Tracking Response Research Articles

Research on the Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network

Visual tracking is one of the key research fields in computer vision. Based on the combination of correlation filter tracking (CFT) model and deep convolutional neural networks (DCNNs), deep correlation filter tracking (DCFT) has recently become a critical issue in visual tracking because of CFT’s rapidity and DCNN’s better feature representation. However, DCNNs are often complex in structure, which most possibly results in the conflict between the rapidity and accuracy of DCFT. To reduce such conflict, this paper proposes a model mainly including: (1) Based on the pre-pruning network obtained by feature channel importance, an optimal global tracking pruning rate (GTPR) is determined in terms of the contribution of filter channels to tracking response. (2) Based on (GTPR), an alternative convolutional kernel is defined to replace non-important channel kernels, which leads to the further pruning of the feature network. (3) An online updating pruned feature network with a structural similarity index is employed to adapt the model to tracking scene changes. (4) The proposed model was performed on OTB2013; experimental results demonstrate the model can effectively enhance speed with a 45% increment while guaranteeing tracking accuracy, and improve tracking accuracy with a 4% increment when tracking scene changes take place.

Minimal-norm state-feedback globally non-overshooting/ undershooting tracking control of multivariable systems

The non-overshooting/undershooting (NOUS) tracking control objective in the output response of the closed-loop system has several practical applications in “positioning” control problems. Recently, a state-feedback gain matrix based on Moore’s eigenstructure assignment technique to achieve the above control objective with a desired convergence rate in the output error from any initial condition is designed, referred to as globally monotonic tracking control, which is a convex optimization problem. From a practical viewpoint, a feedback gain matrix of a minimal norm is often required to install low-cost actuators in the overall closed-loop system. However, there is always a trade-off between the control objectives of achieving a NOUS tracking response with the desired convergence rate and a minimal norm of the feedback gain matrix. Besides, the latter control requirement renders the globally NOUS control optimization problem nonconvex. In this paper, a convex optimization-based iterative algorithm is developed to synthesize a minimal-norm state-feedback globally NOUS tracking controller. An upper bound on the achievable settling time in all output responses for the closed-loop eigenvalues lying in a prespecified region of the complex plane is also provided. The effectiveness of the proposed algorithm is demonstrated through a numerical example, followed by experimental validation on a multitank system.

An Enhanced P&O MPPT Algorithm for PV Systems with Fast Dynamic and Steady-State Response under Real Irradiance and Temperature Conditions

This paper presents an enhanced perturb and observe (P&O) method for reconciling the trade-off problem between the dynamic response and steady-state oscillations in maximum power point tracking (MPPT). The constraint of having to sacrifice either the dynamic response or the steady-state oscillations has been solved. The method uses the relationship between the open-circuit voltage and maximum power voltage from the fractional open-circuit voltage (FOCV) MPPT method to establish a valid, reduced, and confined search space within which an enhanced P&O via dynamic adaptive step size terminates the search for the maximum power point. The feasibility of the proposed method has been validated by comparing its performance with the conventional P&O algorithm. It was noted that the proposed method increased the operational efficiency of the PV module to 99.89%, reduced the tracking time to 1.8 ms, and preserved the good steady-state response with a power attenuation of less than 0.10 W or relative 0.16% under MATLAB environment. An experimental setup was used to collect real irradiance and temperature data which was used in real-time simulations. The enhanced P&O method was able to resist abrupt changes in irradiance and temperature as it effectively and efficiently followed the maximum power point (MPP). Finally, to appreciate the supremacy of the proposed method, it was compared to nineteen different MPPT methods from literature. The comparison showed that the enhanced P&O MPPT method is highly efficient and effective for MPPT in photovoltaic (PV) generation systems.

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the trained ElamnNN is employed to acquire the maximum voltage of the photovoltaic (PV) array. At last, the PID controller and PWM technology are adapted to regulate the switch time of the boost converter. Furthermore, MATLAB/Simulink is adopted to acquire the datasets of irradiance, temperature, and maximum voltage and validate the reliability and superiority of the proposed algorithm under complex atmospheric conditions. The tracking characteristic, response speed, and efficiency of the proposed MPPT algorithm are contrasted with the particle swarm optimization (PSO), ant colony optimization (ACO), ACO-artificial neural network (ACO-ANN), and PSO-RBF neural network (PSO-RBNFNN) algorithm via simulation. The efficiency of the FA-EAS-ElmanNN algorithm is 99.73%, compared with the ACO-ANN, PSO-RBFNN, PSO, and ACO algorithm, which is increased by 0.49%, 0.58%, 1.2% %, and 1.5%, respectively. Additionally, the experimental setup is built to demonstrate the tracking characteristic of the proposed MPPT algorithm.

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

In this paper, the problem of trajectory tracking for robotic manipulator with input deadzone and time-varying disturbances is investigated using a continuous non-singular terminal sliding mode control (SMC). The robotic manipulator plays a vital role in industrial as well as many other applications. However, it faces numerous problems for accurate and fast trajectory tracking response in the presence of unknown uncertainties and external disturbances, that always have adverse affects on system closed-loop performance. This paper proposes a valid control design scheme to address the aforementioned practical issue. First, a non-singular terminal sliding mode surface is designed. Then, a robust control law is developed to guarantee the finite-time converges of system states to the origin. In addition, the sign function is substituted by saturation function, which can effectively reduce the chattering driven by the high-frequency switching item in the traditional SMC method. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The efficiency of the designed control scheme is validated with numerical simulations.

Characterization of Stray Radiation Produced by a Proton Beam in an Anthropomorphic Phantom with Dental Implants Using a Pixel Detector

<h3>Purpose/Objective(s)</h3> We investigated the scattered radiation produced by a collimated proton beam in the case of head and neck cancer using an anthropomorphic head phantom with human-like anatomy, with two dental implants inserted. Using high-resolution semiconductor pixel detectors, we measured and analyzed the backscatter radiation with particle identification resolving power. This research is focused on giving quantitative information about the radiation field components. <h3>Materials/Methods</h3> A treatment plan was designed on a standard X-ray CT which contained the implants at the center of the tumor. Two irradiations of an anthropomorphic head phantom were performed with clinical proton beams: one without metallic implants (plastic inserts) and one with dental implants. Two titanium cylindrical inserts of 1 cm length and 0.33 cm diameter were placed in the phantom's upper jaw like the molar's replacements. The planning target volume (PTV) is shaped so that the dental inserts are centered in the target volume. A scattered fixed proton beam with a nominal energy of 170 MeV was delivered to the phantom using collimators and boluses designed to shape and stop the beam according to the treatment plan. Scattered primary and secondary radiation was detected with hybrid semiconductor pixel detectors, placed at 4 cm behind the PTV. Detailed description of the mixed-radiation field was performed with spectral (energy loss) and tracking (direction) sensitivity. <h3>Results</h3> Measurements included a low intensity proton beam to assess the particle tracking response and identification resolving power in both situations (with and without implants). In this manner, the components of the radiation field were observed and analyzed. Based on their characteristic directional-sensitive spectral-track signal in the detector, both primary and secondary protons can be distinguished from light charged particles. This was done by analyzing single particle pixelated clusters morphology, spectral and directional sensitive parameters. Directional fluxes and linear energy transfer (LET) spectra of charged particle groups provide information about the scattered radiation field. <h3>Conclusion</h3> We present novel information about dental metallic implants' influences in a proton-based treatment. Preliminary results show differences in the LET distributions of the scattered radiation when the implants are in place, respectively, when they are missing. Further analyses will be done to provide a detailed dissemination of contribution for every type of particle.

Long-duration head-down tilt bed rest confirms the relevance of the neutrophil to lymphocyte ratio and suggests coupling it with the platelet to lymphocyte ratio to monitor the immune health of astronauts.

The identification of safe and easily-determined-inflight biomarkers to monitor the immune system of astronauts is mandatory to ensure their well-being and the success of the missions. In this report, we evaluated the relevance of two biomarkers whose determination could be easily implemented in a spacecraft in the near future by using bedridden volunteers as a ground-based model of the microgravity of spaceflight. Our data confirm the relevance of the neutrophil to lymphocyte ratio (NLR) and suggest platelet to lymphocyte ratio (PLR) monitoring to assess long-lasting immune diseases. We recommend coupling these ratios to other biomarkers, such as the quantification of cytokines and viral load measurements, to efficiently detect immune dysfunction, determine when countermeasures should be applied to promote immune recovery, prevent the development of disease, and track responses to treatment.

Prediction of track settlement at high-speed railway transitions between embankment and bridge in the proximity of a turnout

It is well known that transition zones are subject to high differential settlement rates mainly due to the rapid change in track system stiffness as well as materials used. This problem is even more notable in high-speed lines where the dynamic effects are critical. In this paper, a methodology that combines large FE track models and vehicle-track MBS software with relatively low computational costs is proposed to calculate the long-term track behaviour in transition areas. The substructure dynamic characteristics are captured using an equivalent Lump Parameter (LP) model whose parameters are tuned against the FE track model to correctly reproduce the frequency response of the full track. The dynamic behaviour of the whole system is predicted using the in-house vertical vehicle-track software RailDyn. A settlement algorithm accounting for the development of hanging sleepers is then used in iterative simulations. A typical European high-speed transition zone between embankment and bridge is considered as a case study and various scenarios are analysed, including when a switch is located in proximity. Results show that the cement-bound reinforced and layered design considered creates a smooth increase of stiffness through the transition. On the other hand, localised differential settlement is predicted at the position of change in track stiffness, leading to an increase of stress in the ballast layer and a decrease in the layers beneath. The long-term soil behaviour at the switch panel is found to be completely dissociated from the behaviour at the transition when located at or more than 25 m from the bridge, as is the current practice.

Retinal Structure and Function in a Knock-in Mouse Model for the FAM161A-p.Arg523∗ Human Nonsense Pathogenic Variant

Pathogenic variants in FAM161A are the most common cause of retinitis pigmentosa in Israel. Two founder pathogenic variants explain the vast majority of cases of Jewish origin, 1 being a nonsense variant (p.Arg523∗). The aim of this study was to generate a knock-in (KI) mouse model harboring the corresponding p.Arg512∗ pathogenic variant and characterize the course of retinal disease. Experimental study of a mouse animal model. A total of 106 Fam161a knock-in mice and 29 wild-type mice with C57BL/6J background particiapted in this study. Homozygous Fam161a p.Arg512∗ KI mice were generated by Cyagen Biosciences. Visual acuity (VA) was evaluated using optomotor tracking response and retinal function was assessed by electroretinography (ERG). Retinal structure was examined invivo using OCT and fundus autofluorescence imaging. Retinal morphometry was evaluated by histologic and immunohistochemical (IHC) analyses. Visual and retinal function assessments, clinical imaging examinations, quantitative histology, and IHC studies of KI as compared with wild-type (WT) mice retinas. The KI model was generated by replacing 3 bp, resulting in p.Arg512∗. Homozygous KI mice that had progressive loss of VA and ERG responses until the age of 18 months, with no detectable response at 21 months. OCT showed complete loss of the outer nuclear layer at 21 months. Fundus autofluorescence imaging revealed progressive narrowing of blood vessels and formation of patchy hyper-autofluorescent and hypo-autofluorescent spots. Histologic analysis showed progressive loss of photoreceptor nuclei. Immunohistochemistry staining showed Fam161a expression mainly in photoreceptors cilia and the outer plexiform layer (OPL) in WT mice retinas, whereas faint expression was evident mainly in the cilia and OPL of KI mice. The Fam161a - p.Arg512∗ KI mouse model is characterized by widespread retinal degeneration with relatively slow progression. Surprisingly, disease onset is delayed and progression is slower compared with the previously reported knock-out model. The common human null mutation in the KI mouse model is potentially amenable for correction by translational read-through-inducing drugs and by gene augmentation therapy and RNA editing, and can serve to test these treatments as a first step toward possible application in patients. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

An Adaptive PI Controller by Particle Swarm Optimization for Angle Tracking of Steer-by-Wire

In this article, a novel steer-by-wire system architecture called iSteer is designed to realize the high performance of steering angle tracking for self-driving vehicles (especially, self-driving trucks), where the vehicle steers in response to the virtual driver’s command instead of the driver’s hand wheel. Based on a newly steering dynamical model of the iSteer system,an adaptive proportional-integral controller with parameters tuning by particle swarm optimization is developed for real-time or embedded application. Hence, the fast and efficient computation plays an essential role in our control law design. The developed steering controller not only meets this computational demand but also takes into account the self-aligning torque and ground resistance torque (as the disturbances to the iSteer control system). According to the system transfer function, the one-step prediction of steering angle is utilized to find the best particle of the swarm. Through a graph analysis using Nyquist stability criterion, we prove that the steering control system is stable and the error of steering angle tracking is convergent. Some experiments are tested on the physical iSteer system with the developed steering controller, the results of which further verify the effectiveness of the designed architecture and the high performance (e.g., high precision in tracking and fast response time) of the developed control law for steering angle tracking in self-driving vehicle without hand wheel.

Magnetorheological Fluid-Based Bending Actuator for Magnetic Sensing

Magnetorheological (MR) fluids have applications in diverse areas such as automobile suspension, architecture, and vibration control due to their fast response, wide range of controllability, and temperature resistance. However, traditional MR fluids-based applications for robotics lack the capability of sensing that can provide information about the environment. In this letter, we present a MR fluid-based bending actuator for magnetic sensing by integrating a strain gage into the actuator. Its deflection can be detected by reading the change of resistance when the actuator deflects under different external magnetic fields. Subsequently, we obtain the relationship between the magnetic flux density and the deflection by deriving an analytical model. To demonstrate the magnetic actuation capability of the actuator, we fabricate a fish-like robot whose tail is composed of the actuator. We further conduct simulations and experiments to validate the analytical model of magnetic sensing. This model shows good agreement with the simulation and experiment results. We validate the actuator for magnetic sensing by demonstrating that it can exhibit a good tracking response of dynamic signals (maximum absolute error smaller than 2.5 mT). The results illustrate that the MR fluid-based bending actuator has the potential to use in the environmental inspection.

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Experimental and Analytical Investigation into the Effect of Ballasted Track on the Dynamic Response of Railway Bridges under Moving Loads

Ballasted tracks are among the most widespread railway track typologies. The ballast possesses multiple functions. Among them, it significantly affects the dynamic interaction between a rail bridge and a moving load in terms of damping and load distribution. These effects entail accurate modeling of the track–ballast–bridge interaction. The paper presents a finite-difference formulation of the governing equations of the track and the bridge, modeled as Euler–Bernoulli (EB) beams, and coupled by a distributed layer of springs representing the ballast. The two equations are solved under a moving load excitation using a Runge–Kutta family algorithm and the finite-difference method for the temporal and spatial discretization, respectively. The authors validated the mathematical model against the displacement response of a rail bridge with a ballasted substructure. In a first step, the modal parameters of the bridge, obtained from ambient vibration measurements, are used to estimate the bending stiffness of an equivalent EB beam representative of the tested bridge. In a second step, the authors estimated the coupling effect of the ballast by assessing the model sensitivity to the modeling parameters and optimizing the agreement with the experimental data. Comparing the bridge’s experimental displacement responses highlights the ballast’s significant effect on the load distribution and damping. The considerable difference between the damping estimated from output-only identification and that determined from the displacement response under moving load proves the dominant role of the ballast in adsorbing the vibrations transmitted to the bridge under the train passage and the different damping sources under high-amplitude excitation. The authors discuss the tradeoff between model accuracy and computational effort for a reliable estimation of ballasted tracks response under moving loads.

Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance

The thermoelectric generator (TEG) is typically used as a clean power supply to harvest waste heat energy in applications involving a large thermal gradient, such as industrial heat removal and power electronic equipment systems. However, it is often difficult to achieve the optimal output power in the loop of the array system of the TEG owing to different output loads. This article proposes an improved fast terminal sliding-mode variable-structure control algorithm (FTSMC) to maximize power point tracking. The variable-structure sliding-mode control function used in the nonlinear sliding-mode surface of the algorithm allows us to obtain the characteristics of global stability that can enable it to converge to the sliding-mode surface at any position to reduce chatter. Digital modeling and simulation as well as experimental developmental field programmable gate array platforms were built to verify the effectiveness of the proposed FTSMC. It can attain the nonlinear sliding mode more quickly than the traditional sliding-mode algorithm. The results of experiments show that it can reach a tracking response speed of 0.08 s and a maximum conversion efficiency of 99.91%. The work here provides a new way for the efficient use of the TEG array for waste heat recovery.

Calculation of the Dynamic Responses of Rails Subjected to Moving Loads on Ballasted Railway Track

The dynamical responses of railway track has been carried out by different ways. In this work, by developing an analytical model for the ballasted railway track which includes two rails connected to the railway sleeper, a fast method to calculated the dynamic responses of the two rails is presented. The rail is modelled as the infinite beams posed on the periodically supports which are rested on a viscoelastic foundation. We consider the dynamic equation in the steady-state of rails subjected to the moving loads. By using Fourier transform together the periodically conditions, a relation between the reaction force and the beam displacement in the frequency domain has been demonstrated. Then, by performing this relation into the dynamic equation of sleeper laying on viscoelastic foundation, the dynamic responses of the two rails can be obtained with a help of Green’s function. The numerical example demonstrates the effect of the support on the beam responses.&#x0D;

An Efficient MPPT Technique-Based Single-Stage Incremental Conductance for Integrated PV Systems Considering Flyback Central-Type PV Inverter

Central-type photovoltaic (PV) inverters are used in most large-scale standalone and grid-tied PV applications due to the inverter’s high efficiency and low-cost per kW generated. The perturbation and observation (P&amp;O) and incremental conductance (IncCond) have become the most common techniques for maximum power point tracking (MPPT) strategies of PV/wind generation systems. Typically, the MPPT technique is applied in a two-stage operation; the first stage tracks the MPP and boosts the PV voltage to a certain level that complies with grid voltage, whereas the second stage represents the inversion stage that ties the PV system to the grid. Therefore, these common configurations increase the system size and cost as well as reduce its overall footprint. As a result, this paper applies two IncCond MPPT techniques on a proposed single-stage three-phase differential-flyback inverter (DFI). In addition, the three-phase DFI is analyzed for grid current negative-sequence harmonic compensation (NSHC). The proposed system efficiently provides a MPPT of the PV system and voltage boosting property of the DC-AC inverter in a single-stage operation. Moreover, the MPPT technique has been applied through the DFI using the conventional and modified IncCond tracking strategies. Furthermore, the system is validated for the grid-tied operation with the negative-sequence harmonic compensation strategy using computer-based simulation and is tested under uniform, step-change, as well as fast-changing irradiance profiles. The average efficiencies of the proposed system, considering the conventional and modified IncCond MPPT techniques, are 94.16% and 96.4% with tracking responses of 0.062 and 0.035 s and maximum overshoot of 46.15% and 15.38%, respectively.

Response of North Pacific storm tracks to multiscale SST anomalies in a stable state of the Kuroshio extension system during the cold season

In the present study, the response of North Pacific storm tracks to spatial multiscale (large-scale and mesoscale) sea surface temperature anomalies (SSTAs) in stable state of Kuroshio Extension (KE-related SSTAs) system are investigated. The results show that storm tracks are significantly strengthened with local enhanced rainfall in the central North Pacific and near the west coast of the North American continent in response to KE-related large-scale SSTAs, while they shift to the north and are significantly strengthened in the central-eastern North Pacific and Gulf of Alaska with remote impact on precipitation along west coast of North America continent in response to KE-related mesoscale SSTAs. The anomalous storm tracks influenced by KE-related SSTAs at different spatial scales are closely related to the locations of low-level baroclinicity. The response of horizontal advection of temperature to different scales of KE-related SSTAs in the lower atmosphere plays an important role in resulting baroclinicity anomalies.

Efficient maximum power point tracker based on neural network and sliding-mode control for buck converters

Abstract This paper presents detailed design steps of an effective control system aiming to increase the solar energy harvested via photovoltaic power-generation systems. The design of an intelligent maximum power point tracker (MPPT) supported by a robust sliding-mode (SM) controller is discussed in this study. The proposed control scheme is designed to track the MPP and provide a smooth system response by removing the overshoot in the load current during any variation in the connected load. Such a system is suitable for DC–DC buck converter applications. The study begins with modelling the buck converter for a continuous current mode operation. The reference voltage of the tracking system is produced by the proposed neural network (NN) algorithm. The proposed intelligent MPPT integrated with an SM controller is simulated in a MATLAB®/Simulink® platform. The simulation results are analysed to investigate and confirm the satisfaction level of the adopted four-serially connected PV-modules system. The system performance is evaluated at a light intensity of 500 W/m2 and an ambient temperature of 25°C. Applying only the proposed NN algorithm guarantees the MPP tracking response by delivering 100 W at a resistive load of 13 Ω, and 200 W at a load of 6.5 Ω, respectively, with 99.77% system efficiency. However, this simultaneously demonstrates a current spike of ~0.5 A when the load is varied from 50% to 100%. The integrated SM controller demonstrates a robust and smooth response, eliminating the existing current spike.

Optimizing the response to mental health deterioration: Nurses experiences of using a Mental Health Observation Response Chart.

Recognizing mental health deterioration remains a challenge for health systems globally, with evidence of suboptimal care for patients with increasing co-morbid psychiatric illness. Mechanisms exist to detect and respond to physical deterioration with the use of Observation Response Charts, monitoring of vital signs and medical emergency teams but registered nurses lack psychiatric nursing skills to care for this patient population. Currently, there are no validated processes in place to recognize and respond to mental health deterioration in general hospital wards. This qualitative descriptive study explores nurses' experiences of using a Mental Health Observation Response Chartthat uses signs of distress to track and trigger tiered responses to mental health deterioration on general hospital wards. Thirty-five surgical and rehabilitation nurses participated in focus groups from January to March 2020. All qualitative data were thematically analysed using an inductive approach. Analysis resulted in four themes: clinical relevance, a useful chart, identifying distress, and working with doctors. The clinical relevance of the chart was influenced by the level of nursing experience; patient distress; existing escalation pathways and ward culture. The study findings will inform the establishment of a process for nurses to recognize mental health deterioration that could improve patient outcomes and promote staff safety. Further research is needed to validate specific signs of distress with patients in general hospital wards and to develop findings for the clinical relevance of this approach to detect mental health deterioration and improve patient outcomes.

Energy harvesting space-air-sea integrated networks for MEC-enabled maritime Internet of Things

In recent years, various maritime applications such as unmanned surface vehicles, marine environment monitoring, target tracking, and emergency response have developed rapidly in maritime communication networks (MCNs), and these applications are often accompanied by complex computation tasks and low latency requirements. However, due to the limited resources of the vessels, it is critical to design an efficient mobile edge computing (MEC) enabled network for maritime computation. Inspired by this motivation, energy harvesting space-air-sea integrated networks (EH-SASINs) for maritime computation tasks offloading are proposed in this paper. We first make the optimal deployment of tethered aerostats (TAs) with the K-means method. In addition, we study the issue of computation task offloading for vessels, focusing on minimizing the process delay of computation task based on the proposed architecture. Finally, because of the NP-hard properties of the optimization problem, we solve it in two stages and propose an improved water-filling algorithm based on queuing theory. Simulation results show that the proposed EH-SASINs and algorithms outperform the existing scenarios and can reduce about 50% of the latency compared with local computation.