Tracking Scheme Research Articles

A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images

Abstract Multi-threshold image segmentation (MTIS) is a crucial technology in image processing, characterized by simplicity and efficiency, and the key lies in the selection of thresholds. However, the method's time complexity will grow exponentially with the number of thresholds. To solve this problem, an improved arithmetic optimization algorithm (ETAOA) is proposed in this paper, an optimizer for optimizing the process of merging appropriate thresholds. Specifically, two optimization strategies are introduced to optimize the optimal threshold process: elite evolutionary strategy (EES) and elite tracking strategy (ETS). First, to verify the optimization performance of ETAOA, mechanism comparison experiments, scalability tests, and comparison experiments with nine state-of-the-art peers are executed based on the benchmark functions of CEC2014 and CEC2022. After that, to demonstrate the feasibility of ETAOA in the segmentation domain, comparison experiments were performed using ten advanced segmentation methods based on skin cancer dermatoscopy image datasets under low and high thresholds, respectively. The above experimental results show that the proposed ETAOA performs outstanding optimization compared with benchmark functions. Moreover, the experimental results in the segmentation domain show that ETAOA has superior segmentation performance under low and high threshold conditions.

Power tracking and control strategy of regional power grid with high permeability of renewable energy

Power tracking and control strategy of regional power grid with high permeability of renewable energy

A detail tracking strategy analysis of a compound parabolic collector with a heat–pipe receiver based on three–dimensional ray–tracing model

A detail tracking strategy analysis of a compound parabolic collector with a heat–pipe receiver based on three–dimensional ray–tracing model

Analysis on Management Strategies and Digital Transformations with the Help of Web Tracking Tools at DTDC, Bangalore

This study examines how DTDC, a leading logistics and courier company, has utilized web tracking tools to implement digital transformation strategies. The paper explores the integration of management strategies with cutting-edge digital tools to enhance operational efficiency, customer satisfaction, and decision-making. By analysing the role of web tracking systems, this paper sheds light on their impact on organizational agility, data-driven culture, and market competitiveness. The evaluation of digital transformers with web tracking practices are crucial due to the city's role as a major commercial hub in India, characterized by high volume shipping demands and complex logistics networks. Web tracking strategies in this region are imperative for maintaining timely delivery schedules, optimizing inventory turnover, and minimizing operational costs. This study delves into the various aspects of chi square tools examining how strategies such as layout optimization, technological integration, workforce management, and supply chain coordination contribute to enhancing organizational efficiency. Keywords: Digital Transformation, Web Tracking Tools, Management Strategies, DTDC Logistics, Operational Efficiency, Customer Experience, Data Analytics.

Straight-Line Trajectory Tracking Control of Unmanned Sailboat Based on NMPC Velocity and Heading Joint Control

This study proposes a trajectory tracking approach for unmanned sailboats that integrates velocity and heading control using nonlinear model predictive control (NMPC). Unlike conventional methods, which typically rely on separate control strategies for maximum velocity and heading, this study employs a joint control framework based on NMPC. This approach allows for constrained control, ensuring that the sailboat operates safely at the desired velocity, forming a foundation for trajectory tracking. The trajectory tracking strategy, built on the joint control of velocity and heading, is further categorized into upwind and non-upwind tracking based on the wind direction. For non-upwind tracking, the desired heading is determined using the Line-of-Sight (LOS) navigation method, while the desired velocity is calculated through a backstepping method grounded in the Lyapunov stability theorem. For upwind tracking, a zigzag strategy is introduced, using the maximum lateral error to switch headings and ensure that the sailboat remains close to the trajectory. The simulation results show that the proposed method can effectively control the velocity and heading and realize accurate trajectory tracking of the unmanned sailboat under different wind conditions. The average error of velocity and heading control is close to zero. During trajectory tracking, the maximum control error is less than 0.35%.

Lighting Up and Identifying Metal-Binding Proteins in Cells.

Metal ions, either essential or therapeutic, play critical roles in life processes or in the treatment of diseases. Proteins and enzymes are involved in metal homeostasis and the action of metallodrugs. Imaging and identifying these metal-binding proteins will facilitate the elucidation of metal-mediated life processes. The emerging research field of metallomics and metalloproteomics has significantly advanced our understanding of metal homeostasis and the roles that metals play in biology and medicine. Fluorescence-based metalloproteomics offers the possibility of not only visualization but also identification of metal-binding proteins in living cells and tissues. Herein, we summarize different strategies of labeling and tracking of metal-binding proteins with the aid of fluorescent probes. We highlight several examples as showcases of how this fluorescence-based metalloproteomics approach could be utilized in metallobiology and chemical biology. In conclusion, we also discuss the advantages and limitations of fluorescence-based metalloproteomics approaches and point out future directions of metalloproteomics including development of more sensitive and selective fluorescence probes, integration with other omics approaches, as well as application of emerging advanced super-resolution imaging techniques that utilize fluorescent molecules or proteins. We aim to attract more scientists to engage in this exciting field.

DSiam-CnK: A CBAM- and KCF-Enabled Deep Siamese Region Proposal Network for Human Tracking in Dynamic and Occluded Scenes.

Despite the accuracy and robustness attained in the field of object tracking, algorithms based on Siamese neural networks often over-rely on information from the initial frame, neglecting necessary updates to the template; furthermore, in prolonged tracking situations, such methodologies encounter challenges in efficiently addressing issues such as complete occlusion or instances where the target exits the frame. To tackle these issues, this study enhances the SiamRPN algorithm by integrating the convolutional block attention module (CBAM), which enhances spatial channel attention. Additionally, it integrates the kernelized correlation filters (KCFs) for enhanced feature template representation. Building on this, we present DSiam-CnK, a Siamese neural network with dynamic template updating capabilities, facilitating adaptive adjustments in tracking strategy. The proposed algorithm is tailored to elevate the Siamese neural network's accuracy and robustness for prolonged tracking, all the while preserving its tracking velocity. In our research, we assessed the performance on the OTB2015, VOT2018, and LaSOT datasets. Our method, when benchmarked against established trackers, including SiamRPN on OTB2015, achieved a success rate of 92.1% and a precision rate of 90.9%. On the VOT2018 dataset, it excelled, with a VOT-A (accuracy) of 46.7%, a VOT-R (robustness) of 135.3%, and a VOT-EAO (expected average overlap) of 26.4%, leading in all categories. On the LaSOT dataset, it achieved a precision of 35.3%, a normalized precision of 34.4%, and a success rate of 39%. The findings demonstrate enhanced precision in tracking performance and a notable increase in robustness with our method.

Vulnerability Management Best Practices: Developing Strategies for Tracking and Remediating Vulnerabilities within Defined SLAs

Vulnerability Management Best Practices: Developing Strategies for Tracking and Remediating Vulnerabilities within Defined SLAs

Inductor Current-Based Control Strategy for Efficient Power Tracking in Distributed PV Systems

This paper presents an inductor current-based maximum power point tracking (IC-MPPT) strategy and a single-inductor multi-input single-output (SI-MISO) structure with energy storage battery for distributed photovoltaic (PV) systems. In this study framework, the duty cycle of each PV channel can be controlled independently based on the presented IC-MPPT strategy, and the components/sensors costs are reduced through the presented SI-MISO PV system structure. In addition, a model predictive control (MPC) method is presented to regulate DC bus voltage, by controlling the bidirectional converter in the battery circuit. The presented control strategies have been rigorously derived and experimentally validated, and the experimental results demonstrate that each PV module can rapidly and efficiently track to the maximum power point in less than 0.016 s, while the bus voltage is stabilized near the set value, with an overshoot of less than 2.6%.

Adaptive Disturbance Rejection and Power Smoothing Control for Offshore Hydraulic Wind Turbines Based on Pitch and Motor Tilt Angles

This paper investigates an adaptive disturbance rejection control (ADRC) strategy for dual-variable power smoothing for hydraulic wind turbine systems deployed in marine environments. Initially, fluctuations in wind speed induce variations in the output torque and rotational speed of the wind turbine; this study examines the interaction between these two variables and subsequently decouples them. An innovative dual-variable anti-disturbance control strategy is proposed, which independently regulates the pitch angle of the rotor and the swing angle of the variable motor to mitigate fluctuations in both speed and torque, thereby achieving a smoother system output power. The simulation results obtained through MATLAB/Simulink (Version R2022a) indicate that employing the proposed control strategy leads to an 8.31% reduction in power generation compared to optimal power tracking strategies while enhancing output power stability by 56.67%. Furthermore, the effective smoothing of power fluctuations is accomplished without necessitating energy storage devices. Finally, the effectiveness of the power smooth output control strategy proposed in this paper was verified based on a semi-physical simulation experimental platform for a 30 kW hydraulic wind turbine. The control method proposed in this paper provides a theoretical basis for the promotion and application of hydraulic wind turbines with stable power output.

Red flags for potential serious pathologies in people with neck pain: a systematic review of clinical practice guidelines.

We conducted a systematic review of clinical practice guidelines to identify red flags for serious pathologies in neck pain mentioned in clinical practice guidelines, to evaluate agreement in red flag recommendations across guidelines, and to investigate the level of evidence including what study type the recommendations are based on. We searched for guidelines focusing on specific and nonspecific neck pain in MEDLINE, EMBASE, and PEDro up to June 9, 2023. Additionally, we searched for guidelines through citation tracking strategies, by consulting experts in the field, and by checking guideline organization databases. We included 29 guidelines, 12 of which provided a total of 114 red flags for fracture (n = 17), cancer (n = 21), spinal infection (n = 14), myelopathy (n = 15), injury to the spinal cord (n = 1), artery dissection (n = 7), intracranial pathology (n = 3), inflammatory arthritis (n = 2), other systemic disease (n = 6), or unrelated to a specific condition (n = 19). Overall, there is very little agreement (median Fleiss' kappa of 0) between guidelines on the red flags to screen for serious pathologies. Red flags were mainly supported by expert opinions. We also observed a general lack of consensus among guidelines regarding which red flags to endorse. Considering the current limitations of the evidence, specific recommendations on which red flags to use cannot be provided, except for using the Canadian C-Spine rule for screening posttraumatic fractures.

Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

Discrete‐Time Integral Fast Terminal Sliding Mode Predictive Control for Dynamic Positioning Ships With Lumped Uncertainties and Input Constraints

ABSTRACTThis paper proposes a robust discrete‐time integral fast terminal sliding mode predictive control (DIFTSMPC) scheme for DP ship trajectory tracking under the consideration of unmodeled dynamics, environmental disturbance, and input constraints. An improved single closed‐loop control strategy is designed by adopting the earth‐fixed reference velocity as the virtual velocity. The improved nonlinear PID discrete‐time integral fast terminal sliding mode is designed to achieve faster convergence. The unknown nonlinear dynamics and the environmental disturbance are combined as a lumped uncertainty term and estimated using the one‐step delay observation method. Then the corrected sliding model state is optimized to track the reference sliding reaching law in the prediction horizon. The lumped uncertainty estimation is integrated into the sliding mode prediction to guarantee the robustness of the control scheme. The stability of the proposed scheme has been verified. Comparison results demonstrate the effectiveness and superiority in chattering suppressing and constraint handling of the proposed scheme.

Framework for abnormal event detection and tracking based on effective sparse factorization strategy

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.

Lifelong Learning-Based Optimal Trajectory Tracking Control of Constrained Nonlinear Affine Systems Using Deep Neural Networks.

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate the problem of catastrophic forgetting in the multitasking systems. Additionally, the proposed optimal framework addresses state constraints by utilizing a time-varying barrier function (TVBF). The uniform ultimate boundedness (UUB) of the overall closed-loop system is shown using the Lyapunov stability analysis. A two-link robotic manipulator that compares to recent literature shows a 47% total cost reduction, demonstrating the effectiveness of the proposed method.

Optimal parameter identification of adaptive fuzzy logic MPPT based-bald eagle search optimization algorithm to boost the performance of PEM fuel cell

The output power of proton exchange membrane (PEM) fuel cell is dependent on operating temperature and the membrane water content. Changing the operation condition changes the location maximum power point (MPP) on the nonlinear curve of power versus the current. Therefore, a robust MPP tracking strategy is obligatory to grantee the work of PEM fuel cell around or close the MPP and boost the harvested energy. The suggested tracker is based on the bald eagle search (BES) optimization algorithm to identify the best values to entirely advantage the inherent flexibility of fuzzy logic control (FLC) system furthermore to accomplish rapid and precise tracking. Throughout the optimization procedure, the gains of membership functions of FLC are assigned to be decision variables, while the integral of error is the objective function. To approve the advantage of BES, a comparison with gradient-based optimizer (GBO), osprey optimization algorithm (OOA), particle swarm optimization (PSO), pelican optimization algorithm (POA), blue monkey algorithm (BMA) and henry gas solubility optimization (HGS) is performed. The BES has the highest performance compared with other optimizers; it achieves the best value of 1.7638 flowed by GBO (1.7667) whereas the worst value of 1.7987 is obtained by POA. Additionally, the lowest STD of 0.0046 is achieved by BES followed by GBO (0.0078) whereas the worst value of STD of 0.0243 is obtained by HGS. Finally, the optimized FLC-MPPT-based BES is compared with commonly MPPTs including classical FLC and perturb & observe in terms of dynamic and steady state conditions. The comparison demonstrated that the suggested FLC-MPPT-based BES has fast tracking speed and diminishes the oscillations around MPP in steady state.

A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application

Ultra-short-term wind power forecasting (UWPF) is crucial for the large-scale grid connection of wind energy. The state grid in China has strict multi-step forecasting requirements, which pose challenges to on-site efficiency and accuracy. This paper proposes a comprehensive framework of the hybrid method for UWPF with on-site application, consisting of data decomposition, model prediction, and post-processing. In the first stage, rolling decomposition and feature reconstruction are employed to decompose the wind power data into sub-components without future information leakage. The feature extraction and model matching processes are then performed to make full use of different machine learning prediction models and distinct characteristics of wind power components. Finally, a novel error tracking strategy is proposed to enable real-time error correction for multi-step forecasting by capturing the fluctuation characteristics of wind power data. The proposed framework is evaluated on two field wind power datasets through comparative experiments with five benchmark methods and nine reference methods. The experimental results demonstrate that (a) Each module of the proposed method effectively contributes to improving forecasting accuracy. (b) The proposed method significantly outperforms traditional machine learning methods in both single-step and multi-step forecasts, indicating its capability to handle practical UWPF tasks effectively.

Steer-by-wire control algorithm using a dual-layer closed-loop model

Steer-by-wire (SBW) is a crucial chassis electronic control system. Accurate and rapid wheel angle control is key to enhancing system performance. This paper first establishes a dynamics model of the steering actuator. Then, a co-simulation vehicle model with CarSim and Simulink is built, and the feasibility of the SBW model is experimentally verified. Based on this, a front wheel angle compensation difference tracking strategy is proposed using the front wheel steering angle control method. This strategy compensates for the difference between the actual and ideal front wheel steering angles to achieve the desired front wheel angle progressively. A dual-layer controller is designed in the Simulink module and integrated with the CarSim module to achieve closed-loop feedback control of the front wheel angle. The upper layer controller employs a fuzzy PID control method to execute the front wheel angle compensation strategy, enabling two corrections of the front wheel angle and yaw rate. The lower layer controller corrects wheel angle information to improve control effects. Simulation results under double lane change and single lane change conditions show that the fuzzy PID controller with front wheel angle compensation significantly enhances vehicle steering stability. Notably, at high speeds, the peak yaw rate is lower, convergence speed is faster, and response time is shorter.

Tracking Small Extracellular Vesicles Using a Minimally Invasive PicoGreen Labeling Strategy.

Extracellular vesicles (EVs) are cell-secreted lipid bilayer delimited particles that mediate cellular communication. These tiny sacs of cellular information play an important role in cell communication and alter the physiological process under both normal and pathological conditions. As such, tracking EVs can provide valuable information regarding the basic understanding of cell communication, the onset of early malignancy, and biomarker discovery. Most of the current EV-tracking strategies are invasive, altering the natural characteristics of EVs by modifying the lipid bilayer with lipophilic dyes or surface proteins with fluorescent reporters. The invasive labeling strategies could alter the natural processes of EVs and thereby have major limitations for functional studies. Here, we report an alternative minimally invasive EV labeling strategy using PicoGreen (PG), a small molecule that fluoresces at 520 nm when bound to dsDNA. We show that PG binds to dsDNA associated with small EVs (50-200 nm), forming a stable and highly fluorescent PG-DNA complex in EVs (PG-EVs). In both 2D cell culture and 3D organoid models, PG-EV showed efficient tracking properties, including a high signal-to-noise ratio, time- and concentration-dependent uptake, and the ability to traverse a 3D environment. We further validated PG-EV tracking using dual-labeled EVs following two orthogonal labeling strategies: (1) Bioconjugation via surface amine labeling and (2) donor cell engineering via endogenously expressing mCherry-tetraspanin (CD9/CD63/CD81) reporter proteins. Our study has shown the feasibility of using PG-EV as an effective EV tracking strategy that can be applied for studying the functional role of EVs across multiple model systems.

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.