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Background/Objectives: Radial artery tracking (RAT) in the short-axis view is a pivotal step for ultrasound-guided radial artery catheterization (RAC), which is widely employed in various clinical settings. To eliminate disparities and lay the foundations for automated procedures, a pilot study was conducted to explore the feasibility of U-Net and its variants in automatic RAT. Methods: Approved by the institutional ethics committee, patients as potential RAC candidates were enrolled, and the radial arteries were continuously scanned by B-mode ultrasonography. All acquired videos were processed into standardized images, and randomly divided into training, validation, and test sets in an 8:1:1 ratio. Deep learning models, including U-Net and its variants, such as Attention U-Net, UNet++, Res-UNet, TransUNet, and UNeXt, were utilized for automatic RAT. The performance of the deep learning architectures was assessed using loss functions, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). Performance differences were analyzed using the Kruskal–Wallis test. Results: The independent datasets comprised 7233 images extracted from 178 videos of 135 patients (53.3% women; mean age: 41.6 years). Consistent convergence of loss functions between the training and validation sets was achieved for all models except Attention U-Net. Res-UNet emerged as the optimal architecture in terms of DSC and JSC (93.14% and 87.93%), indicating a significant improvement compared to U-Net (91.79% vs. 86.19%, p < 0.05) and Attention U-Net (91.20% vs. 85.02%, p < 0.05). Conclusions: This pilot study validates the feasibility of U-Net and its variants in automatic RAT, highlighting the predominant performance of Res-UNet among the evaluated architectures. [ABSTRACT FROM AUTHOR]

Soft X‐ray spectroscopy is an important technique for measuring the fundamental properties of materials. However, for measurements of samples in the sub‐millimetre range, many experimental setups show limitations. Position drifts on the order of hundreds of micrometres during thermal stabilization of the system can last for hours of expensive beam time. To compensate for drifts, sample tracking and feedback systems must be used. However, in complex sample environments where sample access is very limited, many existing solutions cannot be applied. In this work, we apply a robust computer vision algorithm to automatically track and readjust the sample position in the dozens of micrometres range. Our approach is applied in a complex sample environment, where the sample is in an ultra‐high vacuum chamber, surrounded by cooled thermal shields to reach sample temperatures down to 2.5 K and in the center of a superconducting split coil. Our implementation allows sample‐position tracking and adjustment in the vertical direction since this is the dimension where drifts occur during sample temperature change in our setup. The approach can be easily extended to 2D. The algorithm enables a factor of ten improvement in the overlap of a series of X‐ray absorption spectra in a sample with a vertical size down to 70 µm. This solution can be used in a variety of experimental stations, where optical access is available and sample access by other means is reduced. [ABSTRACT FROM AUTHOR]

To study the influence of speed factors on the stability of a tractor automatic navigation system, combined with the neural network control theory, the authors proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using a back propagation neural network to establish a two-wheel tractor-path dynamics model and a straight-line path tracking deviation model. The overall system simulation was carried out using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed that when the tractor was tracked with the automatic control of a linear path under the condition of variable speed, the maximum deviation of the lateral position deviation was 12.7 cm, and the average absolute deviation was kept within 4.88 cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the conditions of constant speed and variable speed, using the joint sliding mode control method designed by the authors, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations. [ABSTRACT FROM AUTHOR]

Mongolian extratropical cyclones (ETCs) play a crucial role in the weather and climate over East Asia, and the use of datasets with higher spatiotemporal resolutions can improve the understanding of Mongolian ETCs. Based on an automatic detection and tracking algorithm and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) dataset, the characteristics of the frequency, lifetime, moving distance, moving speed, and intensity of Mongolian ETCs in spring during 1979–2021 are investigated. The results show that 92.6% of the Mongolian ETCs are locally formed. There were two decadal changes in the Mongolian ETC frequency in approximately 1993 and 2004, showing a "high–low–high" variation. In terms of ETC passages, there is an increasing trend from central Mongolia to Northeast China and a decreasing trend east of Lake Baikal, indicating that the Mongolian ETC tracks are located more southward. Based on the standard deviation ellipse method and K-means clustering, the Mongolian ETCs are clustered into three categories, namely, southern track (ST, accounting for 53.4% of Mongolian ETCs) ETCs, northern track (NT, accounting for 43.8%) ETCs, and long track (LT, accounting for 2.8%) ETCs. The ST ETCs have a significant increasing trend, while the NT ETCs show a significant decreasing trend. Moreover, the other properties of Mongolian ETCs also show significant decadal variability. The sequences of lifetime, moving distance, moving speed and intensity exhibit decadal changes in the 1990s, with longer lifetime and moving distance, faster moving speed, and stronger intensity after the abrupt change. [ABSTRACT FROM AUTHOR]

Animal collective behavior produces group‐level patterns that emerge from individual‐level interactions among members. Such sophisticated behavioral coordination can be observed across various taxa, indicating evolutionary convergence. Among the most striking examples are two social insect lineages: ants and termites. Ants and termites evolved socially independently, inhabit terrestrial and subterranean environments, and face a similar collective task of maintaining their colonies with many individuals. This results in the convergent evolution of collective phenomena, such as constructing complex nest structures or movement coordination based on chemical and tactile cues. However, although the striking similarity attracts the most attention, little research has focused on how ants and termites differ in collective behaviors at individual and group levels. Here, we review the similarities and distinctions of collective behaviors in ants and termites. Even with similarities in group‐level patterns, rich diversity exists in mechanisms of behavioral coordination and the functions of collective patterns. Comparative analysis of collective behavior is challenging, but recent advances in automatic movement tracking methodologies have greatly expanded the potential for generating further insights. Finally, we conclude that comparative collective behavioral analysis can enhance our ability to understand biodiversity and provide alternative solutions for collective problem‐solving in many fields. [ABSTRACT FROM AUTHOR]

The aim of this work is to propose a new technique for automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform (CWT) method combined with artificial intelligence. Time slices of the variance attribute, derived from the 3D seismic data and chosen by the user, are analysed using the 2D CWT with the 2D Mexican Hat as an analysing wavelet, and the maxima of the modulus of the 2D CWT are mapped for the full range of scales. The ensemble of mapped maxima for the set of time slices is filtered using a Convolutional Neural Network machine. Machine training is performed with a supervised mode using the manually tracked faults as a desired output. Application to real data shows the efficiency and robustness of the proposed method, which can greatly help seismic interpreters in avoiding manual fault tracking, a difficult and time-consuming task. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the current arc suppression method of coil cannot achieve full compensation. First, the calculation model of the full compensation parameters of the controllable voltage source is established by analyzing the grounding fault arc suppression principle of the independent controllable voltage source and the parallel arc suppression coil. Second, by analyzing the arc suppression characteristics of controllable voltage source, a PI automatic regulation method based on phase deviation‐fault residual voltage control is proposed. The method uses fault residual voltage and the change of output voltage amplitude of controllable voltage source to control the adjustment direction and fault residual voltage to control the output voltage amplitude of controllable voltage source. The simulation results show that when the capacitive current of the distribution network is 24.47 A, the fault residual voltage is 2.5 V and the residual current is 446 mA after compensation by the independent controllable voltage source. When the capacitive current is 40.8 A and the deharmonic degree of arc suppression coil is −9%, the fault residual voltage and residual current are 2.2 V and 60 mA after compensated by the parallel arc suppression coil of the controlled voltage source. The proposed PI control method exhibits good stability, resulting in low fault residual voltage, and outperforms traditional control methods, enhancing the arc extinction effect of ground faults. [ABSTRACT FROM AUTHOR]

This study introduces new insights into the climatology of South Atlantic (SAt) cyclones by employing a novel cyclone life cycle detection method, the CycloPhaser. Utilizing the minimum relative vorticity series and its derivative at the cyclone centre, the program effectively identifies distinct phases in the cyclone life cycle. Cyclone tracks are obtained through the analysis of relative vorticity at 850 hPa, using the ERA5 dataset. The study identified six main cyclone life cycle patterns from the analysis of 28,458 systems. The predominant cyclone type, accounting for approximately 60% of the analysed systems, exhibited a four‐phase configuration: incipient, intensification, mature and decay. Detailed statistics for each developmental phase and the overall life cycle are presented, offering valuable comparisons and new insights while corroborating previous research findings. Key genesis regions in the SAt are identified, along with track density maps that reveal distinct preferences in cyclone developmental cycle. The main outcome of this study is the demonstration that the automated classification procedure enables the analysis of cyclones' life cycles to be conducted promptly and with low computing costs, facilitating the comprehensive study of cyclone behaviour with high efficiency. [ABSTRACT FROM AUTHOR]

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit, the existing track detection has some problems such as low efficiency and insufficient detection coverage, so an intelligent and automatic track detection method based on UAV is urgently needed to avoid major safety accidents. At the same time, the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices. As a result, the Dispersed Computing (DCOMP) architecture enables collaborative computing between devices in the Internet of Everything (IoE), promotes low-latency and efficient cross-wide applications, and meets users' growing needs for computing performance and service quality. This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks. Furthermore, the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints. The Markov Decision Process (MDP) model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment. Subsequently, a method based on Double Deep Q-Network (DDQN) is introduced to derive the optimal policy. Simultaneously, an experience replay mechanism is implemented to tackle the issue of increasing dimensionality. The experimental simulations validate the efficacy of the method across various scenarios. [ABSTRACT FROM AUTHOR]

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method. [ABSTRACT FROM AUTHOR]

Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: –2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: –2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts. [ABSTRACT FROM AUTHOR]

Adiabatic calorimeter experiments have quantified the sulfuric acid‐catalyzed reaction of ethylene oxide (EO) with water to form ethylene glycol (EG) and higher glycols. Conditions were selected relevant to industrial safety, such as a scenario of reverse flow of acid from a scrubber to an EO tank. Concentrations of 25–95 wt% EO with acidified water of pH 3.0 down to pH –0.7 were examined. In some experiments, EG was also added to the mix. The best fit apparent reaction rate expression exhibits a 1.5 order in EO concentration, third order in water concentration, nearly first order in acid concentration, and an activation energy of 76,500 kJ/kmol. The reaction model provides an adequate fit of adiabatic self‐heat rate versus temperature, pressure rate versus temperature, pressure versus time, and temperature versus time data for a large number of APTAC (Automatic Pressure Tracking Adiabatic Calorimeter) experiments. The model builds on one previously developed for the neutral reactions of EO with water and EGs. A case study involving a sulfuric acid‐contaminated EO railcar is presented. The reaction model can help quantify the behavior of EO contaminated with sulfuric acid solution below about 120°C. Above this temperature, other reaction pathways not characterized in this study become more prominent. [ABSTRACT FROM AUTHOR]

Autonomous-rail Rapid Transit (ART) tram is a new type of multiple-articulated rubber-tire transit that utilizes intelligent perception, path tracking, and trajectory following control technologies to eliminate reliance on physical railway tracks. The adoption of power batteries, hydrogen energy, wheel-edge motor drive, and other technologies has comprehensively realized the dual advantages of large-capacity rail transportation, which is punctual, high volume, energy-saving, and environmentally friendly, as well as the flexibility and low comprehensive cost of traditional bus operations. This has created a brand-new urban rail transit model. This article first introduces the ART tram systems architecture, operating principles, applicable scenarios. Secondly, it introduces the core subsystems of ART tram vehicle structure, electrical system, and energy storage system. Thirdly, it focuses on analyzing the structure composition and control principles of the Automatic All-Wheel Steering System, which includes two key core subsystems: path tracking control subsystems and trajectory following control subsystems. Then, a horizontal comparison is made between the performance advantages and disadvantages of ART and other transportation systems, and the application status of ART tram is summarized. Finally, some common issues related to the development of ART tram are discussed, and a development plan for future ART systems is proposed to better integrate ART tram into urban transportation and meet people's demands for intelligent, comfortable, fast, and environmentally friendly urban public transportation.

It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average – 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligence (AI) is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and Deep SORT algorithms for target detection and tracking, respectively. In response to the problems encountered in the tracking process of complex and dense scenes, our work (1) Uses optical flow to compensate the Kalman filter, to solve the problem of mismatch between the target bounding box predicted by the Kalman filter (KF) and the input when the target detection in the current frame is complex, thereby improving the prediction accuracy; (2) Using a low confidence trajectory filtering method to reduce false positive trajectories generated by Deep SORT, thereby mitigating the impact of unreliable detection on target tracking. (3) A visual servo controller has been designed for the Unmanned Aerial Vehicle (UAV) to reduce the impact of rapid movement on tracking and ensure that the target is always within the field of view of the UAV camera, thereby achieving automatic tracking tasks. Finally, the system was tested using Tibetan yaks on the Qinghai Tibet Plateau as tracking targets, and the results showed that the system has real-time multi tracking ability and ideal visual servo effect in complex and dense scenes. [ABSTRACT FROM AUTHOR]

Automatic tracking of three-dimensional (3D) human motion pose has the potential to provide corresponding technical support in various fields. However, existing methods for tracking human motion pose suffer from significant errors, long tracking times and suboptimal tracking results. To address these issues, an automatic tracking method for 3D human motion pose using contrastive learning is proposed. By using the feature parameters of 3D human motion poses, threshold variation parameters of 3D human motion poses are computed. The golden section is introduced to transform the threshold variation parameters and extract the features of 3D human motion poses by comparing the feature parameters with the threshold of parameter variation. Under the supervision of contrastive learning, a constraint loss is added to the local–global deep supervision module of contrastive learning to extract local parameters of 3D human motion poses, combined with their local features. After normalizing the 3D human motion pose images, frame differences of the background image are calculated. By constructing an automatic tracking model for 3D human motion poses, automatic tracking of 3D human motion poses is achieved. Experimental results demonstrate that the highest tracking lag is 9%, there is no deviation in node tracking, the pixel contrast is maintained above 90% and only 6 sub-blocks have detail loss. This indicates that the proposed method effectively tracks 3D human motion poses, tracks all the nodes, achieves high accuracy in automatic tracking and produces good tracking results. [ABSTRACT FROM AUTHOR]

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%. Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies. [ABSTRACT FROM AUTHOR]

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure's dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark. [ABSTRACT FROM AUTHOR]

The supercell storm that occurred in southwestern Germany on June 23, 2021, had an exceptionally long lifetime of 7.5 hr, travelled a distance of nearly 190 km, and produced large amounts of hail. During that summer, the Swabian MOSES field campaign was held in that area, and several hydro‐meteorological measurements are available, as the storm passed directly over the main observation site. We present hindcasts of this event with the Icosahedral Non‐hydrostatic model using two horizontal grid spacings (i.e., 2 km, 1 km) with a single‐moment and an advanced double‐moment microphysics scheme. Numerical results show that all 2 km model realizations do not simulate convective precipitation at the correct location and time. For the 1 km grid spacing, changes in aerosol concentration resulted in large changes in convective precipitation. Only the 1 km run assuming a low cloud condensation nuclei (CCNs) concentration is able to realistically capture the storm, whereas no supercell is simulated in the more polluted scenarios. Observed aerosol particle concentrations indicate that CCNs values were the lowest of the month, which suggests that the low aerosol concentration is a reasonable assumption. The thermodynamic structure of the pre‐convective environment, as well as other observations, showed the best agreement to this model run as well, indicating that the good representation of the supercell was obtained for the right reason. The automatic tracking of individual clouds revealed that more convective cells with longer lifetimes are simulated at finer resolution. We also find a negative aerosol–precipitation effect that is not only due to a reduced collision–coalescence process, but also to weaker cold‐rain processes. These findings demonstrate the benefits of using an aerosol‐aware double‐moment microphysics scheme for convective‐scale predictability and that the use of different CCNs concentrations can determine whether a supercell is successfully simulated or not. [ABSTRACT FROM AUTHOR]

The article focuses on the control of an omni-tracked vehicle in a symmetrical fully overlapping track system. The vehicle in question is equipped with four independently controlled tracks. The links of each crawler are equipped with a single rolling roller, fixed at an angle to the direction of the vehicle main axis. The authors propose mathematical description to determine the direction and speed of movement of a single pair of omni-tracks with oppositely arranged rolling rollers. Numerical tests were carried out, the results of which were compared with the mathematical model. The numerical studies were then subjected to experimental verification, using a full-scale prototype. A dynamic direction correction algorithm was also proposed with its effectiveness proved experimentally. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A parked vehicle damaged by a hit-and-run can only be repaired at the expense of the owner, unless the fleeing vehicle is identified and the driver apprehended. Identifying the fleeing vehicle involves using a video investigation method that searches for perpetrators through CCTV footage of the crime scene. When the length of the recorded video is long, the investigation may require an extended amount of time from the investigator, resulting in an added burden on their daily work. Some commercial companies are using object recognition and tracking technology to detect hit-and-run incidents; however, detecting small movements of a vehicle during a minor collision still remains a challenge. Therefore, there is a need for a system that can detect small movement in a vehicle in a lengthy video. Automatic recognition and tracking require a sufficient amount of training dataset. However, such a dataset for hit-and-run incidents is not publicly available. One of the reasons behind this scarcity is that it may violate personal information protection acts. On the other hand, instead of using real accident videos, we could use actors to simulate such accident scenes. Although this may be feasible, creating such a dataset would require substantial costs. In this paper, we describe a new dataset for hit-and-run incidents. We collected 833 hit-and-run videos by recreating a parking lot using miniaturized cars. This dataset has been made publicly available through Kaggle. We used three-dimensional convolution neural network, which is frequently used in the field of action recognition, to detect small movements of vehicles during hit-and-run incidents. In addition, the proportion of the area that surrounds the target vehicle to the min-max box of the vehicle itself and the length of the input frame are varied to compare the accuracy. As a result, we were able to achieve better accuracy by using the lowest proportion and the shortest input frame. [ABSTRACT FROM AUTHOR]

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes. [ABSTRACT FROM AUTHOR]

In this paper, a chained architecture of virtual track train (VTT), gantry virtual track train (G-VTT), was proposed. The distributed virtual driving model (DVD model) for G-VTT was proposed to realise the automatic tracking of paths with arbitrary ommon curves. The core of the DVD model is the cascade modular wheel steering angle (WSA) control algorithm based on the optimal lateral acceleration (OLA) of tracking points. A local tracking objective function based on 2-norm was established to find the OLA in the preview window, thereby the optimal local trajectory was realised. The cascade structure of the algorithm gives the DVD model the advantages of modularity and high scalability, facilitating the cascade control of an n-unit G-VTT. The proposed control strategy was verified by co-simulation of G-VTT dynamics model and the DVD model. Results show that the DVD model can effectively improve the path-following ability and small-curve-passing capacity of the G-VTT with remarkable reduction of the lateral deviation (up to about 99.16%) and turning passageway width (about 59.71%); Besides, the yaw and anti-jackknifing stability of the train are also significantly improved, and the DVD model is well adaptable to speed, curve radius, and payload. [ABSTRACT FROM AUTHOR]

Purpose: MR‐guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium‐filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real‐time catheter tracking during MR‐guided cardiac catheterization is presented. Methods: The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10–20 contiguous saturated slices combined with real‐time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR‐guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated. Results: The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real‐time measurements in the phantom and patients, respectively. Conclusion: The proposed framework enabled real‐time continuous automatic tracking of a gadolinium‐filled catheter balloon during MR‐guided cardiac catheterization. [ABSTRACT FROM AUTHOR]

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled by tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment. [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) has been widely used to measure and regulate human eye aberrations in real time. However, involuntary eye movement will restrict the working ability of the AO system severely. Thus, we propose a dual-camera three-dimensional (3D) automatic tracking technique for the AO system. In this paper, the basic principle of 3D tracking eye positioning and pupil tracking was introduced. The accuracy and feasibility were verified by practical experiments. The results show that the averaged root mean square error of higher-order aberrations from eight subjects is decreased by 11.5%, and the fluctuation variance is decreased significantly. [ABSTRACT FROM AUTHOR]

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece's contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding's normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding. [ABSTRACT FROM AUTHOR]

Cold fronts provide an environment particularly favourable for convective initiation in the mid-latitudes and can also be associated with convective hazards such as flooding, wind, hail and lightning. We build a climatology of cold-frontal convective cells between 2007–2016 for April–September in a cell-front distance framework by combining a radar-based cell detection and tracking dataset and automatic front detection methods applied to reanalysis data. We find that on average around twice as many cells develop on cold-frontal cell days compared to non-cold-frontal cell days. Using the 700 hPa level as a reference point, we show the maximum cell frequency is 350–400 km ahead of the 700 hPa front, which is marginally ahead of the typical surface front location. The 700 hPa front location marks the minimum cell frequency and a clear shift in regime between cells, with a weakened diurnal cycle on the warm side of the 700 hPa cold front and strongly diurnally driven cells on the cold side of the 700 hPa front. High cell frequency is found several hundreds of kilometres ahead of the surface front, and cells in this region are most likely to be associated with mesocyclones, intense convective cores and lightning. Namely, mesocyclones were detected in around 5.0 % of pre-surface-frontal cells compared to only 1.5 % of non-cold-frontal cells. The findings in this study are an important step towards a better understanding of cold-frontal convection climatology and links between cold fronts and convective hazards. [ABSTRACT FROM AUTHOR]

Frequency tracking and amplitude control are essential for piezoelectric transducers. Frequency tracking ensures the piezoelectric transducer operates at the resonant frequency for maximum power output, and amplitude control regulates the mechanical motion of the output. This paper presents a novel driver based on a push–pull inverter for piezoelectric transducers. The proposed driver realizes the frequency tracking and amplitude control scheme by a voltage sensing bridge in the case of transformer secondary matching, guaranteeing automatic frequency tracking and precise mechanical functions regardless of environmental and load variations. The proposed scheme is verified by the ultrasonic scalpel and the ultrasonic motor (USM). The experimental results show that this scheme reduces the build-up time from 10 ms to 3 ms and loaded frequency variations from 250 Hz to 200 Hz. In addition, the amplitude control performance was further observed on USM for various loads. The overshoot is less than 5.4% under different load torques. Therefore, the proposed scheme improves the load adaptability and stability of piezoelectric transducers and promotes the application of piezoelectric transducers under various conditions. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) perfectly fit into the future vision of merchant fleet. MASS autonomous navigation system combines automatic trajectory tracking and supervisor safe trajectory generation subsystems. Automatic trajectory tracking method, using line-of-sight (LOS) reference course generation algorithm, is combined with model predictive control (MPC). Algorithm for MASS trajectory tracking, including cooperation with the dynamic system of safe trajectory generation is described. It allows for better ship control with steadystate cross-track error limitation to the ship hull breadth and limited overshoot after turns. In real MASS ships path is defined as set of straight line segments, so transition between trajectory sections when passing waypoint is unavoidable. In the proposed control algorithm LOS trajectory reference course is mapped to the rotational speed reference value, which is dynamically constrained in MPC controller due to dynamically changing reference trajectory in real MASS system. Also maneuver path advance dependent on the path tangential angle difference, to ensure trajectory tracking for turns from 0 to 90 degrees, without overshoot is used. All results were obtained with the use of training ship in real-time conditions. [ABSTRACT FROM AUTHOR]

The stereotactic treatment of single cranial targets using noncoplanar volumetric modulated arc therapy (VMAT) allows for effective dose delivery to the target, while sparing normal brain tissue. In this study, the dosimetric effect of adding dynamic jaw tracking and automatic collimator angle selection in the optimization of single target cranial VMAT plans was investigated. Twenty-two cranial targets, previously treated with VMAT without dynamic jaw tracking and automatic collimator angle optimization (CAO) were chosen for replanning. Target volumes ranged from 0.441cc to 25.863cc with doses between 18Gy and 30Gy delivered in 1 to 5 fractions. Original plans were reoptimized with automatic CAO, keeping all other objectives the same (CAO plans). Next, original plans were reoptimized with both dynamic jaw tracking and CAO (DJT plans). Original, CAO, and DJT target doses were compared using the Paddick gradient index (GI) and the Paddick inverse conformity index (ICI), while normal tissue dose was compared using the volume of the normal brain receiving 5Gy, 10Gy, and 12Gy. The normal tissue volume was normalized to target size to allow cross comparison between plans. A one-sided t-test was performed to determine whether the changes in the plan metrics were statistically significant. CAO plans had improved GIs compared to the originals (p = 0.03) with insignificant changes in other plan metrics (p > 0.20). The addition of dynamic jaw tracking in DJT plans greatly improved ICIs and normal brain metrics (p < 0.01) compared to the CAO plans with minor improvement in ICIs (p = 0.07). The combined effect of adding dynamic jaw tracking and collimator optimization led to improvements in all metrics of the DJT plans when compared to the original (p < 0.02). The addition of dynamic jaw tracking and CAO led to improvements in both target and normal tissue dose metrics for single-target noncoplanar cranial VMAT plans. [ABSTRACT FROM AUTHOR]

Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into 'control', 'partial feedback', and 'complete feedback' groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (β = −18.9; 95% CI [−29.3, −8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload. [ABSTRACT FROM AUTHOR]

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs. [ABSTRACT FROM AUTHOR]

Context. Investigating the early-stage evolution of an erupting flux rope from the Sun is important for understanding the mechanisms of how it loses its stability and its space-weather impact. Aims. Our aim is to develop an efficient scheme for tracking the early dynamics of erupting solar flux ropes and to use the algorithm to analyse its early-stage properties. The algorithm is tested on a data-driven simulation of an eruption that took place in active region AR12473. We investigate the modelled footpoint movement and magnetic flux evolution of the flux rope and compare these with observational data from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) in the 211 Å and 1600 Å channels. Methods. We used the time-dependent data-driven magnetofrictional model (TMFM) to carry out our analysis. We also performed another modelling run, where we stop the driving of the TMFM midway through the rise of the flux rope through the simulation domain and evolve it instead with a zero-beta magnetohydrodynamic (MHD) approach. Results. The developed algorithm successfully extracts a flux rope and its ascent through the simulation domain. We find that the movement of the modelled flux rope footpoints showcases similar trends in both the TMFM and relaxation MHD runs: the footpoints recede from their respective central location as the eruption progresses and the positive polarity footpoint region exhibits a more dynamic behaviour. The ultraviolet (UV) brightenings and extreme ultraviolet (EUV) dimmings agree well with the models in terms of their dynamics. According to our modelling results, the toroidal magnetic flux in the flux rope first rises and then decreases. In our observational analysis, we capture the descending phase of toroidal flux. Conclusions. The extraction algorithm enables us to effectively study the early dynamics of the flux rope and to derive some of its key properties, such as footpoint movement and toroidal magnetic flux. The results generally agree well with observational data. [ABSTRACT FROM AUTHOR]

Covalent organic frameworks (COFs) stand out as prospective organic‐based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge‐carrier mobility. The "Clean, Uniform, Refined with Automatic Tracking from Experimental Database" (CURATED) COFs is a database of reported experimental COFs that until now remained mostly unexplored for photocatalysis. In this study, the CURATED COFs database is screened for discovering potential photocatalysts using a set of DFT‐based descriptors that cost‐effectively assesses visible light absorption, preliminary thermodynamic feasibility of the desired pair of redox reactions, charge separation, and charge‐carrier mobility. The workflow can shortlist 13 COFs as prospective candidates for water splitting, and identify materials (Nx‐COF (x = 0–3)) that have been reported as candidates for hydrogen evolution reaction. Overall, the strategy addresses the challenge of exploring a large number of COFs by directing future research toward a selective group of COFs, while providing valuable insights into the structural design for achieving a desired photocatalytic process. [ABSTRACT FROM AUTHOR]

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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The modern tram track automatic cleaning car is a crucial equipment in urban rail transportation systems, effectively removing trash, dust, and other debris from the slotted tracks of trams. However, due to the complex and variable structure of turnouts, the cleaning car often requires assistance in accurately detecting their positions. Consequently, the cleaning car needs help in adequately cleaning or bypassing turnouts, which adversely affects cleaning effectiveness and track maintenance quality. This paper presents a novel method for tracking turnout identification called PBE-YOLO based on the improved yolov5s framework. The algorithm enhances yolov5s by optimizing the lightweight backbone network, improving feature fusion methods, and optimizing the regression loss function. The proposed method is trained using a dataset of track turnouts collected through field shots on modern tram lines. Comparative experiments are conducted to analyze the performance of the improved lightweight backbone network, as well as performance comparisons and ablation experiments for the new turnout identification method. Experimental results demonstrate that the proposed PBE-YOLO method achieves a 52.71% reduction in model parameters, a 4.60% increase in mAP@0.5(%), and a 3.27% improvement in precision compared to traditional yolov5s. By improving the track turnout identification method, this paper enables the automatic cleaning car to identify turnouts' positions accurately. This enhancement leads to several benefits, including increased automation levels, improved cleaning efficiency and quality, reduced reliance on manual intervention, and mitigation of collision risks between the cleaning car and turnouts. [ABSTRACT FROM AUTHOR]

Mini-invasive partial nephrectomy (MIPN) has many advantages. However, it is challenging for the surgeon to localise the hidden anatomical structures to be spared or resected during surgery. Augmented reality (AR) is a promising localisation assistance approach. Existing AR-MIPN methods augment the endoscopic view with 3D models from preoperative CT scan. However, they do not track the kidney in real-time, which considerably reduces usability, as AR is only temporarily available on isolated images. We propose an approach to achieve continuous live AR-MIPN. It uses classical camera calibration and manual initial registration. Its key novelty is a keypoint-based automatic kidney tracking module, with three main technical contributions. First, it performs stereo tracking-by-detection from stereo keyframes, exploiting left-right consistency to maximise robustness. Second, it only considers keypoints within the parenchyma, as segmented by a specifically trained neural network. Third, it improves keypoint detection and matching by a new process that we call stereo perspective correction (SPC). It uses the stereo depth-map and surface flattening to generate an image warp that cancels the perspective effect, improving the performance of keypoint detection and matching. We carried out experiments on semi-synthetic and real surgical datasets to compare several tracking methods, showing that our method outperforms. [ABSTRACT FROM AUTHOR]