Yu Fan Du +1 位作者 Xiukun Li Yan Li 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期312-325,共14页
Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in... Accurate time delay estimation of target echo signals is a critical component of underwater target localization.In active sonar systems,echo signal processing is vulnerable to the effects of reverberation and noise in the maritime environment.This paper proposes a novel method for estimating target time delay using multi-bright spot echoes,assuming the target’s size and depth are known.Aiming to effectively enhance the extraction of geometric features from the target echoes and mitigate the impact of reverberation and noise,the proposed approach employs the fractional order Fourier transform-frequency sliced wavelet transform to extract multi-bright spot echoes.Using the highlighting model theory and the target size information,an observation matrix is constructed to represent multi-angle incident signals and obtain the theoretical scattered echo signals from different angles.Aiming to accurately estimate the target’s time delay,waveform similarity coefficients and mean square error values between the theoretical return signals and received signals are computed across various incident angles and time delays.Simulation results show that,compared to the conventional matched filter,the proposed algorithm reduces the relative error by 65.9%-91.5%at a signal-to noise ratio of-25 dB,and by 66.7%-88.9%at a signal-to-reverberation ratio of−10 dB.This algorithm provides a new approach for the precise localization of submerged targets in shallow water environments. 展开更多
关键词 Multi-bright spot echoes Time-delay estimation Target echo signal Frequency sliced wavelet transform Fractional order fourier transform
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