Precise Tracking Research Articles

A Blockchain-Based Model for Traceability and Quality Management of Raw Materials in Clothing Products

In this study, we employ blockchain-based traceability technology to construct a comprehensive model for raw material traceability and quality management in clothing products. Additionally, we investigate engineered water nanostructures (EWNS) for their antibacterial, hydrophilic, and anti-pilling effects on textiles. The EWNS can serve as an effective tool for monitoring quality, enabling precise tracking and verification of nanomaterials during clothing production. The research validated the traceability performance of the blockchain-based model for raw material traceability and quality management in clothing products, achieving a remarkable traceability accuracy of 99.92% and an exceptionally low traceability response time of merely 1.83 seconds. Moreover, after being treated with EWNS, antibacterial rates against E. coli and S. aureus in clothing antibacterial quality management reached 99.9% and 99.0%, respectively. In the hydrophilicity test, EWNS was applied to clothing for a duration of 1 to 7 hours, resulting in a significant reduction in the wetting time of clothing from 10.8 seconds to 1.2 seconds. In the anti-pilling tests, there was a notable enhancement in the effectiveness of anti-pilling properties after EWNS treatment. In summary, our model has achieved remarkable outcomes in material traceability and quality management, offering consumers and the clothing industry a reliable method to ensure product quality and traceability, while simultaneously improving performance aspects such as antibacterial efficacy, hydrophilicity, and resistance against pilling.

LPV interpolation modeling and modal-based pole placement control for ball screw drive with dynamic variations

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime.

Method for noninvasive HV/MV switchgear motion analysis using kernel-based algorithm with adaptive feature extraction

When designing, developing, and testing medium-voltage (MV) and high-voltage (HV) switchgears, it is of utmost importance to analyze the movements of their mechanical parts, such as drive trains, contact nozzles, etc. This ensures the safety of switchgear operations and enables detecting and predicting issues that could lead to component damage, power interruptions, reduced efficiency, or even switch failure. Conventional invasive measuring setups, including encoders and laser distance measurements, are often difficult or expensive to use, due to undesirable environmental properties such as high temperatures and/or high voltages, or dimensional constraints often encountered in testing laboratories, compact substations or confined equipment rooms. Video object tracking can be a viable solution in such conditions, allowing for the extraction of the trajectory of mechanical parts of an object under analysis. This paper proposes a novel kernel-based algorithm with adaptive feature extraction for precise colour-based object tracking through video processing. The implemented method is based on the principles of the CamShift algorithm, augmented with fusion with the Kalman filter for continuous estimation and prediction of the object’s position based on the available measurements while reducing sensitivity to noise. Beyond precise tracking of the object of interest, the algorithm automatically adapts the mask used in the standard CamShift algorithm by extracting and processing the features of the selected object. This approach exhibits flexibility and robustness in adverse industrial environments. It supports modifications according to user preferences and represents a cost-effective alternative to conventional methods, while performing real-time processing. Experimental results underscore this noninvasive approach is flexible, highly robust, and enables tracking of coloured markers even in challenging conditions like poor lighting, significant blur, and low frame rates.

Modeling and Robust Adaptive Practical Predefined Time and Precision Tracking Control of Unmanned Fire Fighting Robot

Modeling and Robust Adaptive Practical Predefined Time and Precision Tracking Control of Unmanned Fire Fighting Robot

Multi-Object Detection and Tracking with Modified Optimization Classification in Video Sequences

The paper presents a novel approach to enhancing multi-object detection and tracking in video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) algorithm. The ASO-DL algorithm synergistically combines the optimization capabilities of ant swarm optimization with the powerful feature extraction abilities of deep learning models, resulting in a robust framework for realtime video analytics. Extensive simulations and experiments demonstrate significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, across various iterations. The proposed method consistently outperforms baseline models, achieving a final best fitness value of 0.96, with an accuracy of 0.98, precision of 0.99, and recall of 0.95. Additionally, classification results across different datasets such as CIFAR-10, IMDB, COCO, and ImageNet highlight the algorithm’s versatility and effectiveness. This research contributes to the field by providing a highly optimized solution for complex multi-object tracking tasks, offering substantial advancements in the accuracy and efficiency of real-time object detection systems. The findings hold significant potential for applications in surveillance, autonomous vehicles, and other domains requiring precise and reliable multi-object tracking.

Data-Driven Quality Improvement for Sustainability in Automotive Packaging

This study addresses sustainability challenges in automotive packaging by introducing a novel data-driven approach to audit and improve packaging quality. Traditional manual auditing processes are both time-consuming and prone to errors. To counter these inefficiencies, we developed an automated system utilizing Microsoft Power Apps and SQL databases to streamline data collection and processing. The automated tools facilitate real-time data capture and visualization through Microsoft Power BI, enabling precise tracking and management of returnable and expendable packaging materials. Our results show that our approach offers a significant enhancement in the accuracy and speed of data analysis, leading to more informed decision-making. Our methodology supports sustainability by reducing waste and reliance on expendable packaging and improving the economic and operational efficiency of automotive packaging systems.

Innovative approaches in enhanced oil recovery: A focus on gas injection synergies with other EOR methods

Enhanced Oil Recovery (EOR) techniques have become crucial in extending the productive life of mature oil fields and maximizing resource extraction. Among these, gas injection methods, particularly those involving CO₂, nitrogen, and hydrocarbon gases, have shown significant promise. This review focuses on the innovative synergies achieved by combining gas injection with other EOR methods to enhance recovery efficiency and economic viability. Gas injection techniques work by increasing reservoir pressure and reducing oil viscosity, facilitating easier flow and extraction. When integrated with other EOR methods such as thermal, chemical, and microbial techniques, the synergistic effects can lead to even greater enhancements in oil recovery. For instance, the combination of gas injection with thermal EOR, like steam injection, can improve the displacement of heavy oils by simultaneously reducing viscosity and enhancing thermal conductivity within the reservoir. Similarly, integrating gas injection with chemical EOR methods, such as polymer flooding, can optimize the sweep efficiency by leveraging the mobility control provided by polymers and the pressure maintenance by gases. Recent advancements in monitoring and simulation technologies have further improved the effectiveness of these combined approaches. Real-time reservoir simulation and 4D seismic monitoring allow for precise tracking of gas and fluid movements, enabling dynamic adjustments to injection parameters for optimal performance. Smart well technologies, equipped with advanced downhole sensors, provide continuous data on reservoir conditions, facilitating more responsive and adaptive EOR strategies. However, these innovative synergies are not without challenges. Economic feasibility remains a significant concern, particularly given the high costs associated with gas procurement and the integration of multiple EOR techniques. Additionally, managing the complex interactions between different EOR methods and heterogeneous reservoir conditions requires sophisticated modeling and robust operational protocols. In conclusion, the integration of gas injection with other EOR methods offers a promising pathway to enhance oil recovery rates and extend the lifespan of mature reservoirs. Continued research and development, coupled with cross-industry collaboration, are essential to overcoming the economic and technical challenges associated with these innovative approaches. This review underscores the potential for gas injection synergies to revolutionize EOR practices and drive sustainable growth in the oil and gas industry.

A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources. The outer loop, contingent on the onboard controller and the robot's GPS system, enabling fine adjustments in angle and position relative to the leader's location. The simulation results illustrated the efficiency of the proposed technique in estimating the orientation angles of the leader and the interference sources. The technique robustness is confirmed through testing the performance on different trajectories. Where the follower perfectly generates a main radiation beam directed towards the leader, effectively mitigates interference signals from neighboring group leaders, and successfully tracks the leader path.

Towards more accurate microbial source tracking via non-negative matrix factorization (NMF).

The microbiome of a sampled habitat often consists of microbial communities from various sources, including potential contaminants. Microbial source tracking (MST) can be used to discern the contribution of each source to the observed microbiome data, thus enabling the identification and tracking of microbial communities within a sample. Therefore, MST has various applications, from monitoring microbial contamination in clinical labs to tracing the source of pollution in environmental samples. Despite promising results in MST development, there is still room for improvement, particularly for applications where precise quantification of each source's contribution is critical. In this study, we introduce a novel tool called SourceID-NMF towards more precise microbial source tracking. SourceID-NMF utilizes a non-negative matrix factorization (NMF) algorithm to trace the microbial sources contributing to a target sample. By leveraging the taxa abundance in both available sources and the target sample, SourceID-NMF estimates the proportion of available sources present in the target sample. To evaluate the performance of SourceID-NMF, we conducted a series of benchmarking experiments using simulated and real data. The simulated experiments mimic realistic yet challenging scenarios for identifying highly similar sources, irrelevant sources, unknown sources, low abundance sources, and noise sources. The results demonstrate the superior accuracy of SourceID-NMF over existing methods. Particularly, SourceID-NMF accurately estimated the proportion of irrelevant and unknown sources while other tools either over- or under-estimated them. In addition, the noise sources experiment also demonstrated the robustness of SourceID-NMF for MST. SourceID-NMF is available online at https://github.com/ZiyiHuang0708/SourceID-NMF.

In vivo biodistribution and tumor uptake of [64Cu]-FAU nanozeolite via positron emission tomography Imaging.

Nanoparticles have emerged as promising theranostic tools for biomedical applications, notably in the treatment of cancers. However, to fully exploit their potential, a thorough understanding of their biodistribution is imperative. In this context, we prepared radioactive [64Cu]-exchanged faujasite nanosized zeolite ([64Cu]-FAU) to conduct positron emission tomography (PET) imaging tracking in preclinical glioblastoma models. In vivo results revealed a rapid and gradual accumulation over time of intravenously injected [64Cu]-FAU zeolite nanocrystals within the brain tumor, while no uptake in the healthy brain was observed. Although a specific tumor targeting was observed in the brain, the kinetics of uptake into tumor tissue was found to be dependent on the glioblastoma model. Indeed, our results showed a rapid uptake in U87-MG model while in U251-MG glioblastoma model tumor uptake was gradual over the time. Interestingly, a [64Cu] activity, decreasing over time, was also observed in organs of elimination such as kidney and liver without showing a difference in activity between both glioblastoma models. Ex vivo analyses confirmed the presence of zeolite nanocrystals in brain tumor with detection of both Si and Al elements originated from them. This radiolabelling strategy, performed for the first time using nanozeolites, enables precise tracking through PET imaging and confirms their accumulation within the glioblastoma. These findings further bolster the potential use of zeolite nanocrystals as valuable theranostic tools.

Multi-species identification and number counting of fish passing through fishway at hydropower stations with LigTraNet

Fishway monitoring can verify the effectiveness of the fishway, optimise the operation mode, and achieve scientific management of fishway operations. Traditional fishway monitoring approaches, hindered by their inefficiency and substantial disruption of fish, are ill-suited for long-term surveillance; thus, employing video monitoring coupled with object detection technology presents an alternative or complementary solution. However, challenges such as the constrained computational capacity of onsite equipment in fishways, complexities involved in model deployment, and sluggish pace of detection are significant hurdles. In this study, by utilising the YOLOv8n model as a benchmark, we engineered a cross-stage partial module with a single convolution (C1) module to replace the existing C2f module with the aim of enhancing performance. We replaced the conventional 2D convolutions in the bottleneck configuration with depthwise separable convolutions and integrated the SimAM module to extract the detailed characteristics of the fish species. By amalgamating LigObNet detection with the DeepSORT algorithm, we established LigTraNet, which is designed to enable precise tracking, identification, and counting of individual fish. The results showed that LigObNet exhibited the lowest complexity and fastest detection speed for underwater fish among similar object recognition and detection models. Compared with the benchmark YOLOv8n model, there were reductions of 8.9% in the network layers, 40.5% in the parameter count, 39.3% in the memory footprint, and 35.8% in the giga floating-point operations and a 38.1% improvement in the inference speed. LigTraNet achieved a total count accuracy rate of 91.8%, demonstrating superior species quantification capabilities over other models with minimal resource usage and rapid inference capabilities, thus offering enhanced practicality for deployment on devices in real-world engineering contexts. This represents a departure from traditional manual monitoring methods for assessing fishway effectiveness, revolutionising aquatic ecological monitoring tools and methodologies and fostering the collaborative advancement of water resource project operations and ecological conservation.

Application of Improved Sliding Mode and Artificial Neural Networks in Robot Control

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency.

Precise acoustic drone localization and tracking via drone noise: Steered response power - phase transform around harmonics

This study introduces a new method for precise acoustic drone localization and tracking via the noise generated by the drone. Drone noises include harmonic frequency components which are related to the rotational speed of the propeller and the number of blades. This study integrates utilization of the frequency components around harmonics into Steered Response Power - Phase Transform (SRP-PHAT). First, a custom discrete Fourier transform (namely DFT-Harmonics) is defined which concentrates only on the vicinities of harmonics to capture the frequency components possibly related to the drone sound. Then, DFT-Harmonics is integrated into SRP-PHAT, which is named SRP-Harmonics. The benefits of SRP-Harmonics are explained and illustrated via SRP maps and videos. Experiments with real microphone array data show that SRP-Harmonics is precise in localizing and tracking a drone, while the ordinary SRP-PHAT can not be reasonably successful. Moreover, SRP-Harmonics after Kaiser window can maintain its performance even when significant level of artificial white noise or natural wind noise exists in the data.

Inner core backtracking by seismic waveform change reversals

The solid inner core, suspended within the liquid outer core and anchored by gravity, has been inferred to rotate relative to the surface of Earth or change over years to decades based on changes in seismograms from repeating earthquakes and explosions1,2. It has a rich inner structure3–6 and influences the pattern of outer core convection and therefore Earth’s magnetic field. Here we compile 143 distinct pairs of repeating earthquakes, many within 16 multiplets, built from 121 earthquakes between 1991 and 2023 in the South Sandwich Islands. We analyse their inner-core-penetrating PKIKP waves recorded on the medium-aperture arrays in northern North America. We document that many multiplets exhibit waveforms that change and then revert at later times to match earlier events. The matching waveforms reveal times at which the inner core re-occupies the same position, relative to the mantle, as it did at some time in the past. The pattern of matches, together with previous studies, demonstrates that the inner core gradually super-rotated from 2003 to 2008, and then from 2008 to 2023 sub-rotated two to three times more slowly back through the same path. These matches enable precise and unambiguous tracking of inner core progression and regression. The resolved different rates of forward and backward motion suggest that new models will be necessary for the dynamics between the inner core, outer core and mantle.

DustSCAN: A Five Year (2018-2022) Hourly Dataset of Dust Plumes From SEVIRI

Airborne mineral dust significantly impacts air quality, human health, and the global climate. Due to sparse ground sensors, particularly in source regions, dust monitoring relies mainly on remote sensing through Aerosol Optical Depth (AOD) retrievals from polar-orbiting satellite optical instruments. These are valuable but lack the temporal resolution for precise plume tracking and source characterization. We introduce DustSCAN, a five-year, hourly dust plume dataset derived from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) images on geostationary-orbit Meteosat satellites. Using multi-channel infrared images, we detect atmospheric dust and track hourly dust-affected pixels. These are clustered into discrete plumes using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DustSCAN includes 9950 discrete plumes over 2018-2022 across the Sahara, the Arabian Desert, and Western and Central Asia. It complements existing resources and provides a framework for detailed analysis of dust sources, trajectories, and impacts. Its distinctive event-based and spatio-temporal detail offers an advancement in unraveling the complexities of dust storm dynamics.

Towards Reliable Identification and Tracking of Drones Within a Swarm

Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

WOMEN SAFETY APP

The Women Safety App operates on a multifaceted approach, incorporating real-time assistance, precise location tracking, and immediate access to emergency services. By capitalizing on the ubiquity of smartphones, this project strives to provide a swift and effective response to potential threats that women may encounter in various situations. The core features of the app include a panic button for instant distress signals, live location sharing to facilitate timely assistance, and a streamlined communication channel with emergency services. These elements collectively contribute to fostering a safer environment for women, instilling a sense of confidence and security as they navigate their daily lives. The Women Safety App project addresses the critical issue of women's safety by leveraging technology to provide a reliable and accessible solution. In today's society, incidents of gender-based violence, harassment, and discrimination underscore the urgent need for effective measures to protect women's well-being and security. Existing safety mechanisms often fall short, relying on manual interventions and traditional communication channels that may not provide immediate assistance during emergencies. The Women Safety App aims to bridge this gap by offering real-time assistance, location tracking, and emergency services through a mobile application. Its key advantage lies in delivering instant distress signals, enabling quick alerts to trusted contacts and emergency responders in threatening situations. Additionally, live location tracking allows users to share their whereabouts for added safety.

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

CANTEEN MANAGEMENT SYSTEM : THE FINAL PROJECT

ABSTRACT : A sophisticated digital solution called the School Canteen Management System (SCMS) was created to streamline the management and operations of school canteens. Essential functions including menu planning, user account administration, inventory management, point of sale (POS), and extensive reporting and analytics are all included in this system. Through process simplification and automation of repetitive tasks, the SCMS increases productivity, lowers error rates, and enhances user experience. Faster service, precise financial tracking, improved stock control, and the capacity to satisfy dietary requirements and preferences are some of the main advantages. In the end, the SCMS ensures convenience and happiness for students, staff, and parents by helping to create a more orderly, transparent, and user-friendly cafeteria environment. INDEX TEARMS – Frontend development HTML, CSS, JavaScript, react js, Database management, mongodb, Chatgpt.

Two-dimensional design strategy to construct smart dual-responsive fluorescent probe for the precise tracking of ischemic stroke

Two-dimensional design strategy to construct smart dual-responsive fluorescent probe for the precise tracking of ischemic stroke