Tracking Mechanism Research Articles

Consecutive one-week model predictions of land surface temperature stay on track for a decade with chaotic behavior tracking

Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer's and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT 'hallucinations', we implemented a context provenance tracking mechanism that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and 'citizen scientists' on the relevance of GPT responses. A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. When evaluating the responses to their questions, human respondents give a "thumbs-up" 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT-generates questions, RAGAS scores 74% on answer relevance. With RAGAS we created a benchmark that can be used to continuously assess the performance of our knowledge base. Full GNQA functionality is embedded in the free GeneNetwork.org web service, an open-source system containing over 25 years of experimental data on model organisms and human. The code developed for this study is published under a free and open-source software license at https://git.genenetwork.org/gn-ai/tree/README.md .

Fast Track Intervention Effects and Mechanisms of Action Through Established Adulthood.

Early preventive interventions can improve outcomes in childhood, but the most effective interventions can continue to deliver benefits through the life course. The Fast Track intervention, a randomized controlled trial for children at risk of conduct problems, has lowered psychopathology, substance use problems, and criminality and elevated happiness at age 25. However, research has not studied whether the intervention's effects continue further into established adulthood. In addition, little is known about the mechanisms through which the intervention may affect adult outcomes. We attempted to answer both questions by simultaneously estimating the intervention's direct effect on adult outcomes at age 31 and the intervention's indirect effects on those outcomes via interpersonal, intrapersonal, and academic competencies gained through the intervention. Participants included the Fast Track intervention (n = 445; 72.4% male) and high-risk control samples (n = 446; 66.4% male). Direct and total effects of random assignment to Fast Track on age 31 outcomes were not significant. However, our analyses showed that Fast Track's improvements to interpersonal and intrapersonal skills in childhood served as catalysts for better life outcomes at age 31. Higher interpersonal skills led to fewer externalizing, internalizing, and substance use problems, reduced criminality and sexual partners, in addition toincreased general health and full-time employment. Improved intrapersonal skills led to greater strength. There were no significant indirect pathways via academic skills. Our findings inform understanding of how a childhood preventive intervention can improve adjustment and behaviors into established adulthood.

Construction of an Adolescent Spinal Health Intelligent Early Warning and Intervention System under the Proactive Health Model

This study explores the construction of an intelligent early warning and intervention system for adolescent spinal health and its application under a proactive health model. The research demonstrates that through real-time monitoring and personalized interventions, the Spinal Health Index of adolescents in the intervention group significantly improved, increasing from 65 to 85 points, while the control group's index slightly decreased from 60 to 58 points. Personalized intervention strategies, such as the combination of exercise and nutritional interventions, were found to be the most effective, indicating that daily activity levels significantly impact spinal health. The system plays a crucial role in the management of adolescent spinal health by facilitating real-time monitoring, personalized interventions, and modifications in health behaviors. Despite limitations including constraints in sample size and geographical scope, a relatively short intervention period, and insufficient data diversity, future research can enhance universality and model generalizability by expanding sample sizes, prolonging the intervention period, and increasing data diversity. Looking forward, the integration of multimodal data, optimization of the user interface, and establishment of long-term tracking mechanisms will further enhance system performance and promote the improvement of spinal health management in adolescents.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

An enhanced global MPPT method to mitigate overheating in PV systems under partial shading conditions

The hot-spot phenomenon in photovoltaic modules is a reliability issue typically caused by many reasons, including partial shading conditions. In such situations, the P-V curve exhibits a unique global maximum power peak (GMPP) and several local maximum power peaks (LMPPs). However, all the existing tracking methods (GMPPT) track the GMPP, even for a slight difference between peaks. For this purpose, an experimental investigation of the relationship between the operating point of a PV module and the temperature of their shaded parts has been carried out. In the studied case, it was found that the shaded cells’ temperature exceeds 99 °C, and it is directly related to the GMPP position on the P-V curve, where it decreased as the operating point is moved from the short circuit current towards the open-circuit voltage. As the shaded cells' temperature may reach high levels, which can result in early degradation of PV modules, an enhanced GMPPT method is proposed in this paper to handle the overheating issue. The proposed method aims to track the secure peak when two peaks or more have closer power values. Consequently, it alleviates the overheating and hot-spotting risks. Furthermore, the scanning voltage interval of this technique is bounded by lower and upper voltage limits. The proposed technique is experimentally validated and compared to two existing methods: the 0.8 Voc and the full sweeping method. The results show that when the power peaks have approximately the same value, the proposed method tracks the peak with the upper voltage (automatically the lower current) to minimize the thermal stress within the shaded cells. Moreover, the proposed method exhibits a 40 % saving in tracking time compared to the full sweeping method thanks to the efficient tracking mechanism that allows skipping certain intervals in the search space.

Photovoltaic penetration potential in the Greek island of Ikaria

The operation of Distribution Networks (DNs) has been affected by the ongoing energy transition, gradually incorporating more Distributed Energy Resources (DERs), mainly Renewable Energy Sources (RES), as well as Energy Storage Systems (ESS) sustainably enhancing DN’s flexibility. In the case of the non-interconnected island of Ikaria, Greece, with high solar and wind potential, the DN includes conventional generators, Photovoltaic systems (PVs), wind farms and a hydro-pumped ESS. Scope of this study is to assess the possible impact of the PVs expansion considering either: i) fixed, ii) single-axis or iii) dual-axis tracking panels. For this purpose, CERTH’s in-house INTEMA.grid platform is used. Tracking mechanism’s effectiveness is studied considering that the expansion doubles or triples the rated power of the existing, fixed 0.4 MW PVs, following the directions of the Distribution System Operator (DSO). Additionally, a monthly analysis is presented, because Ikaria is an island with extremely higher load during summer months due to tourism. According to the results, if the current PV capacity is doubled or tripled, a dual-axis expansion yields 16.0% or 21.3% yearly production increase compared to fixed panels, respectively, with the single-axis effect though being much higher (14% or 18.7%, respectively) than the incremental effect of the second axis (further comparative 1.8% or 2.3%, respectively). The effectiveness of tracking mechanisms is highlighted during summer months and particularly early in the morning or late in the afternoon. Finally, environmental and economic indicators for the proposed installations are assessed.

Tracking Usage Patterns of a Mobile Application for Dementia Risk Assessment in Elderly Individuals.

Among elderly individuals, dementia stands as a prominent contributor to disability. However, multidomain lifestyle interventions have emerged as a promising approach for enhancing cognition. Taking a progressive step, integrating digital elements such as a mobile intervention application for elderly participants becomes crucial. Hence, it's crucial to identify the usage patterns of intervention features. Our aim was to implement a usage tracking mechanism and analyze usage trends and feature preferences over the initial six months of a clinical trial across various clinical sites, comparing intervention and control groups. Our findings reveal the differences in usage between the groups and highlight the most used features.

CubeSats for space debris removal from LEO: Prototype design of a robotic arm-based deorbiter CubeSat

Space debris has become a growing problem, and various studies indicate that the population of space objects has reached a critical density in low Earth orbits (LEO). Therefore, removing debris from the LEO is key to stabilizing the growth of space objects and making some of the orbits reusable for future launches. Deorbiting larger debris objects using miniaturized satellites, CubeSats is both economically viable and requires relatively less time for design & deployment. In this paper, we present a system-level study of orbital debris removal from LEO using CubeSats, detailing various existing debris tracking, rendezvous, capture, and deorbit mechanisms. A deep learning-based object detection algorithm has been utilized to improve the accuracy of detecting and localizing debris. We also present a discussion on the CubeSat-compatible capture & deorbit mechanisms identified from our study and an upper bound on the debris mass that can be deorbited from a given LEO orbit using CubeSats. We aim to demonstrate a case study for the orbital decay analysis of a CubeSat deorbiting the XSat, a microsatellite launched by Singapore. We detail the design of a 27U CubeSat deorbiter prototype equipped with a pair of robotic arms to capture the XSat. This analysis considers active deorbiting using Hall-effect thrusters, for which the required delta-V & propellant mass have also been computed.

RetroSphere: Enabling Low-power and Affordable 3D Input for Lightweight Augmented Reality Devices

RetroSphere introduces a self-contained 6-degree-of-freedom (6DoF) controller tracker, designed for seamless integration with resourceconstrained Augmented Reality (AR) devices such as AR glasses and smartphones. This system employs a tracking mechanism using one to three retro-reflective spheres on a passive controller, thus eliminating the need for any onboard electronics or batteries. Detection is enabled by a stereo pair of mass-produced infrared blob trackers, each outfitted with infrared LED emitters. The system utilizes a lightweight stereo depth-estimation algorithm on an Arduino-compatible ESP32 microcontroller to achieve 6DoF tracking. RetroSphere achieves a tracking accuracy of approximately 96.5%, with positional errors as low as 3.5 cm across a tracking range of 100 cm, while maintaining a power consumption of just about 400 mW. The complete hardware and firmware details for RetroSphere are available as open source on GitHub: https://github.com/AnantaBalaji/RetroSphere

Application of Expressive Arts Group Therapy in Enhancing Subjective Well-being of College Students

This study explores the application of expressive arts group therapy in enhancing college students’ Subjective Well-Being (SWB). It elaborates on this therapy’s concept, theoretical basis, development process, and application areas, analysing its unique advantages in self-exploration, emotional regulation, interpersonal communication, and creative thinking. Practical cases reveal the significant effectiveness of expressive arts group therapy. Suggestions for future practice work are proposed, including strengthening theoretical research, paying attention to individual differences, enhancing counsellor training, and establishing long-term tracking mechanisms. It aims to optimise this therapeutic model further and provide lasting support for enhancing the SWB of college students.

Numerical algorithm for nonlinearity compensation of hardly constrained actuation for trajectory tracking control of deadzone-included dynamic systems

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations. It introduces a trajectory tracking mechanism that, instead of directly following the primary trajectory, adheres to an alternative trajectory capable of stable tracking, gradually converging to the main trajectory while accounting for operational constraints. In practical control systems, the actuator's input-output relationship is often nonlinear and unknown, requiring inversion for model-based control. This paper employs an offline-trained neural network trained on synthetic data to identify and approximate the actuator's behavior. To optimize the control system's performance and ensure stability during sudden error changes, the control input operates in two modes: position and velocity control. This dual-mode control allows for continuous switching between the two, facilitated by an innovative optimization technique based on the gradient descent method with a variable step size. Simulation results validate the effectiveness of the proposed algorithm in controlling systems constrained by hard limits and featuring nonlinear oscillatory actuators, providing a valuable contribution to the field of control systems.

Design of the System of an Assemblable Channel Slope Weeder

If the grass roots of the water conveyance channel slope dig into the soil, it may cause the soil structure to loosen, and then increase the risk of slope landslide. The operation of manual cleaning weeds is dangerous due to the large slope of the channel. In this context, the design of a channel slope weeder that can adapt to different slopes is very important for the protection of the water channel slope. The weeder concept vehicle designed in this paper is mainly composed of four parts: drive car, frame mechanism, track mechanism and weeder mechanism. At the same time, the structure is analyzed by finite element method, and the stability and safety are analyzed by strength. The reference is provided for the design of the algal removal vehicle on the channel slope.

Experimental study of parabolic trough collector in series having a concentric copper tube in evacuated tube for air heating

ABSTRACT Currently, the utilization of solar energy for domestic purposes, particularly in applications such as space heating and hot water, is constrained to flat plate collectors and evacuated tubes due to its compact size, absence of tracking mechanisms, low maintenance, and cost-effectiveness. The objective of this study is to evaluate the practical effectiveness of a locally developed parabolic trough collector system through experimentation. In this configuration, two parabolic trough collectors with one-ended evacuated tubes and inner concentric copper tubes for double pass airflow were connected in series, exhibiting an average air temperature within the range of 130.22°C to 166.72°C, which indicates its potential for use in industrial and domestic applications. Solar intensity, thermal performance, and pressure drop were determined after the first evacuated tube (intermediate point) and after the second evacuated tube. The experiments were carried out on six separate days, with three distinct airflow rates of 0.0056 kg/s, 0.0038 kg/s, and 0.0021 kg/s. Results discovered that the maximum air temperature reached was 210.5°C. The maximum thermal efficiency observed was 39.31%, with an overall average thermal efficiency of 30.56%. Further, the maximum pressure drop achieved was 510 Pa at 0.0056 kg/s.

Development of an early detection and automatic targeting system for cotton weeds using an improved lightweight YOLOv8 architecture on an edge device

Traditional means of weed removal, such as human work or the use of pesticides, frequently require significant amounts of effort, incur high expenses, and can negatively impact the environment. This study introduces a modified version of the YOLOv8 nano architecture that is suitable for running on edge devices for real-time applications. The proposed model uses an augmented version of the well-known CottonWeedDet12 dataset consisting of a total of 16,944 images with characteristic annotations to develop a model capable of correctly distinguishing 12 different cotton weed classes with an increased mean average precision of 97.6 % that is about 1.2 % more than the model trained using original, unaugmented dataset. The final selected model uses a convolutional block attention module (CBAM) and a unique C3Ghost block within the YOLOv8 backbone, which together increase the model's reliability for more accurate predictions with reduced computational complexity. Upon training with the augmented dataset, the proposed model with only 3.6 million parameters was able to achieve an mAP@50 score of 97.6 %, which surpasses all previous studies conducted using this dataset. Additionally, a high F1 score of 94.4 % proves that the model has a good balance between recall and precision. Class Activation Map (CAM) approaches such as EigenCAM, Grad-CAM++, and LayerCAM explainable AI (XAI) showed promising results for each of the customized models upon testing their interpretability for cotton weed detection. Furthermore, based on this model, a fast and cost-efficient targeting system was developed using a yaw-pitch mechanism for automatic weed tracking and herbicide spraying.

Studies on hybrid solar collector with water driven solar tracking unit

ABSTRACT The current solar concentrating collectors can be driven by water gravity without using a torque motor. It saves the electricity required to drive the large solar concentrating collectors. The objective of this study is to simulate and experimentally evaluate a novel hybrid system that combines photovoltaic and thermal technology with a hydro-powered solar tracking unit. The device consists of a hybrid solar collector, which is a multi-reflector compound parabolic collector (MRCPC), an evacuated tube receiver, photovoltaic (PV) panels, and a tracking system. The tracking mechanism attaches the tracking weights to both sides of a chain and sprocket. This hybrid collector rotates around its axis by adjusting the weight of the water in each of the monitoring weights. The system integrates a 12-volt direct current pump (operated by photovoltaic power) for tracking, with flow rate control. A mathematical model is developed and applied to determine the ideal tilt angle for a solar concentrating collector. The model was then used to compare the actual tilt angle and minimize errors in tracking. An experimental investigation used a flow rate of 0.8 LPM. The study achieved a mean tracking error of 2.05º, a maximum thermal efficiency of 43.65%, a maximum solar PV efficiency of 11.30%, and a maximum overall efficiency of 29.81%. An analytical method was developed to simulate the collector’s outlet temperature.

Crowd Scene Anomaly Detection in Online Videos

Abstract. The prevalence of surveillance cameras in public places has led to an extremely pressing need for effective position and crowd monitoring, as well as anomaly detection. This paper tends to exhibit an incorporated approach that combines state-of-the-art computer vision techniques for comprehensive crowd surveillance. The main features of our approach are summarized into four steps: (a) Object detection and tracking; (b) Geometric rectification for positioning; (c) Motion extraction; and (d) Anomaly detection. First, this uses YOLOv5's Convolutional Neural Network (CNN) model in making efficient detection of objects, focusing on spotting individuals within crowded scenes. After detection, a strong mechanism for tracking is established with the help of the DeepSORT algorithm, which can track the person across frames. It must gain the people's position in the video frame and analyze motion data with the guarantee of capture of camera-scene geometry. Each frame thus gets converted from the 3D perspective to a 2D bird's eye view within the surveillance video, giving a guarantee of capture of the geometry of a camera scene. Motion anomaly detection is addressed through statistical methods, with Kernel Density Estimation (KDE) being employed to identify deviations from normal motion patterns. Extensive experiments conducted on different online crowd scene video datasets validate the effectiveness of the proposed anomaly detection mechanism. Overall, this integrated approach proposes a promising solution to crowd surveillance, further development of object detection, tracking, and anomaly analysis for monitoring public spaces.

Streamlining last-mile delivery in Delta State: Enhancing logistics management for efficient distribution

This study investigates the challenges and potential solutions of last-mile delivery experienced by customers purchasing from online and physical stores in Delta State, Nigeria. Utilizing a mixed-method approach, data were collected from a sample size of 349 participants, comprising customers of both online and physical retail outlets. Quantitative data analysis involved the examination of contingency tables and the calculation of chi-square statistics to determine the significance of differences in last-mile delivery challenges between online and physical stores. Additionally, qualitative data were obtained through open-ended survey questions and thematic analysis to explore potential strategies for addressing identified challenges. The findings reveal that customers encounter similar challenges in last-mile delivery across both online and physical stores. While there may be variations in the frequencies of these challenges between the two types of stores, statistical analysis in this study indicates that these differences are not significant. Furthermore, findings reveal that the identified challenges have a substantial impact on customer satisfaction and purchasing behavior, particularly in online stores. Strategies were identified to address these challenges such as the integration of seamless payment options, address verification technology, involvement of locals in the delivery process, among others. These recommendations emphasizes the importance of implementing tailored strategies to improve last-mile delivery services and enhance the overall shopping experience for customers in Delta State. This study contributes to the existing literature by providing insights into the challenges and potential solutions of last-mile delivery in the context of both online and physical retail environments. The findings offer practical implications for logistics management and customer service strategies, guiding retailers in addressing common challenges and driving business growth in the competitive retail landscape of Delta State. Keywords: Last-Mile Delivery, Route Optimization, Innovative Delivery Models, Delivery Tracking Mechanism, Operational Efficiency, Customer Satisfaction

Developing a System on the Dynamics and Implications of Parental and School Tracking of Children's Activities and Academic Progress

This paper explores the dynamics and effects of the school and parent tracking system at École Secondaire Islamique de Gisenyi. The study addresses the problem of monitoring students' academic performance and attendance by examining the extent to which parents and the school utilize tracking mechanisms. Motivations, benefits, and challenges associated with parental and school tracking practices are explored, considering factors like privacy concerns, parental involvement, and student autonomy. The research objectives focus on understanding how the implementation of a tracking system can enhance student success and the overall educational experience through collaborative efforts between parents and the school. By investigating the impact of parental tracking, the study aims to strengthen the bond between parents and the school, fostering a more supportive and engaged learning environment. The methodology involves qualitative data collection methods, including interviews and observations, to provide a comprehensive understanding of the dynamics and effects of the tracking system. The expected results are based on the assumption that the implementation of a tracking system at École Secondaire Islamique de Gisenyi will positively impact student progress, parental involvement, and overall school dynamics. The study's findings aim to contribute to the broader discourse on educational monitoring practices, inform policy recommendations, and empower stakeholders to make informed decisions regarding student tracking in Rwandan secondary schools. In conclusion, this research sheds light on the dynamics and effects of the school and parent tracking system at École Secondaire Islamique de Gisenyi. The anticipated positive outcomes of implementing a robust and user-friendly tracking system include enhancing student success and fostering collaboration between parents and school staff. Keywords: parental tracking, school tracking, data collection, tracking practices.