An improved innovation-based protection level evaluation method for precise point positioning based on Kalman filter

This study aims to analyze how audiences interpret the flexing content of a local skincare brand owner and the role of digital literacy in this process. Using a qualitative netnographic approach, data was obtained through analysis of the brand owner's TikTok content and in-depth interviews with four informants who are both audience members and consumers. The results show that flexing as a symbolic branding strategy is interpreted in various ways, ranging from admiration to suspicion. Digital literacy plays a crucial role in filtering information, assessing content credibility, and influencing purchasing decisions. The contrasting reactions in netizen comments confirm that digital literacy levels determine how society, particularly the lower-middle class, forms perceptions of flexing practices on social media.

A novel extended Kalman filter for earthquake ground motion estimation using partial measurements under non-Gaussian noise

Secure cyber-physical systems: Identification and mitigation strategies for Markovian chain-based FDI attacks.

Hybrid long short-term memory network and Bayesian adaptive extended Kalman filter approach for reliable lithium-ion battery state-of-charge estimation in electric vehicles

Robust state of charge estimation in electric vehicle batteries using neural-network aided Kalman filter

Safety-oriented passenger flow control at a congested metro hub: A microscopic approach.

Interactive data-model hybrid method based on unscented Kalman filter and bidirectional long short-term memory network with self-attention for lithium-ion battery remaining useful life prediction

Nanocrystalline materials are the basis of many novel engineered systems, including batteries, nanocomposites, and glass ceramics. Three-dimensional electron diffraction (3D ED) has become a key technique for structural analysis of such materials, offering clear advantages over conventional X-ray diffraction. Commercial routine 3D ED acquisition allowing for measurements of crystals down to ∼750 nm is now standard, but pushing the measurable size towards a few tens of nanometers introduces new challenges, requiring robust crystal-tracking methods. At this scale, TEM automation, reliable object detection, and high mechanical precision of the goniometer are essential. PyFast-ADT is introduced as a modular automation framework for 3D ED data collection, extending the measurable size range through improved crystal tracking routines. Its Python architecture enhances shareability and promotes facility automation within the 3D ED and Cryo-EM communities. The PatchworkCC algorithm combines Cross-Correlation with Kalman Filtering to achieve fully automatic crystal tracking with improved accuracy and minimal user supervision. Characterization of goniometer reproducibility revealed a rapid decrease behaviour degrading precision, addressed by the HiPerGonio procedure, which stabilizes performance and supports optimal TEM/sample holder choices. Together, these developments enable fully automated 3D ED data collection on 25 nm nanocrystals embedded in a glass-ceramic matrix, increasing throughput up to sixfold and advancing reproducible, high-throughput structure determination at the nanometer scale.

Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facing nonlinear systems, Gaussian filters such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) typically rely on certain linearization techniques, which can introduce large estimation errors. To address this issue, this paper reconstructs the prediction and update steps of Gaussian filtering as solutions to two distinct optimization problems, whose optimal conditions are found to have analytical forms from Stein's lemma. It is observed that the stationary point for the prediction step requires calculating the first two moments of the prior distribution, which is equivalent to that step in existing moment-matching filters. In the update step, instead of linearizing the model to approximate the stationary points, we propose an iterative approach to directly minimize the update step's objective to avoid linearization errors. For the purpose of performing the steepest descent on the Gaussian manifold, we derive its natural gradient that leverages Fisher information matrix to adjust the gradient direction, accounting for the curvature of the parameter space. Combining this update step with moment matching in the prediction step, we introduce a new iterative filter for nonlinear systems called Natural Gradient Gaussian Approximation filter, or NANOfilter for short. We prove that NANO filter locally converges to the optimal Gaussian approximation at each time step. Furthermore, the estimation error is proven exponentially bounded for nearly linear measurement equation and low noise levels through constructing a supermartingale-like property across consecutive time steps. Real-world experiments demonstrate that, compared to popular Gaussian filters such as EKF, UKF, iterated EKF, and posterior linearization filter, NANO filter reduces the average root mean square error by approximately 45% while maintaining a comparable computational burden.

Distinguishing the theoretical components of positive body image via expert consensus.

Purpose This paper aims to propose a novel methodology to mitigate welding torch vibration caused by sensor measurement noise during laser vision-based seam tracking, which compromises robotic welding quality. Design/methodology/approach A Kalman filter is applied to smooth the noisy sensor data for precise tracking. To avoid costly and inefficient manual tuning of the filter’s hyperparameters, Bayesian optimization is utilized to approximate optimal settings with minimal experimental iterations. Its efficiency is validated through comparative experiments with grid search and random search. Findings Experimental results demonstrate that Bayesian optimization identifies a configuration meeting the accuracy requirement within 3 iterations, compared to approximately 20 iterations for grid and random search. This achieves over 80% greater efficiency, significantly reducing tuning time and cost. Originality/value This paper proposes a sample-efficient method that ensures effective vibration suppression and tracking accuracy while significantly reducing optimization iterations. The approach offers a practical solution for the efficient calibration of welding robots and other high-cost control systems.

ABSTRACT Nearshore bathymetry estimation is crucial for human marine activities. Synthetic Aperture Radar (SAR) plays an essential role in nearshore bathymetric mapping. In recent years, nearshore bathymetry estimation based on the variations in ocean wave characteristics from SAR imagery has garnered significant attention. However, existing Fast Fourier Transform-basedmethods are not accurate in estimating the wavelength when spectral peaks are not sharp, resulting in high bathymetric errors. To address this problem, we propose a new estimation method utilizing an adaptive windowing strategy. Specifically, the method adaptively sets the window angle based on the local wave direction of each sub-image and consequently generates multi-angle windows. The final wavelength is estimated using the Kalman filter, which leverages the differences of multi-angle windows to enhance bathymetric accuracy. Simulation and real SAR images are used to verify the effectiveness of this method. Compared with the traditional method, the proposed method can distinguish more subtle bathymetric changes and significantly reduce the bathymetric errors.

Phase-shifting profilometry (PSP) suffers from motion-induced phase-step variations in dynamic scenes. The breakdown of the fixed phase shift assumption results in issues such as ripples, distortions and accuracy decline in PSP systems. To reduce motion-induced phase errors, we propose a wavelet-assisted adaptive extended Kalman filter (WAEKF) to estimate varied pixel-wise phase shift. A wavelet-based strategy is presented to extract an initial spatial carrier frequency at each row from fringe patterns for EKF estimation. A state-space model employing the quadrature phase component and carrier frequency is established in this paper. The unknown phase shifts can be evaluated by using a forward-backward filter. Experiments show that the proposed method can acquire an accurate initial carrier frequency and phase shift map, which effectively reduces 3D reconstruction error and can be extended to N-step PSP systems.

ABSTRACT To solve the problems of excessive overshoot, insufficient precision, and susceptibility to external disturbances in the speed control of permanent magnet synchronous motors (PMSM), a novel sensorless control method is proposed herein. First, a new exponential convergence law was designed and a new sliding mode speed controller was constructed based on this law. An optimisation algorithm combining genetic algorithms and particle swarm optimisation (GAPSO) is proposed to optimise the parameters of the sliding-mode speed controller. Second, inspired by the Cholesky triangular decomposition, the symmetric strong tracking extended Kalman filter (SSTEKF) algorithm is derived from the strong tracking extended Kalman filter (STEKF) algorithm. The experimental results show that the GAPSO method for optimising the sliding mode controller parameters reduces the overshoot and accelerates the convergence speed, improving the control effectiveness of the PMSM, particularly under sudden load changes. Compared with the STEKF algorithm, the improved SSTEKF algorithm has higher accuracy in estimating the rotor speed and position, with the error in estimating the rotor speed reduced from 0.3 to 0.2 and the error in estimating the rotor position reduced from 0.01 to 0.005. Even with sudden load changes, a better observation performance can be achieved.

Reliable navigation under GNSS degradation requires exploiting complementary sensors and estimation methods that can tolerate nonlinearity and outliers. This paper presents a multi-source integrated navigation approach for unmanned aerial vehicles (UAVs) that combines an inertial measurement unit (IMU), GNSS, a ground-based laser ranging-and-angle sensor, and a ground-based RF radar. A practical calibration and alignment pipeline is first established, including IMU intrinsic calibration (misalignment, scale factors, and biases), GNSS lever-arm compensation, and weighted least-squares calibration for range/angle channels of the laser sensor and radar. On this basis, a sliding-window factor-graph optimization framework is constructed with IMU preintegration as the time backbone, while GNSS, laser, and radar measurements are introduced as factors. Marginalization is applied to bound the problem size, and residual-based down-weighting is used to suppress gross errors. Simulation results on a maneuvering UAV trajectory demonstrate clear accuracy gains over an extended Kalman filter (EKF): the mean position error decreases from about 2.162.20 m to 0.690.79 m, and the mean velocity error decreases from about 0.240.28 m/s to 0.100.11 m/s. These results indicate that factor-graph smoothing can provide more accurate and stable navigation estimates for multi-rate heterogeneous sensing.

The adoption of digital twins has revolutionized industrial process simulation, monitoring, and control effectiveness. However, practical implementations of digital twins are hindered by substantial challenges, including extended development time, diminishing model accuracy, and restricted interactive capabilities. Addressing these critical issues, this paper proposes a comprehensive digital twin development framework that integrates digital twin identification, real-time model updating, and advanced process control. The proposed approach first identifies the offline digital twin model through the sparse identification of a nonlinear dynamics algorithm, reducing the digital twin development time while maintaining model fidelity. Then, the identified model is updated by the extended Kalman filter to mitigate the problem of diminishing accuracy. Finally, incorporating the latest updated model into the model predictive control facilitates the control inputs optimization and enhances the interactive capacity of digital twins. Through one industrial case study and two simulation examples, the advantages of the proposed algorithm are demonstrated.

To improve the accuracy and real-time performance of trajectory tracking control for a four-wheel differential drive intelligent sweeping vehicle, a trajectory tracking control method based on an adaptive strong tracking extended Kalman filter (ASTEKF) state estimator and a Laguerre-based model predictive controller (LMPC) is proposed. Based on the kinematic model of the intelligent sweeping vehicle, an ASTEKF state estimator is designed for vehicle state estimation, and a Laguerre-function-based model predictive controller is developed for trajectory tracking control, thereby enhancing the control accuracy and stability of the vehicle. Simulation results demonstrate that compared with the conventional MPC algorithm, the proposed ASTEKF–LMPC algorithm reduces the maximum lateral error by 44.65% and the maximum heading angle error by 40.96% during sweeping operations, while under normal driving conditions, the maximum lateral error and maximum heading angle error are reduced by 36.27% and 40.03%, respectively. Furthermore, experimental tests conducted on an intelligent sweeping vehicle platform show that the proposed method reduces the maximum lateral error by 34.25% and the maximum heading angle error by 23.18%, thereby validating the effectiveness of the proposed algorithm in intelligent sweeping operations.

The purpose of the study is to develop methods for optimal estimation of unknown non-random factors affecting the quality of production equipment by the criterion of maximum likelihood, as well as random factors by the criterion of minimum standard error based on the processing of information related to claims received using the mathematical theory of random point processes and the theory of statistical solutions. The research method consists in applying the well-known hypothesis about the distribution of operating time for failure of technical systems in the form of an exponential distribution depending on the failure rate function. The fact is used that the corresponding distribution of the number of failures is distributed according to the Poisson law with the same function of failure rates. It is assumed that the intensity function depends not only on time, but also on a set of unknown non-random parameters, or on random parameters. It is emphasized that such factors may reflect the generalized state of the technical system, and information about this may be contained in the facts of product claims. The task of optimal estimation of the parameters on which the failure rate function depends is set. Since in this formulation of the problem, only the facts of filing claims, as well as the times of their presentation, are available for processing, the maximum likelihood function method is used for optimal estimation of nonrandom parameters, and the optimal Kalman filter is used for random parameters. The problem of optimal estimation of unknown parameters from a multiplicatively separable failure rate function, i.e. one that is representable as a product of a separate function of time and a vector function of unknown parameters, is considered. It is shown that for such a function, the optimal estimation problem is reduced to the problem of estimating a single scalar parameter that scales the time function. The well-known Kalman algorithm for continuous parameters is applied to the case of the observed process in the form of the number of claims’ events and the time of their occurrence. Examples of evaluation of both unknown and random factors are given for unified real data on tissue defects, and confirm the operability of the algorithms and their applicability for the simplest assessments of the condition of production equipment. The new results include the formulation of the problem of studying a failure intensity function that depends on a set of unknown nonrandom parameters, the application of the maximum likelihood method and Kalman algorithm for optimal estimation of these parameters, and the proof that for a separable failure intensity function, the optimal estimation reduces to the estimation of a scalar quantity that scales the time-dependent intensity function. The conclusion states that examples of assessment of factors affecting the function of the failure rate confirm the operability of the algorithm and its applicability for the simplest assessments of the condition of production equipment. A separate task is to develop analytical expressions for the failure rate function that depends on parameters, as well as methods for comparing estimates obtained by different methods. Solving these tasks will make it possible to develop methods for clarifying the condition of production equipment.

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar's environmental robustness alongside UWB's precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system's potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety.