State Tracking Research Articles

Safety evaluation for bogie with side bearer pad based on US CFR standard

Abstract Side bearer pad is a typical structure widely adopted in the design of the bogie supporting the subway vehicles in US. This type of bogie should meet the Code of Federal Regulations (CFR), so the safety of this type of bogie needs to be evaluated, especially the safety against derailment. Usually, the lateral and vertical force will be calculated from dynamics model by VAMPIRE or SIMPACK and the result compared with the Nadal limit to evaluate the derailment safety margin. Track condition, guard rail condition, wheel/rail CoF, side bearer pad CoF, speed are the major factors which affect the L/V. This paper will introduce the structure of bogie with side bearer pad and discuss the safety against derailment evaluation based on the sensitivity of the above factors.

Probing Spatiotemporal Effects of Intertrack Recombination with a New Implementation of Simultaneous Multiple Tracks in TRAX-CHEM

Among the most investigated hypotheses for a radiobiological explanation of the mechanism behind the FLASH effect in ultra-high dose rate radiotherapy, intertrack recombination between particle tracks arriving at a close spatiotemporal distance has been suggested. In the present work, we examine these conditions for different beam qualities and energies, defining the limits of both space and time where a non-negligible chemical effect is expected. To this purpose the TRAX-CHEM chemical track structure Monte Carlo code has been extended to handle several particle tracks at the same time, separated by pre-defined spatial and temporal distances. We analyzed the yields of different radicals as compared to the non-interacting track conditions and we evaluated the difference. We find a negligible role of intertrack for spatial distances larger than 1 μm, while for temporal distances up to μs, a non-negligible interaction is observed especially at higher LET. In addition, we emphasize the non-monotonic behavior of some relative yield as a function of the time separation, in particular of H2O2, due to the onset of a different reaction involving solvated electrons besides well-known OH· recombination.

Spontaneous oxycodone withdrawal disrupts sleep, diurnal, and electrophysiological dynamics in rats.

Opioid dependence is defined by an aversive withdrawal syndrome upon drug cessation that can motivate continued drug-taking, development of opioid use disorder, and precipitate relapse. An understudied but common opioid withdrawal symptom is disrupted sleep, reported as both insomnia and daytime sleepiness. Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions. The goal of this study was to establish an in-depth, temporal signature of spontaneous oxycodone withdrawal effects on the diurnal composition of discrete sleep stages and the dynamic spectral properties of the electroencephalogram (EEG) signal in male rats. We continuously recorded EEG and electromyography (EMG) signals for 8 d of spontaneous withdrawal after a 14-d escalating-dose oxycodone regimen (0.5-8.0 mg/kg, 2×d; SC). During withdrawal, there was a profound loss (peaking on days 2-3) and gradual return of diurnal structure in sleep, body temperature, and locomotor activity, as well as decreased sleep and wake bout durations dependent on lights on/off. Withdrawal was associated with significant alterations in the slope of the aperiodic 1/f component of the EEG power spectrum, an established biomarker of arousal level. Early in withdrawal, NREM exhibited an acute flattening and return to baseline of both low (1-4 Hz) and high (15-50 Hz) frequency components of the 1/f spectrum. These findings suggest temporally dependent withdrawal effects on sleep, reflecting the complex way in which the allostatic forces of opioid withdrawal impinge upon sleep and diurnal processes. These foundational data based on continuous tracking of vigilance state, sleep stage composition, and spectral EEG properties provide a detailed construct with which to form and test hypotheses on the mechanisms of opioid-sleep interactions.

Determining the dynamic indicators of the pneumatic spring for high-speed rolling stock in the zone of a rail joint along a railroad track

The object of this study is the pneumatic spring of the high-speed railroad rolling stock that moves over the rail joint of a railroad track with a vertical ledge. The task solved was establishing the dynamic behavior of the rubber cord shell of the pneumatic spring of the high-speed railroad rolling stock, taking into account the design features of the railroad track, namely the rail joint. The methodology for experimental testing of the pneumatic spring of the high-speed railroad rolling stock using the proposed dynamic test installation is given. Experimental tests of the pneumatic spring were carried out within the rail joint of the railroad track, which has a vertical ledge of 7.0 mm. It was established that the maximum value of the accelerations of the rubber cord shell of the pneumatic spring occurs in the vertical plane. The maximum vertical accelerations of the rubber cord shell of the pneumatic spring were 2.4 m/s2, horizontal transverse accelerations – 0.85 m/s2, and horizontal longitudinal accelerations – 0.9 m/s2. It was determined that the deformations of the pneumatic spring in the vertical plane are higher than the deformations in the horizontal plane. The value of the maximum vertical deformations of the pneumatic spring was 4.1 mm, while the maximum value of horizontal deformations was 1.2 mm. The natural frequencies and logarithmic decrements of oscillations damping were determined based on the obtained records of the free oscillations of the rubber cord shell of the pneumatic spring. It was established that the value of the first natural frequency of oscillations of the pneumatic spring is 3.21 Hz. The logarithmic decrement of oscillation damping of the rubber cord shell of the pneumatic spring was determined based on the constructed graph of oscillation damping with an approximating exponent. It is 0.2147. The obtained values of the dynamic indicators of the new pneumatic spring could be used in the future to control changes in the physical and mechanical properties of the rubber cord shell of the pneumatic spring under the operational conditions of the railroad track. In practice, engineers and scientists will be able to take into account the obtained dynamic parameters of the spring when designing and improving the pneumatic spring for high-speed train movement

Sliding Mode Control Approach for Vision-Based High-Precision Unmanned Aerial Vehicle Landing System Under Disturbances

Unmanned aerial vehicles (UAVs) face significant challenges when landing on moving targets due to disturbances, such as wind, that affect landing precision. This study develops a system that leverages global navigation satellite system (GNSS) signals and UAV visual data to enable real-time precision landings, and incorporates a sliding mode controller (SMC) to mitigate external disturbances throughout the landing process. To this end, a reference-model-based SMC is proposed, which defines reference values for each state to enhance the steadiness and safety of the velocity control system, thereby improving velocity state tracking and accuracy. The stability of the proposed controller is demonstrated using the Lyapunov method and comparing its performance against other controllers, including backstepping, linear-quadratic regulator (LQR), and proportional–integral–derivative (PID). The experimental results reveal a 75% reduction in maximum velocity tracking error and an 80% reduction in maximum landing error with the proposed controller. Finally, extensive real-flight tests confirm the stability and feasibility of the system.

Optimization of Curve Correction in the Plan Using Computer Modeling to Improve Train Safety

Purpose. The authors intend to develop and implement innovative approaches to optimizing curve correction in the plan of railway tracks using computer modeling. This process aims to: improve traffic safety; improve the smoothness of the ride, which increases passenger comfort; eliminate speed limits on curved sections, which reduces travel time and increases railroad capacity; reduce track maintenance and rolling stock repair costs. The use of special software to simulate various scenarios of train traffic along the adjusted curves will allow us to assess the impact of the train flow structure on the speed, rolling stock dynamics, and track condition. Methodology. The study is based on the theory of mathematical modeling and numerical calculation methods. To find the best option for the reconstruction of railway sections, the line is considered as a complex system consisting of various infrastructure facilities. Due to their unsatisfactory technical condition, these facilities may limit the speed of traffic on certain sections. The solution to this problem is complicated due to the interdependence of such objects: the reduction of travel time on a particular section is not linear, and obtaining accurate results requires traction calculations taking into account various options for eliminating speed limits. Findings. A calculation algorithm for optimizing long sections of the railway plan is proposed. The algorithm allows to take into account the desired direction of shifts in the curves, which greatly facilitates the optimization process. An example of calculations based on the proposed algorithm is given, which demonstrates the effectiveness of the approach. Originality. The scientific and applied problem of optimizing the process of adjusting railway curves in the plan has been solved. This helps to improve the safety and speed of trains through the introduction of the latest computer modeling methods. Practical value. The implementation of the proposed technology for optimizing the railway plan will significantly increase the speed and safety of train traffic, as well as increase the throughput capacity of railway lines. This will be an important step for the integration of Ukrainian railways into the European transportation system.

Ultra-local model based prescribed performance adaptive control of overhead cranes under uncertainties

In this paper, a model-free adaptive control scheme is proposed to address the problem of system performance variations due to inaccurate models of overhead cranes, and external disturbances. First, an adaptive fast integral terminal sliding mode controller with a prespecified performance setting is proposed to accelerate the convergence of the sliding mode surface. In addition, an improved form of performance function is introduced to ensure the high accuracy convergence of state errors, while effectively enhance response performance and state tracking accuracy. Second, a prespecified performance nonlinear disturbance observer is introduced to estimate the lumped dynamics part of the ultra-local model, which can guarantee effective model-free properties. Finally, the stability of the closed-loop control scheme is analyzed by means of Lyapunov stability theory, and the effectiveness and superiority of the proposed method is proved by means of numerical simulation results.

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

Non-singular fast terminal sliding mode control of robotic manipulator with prescribed performance

Considering the improvement of transient response when tracking the trajectory of the robotic manipulator, a novel prescribed performance non-singular fast terminal sliding mode (PPNFTSM) controller is designed. The method performs the controller design by introducing performance functions with constraint effects, and, at the same time, transforms the trajectory tracking deviation using the hyperbolic tangent function, and a new error performance index function is designed accordingly to solve the dilemma that the prescribed performance control (PPC) depends on the first constrained value of the system; the error obtained from the transformation is then combined with the non-singular fast terminal sliding mode (NFTSM) for controller design. The proof of the stability is established based on Lyapunov functions. The proposed method does not require an initial tracking condition. This is verified by simulation and ensures that the tracking deviation approaches a delimited region near the equilibrium point with a prescribed transient performance and strong robust performance.

Related Work on Few-shot Method: A Review

Abstract. As Natural Language Processing (NLP) technologies continue to expand into novel domains, few-shot learning emerges as a pivotal approach to addressing the challenge of data scarcity. Traditional neural networks, due to their strong reliance on abundant data, have posed limitations in their applicability to new domains. Consequently, there is a pressing need to introduce fresh research perspectives and solutions within this realm, aiming to propel its development towards greater practicality and efficiency. Firstly, solving the zero or few-shot Dialogue State Tracking problem has become necessary as the demand for deploying such systems in new domains continues to grow. This article explores the performance of D-REPTILE, a meta learner for Destination Address (DST) problems, in unknown domains. Then PET is extensively studied. Real-world tests on RAFT show cue-based learning works in low-sample settings, reinforcing the importance of instruction-based learning for human-like few-shot capabilities. Thirdly, to address the overfitting problem, this paper also explores the LA- UCL model, as well as its application, development, and challenges, which enables the LA-UCL model to enhance the Large Language Model data expansion effect through two modules. Finally, CausalCollab is introduced, which uses Incremental Stylistic Effects (ISE) as a guiding estimator for assessing the effectiveness of LM-human cooperation tactics through time.

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

Alamouti coding enabled polarization insensitive simplified self-homodyne coherent system for short-reach optical interconnects

Coherent technology inherent with more available degrees of freedom is deemed as a competitive solution for next-generation ultra-high-speed short-reach optical interconnects. However, the high system cost, elevated power consumption, and large footprint size pose formidable barriers to the application of conventional coherent systems in short-reach scenarios. Self-homodyne coherent detection exhibits its potential for short-reach applications due to the sharing of the signal carrier and the local oscillator (LO), which not only reduces the system cost but also eliminates the frequency offset. Nevertheless, the self-homodyne coherent technique suffers from the polarization fading issue due to the random polarization states of the remote LO. In this paper, an Alamouti coding enabled simplified self-homodyne coherent detection technique is proposed to solve the polarization fading issue, resulting in a polarization-insensitive receiver. Besides, to further reduce the computational complexity of the system, the digital subcarrier multiplexing (DSCM) technique is discussed to alleviate the complexity of chromatic dispersion compensation (CDC), which is another dominant power consumption module of the receiver-side digital signal processing. The performance of the proposed scheme is demonstrated with a 50Gbaud 4-subcarrier 16/32QAM DSCM signal. The results show that polarization-insensitive self-homodyne detection is achieved by the transmitter-side Alamouti coding technique, circumventing the sophisticated automatic polarization controller for the polarization state tracking of the remote LO. In addition, by applying the joint dispersion compensation and equalization (JDCE) method for the DSCM signal, the CD-induced penalty can be fully addressed by increasing 4 taps in the equalizer for an 80 km single-mode fiber transmission without an individual CDC module, reducing the computational complexity of the system significantly.

State Tracking in Nonadiabatic Molecular Dynamics Using Only Forces and Energies.

A new algorithm for the identification of unavoided (trivial) crossings in nonadiabatic molecular dynamics calculations is reported. The approach does not require knowledge of wave functions or wave function time overlaps and uses only information on state energies and gradients. In addition, a simple phase consistency correction algorithm for time-derivative nonadiabatic couplings is proposed for situations in which wave function time overlaps are not available. The performance of the two algorithms is demonstrated using several state crossing models. The approaches work best for systems with localized nonadiabatic coupling regions but may have difficulties for those with extended regions of nonadiabatic coupling. It is found that state tracking alone is not sufficient for producing correct population dynamics and that nonadiabatic coupling phase correction is required.

Multi-domain dialogue state tracking via dual dynamic graph with hierarchical slot selector

Dialogue state tracking aims to maintain user intent as a consistent state across multi-domains to accomplish natural dialogue systems. However, previous researches often fall short in capturing the difference of multiple slot types and fail to adequately consider the selection of discerning information. The increase in unnecessary information correlates with a decrease in predictive performance. Therefore, the careful selection of high-quality information is imperative. Moreover, considering that the types of essential and available information vary for each slot, the process of selecting appropriate information may also differ. To address these issues, we propose HS2DG-DST, a Hierarchical Slot Selector and Dual Dynamic Graph-based DST. Our model is designed to provide maximum information for optimal value prediction by clearly exploiting the need for differentiated information for each slot. First, we hierarchically classify slot types based on the multiple properties. Then, two dynamic graphs provide highly relevant information to each slot. Experimental results on MultiWOZ datasets demonstrate that our model outperforms state-of-the-art models.

Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework

Multi-object tracking faces critical challenges, including occlusions, ID switches, and erroneous detection boxes, which significantly hinder tracking accuracy in complex environments. To address these issues, this study proposes a single-task joint learning (STJL) model integrated into an online multi-object tracking framework to enhance feature extraction and model robustness across diverse scenarios. Employing cross-dataset training, the model has improved generalization capabilities and can effectively handle various tracking conditions. A key innovation is the refined tracker initialization strategy that combines detection and tracklet confidence, which significantly reduces the number of false positives and ID switches. Additionally, the framework employs a combination of Mahalanobis and cosine distances to optimize data association, further improving tracking accuracy. The experimental results demonstrate that the proposed model outperformed state-of-the-art methods on standard benchmark datasets, achieving superior MOTA and reduced ID switches, confirming its effectiveness in dynamic and occlusion-heavy environments.

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

This paper proposes an improved Active Disturbance Rejection Control (ADRC) scheme for nonlinear control-affine systems, achieving superior robustness/ performance trade-offs. By leveraging the flatness property within a closed-loop structure, Nonlinear Internal Model Control (NLIMC) is employed as a model-based controller utilises partial plant knowledge to enhance control performance and eliminate static tracking errors. Subsequently, an improved Extended State Observer (IESO) is incorporated with NLIMC to address unmodelled dynamics, disturbances, and parameter uncertainties in addition to the full state's estimation. The IESO's added hyperbolic function dynamic ensures higher sensitivity and accuracy compared to conventional linear ESOs, especially in case of large errors. Control performances are evaluated through a Remotely Operated Vehicle (ROV) application. Numerical simulations have been carried out and the results confirm its effectiveness and superiority compared to Ocean Disturbance Observer-based Double-loop Sliding Mode Controller (ODSMC) and the linear ADRC.

Improving Freight Train Wheel Monitoring with Smart Sensors

Onboard monitoring of freight car axleboxes enhances safety, reduces maintenance costs, and improves track conditions by preventing secondary damage. Installing wireless sensors on freight cars without a nearby power source should be cost-effective, given the large quantities involved. To address this, a new wireless smart sensor node has been deployed. The sensor automatically recognizes stable operating conditions, detects wheel rotational speed from vibrations, performs real-time condition monitoring, and transmits the results to the cloud. This study outlines the smart sensor concept and the pilot field test conducted with real freight cars. The results demonstrate the ability to estimate wheel rotational speed from vibrations and the potential for detecting wheel out-of-roundness (OOR) using a newly developed condition indicator for low-power real-time operations.

Estimation of genetic parameters for racing time and ranking in Thoroughbred horses

This study aimed to estimate genetic parameters for race time in seconds and final ranking, as well as to analyze the genetic trends associated with race time. The study utilized a dataset consisting of 23,290 records of race times and final ranks at distances of 1,000, 1,600, and 2,000 m from 6,213 Thoroughbred horses from the São Paulo Jockey Club. Our model considered the year of the run, animal sex, race class, track conditions, the linear effect of horse weight and age, and the quadratic effect of age as fixed covariates. Random effects included direct additive genetic, animal permanent environmental, and residual effects. Heritability estimates ranged from 0.01±0.03 to 0.17±0.04 (race time); 0.04±0.03 to 0.09±0.04 (final ranking), and repeatability from 0.19±0.02 to 0.37±0.02 (race time) and 0.15±0.02 to 0.23±0.04 (final ranking). Pearson's correlations of estimated breeding values between race time and ranking ranged from -0,027±0.16 to 0.84±0.01. When analyzing the genetic trend for races at a distance of 1,000 m, we observed a decrease in race time over time. However, for races at 1,600 and 2,000 m, a clear pattern of time reduction was not apparent. Race time offers a more promising response to selection, particularly in shorter races. Selecting for race time is expected to have a greater indirect impact on final rank. Genetic trends have revealed limited genetic progress over the years; therefore, it is advisable to reevaluate the current selection process.

Distributed minimum error entropy with fiducial points Kalman filter for state tracking

With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information. Then, by bringing the minimum error entropy with fiducial points (MEEF) standard into the process of information fusion, a robust algorithm named centralized MEEF KF (CMEEF-KF) is presented, which is robust to non-Gaussian noise and unusual data. Furthermore, to overcome the communication burden of CMEEF-KF in sensor networks, the distributed MEEF-KF (DMEEF-KF) is developed, which construct a framework of consensus average method for node information fusion. Specifically, each sensor only exchanges the key information with its neighborhoods. In addition, in order to make the algorithm able to cope with the nonlinear state estimation problem, the distributed MEEF extended Kalman filter is also proposed. Eventually, the effectiveness of the suggested algorithms is demonstrated by land vehicle navigation and power system tracking state estimation using a 10-node sensor network.

Truncated predictive control for delayed cyber–physical systems under deception attacks

In this work, the state tracking, input delay prediction, uncertainty and disturbance rejection problems are studied for the delayed cyber–physical systems under uncertainties, nonlinearities and external disturbances in which the addressed model is described by the fuzzy technique. More specifically, such an input-delayed system is linearized through an interval type-2 fuzzy technique accompanied by a parallel distribution compensation method. Further, the presence of constant input delays in the examined system is proficiently compensated with the assistance of a truncated predictor feedback approach. In parallel, the impacts of external disturbances and uncertainties that are occurred in the system are estimated by means of uncertainty and disturbance estimator method in an efficacious way. With the help of this estimated information, an uncertainty and disturbance estimator-based truncated predictive tracking control protocol is formulated to acquire the state tracking results of the underlying system in the presence of external disturbances and uncertainties. Remarkably, in this study, the interval type-2 fuzzy is blended with the designed reference system. Furthermore, the secure tracking control law is designed for the considered system under the random deception attacks. To address the deception attacks, the Bernoulli stochastic distribution is considered in this study. Moreover, the constraints are expressed for computing the truncated predictive feedback control gain matrices. Conclusively, the numerical simulation analysis is carried out to demonstrate the usefulness of the devised approach.