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The task of multi-object tracking of fish fry poses significant challenges, as the majority of the fish fry individuals exhibit highly similar appearances, and the feature distinctions between individual targets are not readily apparent. Consequently, fish tracking algorithms relying primarily on appearance-based features for data association often suffer from low accuracy and poor robustness. To address the challenges inherent in multi-object tracking of fish fry, this study presents an improved DeepSort-based algorithm, dubbed PLCFishMOT, designed specifically for enhanced performance in this domain. Furthermore, the fish fry trajectories may exhibit nonlinear characteristics due to external perturbations. To address this, the original Kalman filtering method has been replaced with a particle filtering approach, which is more suitable for handling nonlinear and non-Gaussian problems. This modification serves to enhance the accuracy of the trajectory prediction process. To further bolster the accuracy of the data association process, the proposed framework incorporates a large separable kernel attention mechanism into the original feature extraction network. This mechanism leverages convolutional kernels of varying sizes to extract target features with differing receptive field dimensions, thereby enhancing the overall effectiveness of the feature representation. The proposed approach effectively addresses the challenge of incorrect ID assignment, which can arise due to the close parallel swimming patterns exhibited by the fish fry. This is achieved by leveraging the cosine angle value between the fry detection frame and the trajectory frame as a discriminating factor. The experimental evaluation of the proposed algorithm on an open-source video dataset demonstrates its strong performance, with the algorithm achieving an IDF1 score of 75.8%, a MOTA score of 98.1%, and IDs is 10, respectively. Furthermore, to assess the generalization capabilities of the proposed approach, validation experiments were conducted using a fish fry video dataset captured in real-world aquaculture scenarios. The experimental results demonstrate that the PLCFishMOT algorithm achieves the best tracking performance compared to other advanced multi-object tracking algorithms. [ABSTRACT FROM AUTHOR]

Development of advanced health monitoring sensors and high-performance computing enabled multi-sensors information to analyse degradation in complex engineering systems/components. These sensors are often aimed to capture different physical aspects of a system. Thus, each sensor only contains partial information about the same degradation process. A significant need is to devise a contemporary data fusion method that effectively integrates independent multi-sensors measurements leading to a better prognosis of the degradation process. Unlike conventional data fusion methodologies that fuse multiple sensors' information prior implementation of prognosis step, this paper presents a novel fusion framework based on multi-sensor prognosis data. The framework is developed using Particle Filtering (PF) technique coupled with Auto-Regressive Integrated Moving Average (ARIMA) model. The proposed framework is applied on historical Non-Destructive Testing (NDT) database generated through Ultrasonic Probe Listening and Thermal Imaging of in-service aerial bundled cables (ABCs) installed in coastal regions to evaluate cable degradation over time. Promising results of $f$ f -steps prediction scheme from fused degradation values, as compared to using individual measurement mode data, indicates the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies statistical inference for a Lindley random walk model when the increment process driving the walk is strictly stationary. Lindley random walks govern customer waiting times in many queueing models and several natural and business processes, including snow depths, frozen soil depths, inventory quantities, etc. The probabilistic properties of a Lindley walk with time-correlated stationary changes are first reviewed. We provide a streamlined argument that the process has a proper limiting distribution when the mean of the incremental changes is negative, and that the Lindley process is strictly stationary when starting from this stationary distribution. Next, the Markov characteristics of the process are explored when the change process has a Markov structure of first or higher order. A derivation of the model's likelihood is given when the change process is a Gaussian autoregressive time series. An efficient particle filtering method for evaluating and optimizing the likelihood with Gaussian changes is then devised and studied via simulation. [ABSTRACT FROM AUTHOR]

In environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self‐potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single‐frame inversion, overlooking the temporal correlation in time‐series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time‐lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab‐measured dataset. The findings demonstrate the efficacy of the time‐lapse inversion algorithm in precisely estimating dynamic models, outperforming static single‐frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell‐shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time‐lapse inversion of time‐series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states. [ABSTRACT FROM AUTHOR]

In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications. [ABSTRACT FROM AUTHOR]

Objective The Coriolis flowmeter can directly measure the mass flow rate of fluids and is widely used in the field of petrochemicals. This paper proposes a phase difference algorithm based on particle filter to meet the high-precision measurement requirements of Coriolis flow meters. Methods By analyzing the characteristics of the Coriolis flowmeter signal, the state transition equation and observation equation of the vibration signal system were established. The particle filter algorithm was used to track the signal amplitude, angular frequency, and phase parameters, and the phase difference was calculated from the updated state values. Results The simulation analysis results show that compared with typical Hilbert transform processing results, the relative error of phase calulation results based on particle filter is significantly reduced. Conclusions The processing results of actual vibration raw signals of Coriolis flow meters show that the algorithm based on particle filter can be effectively applied to the processing of measured vibration signals of Coriolis flowmeters, and to a certain extent, it improves the measurement accuracy of time difference or phase difference. [ABSTRACT FROM AUTHOR]

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used "linear approximation" model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias. [ABSTRACT FROM AUTHOR]

The problem addressed in this work is the conditional simulation of a regionalized variable that is modeled as a realization of a non-monotonic transform of a Gaussian random field. As an alternative to Markov Chain Monte Carlo methods that often suffer from a slow convergence to the target distribution, we propose the use of sequential Monte Carlo approaches, with different variants of particle filtering. These variants are tested on synthetic and real datasets, to showcase their applicability and effectiveness under a proper setup of the importance sampling strategy, visiting sequence, number of particles, block size and kriging neighborhood used. The real case study, which deals with the simulation of gold grades in a porphyry copper-gold deposit, shows that the multi-Gaussian model based on a non-monotonic anamorphosis better assesses uncertainty than the traditional model based on a strictly monotonic anamorphosis, and that a moving neighborhood implementation of sequential Monte Carlo approaches can be successful, opening the door to applications to large-size problems in spatial uncertainty modeling. [ABSTRACT FROM AUTHOR]

Probabilistic dynamical models used in applications in tracking and prediction are typically assumed to be Gaussian noise driven motions since well-known inference algorithms can be applied to these models. However, in many real world examples deviations from Gaussianity are expected to appear, e.g., rapid changes in speed or direction, which cannot be reflected using processes with a smooth mean response. In this work, we introduce the non-Gaussian process (NGP) dynamical model which allow for straightforward modelling of heavy-tailed, non-Gaussian behaviours while retaining a tractable conditional Gaussian process (GP) structure through an infinite mixture of non-homogeneous GPs representation. We present two novel inference methodologies for these new models based on the conditionally Gaussian formulation of NGPs which are suitable for both MCMC and marginalised particle filtering algorithms. The results are demonstrated on synthetically generated data sets.

Aiming at the problem of detecting and locating the leakage position of urban pipelines, an underwater navigation and positioning method combining the jet link inertial navigation system and the simultaneous composition positioning algorithm is proposed. The sonar sensor is used to collect the characteristic position information of urban pipelines, and the pipeline map is constructed under the action of the simultaneous composition positioning algorithm to obtain high-precision positioning information. The positioning information obtained above is then combined with the Jet link inertial navigation system using a particle filtering algorithm to compensate for its position error accumulation. The simulation experiment results show that the positioning accuracy of the described combination method is high, reaching 0.1% of the total range. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) for in‐service systems plays an important role in ensuring efficient operation of industrial equipment and in preventing unexpected equipment failures. In this paper, we present a prognostic framework for real‐time RUL prediction based on joint modeling of both degradation signals and time‐to‐event data. The proposed model employs a change point‐based general path model to capture signal non‐linearity and Neural network (NN) based Cox model to link the time‐to‐event data with the estimated degradation trend. An empirical two‐step scheme for hyperparameter estimation is proposed to enhance prognostic accuracy. Furthermore, an efficient Bayesian model updating procedure, integrated with recursive particle filtering, is used to facilitate online prediction, achieving accurate RUL prediction in real‐time and accounting for uncertainties in RUL prediction. Simulation and real‐life case studies demonstrate the advantages of the proposed method over existing approaches. [ABSTRACT FROM AUTHOR]

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since their information is most likely to be trustworthy. This study stochastically investigates the three most common fusion techniques: Kalman filtering, particle filtering and Bayesian probability (which is the basis of other techniques). The paper focuses on using fusion techniques in the context of state estimation for dynamic systems to improve reliability and accuracy. The fusion methods are investigated using different types of datasets to find out their performance and accuracy in state estimation. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, we develop a framework to enhance the control of autonomous vehicles within signalized intersections by integrating system dynamics with imperfect sensor data. Although sensor data for autonomous vehicles is often partial, noisy, or missing, its alignment with underlying physics has the potential to significantly improve prediction accuracy. Our methodology involves a physics-based representation of system dynamics, accounting for stochastic elements originating from differences between real-world scenarios and model-based representations, using a Markov decision process (MDP) model that embraces system physics while accommodating uncertainties. Leveraging partial sensor data, we aim to diminish uncertainty associated with segments of the system model reflected in the data and better estimate the other physical variables that are not measurable through data. This framework develops particle-based reinforcement learning action policies, seamlessly integrating data and physics for controlling autonomous vehicles approaching signalized intersections. Numerical experiments showcase the effectiveness of these policies in ensuring safe control and decision-making in different scenarios of missing state variables and varying levels of data sparsity. [ABSTRACT FROM AUTHOR]

The Virtual Track Train (VTT) represents an innovative urban public transportation system that combines tire-based running gears with rail transit management. Effective control of such a system necessitates precise state estimation, a task rendered complex by the multi-articulated nature of the vehicles. This study addresses the challenge by focusing on state estimation for the first unit under significant interference, introducing a fusion state estimation strategy utilizing Gaussian Process Regression (GPR) and Interacting Multiple Model (IMM) techniques. First, a joint model for the first unit is established, comprising the dynamics model as the main model and a residual model constructed based on GPR to accommodate the main model's error. The proposed fusion strategy comprises two components: a kinematic model-based method for handling transient and high-acceleration phases, and a joint-model-based method suitable for near-steady-state and low-acceleration conditions. The IMM method is employed to integrate these two approaches. Subsequent units' states are computed from the first unit's state, articulation angles, and yaw rates' filtered data. Validation through hardware-in-the-loop (HIL) simulation demonstrates the strategy's efficacy, achieving high accuracy with an average lateral speed estimation error below 0.02 m/s and a maximum error not exceeding 0.22 m/s. Additionally, the impact on VTT control performance after incorporating state estimation is minimal, with a reduction of only 3–6%. [ABSTRACT FROM AUTHOR]

In this paper, we focus on studying the Mixed-Effects State-Space (MESS) models previously introduced by Liu et al. [Liu D, Lu T, Niu X-F, et al. Mixed-effects state-space models for analysis of longitudinal dynamic systems. Biometrics. 2011;67(2):476–485]. We propose an estimation method by combining the auxiliary particle learning and smoothing approach with the Expectation Maximization (EM) algorithm. First, we describe the technical details of the algorithm steps. Then, we evaluate their effectiveness and goodness of fit through a simulation study. Our method requires expressing the posterior distribution for the random effects using a sufficient statistic that can be updated recursively, thus enabling its application to various model formulations including non-Gaussian and nonlinear cases. Finally, we demonstrate the usefulness of our method and its capability to handle the missing data problem through an application to a real dataset. [ABSTRACT FROM AUTHOR]

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The localization of a robot is an important problem for accurate mapping via the mobile robot. In this paper, we propose minimax particle filtering (MPF) for the pose estimation of a mobile robot in the problem of simultaneous localization and mapping (SLAM) based on 2D LIDAR scans. Standard particle filtering has generic drawbacks such as particle degeneracy and particle impoverishment issues that cause the degraded quality of particles and result in unsatisfactory performance in practice while particle filtering is expected to show optimal performance in nonlinear problems, theoretically. Recently proposed MPF overcomes these limitations in PF implementation with increased quality of the particles in terms of particle diversity and variance of the weights. We test the proposed SLAM algorithm based on MPF with the datasets that were used for testing the standard Rao-Blackwellized PF (RBPF) SLAM and show the outperforming results, particularly in terms of the maximum translational/rotational errors that result in the overall diminished average errors. [ABSTRACT FROM AUTHOR]

In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications.

The prediction of the evolution of epidemics plays an important role in limiting the transmissibility and the burdensome consequences of infectious diseases, which leads to the employment of mathematical modeling. In this paper, we propose a stochastic particle filtering extended SEIRS model with repeated vaccination and time-dependent parameters, aiming to efficiently describe the demanding dynamics of time-varying epidemics. The validity of our model is examined using daily records of COVID-19 in Italy for a period of 525 days, revealing a notable capacity to uncover the hidden dynamics of the pandemic. The main findings include the estimation of asymptomatic cases, which is a well-known feature of the current pandemic. Unlike other proposed models that employ extra compartments for asymptomatic cases, which force the estimation of this proportion and significantly increase the model's complexity, our approach leads to the evaluation of the hidden dynamics of COVID-19 without additional computational burden. Other findings that confirm the model's appropriateness and robustness are its parameter evolution and the estimation of more ICU-admitted cases compared to the official records during the most prevalent infection wave of January 2022, attributed to the intensified increase in admissions that may have led to full occupancy in ICUs. As the vast majority of datasets contain time series of total recovered and vaccinated cases, we propose a statistical algorithm to estimate the currently recovered and protected through vaccination cases. This necessity arises from the attenuation of antibodies after vaccination/infection and is necessary for long-time interval predictions. Finally, we not only present a novel stochastic epidemiological model and test its efficiency but also investigate its mathematical properties, such as the existence and stability of epidemic equilibria, giving new insights to the literature. The latter provides additional details concerning the system's long-term behavior, while the conclusions drawn from the R0 index provide perspectives on the severity and future of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method. [ABSTRACT FROM AUTHOR]

The ensemble Gaussian mixture filter (EnGMF) combines the simplicity and power of Gaussian mixture models with the provable convergence and power of particle filters. The quality of the EnGMF heavily depends on the choice of covariance matrix in each Gaussian mixture. This work extends the EnGMF to an adaptive choice of covariance based on the parameterized estimates of the sample covariance matrix. Through the use of the expectation maximization algorithm, optimal choices of the covariance matrix parameters are computed in an online fashion. Numerical experiments on the Lorenz '63 equations show that the proposed methodology converges to classical results known in particle filtering. Further numerical results with more advanced choices of covariance parameterization and the medium-size Lorenz '96 equations show that the proposed approach can perform significantly better than the standard EnGMF and other classical data assimilation algorithms. [ABSTRACT FROM AUTHOR]

Geodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications, including to determine crustal velocity fields and to detect significant displacement from earthquakes. The relationships inherent in these GPS displacement observations have not been fully explored. This study employs the sequential Monte Carlo method, specifically particle filtering (PF), to develop a time-varying analysis of the relationships among GPS displacement time-series within a network, with the aim of uncovering network dynamics. Additionally, we introduce a proposed graph representation to enhance the understanding of these relationships. Using the 1-Hz GEONET GNSS network data of the Tohoku-Oki Mw9.0 2011 as a demonstration, the results demonstrate successful parameter tracking that clarifies the observations' underlying dynamics. These findings have potential applications in detecting anomalous displacements in the future. [ABSTRACT FROM AUTHOR]

This article explores a particle fusion approach for distributed filtering and smoothing in unmanned systems. The authors propose a drop-in replacement for the pointwise product operation using the generalized H¿older's inequality, which allows for tighter approximations in particle-based distributions. They validate their approach through simulations and experiments with miniature autonomous blimps, showing improved tracking performance. The text also discusses the concepts of distributed filtering and smoothing, as well as the use of factor graphs and the Belief Propagation algorithm in the context of distributed autonomous vehicles. The proposed methods are demonstrated to be effective through simulation examples and experimental results. The authors suggest future work involving the use of machine learning models to handle higher-dimensional data. [Extracted from the article]

In the scerinao of impulsive noise, this paper proposes a multi-Bernoulli enhanced auxiliary particle filtering (MB-EAPF) algorithm for multi-source direction of arrival tracking by utilizing uniform linear array configuration. By proposing an EAPF to solve the particle degradation issue raised by the resampling method of the conventional multi-target multi-Bernoulli (MeMBer) filtering. Moreover, since the measurement data is disturbed by impulsive noise and its second-order statistic fails, the phased fractional low order moment matrix is utilized as an alternate covariance matrix. Furthermore, the likelihood function of the MeMBer filtering is replaced by the multiple signal classification spatial spectral function and exponentially weighted to obtain more particles closer to the posterior distribution. Simulation results demonstrate that the proposed algorithm provides better tracking performance and more accurate estimation than the conventional MeMBer filtering. [ABSTRACT FROM AUTHOR]

In this paper, we study the application of Itô-vector projection [1] to the optimal filtering problem. The algorithm projects one SDE to another, possibly lower dimensional, SDE by minimizing an Itô–Taylor expansion of the local projection error’s L2 norm. We explicitly derive the projection filter equation for a general class of parametric densities, and then specifically apply it to exponential families. We demonstrate that for the case where the measurement drift function is in the span of the natural statistics, the Itô-vector projection filter (IVPF) coincides with the Stratonovich-projection filter (SPF) [2]. We then compare the performance of the IVPF against the SPF (with both being implemented using the Gaussian bijection proposed in [3] and the sparse Gauss–Patterson numerical integration) for two-dimensional optimal filtering problem to show the effectiveness of the proposed algorithm. We vary the measurement drift function to four different functions that are not in the span of natural statistics. Based on one hundred Monte Carlo simulations for each measurement drift, we found that their performances are comparable, with the IVPF potentially offering a slightly more robust performance. However, in our current numerical implementation, the SPF consistently outperforms the IVPF in terms of speed.

Abstract Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients’ characteristic data, be more relevant to different patients, and help achieve precision medicine.

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since their information is most likely to be trustworthy. This study stochastically investigates the three most common fusion techniques: Kalman filtering, particle filtering and Bayesian probability (which is the basis of other techniques). The paper focuses on using fusion techniques in the context of state estimation for dynamic systems to improve reliability and accuracy. The fusion methods are investigated using different types of datasets to find out their performance and accuracy in state estimation.

This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method.

Abstract Passive acoustic telemetry is widely used to study the movements of aquatic animals. However, a holistic, mechanistic modelling framework that permits the reconstruction of fine‐scale movements and emergent patterns of space use from detections at receivers remains lacking. Here, we introduce an integrative modelling framework that recapitulates the movement and detection processes that generate detections to reconstruct fine‐scale movements and patterns of space use. This framework is supported by a new family of algorithms designed for detection and depth observations and can be flexibly extended to incorporate other data types. Using simulation, we illustrate applications of our framework and evaluate algorithm utility and sensitivity in different settings. As a case study, we analyse movement data collected from the Critically Endangered flapper skate (Dipturus intermedius) in Scotland. We show that our methods can be used to reconstruct fine‐scale movement paths, patterns of space use and support habitat preference analyses. For reconstructing patterns of space use, simulations show that the methods are consistently more instructive than the most widely used alternative approach (the mean‐position algorithm), particularly in clustered receiver arrays. For flapper skate, the reconstruction of movements reveals responses to disturbance, fine‐scale spatial partitioning and patterns of space use with significant implications for marine management. We conclude that this framework represents a widely applicable methodological advance with applications to studies of pelagic, demersal and benthic species across multiple spatiotemporal scales.

The DC circuit breaker is a crucial equipment for eliminating faults in DC transmission lines. The electromagnetic release functions as a critical component that restricts the circuit breaker's lifespan. It is essential to prioritize its safety and reliability during usage and predicting its remaining useful life (RUL) is paramount. This paper proposes a new prediction technique based on particle filtering (PF) and the whale optimization algorithm (WOA) for the remaining useful life of electromagnetic release. The particle filtering algorithm is a commonly used technique in practical engineering fields such as target tracking and RUL prediction. It is a mainstream method for solving the parameter estimation of non-linear non-Gaussian systems. The WOA is introduced to improve the PF algorithm in order to ensure the diversity of particles and lessen the effect of particle degradation. The WOA replaces the traditional resampling process, and after each computation is finished, the weights of the particles in the particle set are reassigned in order to improve the particle distribution and increase algorithm accuracy. The rate of loss of the spring reaction force and the striker counterforce are chosen as the degradation characteristics after the degradation factors of electromagnetic release are analyzed. The degradation curves at different temperatures of electromagnetic release are obtained using the accelerated life test, and the full-life data during normal operation are derived using the Arrhenius equation. Finally, the RUL is predicted by comparing this paper's method with the conventional PF method. The experimental results demonstrate that the method presented in this paper can more accurately predict the RUL of the electromagnetic release and has a higher prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predictive controller (SNMPC) that learns a tyre–road friction model online using standard automotive-grade sensors. Learning the entire tyre–road friction model in real time requires driving in the nonlinear, potentially unstable regime of the vehicle dynamics, using a prediction model that may not have fully converged. To handle this, we formulate the tyre-friction model learning in a Bayesian framework and propose two estimators that learn different aspects of the tyre–road friction. The estimators output the estimate of the tyre-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes. The SNMPC exploits the uncertainty estimate in its prediction model to take proper action when the uncertainty is large. We validate the approach in an extensive Monte Carlo study using real vehicle parameters and in CarSim. The results when comparing to various MPC approaches indicate a substantial reduction in constraint violations, as well as a reduction in closed-loop cost. We also demonstrate the real-time feasibility in automotive-grade processors using a dSPACE MicroAutoBox-II rapid prototyping unit, showing a worst-case computation time of roughly 40 ms. [ABSTRACT FROM AUTHOR]

This study presents a machine learning model for predicting representative surface distresses (crack, durability, patching, joint spall) in concrete pavements, focusing on South Korean examples. It thoroughly analyzes specific distress types using time series data to understand their development over time, aiming to surpass traditional regression methods in forecasting pavement conditions. The research fills a gap by applying machine learning algorithms to detailed long-term data, enhancing the accuracy of distress progression predictions, which is crucial for efficient pavement management. A notable aspect of this study is the use of particle filtering, recognized for its effective resampling in analyzing time series data. To validate predictions, we compared the results from particle filtering with those from traditional regression models, long short-term memory (LSTM) networks, and Deep Neural Networks (DNNs). The accuracy varied significantly, with differences ranging from 3.32% to 23.64%, indicating particle filtering's suitability for time-series-based pavement condition predictions. These findings are especially relevant in the context of current image-based machine learning and AI research in pavement distress detection and prediction. This research offers a comprehensive reference that is especially valuable due to the lack of studies using long-term usage data, thereby making a significant contribution to pavement management research and practice. [ABSTRACT FROM AUTHOR]

In this research paper, we investigate the problem of remote state estimation for nonlinear discrete systems. Specifically, we focus on scenarios where event-triggered sensor schedules are utilized and where packet drops occur between the sensor and the estimator. In the sensor scheduler, the SOD mechanism is proposed to decrease the amount of data transmitted from the sensor to a remote estimator and the phenomena of packet drops modeled with random variables obeying the Bernoulli distribution. As a consequence of packet drops, the assumption of Gaussianity no longer holds at the estimator side. By fully considering the non-linearity and non-Gaussianity of the dynamic system, this paper develops an event-trigger particle filter algorithm to relieve the communication burden and achieve an appropriate estimation accuracy. First, we derive an explicit expression for the likelihood function when an event trigger occurs and the possible occurrence of packet dropout is taken into consideration. Then, using a special form of sequential Monte–Carlo algorithm, the posterior distribution is approximated and the corresponding minimum mean-squared error is derived. By contrasting the error covariance matrix with the posterior Cramér–Rao lower bound, the estimator's performance is assessed. An illustrative numerical example shows the effectiveness of the proposed design. [Display omitted] • Limited bandwidth networked systems require sensors scheduling algorithm design. • Event-based particle filter is designed in conjunction with scheduling algorithms and packet dropout. • With Cramér–Rao lower bound the proposed scheduling algorithms improve clearly the filter performance. • Innovation scheduling allows robustness against packet dropouts. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine. [ABSTRACT FROM AUTHOR]

South Korea is implementing various policies to address the aging of infrastructures and improve road infrastructure management. Moreover, numerous research projects aiming at the development of necessary technologies for the proper implementation of these policies are underway. This study specifically aims to overcome existing problems in bridge pavement maintenance, such as the inaccuracy of future condition predictions and the selection of incorrect evaluation indicators. Our goal is to provide a new approach for the improved management of the bridge pavement management system (BPMS). To address the issues of accuracy in future condition prediction and evaluation indicator selection within the existing maintenance system, we utilized particle filtering, a Kalman filter method among machine learning techniques. This method allows for the prediction of future conditions, based on the nonlinearly collected bridge pavement conditions within BPMS. Furthermore, we proposed a systematic bridge pavement management strategy. This strategy utilizes traffic volume (ESALs; equivalent single axle loadings), a factor that can influence the future condition of bridge pavement, in correlation with the future condition predicted through particle filtering within BPMS. [ABSTRACT FROM AUTHOR]

With the widespread use of radioactive sources in various fields, incidents of loss of radioactive sources occur from time to time aiming at the problem of searching for a single radioactive sources, a particle filtering method based on the angle of arrival was proposed to improve the computational efficiency. The particle set was established to describe the position and intensity of the radioactive source, the angle of arrival was introduced into the particle update process, and the updated particles were selected according to the relative angle of the particles. Next, the following steps include: complete normalization, resampling and adaptive update. Establishing an observation vector set, whenever a new environmental observation data is measured, the optimal estimated position of the radioactive source reflected by all the current observation vectors can be obtained, and the search path can be guided closer to the position of the radioactive source, and when the particle filter meets the convergence conditions, the particles are weighted to obtain the final estimated position of the radioactive source. Simulation experiments show that this method can be applied to source seeking on mobile platform, its efficiency is higher than the existing particle filter method, and its detection area also significantly increases.

The particle filter is known to be a powerful tool for the estimation of moving targets guided by nonlinear dynamics and sensors. The filter, however, is known to suffer from degeneracy — a feature of one particle gathering all the weight, thus causing the filter to completely diverge. Degeneracy problems become more evident when the sensors are accurate and/or the target maneuvers greatly. The resampling step in the particle filter is critical because it avoids degeneracy of particles by eliminating the wasteful use of particles that do not contribute to the posterior probability density function. The conventional resampling methods, despite being unbiased in approximating the posterior density, involve exhaustive and sequential communication within the particles and thus are computationally expensive. Hence conventional resampling is a major bottleneck for fast implementation of particle filters for real-time tracking. In this paper, we propose a new approach of filtering that requires resampling of only a minimum number of the most important particles that contribute to the posterior density. Minimizing the resampling operation to over a few important particles substantially accelerates the filtering process. We show the merits of the proposed method via simulations using a nonlinear example. [ABSTRACT FROM AUTHOR]