Tracking Precision Research Articles

Numerical energy evaluation of an optimized hybrid energy harvesting system (CPV-TEG) considering the effects of solar tracking

Currently, the energy demand at a global level is accelerated, and the use of non-renewable energies increases the problems related to global warming. Solar technologies, mainly photovoltaics, have been used as an alternative, but conversion efficiency is still low. Hybrid generation systems, which integrate photovoltaic and thermoelectric technologies, seek to provide a solution to efficiency and high costs per area unit. These systems need high tracking precision to concentrate solar energy, which requires robotic tracking systems, which generate energy waste due to the tracking action. The studies and developments do not contemplate in their analysis the losses due to tracking or the precision required by the optical concentration element. The present research develops a novel methodology for the design of optimized hybrid energy harvesting systems, integrating the concentrated photovoltaic and thermoelectric generator technologies and including the effects of solar tracking in the energy sense. In addition, a process for the numerical evaluation of hybrid systems is presented, considering thermal and tracking simulations to estimate the global energy balance. Consequently, the design methodology and numerical evaluation are implemented, comparing three concentration configurations. Additionally, a focus control device is designed for a high concentration index and evaluated. The mean total electrical efficiency of the hybrid system is 40.6763%, 38.84%, 13.3439%, and 35.0484%, respectively. With global gain estimates of 189.48 Wh, 201.83 Wh, 47.43 Wh, and 141.91 Wh. Finally, the results are compared with other developments, concluding that the hybrid system has high energy production. In conclusion, the proposal increases the overall performance of hybrid energy harvesting systems, allowing the development of future research regarding novel concentration devices, cooling methods, harvesting systems, and absorbing designs, among other innovations in more realistic energy evaluation terms.

Efficient and accurate flexible multibody dynamics modeling for complex spacecraft with integrated control applications

This study presents a novel flexible multibody dynamics modeling method tailored to spacecraft with intricate motion features such as multi-axis antenna actuation, robotic manipulations, and space station module rotation. Using a floating base and a tree-topology structure, the model effectively simulates the kinematics and dynamics of large-angle joint rotations and elastic deformations, leveraging the Lagrangian framework and finite element analysis. An advanced augmented proportional–derivative (APD) control scheme is also introduced, incorporating system dynamics for enhanced predictive control, accommodating complex interactions and nonlinear system behaviors. Our modeling approach, compared with the commercial Adams software, shows similar system responses while reducing computation time by about 70%, highlighting its efficiency and accuracy. Moreover, incorporating the APD strategy into our system enhances tracking precision, achieving a 90% reduction in error compared with conventional proportional–derivative controls, underscoring the benefits of combining dynamic modeling with control improvements.

Adaptive Online Neural Predictive Control of Hydraulic Actuator Using FPGA Acceleration

Hydraulic actuators have recently been widely used in control systems as efficient output devices. However, high-precision control of hydraulic actuators in industrial applications remains challenging due to the conflict between controllers' complexity, especially in theoretical modeling, and their generalization abilities, including adaptability to parametric uncertainties and versatility for various working conditions. To address this issue, we propose an adaptive online neural predictive control (AONPC) scheme, which is capable of handling unmodeled nonlinearities and uncertainties with a simple structure and low demand for system modeling. Specifically, an artificial neural network (ANN) is responsible for data-driven system identification and predictive control, while upper confidence bound (UCB) algorithm is introduced for hyperparameter self-adaptation. The ANN structure and the iteration number for solving the optimization problem are determined through a simulation study. Then, to complete the whole control loop within a short and fixed tracking interval, the proposed controller is implemented on a field programmable gate array (FPGA), which can effectively improve the tracking precision. Finally, extensive experimental results on a realistic hydraulic actuator device demonstrate the superiority of the proposed controller.

Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft

This paper concerns the problem of robust attitude control of a quadrotor aircraft subjected to modeling inaccuracies and wind disturbances. First, the model dynamics and state space representation of the quadrotor system are designed by considering the impact of disturbances and uncertainties. Then, a novel robust backstepping non-singular integral terminal sliding mode control (BNITSMC) is synthesized for the quadrotor attitude with the aim of enhancing the tracking accuracy. To strengthen the system’s robustness and guarantee the reachability of the sliding surface while reducing chattering, the supertwisting algorithm (ST) is used. Closed-loop stability and state convergence are guaranteed via the Lyapunov theory. Some experimental flight tests have been executed to validate the workability of the developed flight control. The obtained results prove that the recommended technique can provide improved performance in terms of stabilization and tracking precision.

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.

Comparative Analysis of Force and Eddy Current Position Sensing Approaches for a Magnetic Levitation Platform with an Exceptional Hovering Distance

This paper provides a comparative analysis between a force sensor and an eddy current sensor, focusing on their usability to determine the position of a circular levitating permanent magnet (PM) mover within an axially symmetric magnetic levitation platform (MLP) with an exceptionally large air gap. The sensors enable closed-loop control, which is essential for accurately and stably maintaining the mover’s radial position. For the considered MLP, a change in radial position in principle results in a tilting of the mover, i.e., a deviation from the parallel alignment relative to the stator. As both the radial position and the tilting angle affect the sensors’ (force and eddy current) output voltage, an observer must deduce the radial position from the output sensor’s voltage, requiring a comprehensive MLP dynamic model and calibration of the models for both sensing approaches. The paper discusses the advantages and weaknesses of each sensor concept, exploring operational principles and performance in levitation tests. The force sensor exhibits versatility, proving functional across various application scenarios, such as when the mover is sealed in a conductive, non-magnetic chamber. In contrast, due to its high-frequency operation, the eddy current sensor is more straightforward to characterize, simplifying its behavior relative to the mover’s slower dynamics. Measurements are conducted to validate the models, showing the eddy current sensor’s robustness against disturbances and imperfections in the MLPs and its immunity to cross-axis interference. Conclusively, in levitation experiments where the mover is vertically distanced at 104 mm from the stator, the eddy current sensor achieves a position tracking precision about ten times better and a signal-to-noise ratio (SNR) ten times higher compared to the off-the-shelf force sensor, confirming its better performance and reliability; however, it cannot be used in applications where conductive objects are present in the air gap. Furthermore, additional experiments are conducted on the MLP using the eddy current sensor to show the controller’s robustness and dynamic reference tracking capability, with and without a payload.

Vehicle Multi-Object Detection and Tracking Algorithm Based on Improved You Only Look Once 5s Version and DeepSORT

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads.

Design and analysis of shadow detection and removal scheme in real-time still images

This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.

SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention

The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale information, the accuracy and speed of such tracking algorithms still need to be improved in complex environments such as rapid motion and illumination variation. In response to the above problems, we propose SSTrack, an object tracking algorithm based on spatial scale attention. We use dilated convolution branch and covariance pooling to build a spatial scale attention module, which can extract the spatial and scale information of the target object. By embedding the spatial scale attention module into Swin Transformer as the backbone, the ability to extract local detailed information has been enhanced, and the success rate and precision of tracking have been improved. At the same time, to reduce the computational complexity of self-attention, Exemplar Transformer is applied to the encoder structure. SSTrack achieved 71.5% average overlap (AO), 86.7% normalized precision (NP), and 68.4% area under curve (AUC) scores on the GOT-10k, TrackingNet, and LaSOT. The tracking speed reached 28fps, which can meet the need for real-time object tracking.

Repeatability and reproducibility of prostate apparent diffusion coefficient values on a 1.5 T magnetic resonance linear accelerator

Background and PurposeIntegrated magnetic resonance linear accelerator (MR-Linac) systems offer potential for biologically based adaptive radiation therapy using apparent diffusion coefficient (ADC). Accurate tracking of longitudinal ADC changes is key to establishing ADC-driven dose adaptation. Here, we report repeatability and reproducibility of intraprostatic ADC using deformable image registration (DIR) to correct for inter-fraction prostate changes. Materials and MethodsThe study included within-fraction repeat ADC measurements for three consecutive fractions for 20 patients with prostate cancer treated on a 1.5 T MR-Linac. We deformably registered successive fraction T2-weighted images and applied the deformation vector field to corresponding ADC maps to align to fraction 2. We delineated gross tumour volume (GTV), peripheral zone (PZ) and prostate clinical target volume (CTV) regions-of-interest (ROIs) on T2-weighted MRI and copied to ADC maps. We computed intraclass correlation coefficients (ICC) and percent repeatability coefficient (%RC) to determine within-fraction repeatability and between-fraction reproducibility for individual voxels, mean and 10th percentile ADC values per ROI. ResultsThe ICC between repeats and fractions was excellent for mean and 10th percentile ADC in all ROIs (ICC > 0.86), and moderate repeatability and reproducibility existed for individual voxels (ICC > 0.542). Similarly, low %RC within-fraction (4.2–17.9 %) mean and 10th percentile ADC existed, with greater %RC between fractions (10.2–36.8 %). Higher %RC existed for individual voxel within-fraction (21.7–30.6 %) and between-fraction (32.1–34.5 %) ADC. ConclusionsResults suggest excellent ADC repeatability and reproducibility in clinically relevant ROIs using DIR to correct between-fraction anatomical changes. We established the precision of voxel-level ADC tracking for future biologically based adaptation implementation.

Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems

In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV’s performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter’s presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.

Space dynamic target tracking method based on five-frame difference and Deepsort

For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.

Probabilistic 3D motion model for object tracking in aerial applications

AbstractVisual object tracking, crucial in aerial applications such as surveillance, cinematography, and chasing, faces challenges despite AI advancements. Current solutions lack full reliability, leading to common tracking failures in the presence of fast motions or long‐term occlusions of the subject. To tackle this issue, a 3D motion model is proposed that employs camera/vehicle states to locate a subject in the inertial coordinates. Next, a probability distribution is generated over future trajectories and they are sampled using a Monte Carlo technique to provide search regions that are fed into an online appearance learning process. This 3D motion model incorporates machine‐learning approaches for direct range estimation from monocular images. The model adapts computationally by adjusting search areas based on tracking confidence. It is integrated into DiMP, an online and deep learning‐based appearance model. The resulting tracker is evaluated on the VIOT dataset with sequences of both images and camera states, achieving a 68.9% tracking precision compared to DiMP's 49.7%. This approach demonstrates increased tracking duration, improved recovery after occlusions, and faster motions. Additionally, this strategy outperforms random searches by about 3.0%.

Generating Frequency Selective Vibrations in Remote Moving Magnets

Extensive efforts in providing upper limb amputees with sensory feedback have primarily focused on the restoration of tactile capabilities, while challenges in evoking proprioceptive sensations have been poorly addressed. Previously, an human–machine interface (HMI) was proposed based on permanent magnets implanted in residual muscles of an amputee, namely the myokinetic interface, to control robotic limb prostheses. Besides control, implanted magnets offer an unprecedent opportunity to trigger musculotendon proprioceptors via untethered selective vibrations. Herein, the challenge of tracking multiple moving magnets is addressed (e.g., following muscle contractions) while being vibrated by controlled magnetic fields produced by external coils. Results demonstrate the viability of a real‐time (RT) system capable of simultaneously tracking and vibrating multiple moving magnets within a three‐dimensional workspace. Highly selective torsional vibrations in the frequency span eliciting movement illusions (70, 80, and 90 Hz) are achieved on two moving magnets, with efficiencies above 0.82 (over 80% of spectral power at the desired frequency). Tracking accuracy and precision remain robust to the coil magnetic field, with position median errors below 1.2 mm and median displacement errors below 0.95 mm. This study represents a crucial step towards the development of a bench system to study proprioception in humans.

Deep Reinforcement Learning for Load Frequency Control in Isolated Microgrids: A Knowledge Aggregation Approach with Emphasis on Power Symmetry and Balance

To address the issues of instability and inefficiency that the fluctuating and uncertain characteristics of renewable energy sources impose on low-carbon microgrids, this research introduces a novel Knowledge-Data-Driven Load Frequency Control (KDD-LFC) approach. This advanced strategy seamlessly combines pre-existing knowledge frameworks with the capabilities of deep learning neural networks, enabling the adaptive management and multi-faceted optimization of microgrid functionalities, with a keen emphasis on the symmetry and equilibrium of active power. Initially, the process involves the cultivation of foundational knowledge through established methodologies to augment the reservoir of experience. Following this, a Knowledge-Aggregation-based Proximal Policy Optimization (KA-PPO) technique is employed, which proficiently acquires an understanding of the microgrid’s state representations and operational tactics. This strategy meticulously navigates the delicate balance between the exploration of new strategies and the exploitation of known efficacies, ensuring the harmonization of frequency stability, precision in tracking, and the optimization of control expenditures through the strategic formulation of the reward function. The empirical validation of the KDD-LFC method’s effectiveness and its superiority are demonstrated via simulation tests conducted on the load frequency control (LFC) framework of the Sansha isolated island microgrid, which is under the administration of the China Southern Grid.

UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.

Development of AI-based process controller of sour water treatment unit using deep reinforcement learning

BackgroundDue to the variability in the feedstock conditions and the nonlinearity of the sour water stripping process, determining the optimal operating conditions for Sour Water Treatment Unit (SWTU) is a huge challenge. MethodsIn this study, we propose an AI-Based Process Controller (AIPC) for optimizing the SWTU, combining deep reinforcement learning (DRL) and expert knowledge. A surrogate model of an industrial SWTU digital twin was developed to serve as the environment for DRL. A reward function was designed and compared with others for evaluation. A method for seamless switching was devised to guarantee uninterrupted device operation by preventing any interference from the policy network. Significant FindingsIn contrast to the alternative control schemes, the AIPC not only demonstrates superior performance in mitigating overshooting and enhancing setpoint tracking precision but achieves a reduction in stripping steam usage. The proposed method has great potential in the field of real-time optimization.

Fixed-Time Sliding Mode Control for Linear Motor Traction Systems with Prescribed Performance

In this research, we propose a fixed-time sliding mode controller using a prescribed performance control approach to address the speed tracking problem in linear motor traction systems, which are powered by high-power permanent magnet linear synchronous motors (PMLSMs). Initially, to tackle the issue of the convergence time and dynamic response associated with traditional finite-time sliding mode controllers, we introduce a fixed-time sliding mode controller. This controller guarantees that the system state converges to the origin within a specified upper time limit. Subsequently, to enhance the dynamic response of the PMLSM and minimize speed errors, we integrate the prescribed performance control strategy with a fixed-time sliding mode controller. This effectively limits the motor’s speed error within the predefined function boundaries, reduces system overshoo, and mitigates system jitter to a certain degree. Finally, simulation results are presented to validate that the proposed control strategy significantly enhances precision of speed tracking in PMLSMs.

Neuroadaptive Tracking Control of Affine Nonlinear Systems Using Echo State Networks Embedded With Multiclustered Structure and Intrinsic Plasticity.

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.