Efficient Tracking Research Articles

Optimization of logistics cargo tracking and transportation efficiency based on data science deep learning models

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. These research results provide important support to further promote the intelligent development of the logistics industry.

An Innovative AgNP-based Solar Panel Coating and Farmland Fertility Optimization (FFO) based Power Extraction Methodology for Grid Systems

In the power electronic system, coated solar panels attracted a lot of interest in present times. The proposed work aims to achieve two key objectives: maximal power extraction and solar panel coating. To reduce the cost of coating material for solar panels, Silver Nano Particles (AgNPs) are first collected from the leaves of Rose periwinkle plants. This strategy aims to achieve maximal power extraction by coating solar panels with green synthesized silver nanoparticles. To reduce the cost of coating material, Rosy periwinkle plant leaves are used to synthesize silver nanoparticles or AgNPs. To ascertain the framework's capacity for measuring energy both before and after the panels are coated with AgNP, this study theoretically analyses the data. The power current and voltage-current characteristics of the study were validated, enabling an examination of the study's effectiveness. The coated type outperformed the normal solar panel by 2%, according to the results. With a new approach called Farmland Fertility Optimization – Maximum Power Position Tracking, the precise peak site for increased energy yield is discovered. The bi-directional converter is also utilized to mitigate stress and increase voltage gain. To improve the power quality with fewer harmonics, the 3-phase inverter and the LC filtering circuits are used. Finally, a variety of performance measures are used to confirm the results of coated solar panels using power-tracking control techniques. The findings suggest that AgNP-coated solar panels provide the best possible electrical energy with improved voltage, current, and power quality. Performance evaluation shows that the coated solar panel's power tracking efficiency has increased to 99% with decreased harmonics of 2.52%.

Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency

Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.

Development of a multi-level feature fusion model for basketball player trajectory tracking

To solve the problems of low matching degree, long tracking time, and low accuracy of multi-target tracking in the process of athlete motion trajectory tracking using deep learning technology, a new athlete motion trajectory tracking model was proposed in this study. The study first optimized the current object detection algorithm in basketball, utilized a hybrid attention mechanism to extract object features, and improved the non-maximum suppression strategy. Then, a hybrid branch network was introduced to improve the residual network and a new athlete identity recognition model was proposed. Finally, a new trajectory tracking model was designed by combining the object detection model and the athlete identity recognition model. The research results indicated that in the object detection experiment, the detection time of the proposed object detection algorithm was always below 0.4 s, and its average accuracy reached up to 0.63. In trajectory tracking testing, the final built tracking model had a multi-target tracking accuracy of up to 0.98, and its tracking overlap rate was as low as 0.02. This study has the following two contributions. Firstly, a new model of athlete trajectory tracking is proposed, which improves the accuracy and efficiency of multi-target tracking by optimizing object detection algorithm and introducing hybrid branch network. Second, the model has excellent performance in both object detection and track tracking, which can not only provide a new solution for athletes' motion trajectory tracking, but also significantly improve the effect of motion tracking.

Model-based predictive greenhouse parameter control of aquaponic system.

The effectiveness of an aquaponic system significantly relies on the habitat provided for both the fish and plants. As an integral component of aquaponics, hydroponic cultivation benefits greatly from the controlled environment of a greenhouse. Within this environment, factors such as temperature, carbon dioxide levels, humidity, and light can be carefully adjusted to maximize plant growth and development. This precise regulation ensures an ideal growing environment, fostering the flourishing of plants and contributing to the overall success of the aquaponic ecosystem. This study presented a control approach for an aquaponic greenhouse system. It aims to keep the greenhouse climate parameters (temperature, CO2 concentration, and humidity) at their ideal levels. The proposed control strategy is a two-layered mechanism in which the first layer presents an optimization framework using particle swarm optimization (PSO) algorithm to give the setpoints for the controller, and the second layer demonstrates a constrained discrete model predictive control (CDMPC) strategy to maintain the desired trajectories received from the optimization layer. To validate the results obtained using PSO, this study incorporates genetic algorithms (GA) and assesses their performance in comparison. Given similar computational efficiency and low computational time for both algorithms, the optimal values identified by particle swarm optimization (PSO) are adopted as the setpoints. Two performance criteria, relative average deviation (RAD) and mean relative deviation (MRD), are derived to evaluate the tracking performance of the proposed CDMPC controller under external disturbances. A comparison of the proposed CDMPC with the PI controller is also offered. According to the comparison results, our proposed CDMPC performs better than the PI controller with lower RAD values (temperature, 1.1315; CO2 concentration, 0.9225; humidity, 2.547) and MRD values (temperature, 0.315; CO2 concentration, 0.25; humidity: 1.013). The controller is validated to be efficient by its strong control performance, highlighted by robustness, efficient setpoint tracking, and adequate disturbance rejection. This novel approach might prove to be a useful technique for developing environmental control strategies that can be used for potentially boosting production rates of aquaponic greenhouse systems, maximizing profitability, and reducing labor needs. By maintaining optimal conditions, it can enhance ecosystem health, improve yields, and streamline operations, paving the way for greater system performance and sustainability.

Optimizing Vehicle Detection and Tracking Efficiency Through YOLO-Based Multi-Objective Approach

In traffic management, a careful detection of cars plays and monitors a vital role in ensuring safety and efficiency. This paper proposes a way to enhance the discovery of vehicles by dividing roads into remote areas and closing. The newly developed approach aims to define a vehicle and calculate vehicles in both fields. Yolov5M network is easy to discover and localize vehicles, exceed the traditional speed tracking methods and enable microorganisms. The proposed form achieves a higher accuracy of 0.833, outperform the Yolov5 Standard Form of 0.67. It also demonstrates the performance enhancement in the results of summons and mean average precision by keeping the training parameters reduction trend the same. Technically speaking, the split of the roads surface is divided into sections using the latest retail techniques and the fine tuning of the Yolov5M network to positively affect the detection and classification of vehicles.

An efficient object tracking based on multi‐head cross‐attention transformer

AbstractObject tracking is an essential component of computer vision and plays a significant role in various practical applications. Recently, transformer‐based trackers have become the predominant method for tracking due to their robustness and efficiency. However, existing transformer‐based trackers typically focus solely on the template features, neglecting the interactions between the search features and the template features during the tracking process. To address this issue, this article introduces a multi‐head cross‐attention transformer for visual tracking (MCTT), which effectively enhance the interaction between the template branch and the search branch, enabling the tracker to prioritize discriminative feature. Additionally, an auxiliary segmentation mask head has been designed to produce a pixel‐level feature representation, enhancing and tracking accuracy by predicting a set of binary masks. Comprehensive experiments have been performed on benchmark datasets, such as LaSOT, GOT‐10k, UAV123 and TrackingNet using various advanced methods, demonstrating that our approach achieves promising tracking performance. MCTT achieves an AO score of 72.8 on the GOT‐10k.

Bug Tracker User Guide

ABSTRACT___ The Bug Tracking Tool project addresses the pressing need for efficient bug tracking and management in contemporary software development. By leveraging modern web technologies and agile methodologies, the project aims to develop a comprehensive solution that streamlines bug identification, tracking, and resolution processes. Through a literature survey, key observations and shortcomings in existing bug tracking tools were identified, guiding the proposed work. The project’s methodology encompasses requirements gathering, design, implementation, testing, and deployment, focusing on user-centric design and functionality. Preliminary results demonstrate progress towards achieving project objectives, highlighting the tool’s potential to enhance software quality assurance practices and user satisfaction. Despite encountering challenges such as technical complexity and resource constraints, the project lays a solid foundation for future enhancements and optimizations. Overall, the Bug Tracking Tool project represents a significant step towards improving bug tracking practices and delivering higher-quality software products to end-users INDEX TEARMS – HTML, CSS , JavaScript, Python, Django Framework, SQLlite.

Eddy Current Sensor Array for Electromagnetic Sensing and Crack Reconstruction with High Lift-Off in Railway Tracks.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

Kirigami enabled reconfigurable three-dimensional evaporator arrays for dynamic solar tracking and high efficiency desalination.

A kirigami-engineered composite hydrogel membrane is exploited for the construction of three dimensional (3D) solar-tracking evaporator arrays with outstanding evaporation performance and salt tolerance. The hybrid nanofiber network in the hydrogel membrane offers favorable water transport dynamics combined with excellent structural robustness, which are beneficial for the engineering of 3D dynamic structures. Periodic triangular cuts patterned into the membrane allow formation and reconfiguration of 3D conical arrays controlled by uniaxial stretching. With these structures, the tilt angles of the membrane surface are actively tuned to follow the solar trajectory, leading to a solar evaporation rate ~80% higher than that of static planar devices. Furthermore, the tapered 3D flaps and their micro-structured surfaces are capable of localized salt crystallization for prolonged solar desalination, enabling a stable evaporation rate of 3.4 kg m-2 hour-1 even in saturated brine. This versatile design may facilitate the implementation of solar evaporators for desalination and provide inspirations for other soft functional devices with dynamic 3D configurations.

Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

Path Planning Algorithm of Orchard Fertilization Robot Based on Multi-Constrained Bessel Curve

Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots.

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

Design and development of new charge controller for GPS tracker for electric vehicles

This investigation introduces a new charge controller for GPS trackers, with the objective of offering long-lasting and efficient tracking solutions. The article emphasizes the widespread use of GPS trackers in various sectors, as well as the critical need for sustainable power sources to maintain their effectiveness. The charge controller ensures optimal performance and durability by meticulously choosing components. Experimental investigations confirm the efficacy of the charge controller to deliver a steady power supply to the GPS tracker. The integration of GPS technology with solar PV charging allows for continuous monitoring. The results show that the solar photovoltaic (PV) system reached a peak output voltage of 10 V, ensuring that the battery remained at a stable level of 12.4 V and continually providing the GPS tracker with a steady 5 V. These findings validate the system's capacity to deliver dependable and enduring electricity, signifying notable progress in remote monitoring, asset tracking, and fleet management.

Exploring Blockchain for Nuclear Material Tracking: A Scoping Review and Innovative Model Proposal

Ensuring safe and transparent tracking of nuclear materials in the modern era is critical for global security and compliance with international regulations. Blockchain technology, a decentralized and immutable ledger, offers a new approach to recording transactions, increasing trust without intermediaries. In this study, it was investigated whether nuclear material tracking was performed with advanced technology blockchain from past to present; it was seen that there needed to be a study on this subject in the literature, and that there was a gap. Search results proving this are presented. The authors present a model that can enable nuclear material tracking with blockchain technology, which will create a solid structure for recording and verifying every process step in the nuclear supply chain, from the creation of the first product to destruction. This model discusses how nuclear materials, which are very important to track from the beginning until they become waste, can be tracked with blockchain technology, and the contributions they can make nationally and internationally are explained. As a result of the research, it is shown that blockchain technology has the potential to pave the way for more resilient and precise nuclear supply chains by significantly increasing the security and efficiency of nuclear material tracking.

An Efficient Multi-Object Tracking Guided by Spatial Clustering on Vision Sensors

An Efficient Multi-Object Tracking Guided by Spatial Clustering on Vision Sensors

On-grid optimal MPPT for fine-tuned inverter based PV system using golf optimizer considering partial shading effect

Improving photovoltaic systems to better withstand variations in temperature and irradiance, lessen steady-state ripples, maintain high efficiency, require less tracking time, and have minimal complexity is becoming increasingly important. The PV array's overall power output is negatively impacted by partial shading because the shaded cells produce less power and consume less power overall, which lowers efficiency and creates localized hotspots. Although bypass diodes may be used to counteract these impacts by rerouting current around cells that are shaded, they can result in several peaks, which complicates maximum power point tracking (MPPT). To manage several peaks and optimize power output, metaheuristic algorithms are compulsory. A recently developed game-based optimization methodology known as Golf Optimization Approach (GOA) is used in this study to find a way to MPPT in a grid-connected photovoltaic (PV) system under partial shading conditions (PSCs). Additionally, the same suggested approach is applied to get optimal parameters for the inverter with the goal of optimizing its voltage and current regulators. It aims to evaluate a plan to lower the output voltage and current's total harmonic distortion and DC voltage error. The proposed technique is contrasted to other tracking approaches such as the traditional Perturb & Observe (P&O), Gray Wolf Optimization (GWO), and Sea-gull Optimization Approach (SOA). Simulation is performed in MATLAB R2022b/SIMULINK environment. The obtained outcomes demonstrate the ability of the suggested technique to enhance the utility grid's power quality and improve the tracking system under various partial shading effects and temperature alterations. The proposed technique achieves a tracking efficiency of 99.8 %, surpassing the effectiveness of both of the previous methods which are at 97.2 % and 96.7 %.

Use of Yolo Detection for 3D Pose Tracking of Cardiac Catheters Using Bi-Plane Fluoroscopy

The increasing rate of minimally invasive procedures and the growing prevalence of cardiovascular disease have led to a demand for higher-quality guidance systems for catheter tracking. Traditional methods for catheter tracking, such as detection based on single points and applying masking techniques, have been limited in their ability to provide accurate pose information. In this paper, we propose a novel deep learning-based method for catheter tracking and pose detection. Our method uses a Yolov5 bounding box neural network with postprocessing to perform landmark detection in four regions of the catheter: the tip, radio-opaque marker, bend, and entry point. This allows us to track the catheter’s position and orientation in real time, without the need for additional masking or segmentation techniques. We evaluated our method on a dataset of fluoroscopic images from two distinct datasets and achieved state-of-the-art results in terms of accuracy and robustness. Our model was able to detect all four landmark features (tip, marker, bend, and entry) used to generate a pose for a catheter with 0.285 ± 0.143 mm, 0.261 ± 0.138 mm, 0.424 ± 0.361 mm, and 0.235 ± 0.085 mm accuracy. We believe that our method has the potential to significantly improve the accuracy and efficiency of catheter tracking in medical procedures that utilize bi-plane fluoroscopy guidance.

An alternative approach to efficiency tracking in thorium determination in fertilizers by alpha spectrometry

Fertilizers play a key role in increasing crop yield per unit land answering the growing demand for food production. However, excessive or improper use of fertilizers can lead to several environmental issues including soil contamination. One of the known contaminants attributed to fertilizers are radionuclides. The goal of this study was to determine the concentration of thorium isotopes in several types of commonly used fertilizers produced in Poland. The methodology included the use of an alternative tracer (namely 228Th) to evaluate chemical recovery. The correctness of the proposed method was confirmed by analyzing certified reference materials. The obtained results showed that the activity concentration of 232Th was ranged from <0.34 Bq kg−1 for nitrogenous fertilizer up to 97 ± 22 Bq kg−1 for pure phosphate fertilizer.

UAV data and deep learning: efficient tools to map ant mounds and their ecological impact

AbstractHigh‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.